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Advances on Resources Management for Multi-Platform Infrastructures
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Advances on Resources Management for Multi-Platform Infrastructures

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 September 2018) | Viewed by 132594

Special Issue Editors

Prof. Dr. Luis Javier Garcia Villalba

E-Mail Website
Guest Editor
Group of Analysis, Security and Systems (GASS), Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain
Interests: artificial intelligence; big data; computer networks; computer security; information theory; IoT; multimedia forensics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Anura P. Jayasumana

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Guest Editor
Electrical and Computer Engineering Department, Colorado State University, Fort Collins, CO 80523-1373, USA
Interests: Computer and Communication Networking; Application of sensor networks and embedded systems; VLSI
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jun Bi

E-Mail Website
Guest Editor
Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing, China
Interests: SAVA; SDN; NDN; Routing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of new technologies in micro‐electro‐mechanical systems (MEMS) have increased the capacity of automatically recording, processing and sending information through different infrastructures. This new generation of devices, sensors, and actuators (RFID, Bluetooth Devices, Wireless Sensor Networks WSN, Embedded Systems, and Near Field Communication NFC), which exchange information with other on‐line, connected devices, has created a new market, based on services and applications. However, the coordination tasks between different architectures (sensor, fixed, mobile) bring key challenges in terms of theoretical foundation, security, programmability, energy efficiency, and management. This Special Issue intends to collect current development and the future directions in resource and information management in different infrastructures. We invite authors to submit their original papers. Potential topics include, but are not limited to:

  • Multiplatform Integration for fixed sensors and mobile devices.
  • Data plane and control plane architectures in fixed sensors and mobile devices.
  • Monitoring of multiplatform communication.
  • Sensors and Multiplatform Information Processing.
  • Sensors and Mobile Integration and Communication.
  • Load adaptive in SDN fixed and mobile networks.
  • Simulation for multi‐platform architectures.
  • Virtualization of data and control planes in sensors and fixed infrastructures.
  • Reliable and robust communication for multi‐platform architectures.
  • Theoretical foundation of integrated architectures.
  • Quality of Service in multiplatform schemes.

Prof. Luis Javier Garcia Villalba
Prof. Anura P. Jayasumana
Prof. Jun Bi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (21 papers)

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Research

35 pages, 4968 KiB  
Article
New DoS Defense Method Based on Strong Designated Verifier Signatures
by Marcone Pereira De Almeida, Rafael Timóteo De Sousa Júnior, Luis Javier García Villalba and Tai-Hoon Kim
Sensors 2018, 18(9), 2813; https://doi.org/10.3390/s18092813 - 26 Aug 2018
Cited by 10 | Viewed by 4417
Abstract
We present a novel technique for source authentication of a packet stream in a network, which intends to give guarantees that a specific network flow really comes from a claimed origin. This mechanism, named packet level authentication (PLA), can be an essential tool [...] Read more.
We present a novel technique for source authentication of a packet stream in a network, which intends to give guarantees that a specific network flow really comes from a claimed origin. This mechanism, named packet level authentication (PLA), can be an essential tool for addressing Denial of Service (DoS) attacks. Based on designated verifier signature schemes, our proposal is an appropriate and unprecedented solution applying digital signatures for DoS prevention. Our scheme does not rely on an expensive public-key infrastructure and makes use of light cryptography machinery that is suitable in the context of the Internet of Things (IoT). We analyze our proposed scheme as a defense measure considering known DoS attacks and present a formal proof of its resilience face to eventual adversaries. Furthermore, we compare our solution to already existent strategies, highlighting its advantages and drawbacks. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
►▼ Show Figures

Figure 1

Figure 1
<p>Signature of a message <span class="html-italic">m</span> by the sender, and the verification process by the receiver.</p> Full article ">Figure 2
<p>(<b>a</b>) The challenger Alice takes two messages randomly chosen and sends them to the signer Bob. (<b>b</b>) The signer flips a coin and chooses either 0 or 1, then he encrypts one of the messages and sends the enciphered message back to the challenger, who wins the game if she finds which <span class="html-italic">m</span> was encrypted.</p> Full article ">Figure 3
<p>In undeniable signature, (<b>a</b>) the receiver is not able to verify the validity of a signature until (<b>b</b>) the signer agrees to perform a validation interaction.</p> Full article ">Figure 4
<p>Bob is able to verify the validity of the signature as the designated verifier. The view of Cindy is that the signatures are indistinguishable of any other created by Bob.</p> Full article ">Figure 5
<p>In SDVS, a curious Cindy cannot decide who is the author of the signature, because this one is indistinguishable from any other that anybody could create.</p> Full article ">Figure 6
<p>The proposed mode of generating packets signatures by the sender, thus producing packets with dummy and coherent signatures and packets with no signature.</p> Full article ">Figure 7
<p>A possible utilization scenario for the proposed DoS defense. During a DoS attack, the target server requires signed traffic from the senders and, since in this case just <span class="html-italic">Network-A</span> and <span class="html-italic">Network-B</span> can do it, the other sender is blocked.</p> Full article ">Figure 8
<p>Overview of the proposed defense dynamics showing the interaction between sender and receiver.</p> Full article ">Figure 9
<p>A common IoT Instance (<b>a</b>); and its corresponding oneM2M functional configuration (<b>b</b>).</p> Full article ">Figure 10
<p>A fully distributed IDS for IoT.</p> Full article ">Figure 11
<p>Overview of a state machine. The process reads the first character and, for every “c” found, a transition is realized.</p> Full article ">Figure 12
<p>An overview of transition rules on a finite state machine: (<b>a</b>) the sender transmits a message to the receiver after reading <math display="inline"><semantics> <msubsup> <mi>m</mi> <mi>S</mi> <mi>i</mi> </msubsup> </semantics></math>; and (<b>b</b>) the receiver responds to the sender after he gets the sender message.</p> Full article ">Figure 13
<p>The finite state machine for the receiver (<b>a</b>) and the sender (<b>b</b>) in our proposed protocol.</p> Full article ">Figure 14
<p>Relation between the number of packets the adversary manipulates and the probability <math display="inline"><semantics> <msub> <mi>π</mi> <mrow> <mi>N</mi> <mi>A</mi> </mrow> </msub> </semantics></math> of the receiver not holding the authenticated status for a sender.</p> Full article ">Figure 15
<p>Relation between the number of packets the adversary manipulates, the probability of hitting valid signatures <math display="inline"><semantics> <mi>δ</mi> </semantics></math> and the probability <math display="inline"><semantics> <msub> <mi>π</mi> <mrow> <mi>N</mi> <mi>A</mi> </mrow> </msub> </semantics></math> of invalidating an authentication, working with <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>. In this case, the adversary gets a higher chance only after dropping most of the packets (more than 19 packets in 20).</p> Full article ">Figure 16
<p>Relation between the number of packets the adversary manipulates, the probability of hitting valid signatures <math display="inline"><semantics> <mi>δ</mi> </semantics></math> and the probability <math display="inline"><semantics> <msub> <mi>π</mi> <mrow> <mi>N</mi> <mi>A</mi> </mrow> </msub> </semantics></math> of invalidating an authentication, working with <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>. In this case, the adversary has a chance only after dropping the total of the packets. With these settings, the all-or-nothing nature of our defense scheme is clear.</p> Full article ">
22 pages, 22998 KiB  
Article
Watermarking Based on Compressive Sensing for Digital Speech Detection and Recovery †
by Wenhuan Lu, Zonglei Chen, Ling Li, Xiaochun Cao, Jianguo Wei, Naixue Xiong, Jian Li and Jianwu Dang
Sensors 2018, 18(7), 2390; https://doi.org/10.3390/s18072390 - 23 Jul 2018
Cited by 19 | Viewed by 4638
Abstract
In this paper, a novel imperceptible, fragile and blind watermark scheme is proposed for speech tampering detection and self-recovery. The embedded watermark data for content recovery is calculated from the original discrete cosine transform (DCT) coefficients of host speech. The watermark information is [...] Read more.
In this paper, a novel imperceptible, fragile and blind watermark scheme is proposed for speech tampering detection and self-recovery. The embedded watermark data for content recovery is calculated from the original discrete cosine transform (DCT) coefficients of host speech. The watermark information is shared in a frames-group instead of stored in one frame. The scheme trades off between the data waste problem and the tampering coincidence problem. When a part of a watermarked speech signal is tampered with, one can accurately localize the tampered area, the watermark data in the area without any modification still can be extracted. Then, a compressive sensing technique is employed to retrieve the coefficients by exploiting the sparseness in the DCT domain. The smaller the tampered the area, the better quality of the recovered signal is. Experimental results show that the watermarked signal is imperceptible, and the recovered signal is intelligible for high tampering rates of up to 47.6%. A deep learning-based enhancement method is also proposed and implemented to increase the SNR of recovered speech signal. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
►▼ Show Figures

Figure 1

Figure 1
<p>Sketch of watermark embedding procedure.</p> Full article ">Figure 2
<p>Sketch of watermark recovery procedure.</p> Full article ">Figure 3
<p>The SNR values of all the recovered speech signal.</p> Full article ">Figure 4
<p>Original speech signal (<b>a</b>) Waveform (<b>b</b>) Spectrogram.</p> Full article ">Figure 4 Cont.
<p>Original speech signal (<b>a</b>) Waveform (<b>b</b>) Spectrogram.</p> Full article ">Figure 5
<p>Watermarked speech signal (<b>a</b>) Waveform (<b>b</b>) Spectrogram.</p> Full article ">Figure 6
<p>Damaged speech signal, the tampering percentage is 19.5% (<b>a</b>) Waveform (<b>b</b>) Spectrogram.</p> Full article ">Figure 6 Cont.
<p>Damaged speech signal, the tampering percentage is 19.5% (<b>a</b>) Waveform (<b>b</b>) Spectrogram.</p> Full article ">Figure 7
<p>Recovered speech signal, the tampering percentage is 19.5% (<b>a</b>) Waveform (<b>b</b>) Spectrogram.</p> Full article ">Figure 8
<p>Damaged speech signal, the tampering percentage is 47.6% (<b>a</b>) Waveform (<b>b</b>) Spectrogram.</p> Full article ">Figure 8 Cont.
<p>Damaged speech signal, the tampering percentage is 47.6% (<b>a</b>) Waveform (<b>b</b>) Spectrogram.</p> Full article ">Figure 9
<p>Recovered speech signal, the tampering percentage is 47.6% (<b>a</b>) Waveform (<b>b</b>) Spectrogram.</p> Full article ">Figure 10
<p>Damaged speech signal, different parts of the signal are tampered separately (<b>a</b>) Waveform (<b>b</b>) Spectrogram.</p> Full article ">Figure 10 Cont.
<p>Damaged speech signal, different parts of the signal are tampered separately (<b>a</b>) Waveform (<b>b</b>) Spectrogram.</p> Full article ">Figure 11
<p>Recovered speech signal, different parts of the signal are tampered separately (<b>a</b>) Waveform (<b>b</b>) Spectrogram.</p> Full article ">Figure 12
<p>Spectrograms. (<b>a</b>) Watermarked speech signal; (<b>b</b>) Recovered speech signal without processing; (<b>c</b>) Four hidden layers, decrement of nodes; (<b>d</b>) Four hidden layers, same number of nodes.</p> Full article ">Figure 12 Cont.
<p>Spectrograms. (<b>a</b>) Watermarked speech signal; (<b>b</b>) Recovered speech signal without processing; (<b>c</b>) Four hidden layers, decrement of nodes; (<b>d</b>) Four hidden layers, same number of nodes.</p> Full article ">Figure 13
<p>Waveforms. (<b>a</b>) Watermarked speech signal; (<b>b</b>) Recovered speech signal without processing; (<b>c</b>) Four hidden layers, decrement of nodes; (<b>d</b>) Four hidden layers, same number of nodes.</p> Full article ">Figure 13 Cont.
<p>Waveforms. (<b>a</b>) Watermarked speech signal; (<b>b</b>) Recovered speech signal without processing; (<b>c</b>) Four hidden layers, decrement of nodes; (<b>d</b>) Four hidden layers, same number of nodes.</p> Full article ">Figure 14
<p>Spectrograms of recovered speech signal using different numbers of hidden layers of DNN (<b>a</b>) 4 hidden layers, (<b>b</b>) 7 hidden layers.</p> Full article ">Figure 14 Cont.
<p>Spectrograms of recovered speech signal using different numbers of hidden layers of DNN (<b>a</b>) 4 hidden layers, (<b>b</b>) 7 hidden layers.</p> Full article ">Figure 15
<p>Waveforms of recovered speech signal by using different numbers of hidden layers of DNN (<b>a</b>) 4 hidden layers (<b>b</b>) 7 hidden layers.</p> Full article ">Figure 16
<p>Spectrograms of recovered speech signal by using different numbers of iterations of DNN (<b>a</b>) 100 iterations (<b>b</b>) 200 iterations(<b>c</b>) 500 iterations.</p> Full article ">Figure 17
<p>Waveforms of recovered speech signal by using different iterations of DNN (<b>a</b>) 100 iterations (<b>b</b>) 200 iterations(<b>c</b>) 500 iterations.</p> Full article ">
32 pages, 4213 KiB  
Article
A Personalized QoS Prediction Approach for CPS Service Recommendation Based on Reputation and Location-Aware Collaborative Filtering
by Li Kuang, Long Yu, Lan Huang, Yin Wang, Pengju Ma, Chuanbin Li and Yujia Zhu
Sensors 2018, 18(5), 1556; https://doi.org/10.3390/s18051556 - 14 May 2018
Cited by 53 | Viewed by 4636
Abstract
With the rapid development of cyber-physical systems (CPS), building cyber-physical systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedure of building Cyber-physical systems, it has been found that a large number of [...] Read more.
With the rapid development of cyber-physical systems (CPS), building cyber-physical systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedure of building Cyber-physical systems, it has been found that a large number of functionally equivalent services exist, so it becomes an urgent task to recommend suitable services from the large number of services available in CPS. However, since it is time-consuming, and even impractical, for a single user to invoke all of the services in CPS to experience their QoS, a robust QoS prediction method is needed to predict unknown QoS values. A commonly used method in QoS prediction is collaborative filtering, however, it is hard to deal with the data sparsity and cold start problem, and meanwhile most of the existing methods ignore the data credibility issue. Thence, in order to solve both of these challenging problems, in this paper, we design a framework of QoS prediction for CPS services, and propose a personalized QoS prediction approach based on reputation and location-aware collaborative filtering. Our approach first calculates the reputation of users by using the Dirichlet probability distribution, so as to identify untrusted users and process their unreliable data, and then it digs out the geographic neighborhood in three levels to improve the similarity calculation of users and services. Finally, the data from geographical neighbors of users and services are fused to predict the unknown QoS values. The experiments using real datasets show that our proposed approach outperforms other existing methods in terms of accuracy, efficiency, and robustness. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
►▼ Show Figures

Figure 1

Figure 1
<p>The quality of service (QoS) prediction framework for cyber-physical system (CPS) services.</p> Full article ">Figure 2
<p>Process of QoS prediction, based on geography and reputation-aware.</p> Full article ">Figure 3
<p>Hierarchical relationship of <span class="html-italic">Provider-Level</span>, <span class="html-italic">SAS-Level</span>, and <span class="html-italic">SCluster-Level</span>.</p> Full article ">Figure 4
<p>(<b>a</b>) Impact of untrustworthy users (NMAE); (<b>b</b>) Impact of untrustworthy users (RMSE).</p> Full article ">Figure 5
<p>(<b>a</b>) Impact of Threshold <span class="html-italic">δ</span> (MD = 5%); (<b>b</b>) Impact of Threshold <span class="html-italic">δ</span> (MD = 20%).</p> Full article ">Figure 5 Cont.
<p>(<b>a</b>) Impact of Threshold <span class="html-italic">δ</span> (MD = 5%); (<b>b</b>) Impact of Threshold <span class="html-italic">δ</span> (MD = 20%).</p> Full article ">Figure 6
<p>(<b>a</b>) Impact of <span class="html-italic">UK</span> (MD = 5%, NMAE); (<b>b</b>) Impact of <span class="html-italic">UK</span> (MD = 20%, NMAE); (<b>c</b>) Impact of <span class="html-italic">UK</span> (MD = 5%, RMSE); (<b>d</b>) Impact of <span class="html-italic">UK</span> (MD = 20%, RMSE).</p> Full article ">Figure 6 Cont.
<p>(<b>a</b>) Impact of <span class="html-italic">UK</span> (MD = 5%, NMAE); (<b>b</b>) Impact of <span class="html-italic">UK</span> (MD = 20%, NMAE); (<b>c</b>) Impact of <span class="html-italic">UK</span> (MD = 5%, RMSE); (<b>d</b>) Impact of <span class="html-italic">UK</span> (MD = 20%, RMSE).</p> Full article ">Figure 6 Cont.
<p>(<b>a</b>) Impact of <span class="html-italic">UK</span> (MD = 5%, NMAE); (<b>b</b>) Impact of <span class="html-italic">UK</span> (MD = 20%, NMAE); (<b>c</b>) Impact of <span class="html-italic">UK</span> (MD = 5%, RMSE); (<b>d</b>) Impact of <span class="html-italic">UK</span> (MD = 20%, RMSE).</p> Full article ">Figure 7
<p>(<b>a</b>) Impact of matrix density(NMAE); (<b>b</b>) Impact of matrix density(RMSE).</p> Full article ">Figure 8
<p>(<b>a</b>) Impact of <span class="html-italic">K<sub>S</sub></span> (MD = 5%, NMAE); (<b>b</b>) Impact of <span class="html-italic">K<sub>S</sub></span> (MD = 20%, NMAE); (<b>c</b>) Impact of <span class="html-italic">K<sub>S</sub></span> (MD = 5%, RMSE); (<b>d</b>) Impact of <span class="html-italic">K<sub>S</sub></span> (MD = 20%, RMSE).</p> Full article ">Figure 8 Cont.
<p>(<b>a</b>) Impact of <span class="html-italic">K<sub>S</sub></span> (MD = 5%, NMAE); (<b>b</b>) Impact of <span class="html-italic">K<sub>S</sub></span> (MD = 20%, NMAE); (<b>c</b>) Impact of <span class="html-italic">K<sub>S</sub></span> (MD = 5%, RMSE); (<b>d</b>) Impact of <span class="html-italic">K<sub>S</sub></span> (MD = 20%, RMSE).</p> Full article ">Figure 9
<p>(<b>a)</b> Impact of <span class="html-italic">K<sub>NS</sub></span> (NMAE); (<b>b</b>) impact of <span class="html-italic">K<sub>NU</sub></span> (NMAE); (<b>c</b>) Impact of <span class="html-italic">K<sub>NS</sub></span> (RMSE); (<b>d</b>) impact of <span class="html-italic">K<sub>NU</sub></span> (RMSE).</p> Full article ">Figure 9 Cont.
<p>(<b>a)</b> Impact of <span class="html-italic">K<sub>NS</sub></span> (NMAE); (<b>b</b>) impact of <span class="html-italic">K<sub>NU</sub></span> (NMAE); (<b>c</b>) Impact of <span class="html-italic">K<sub>NS</sub></span> (RMSE); (<b>d</b>) impact of <span class="html-italic">K<sub>NU</sub></span> (RMSE).</p> Full article ">Figure 9 Cont.
<p>(<b>a)</b> Impact of <span class="html-italic">K<sub>NS</sub></span> (NMAE); (<b>b</b>) impact of <span class="html-italic">K<sub>NU</sub></span> (NMAE); (<b>c</b>) Impact of <span class="html-italic">K<sub>NS</sub></span> (RMSE); (<b>d</b>) impact of <span class="html-italic">K<sub>NU</sub></span> (RMSE).</p> Full article ">
16 pages, 841 KiB  
Article
Learning Perfectly Secure Cryptography to Protect Communications with Adversarial Neural Cryptography
by Murilo Coutinho, Robson De Oliveira Albuquerque, Fábio Borges, Luis Javier García Villalba and Tai-Hoon Kim
Sensors 2018, 18(5), 1306; https://doi.org/10.3390/s18051306 - 24 Apr 2018
Cited by 45 | Viewed by 7113
Abstract
Researches in Artificial Intelligence (AI) have achieved many important breakthroughs, especially in recent years. In some cases, AI learns alone from scratch and performs human tasks faster and better than humans. With the recent advances in AI, it is natural to wonder whether [...] Read more.
Researches in Artificial Intelligence (AI) have achieved many important breakthroughs, especially in recent years. In some cases, AI learns alone from scratch and performs human tasks faster and better than humans. With the recent advances in AI, it is natural to wonder whether Artificial Neural Networks will be used to successfully create or break cryptographic algorithms. Bibliographic review shows the main approach to this problem have been addressed throughout complex Neural Networks, but without understanding or proving the security of the generated model. This paper presents an analysis of the security of cryptographic algorithms generated by a new technique called Adversarial Neural Cryptography (ANC). Using the proposed network, we show limitations and directions to improve the current approach of ANC. Training the proposed Artificial Neural Network with the improved model of ANC, we show that artificially intelligent agents can learn the unbreakable One-Time Pad (OTP) algorithm, without human knowledge, to communicate securely through an insecure communication channel. This paper shows in which conditions an AI agent can learn a secure encryption scheme. However, it also shows that, without a stronger adversary, it is more likely to obtain an insecure one. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
►▼ Show Figures

Figure 1

Figure 1
<p>Alice, Bob, and Eve, with a symmetric cryptosystem.</p> Full article ">Figure 2
<p>Alice, Bob, and Eve, and the CPA-ANC setup. Alice and Bob share a secret key <span class="html-italic">K</span>. Eve chooses two messages <math display="inline"> <semantics> <msub> <mi>P</mi> <mn>0</mn> </msub> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>P</mi> <mn>1</mn> </msub> </semantics> </math>. Alice randomly chooses one message to encrypt producing the ciphertext <span class="html-italic">C</span>. Bob uses the key <span class="html-italic">K</span> to decrypt <span class="html-italic">C</span> producing <math display="inline"> <semantics> <msub> <mi>P</mi> <mrow> <mi>Bob</mi> </mrow> </msub> </semantics> </math>. Eve receives the ciphertext <span class="html-italic">C</span> and tries to guess which message was encrypted outputting 0 if believes <math display="inline"> <semantics> <msub> <mi>P</mi> <mn>0</mn> </msub> </semantics> </math> was encrypted and 1 if believes <math display="inline"> <semantics> <msub> <mi>P</mi> <mn>1</mn> </msub> </semantics> </math> was encrypted.</p> Full article ">Figure 3
<p>The proposed neural network (<span class="html-italic">CryptoNet</span>). The bits of the plaintext are represented by [<math display="inline"> <semantics> <msub> <mi>p</mi> <mn>0</mn> </msub> </semantics> </math>, <math display="inline"> <semantics> <msub> <mi>p</mi> <mn>1</mn> </msub> </semantics> </math>, …, <math display="inline"> <semantics> <msub> <mi>p</mi> <mrow> <mi>n</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </semantics> </math>]. The bits of the key are represented by [<math display="inline"> <semantics> <msub> <mi>k</mi> <mn>0</mn> </msub> </semantics> </math>, <math display="inline"> <semantics> <msub> <mi>k</mi> <mn>1</mn> </msub> </semantics> </math>, …, <math display="inline"> <semantics> <msub> <mi>k</mi> <mrow> <mi>n</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </semantics> </math>]. The function <span class="html-italic">f</span> (see Equation (<a href="#FD9-sensors-18-01306" class="html-disp-formula">9</a>)) transforms the bits into angles [<math display="inline"> <semantics> <msub> <mi>a</mi> <mn>0</mn> </msub> </semantics> </math>, <math display="inline"> <semantics> <msub> <mi>a</mi> <mn>1</mn> </msub> </semantics> </math>, … <math display="inline"> <semantics> <msub> <mi>a</mi> <mrow> <mn>2</mn> <mi>n</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </semantics> </math>]. A fully connected layer combines the angles forming the variables [<math display="inline"> <semantics> <msub> <mi>h</mi> <mn>0</mn> </msub> </semantics> </math>, <math display="inline"> <semantics> <msub> <mi>h</mi> <mn>1</mn> </msub> </semantics> </math>, …, <math display="inline"> <semantics> <msub> <mi>h</mi> <mrow> <mi>n</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </semantics> </math>]. The function <math display="inline"> <semantics> <msup> <mi>f</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics> </math> (see Equation (<a href="#FD10-sensors-18-01306" class="html-disp-formula">10</a>)) transforms the combined angles into continuous bits (real numbers in the interval <math display="inline"> <semantics> <mrow> <mo stretchy="false">[</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo stretchy="false">]</mo> </mrow> </semantics> </math>) [<math display="inline"> <semantics> <msub> <mi>c</mi> <mn>0</mn> </msub> </semantics> </math>, <math display="inline"> <semantics> <msub> <mi>c</mi> <mn>1</mn> </msub> </semantics> </math>, …, <math display="inline"> <semantics> <msub> <mi>c</mi> <mrow> <mi>n</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </semantics> </math>] representing the ciphertext.</p> Full article ">Figure 4
<p>The learning curves forThe challenge between Alice and Bob against Eve who tries to minimize its decryption error. Alice and Bob try to minimize Bob’s decryption error while maximizing Eve’s decryption error. Eve is represented in green and Alice and Bob are represented in blue. The number of steps denote the number of “minibatches” on training phase.</p> Full article ">Figure 5
<p>Eve’s neural network (<span class="html-italic">CPA-CryptoNet</span>). Eve receives as input two plaintexts <math display="inline"> <semantics> <msub> <mi>P</mi> <mn>0</mn> </msub> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>P</mi> <mn>1</mn> </msub> </semantics> </math>. The bits of each plaintext are represented by [<math display="inline"> <semantics> <msub> <mi>p</mi> <mrow> <mn>0</mn> <mo>,</mo> <mn>0</mn> </mrow> </msub> </semantics> </math>, …, <math display="inline"> <semantics> <msub> <mi>p</mi> <mrow> <mn>0</mn> <mo>,</mo> <mi>n</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </semantics> </math>] for <math display="inline"> <semantics> <msub> <mi>P</mi> <mn>0</mn> </msub> </semantics> </math> and [<math display="inline"> <semantics> <msub> <mi>p</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>0</mn> </mrow> </msub> </semantics> </math>, …, <math display="inline"> <semantics> <msub> <mi>p</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>n</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </semantics> </math>] for <math display="inline"> <semantics> <msub> <mi>P</mi> <mn>1</mn> </msub> </semantics> </math>. Eve also receives the ciphertext <span class="html-italic">C</span> represented by [<math display="inline"> <semantics> <msub> <mi>c</mi> <mn>0</mn> </msub> </semantics> </math>, …, <math display="inline"> <semantics> <msub> <mi>c</mi> <mrow> <mi>n</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </semantics> </math>]. The function <span class="html-italic">f</span> (see Equation (<a href="#FD9-sensors-18-01306" class="html-disp-formula">9</a>)) transforms the bits into angles [<math display="inline"> <semantics> <msub> <mi>a</mi> <mn>0</mn> </msub> </semantics> </math>, <math display="inline"> <semantics> <msub> <mi>a</mi> <mn>1</mn> </msub> </semantics> </math>, … <math display="inline"> <semantics> <msub> <mi>a</mi> <mrow> <mn>3</mn> <mi>n</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </semantics> </math>]. A fully connected layer combines the angles generating the hidden variables [<math display="inline"> <semantics> <msubsup> <mi>h</mi> <mn>0</mn> <mn>0</mn> </msubsup> </semantics> </math>, <math display="inline"> <semantics> <msubsup> <mi>h</mi> <mn>1</mn> <mn>0</mn> </msubsup> </semantics> </math>, …, <math display="inline"> <semantics> <msubsup> <mi>h</mi> <mrow> <mi>R</mi> <mo>−</mo> <mn>1</mn> </mrow> <mn>0</mn> </msubsup> </semantics> </math>], where <span class="html-italic">R</span> is the number of rules. The function <math display="inline"> <semantics> <msup> <mi>f</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics> </math> (see Equation (<a href="#FD10-sensors-18-01306" class="html-disp-formula">10</a>)) transforms the combined angles into continuous bits (real numbers in the interval <math display="inline"> <semantics> <mrow> <mo stretchy="false">[</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo stretchy="false">]</mo> </mrow> </semantics> </math>) [<math display="inline"> <semantics> <msubsup> <mi>h</mi> <mn>0</mn> <mn>1</mn> </msubsup> </semantics> </math>, <math display="inline"> <semantics> <msubsup> <mi>h</mi> <mn>1</mn> <mn>1</mn> </msubsup> </semantics> </math>, …, <math display="inline"> <semantics> <msubsup> <mi>h</mi> <mrow> <mi>R</mi> <mo>−</mo> <mn>1</mn> </mrow> <mn>1</mn> </msubsup> </semantics> </math>]. Another fully connected layer brings the hidden variables to logits through a softmax layer resulting in a probability <math display="inline"> <semantics> <msub> <mi>π</mi> <mn>0</mn> </msub> </semantics> </math> of <span class="html-italic">C</span> being a ciphertext of <math display="inline"> <semantics> <msub> <mi>P</mi> <mn>0</mn> </msub> </semantics> </math> and a probability <math display="inline"> <semantics> <msub> <mi>π</mi> <mn>1</mn> </msub> </semantics> </math> of <span class="html-italic">C</span> being a ciphertext of <math display="inline"> <semantics> <msub> <mi>P</mi> <mn>1</mn> </msub> </semantics> </math>.</p> Full article ">Figure 6
<p>The challenge of Alice and Bob against Eve. Eve tries to maximize its classification rate while Alice and Bob try to minimize Eve’s classification rate and minimize Bob’s decryption error. In the figure on the left, Bob’s decryption error decreases over time in blue color. Also, on the right, one can see that Eve increases its classification rate in red color, however, when Alice and Bob learn a secure cryptosystem, in this case the OTP, Eve’s classification rate becomes no better than random. The number of steps denote the number of “minibatches” on training phase. In black, we have a smooth version of the red curve.</p> Full article ">
23 pages, 3456 KiB  
Article
Clustering and Flow Conservation Monitoring Tool for Software Defined Networks
by Jesús Antonio Puente Fernández, Luis Javier García Villalba and Tai-Hoon Kim
Sensors 2018, 18(4), 1079; https://doi.org/10.3390/s18041079 - 3 Apr 2018
Cited by 3 | Viewed by 4113
Abstract
Prediction systems present some challenges on two fronts: the relation between video quality and observed session features and on the other hand, dynamics changes on the video quality. Software Defined Networks (SDN) is a new concept of network architecture that provides the separation [...] Read more.
Prediction systems present some challenges on two fronts: the relation between video quality and observed session features and on the other hand, dynamics changes on the video quality. Software Defined Networks (SDN) is a new concept of network architecture that provides the separation of control plane (controller) and data plane (switches) in network devices. Due to the existence of the southbound interface, it is possible to deploy monitoring tools to obtain the network status and retrieve a statistics collection. Therefore, achieving the most accurate statistics depends on a strategy of monitoring and information requests of network devices. In this paper, we propose an enhanced algorithm for requesting statistics to measure the traffic flow in SDN networks. Such an algorithm is based on grouping network switches in clusters focusing on their number of ports to apply different monitoring techniques. Such grouping occurs by avoiding monitoring queries in network switches with common characteristics and then, by omitting redundant information. In this way, the present proposal decreases the number of monitoring queries to switches, improving the network traffic and preventing the switching overload. We have tested our optimization in a video streaming simulation using different types of videos. The experiments and comparison with traditional monitoring techniques demonstrate the feasibility of our proposal maintaining similar values decreasing the number of queries to the switches. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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<p>SDN Architecture.</p> Full article ">Figure 2
<p>Topology Tested.</p> Full article ">Figure 3
<p>Enhanced vs. Non-Enhanced Data and Error Rate of Highway_cif.</p> Full article ">Figure 3 Cont.
<p>Enhanced vs. Non-Enhanced Data and Error Rate of Highway_cif.</p> Full article ">Figure 3 Cont.
<p>Enhanced vs. Non-Enhanced Data and Error Rate of Highway_cif.</p> Full article ">Figure 4
<p>Enhanced vs. Non-Enhanced Data and Error Rate of Akiyo_cif.</p> Full article ">Figure 5
<p>Enhanced vs. Non-Enhanced Data and Error Rate of Bridge-far_cif.</p> Full article ">Figure 6
<p>Enhanced vs. Non-Enhanced Data and Error Rate of Clarie_cif.</p> Full article ">
27 pages, 624 KiB  
Article
Security Architecture and Protocol for Trust Verifications Regarding the Integrity of Files Stored in Cloud Services
by Alexandre Pinheiro, Edna Dias Canedo, Rafael Timoteo De Sousa Junior, Robson De Oliveira Albuquerque, Luis Javier García Villalba and Tai-Hoon Kim
Sensors 2018, 18(3), 753; https://doi.org/10.3390/s18030753 - 2 Mar 2018
Cited by 9 | Viewed by 5231
Abstract
Cloud computing is considered an interesting paradigm due to its scalability, availability and virtually unlimited storage capacity. However, it is challenging to organize a cloud storage service (CSS) that is safe from the client point-of-view and to implement this CSS in public clouds [...] Read more.
Cloud computing is considered an interesting paradigm due to its scalability, availability and virtually unlimited storage capacity. However, it is challenging to organize a cloud storage service (CSS) that is safe from the client point-of-view and to implement this CSS in public clouds since it is not advisable to blindly consider this configuration as fully trustworthy. Ideally, owners of large amounts of data should trust their data to be in the cloud for a long period of time, without the burden of keeping copies of the original data, nor of accessing the whole content for verifications regarding data preservation. Due to these requirements, integrity, availability, privacy and trust are still challenging issues for the adoption of cloud storage services, especially when losing or leaking information can bring significant damage, be it legal or business-related. With such concerns in mind, this paper proposes an architecture for periodically monitoring both the information stored in the cloud and the service provider behavior. The architecture operates with a proposed protocol based on trust and encryption concepts to ensure cloud data integrity without compromising confidentiality and without overloading storage services. Extensive tests and simulations of the proposed architecture and protocol validate their functional behavior and performance. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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<p>Trust-oriented protocol for continuous monitoring of stored files in the cloud (TOPMCloud) processes.</p> Full article ">Figure 2
<p>Time required to complete a file-checking cycle.</p> Full article ">Figure 3
<p>Expected best performing trust level evolution for a CSS.</p> Full article ">Figure 4
<p>Time to upgrade the trust level according to the number of monitored files.</p> Full article ">Figure 5
<p>Number of file-checking failures needed to downgrade to each distrust level.</p> Full article ">Figure 6
<p>Trust level downgrade according to the number of monitored files.</p> Full article ">Figure 7
<p>The client interface showing the “Upload File” function.</p> Full article ">Figure 8
<p>The monitoring module entry screen.</p> Full article ">Figure 9
<p>File status query screen.</p> Full article ">Figure 10
<p>Average time for file encryption and hash generation by file size.</p> Full article ">Figure 11
<p>Required average time for computing cycles and distributing chunks.</p> Full article ">Figure 12
<p>Average time for data block hashing.</p> Full article ">Figure 13
<p>Required average time for sending an information table to the integrity check service (ICS).</p> Full article ">Figure 14
<p>Average daily network bandwidth consumption by stored file.</p> Full article ">Figure 15
<p>Time spent to conclude the processing of a challenge by file size.</p> Full article ">Figure 16
<p>Average time spent by the cloud storage service (CSS) to answer ICS challenges, by CSS and file size.</p> Full article ">Figure 17
<p>Detection by the ICS of integrity faults in files stored in a CSS.</p> Full article ">
16 pages, 2505 KiB  
Article
A Custom Approach for a Flexible, Real-Time and Reliable Software Defined Utility
by Agustín Zaballos, Joan Navarro and Ramon Martín De Pozuelo
Sensors 2018, 18(3), 718; https://doi.org/10.3390/s18030718 - 28 Feb 2018
Cited by 5 | Viewed by 3977
Abstract
Information and communication technologies (ICTs) have enabled the evolution of traditional electric power distribution networks towards a new paradigm referred to as the smart grid. However, the different elements that compose the ICT plane of a smart grid are usually conceived as isolated [...] Read more.
Information and communication technologies (ICTs) have enabled the evolution of traditional electric power distribution networks towards a new paradigm referred to as the smart grid. However, the different elements that compose the ICT plane of a smart grid are usually conceived as isolated systems that typically result in rigid hardware architectures, which are hard to interoperate, manage and adapt to new situations. In the recent years, software-defined systems that take advantage of software and high-speed data network infrastructures have emerged as a promising alternative to classic ad hoc approaches in terms of integration, automation, real-time reconfiguration and resource reusability. The purpose of this paper is to propose the usage of software-defined utilities (SDUs) to address the latent deployment and management limitations of smart grids. More specifically, the implementation of a smart grid’s data storage and management system prototype by means of SDUs is introduced, which exhibits the feasibility of this alternative approach. This system features a hybrid cloud architecture able to meet the data storage requirements of electric utilities and adapt itself to their ever-evolving needs. Conducted experimentations endorse the feasibility of this solution and encourage practitioners to point their efforts in this direction. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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<p>Main software-defined utility modules: context-aware security, Hybrid Cloud Data Management (HCDM) system, web of energy.</p> Full article ">Figure 2
<p>Epidemic replication protocol with three layers. Nodes from public or private clouds can be added to any layer at will. (<b>a</b>) Version 1 of data is generated at the core layer. (<b>b</b>) Version 1 is propagated to the next layer and a new version (i.e., version 2) is generated at core layer. (<b>c</b>) Data from Version 1 and 2 are propagated to their subsequent layers and a new version (i.e., version 3) is generated at the core layer.</p> Full article ">Figure 3
<p>Service composition diagram of the HCDM.</p> Full article ">Figure 4
<p>Test environment built on the FINESCE project facilities. There are three physical locations with three FIDEVs (FInesce DEVices) hosted at each location. The proposed HCDM Application Programming Interface (API) under evaluation is running at all FIDEVs. Each FIDEV is in charge of ten smart meters.</p> Full article ">Figure 5
<p>Evolution of the FIDEVs when exposed to different situations. Ratio of data requests (reads %), data generation (updates %), and overall latency from all the nodes are depicted on the bottom. FIDEVs configurations are depicted on top.</p> Full article ">
20 pages, 932 KiB  
Article
Robust Rate Maximization for Heterogeneous Wireless Networks under Channel Uncertainties
by Yongjun Xu, Yuan Hu and Guoquan Li
Sensors 2018, 18(2), 639; https://doi.org/10.3390/s18020639 - 21 Feb 2018
Cited by 14 | Viewed by 4629
Abstract
Heterogeneous wireless networks are a promising technology in next generation wireless communication networks, which has been shown to efficiently reduce the blind area of mobile communication and improve network coverage compared with the traditional wireless communication networks. In this paper, a robust power [...] Read more.
Heterogeneous wireless networks are a promising technology in next generation wireless communication networks, which has been shown to efficiently reduce the blind area of mobile communication and improve network coverage compared with the traditional wireless communication networks. In this paper, a robust power allocation problem for a two-tier heterogeneous wireless networks is formulated based on orthogonal frequency-division multiplexing technology. Under the consideration of imperfect channel state information (CSI), the robust sum-rate maximization problem is built while avoiding sever cross-tier interference to macrocell user and maintaining the minimum rate requirement of each femtocell user. To be practical, both of channel estimation errors from the femtocells to the macrocell and link uncertainties of each femtocell user are simultaneously considered in terms of outage probabilities of users. The optimization problem is analyzed under no CSI feedback with some cumulative distribution function and partial CSI with Gaussian distribution of channel estimation error. The robust optimization problem is converted into the convex optimization problem which is solved by using Lagrange dual theory and subgradient algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm by the impact of channel uncertainties on the system performance. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>An example of the two-tier HetNet consisting of one macrocell and multiple femtocells.</p> Full article ">Figure 2
<p>Algorithm flow chart.</p> Full article ">Figure 3
<p>Convergence performance of the proposed algorithm.</p> Full article ">Figure 4
<p>Sum data rate of FUs versus the maximum transmit power under different MU outage probabilities.</p> Full article ">Figure 5
<p>Sum data rate of FUs versus the maximum transmit power under different FU outage probabilities.</p> Full article ">Figure 6
<p>Comparison of interference power to the MU versus maximum transmit power.</p> Full article ">Figure 7
<p>The effect of channel uncertainty on the data rate of FUs.</p> Full article ">
5000 KiB  
Article
Application of a Multimedia Service and Resource Management Architecture for Fault Diagnosis
by Alfonso Castro, Andrés A. Sedano, Fco. Javier García, Eduardo Villoslada and Víctor A. Villagrá
Sensors 2018, 18(1), 68; https://doi.org/10.3390/s18010068 - 28 Dec 2017
Cited by 4 | Viewed by 4924
Abstract
Nowadays, the complexity of global video products has substantially increased. They are composed of several associated services whose functionalities need to adapt across heterogeneous networks with different technologies and administrative domains. Each of these domains has different operational procedures; therefore, the comprehensive management [...] Read more.
Nowadays, the complexity of global video products has substantially increased. They are composed of several associated services whose functionalities need to adapt across heterogeneous networks with different technologies and administrative domains. Each of these domains has different operational procedures; therefore, the comprehensive management of multi-domain services presents serious challenges. This paper discusses an approach to service management linking fault diagnosis system and Business Processes for Telefónica’s global video service. The main contribution of this paper is the proposal of an extended service management architecture based on Multi Agent Systems able to integrate the fault diagnosis with other different service management functionalities. This architecture includes a distributed set of agents able to coordinate their actions under the umbrella of a Shared Knowledge Plane, inferring and sharing their knowledge with semantic techniques and three types of automatic reasoning: heterogeneous, ontology-based and Bayesian reasoning. This proposal has been deployed and validated in a real scenario in the video service offered by Telefónica Latam. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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<p>Agent system structure.</p> Full article ">Figure 2
<p>Whole management ontology.</p> Full article ">Figure 3
<p>Video service environment.</p> Full article ">Figure 4
<p>Diagnosis architecture.</p> Full article ">Figure 5
<p>Diagnosis results graph.</p> Full article ">Figure 6
<p>Shared Knowledge Plane schema.</p> Full article ">Figure 7
<p>Normalized entropy of various root causes of faults.</p> Full article ">Figure 8
<p>Fault root cause clusters.</p> Full article ">Figure 9
<p>Density plot of diagnosis duration (in seconds).</p> Full article ">Figure 10
<p>KPI monthly evolution.</p> Full article ">Figure 11
<p>KQI evolution.</p> Full article ">
5374 KiB  
Article
An Indoor Location-Based Control System Using Bluetooth Beacons for IoT Systems
by Jun-Ho Huh and Kyungryong Seo
Sensors 2017, 17(12), 2917; https://doi.org/10.3390/s17122917 - 19 Dec 2017
Cited by 118 | Viewed by 10899
Abstract
The indoor location-based control system estimates the indoor position of a user to provide the service he/she requires. The major elements involved in the system are the localization server, service-provision client, user application positioning technology. The localization server controls access of terminal devices [...] Read more.
The indoor location-based control system estimates the indoor position of a user to provide the service he/she requires. The major elements involved in the system are the localization server, service-provision client, user application positioning technology. The localization server controls access of terminal devices (e.g., Smart Phones and other wireless devices) to determine their locations within a specified space first and then the service-provision client initiates required services such as indoor navigation and monitoring/surveillance. The user application provides necessary data to let the server to localize the devices or allow the user to receive various services from the client. The major technological elements involved in this system are indoor space partition method, Bluetooth 4.0, RSSI (Received Signal Strength Indication) and trilateration. The system also employs the BLE communication technology when determining the position of the user in an indoor space. The position information obtained is then used to control a specific device(s). These technologies are fundamental in achieving a “Smart Living”. An indoor location-based control system that provides services by estimating user’s indoor locations has been implemented in this study (First scenario). The algorithm introduced in this study (Second scenario) is effective in extracting valid samples from the RSSI dataset but has it has some drawbacks as well. Although we used a range-average algorithm that measures the shortest distance, there are some limitations because the measurement results depend on the sample size and the sample efficiency depends on sampling speeds and environmental changes. However, the Bluetooth system can be implemented at a relatively low cost so that once the problem of precision is solved, it can be applied to various fields. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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<p>A basic unit space with beacons.</p> Full article ">Figure 2
<p>Indoor space partitioning for a large space.</p> Full article ">Figure 3
<p>An example of trilateration.</p> Full article ">Figure 4
<p>Selecting the target points for trilateration.</p> Full article ">Figure 5
<p>The COG of a Polygon.</p> Full article ">Figure 6
<p>The schematic of indoor location-based control system.</p> Full article ">Figure 7
<p>The operation process of the localization server.</p> Full article ">Figure 8
<p>A basic screen of the camera-based monitoring program.</p> Full article ">Figure 9
<p>Implementation of the screen settings for camera-based monitoring program.</p> Full article ">Figure 10
<p>Execution of user application (Scenario 1).</p> Full article ">Figure 11
<p>The coordinate system and location beacons deployment plot of the testing space.</p> Full article ">Figure 12
<p>Actual deployment status of location beacons in the testing space.</p> Full article ">Figure 13
<p>Bluetooth module (HM-10) and location beacon experiment tool (Using Scenario 1).</p> Full article ">Figure 14
<p>Distribution graph for the estimated locations.</p> Full article ">Figure 15
<p>TI CC2540 module (Using Scenario 2).</p> Full article ">Figure 16
<p>Position measuring application (Scenario 2). (<b>a</b>) User Interface (1); (<b>b</b>) User Interface (1).</p> Full article ">Figure 17
<p>Trapezoid integration.</p> Full article ">Figure 18
<p>A method of measuring position of equipment.</p> Full article ">Figure 19
<p>An Android application that measures distance to a Beacon (Scenario 2).</p> Full article ">Figure 20
<p>Signal changes after using a smooth filter.</p> Full article ">Figure 21
<p>A concept diagram of the indoor positioning system application.</p> Full article ">
1717 KiB  
Article
Optimal Power Allocation Strategy in a Joint Bistatic Radar and Communication System Based on Low Probability of Intercept
by Chenguang Shi, Fei Wang, Sana Salous and Jianjiang Zhou
Sensors 2017, 17(12), 2731; https://doi.org/10.3390/s17122731 - 25 Nov 2017
Cited by 15 | Viewed by 5960
Abstract
In this paper, we investigate a low probability of intercept (LPI)-based optimal power allocation strategy for a joint bistatic radar and communication system, which is composed of a dedicated transmitter, a radar receiver, and a communication receiver. The joint system is capable of [...] Read more.
In this paper, we investigate a low probability of intercept (LPI)-based optimal power allocation strategy for a joint bistatic radar and communication system, which is composed of a dedicated transmitter, a radar receiver, and a communication receiver. The joint system is capable of fulfilling the requirements of both radar and communications simultaneously. First, assuming that the signal-to-noise ratio (SNR) corresponding to the target surveillance path is much weaker than that corresponding to the line of sight path at radar receiver, the analytically closed-form expression for the probability of false alarm is calculated, whereas the closed-form expression for the probability of detection is not analytically tractable and is approximated due to the fact that the received signals are not zero-mean Gaussian under target presence hypothesis. Then, an LPI-based optimal power allocation strategy is presented to minimize the total transmission power for information signal and radar waveform, which is constrained by a specified information rate for the communication receiver and the desired probabilities of detection and false alarm for the radar receiver. The well-known bisection search method is employed to solve the resulting constrained optimization problem. Finally, numerical simulations are provided to reveal the effects of several system parameters on the power allocation results. It is also demonstrated that the LPI performance of the joint bistatic radar and communication system can be markedly improved by utilizing the proposed scheme. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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<p>Joint bistatic radar and communication system with a dedicated transmitter, a radar receiver, and a communication receiver.</p> Full article ">Figure 2
<p>Probability of detection <math display="inline"> <semantics> <msub> <mi>p</mi> <mi mathvariant="normal">D</mi> </msub> </semantics> </math> versus transmit power for radar waveform <math display="inline"> <semantics> <msub> <mi>P</mi> <mi>rad</mi> </msub> </semantics> </math> with different <math display="inline"> <semantics> <msub> <mi>γ</mi> <mi>rad</mi> </msub> </semantics> </math> (<math display="inline"> <semantics> <mrow> <msub> <mi>δ</mi> <mi>FA</mi> </msub> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>-</mo> <mn>6</mn> </mrow> </msup> </mrow> </semantics> </math>).</p> Full article ">Figure 3
<p>Probability of detection <math display="inline"> <semantics> <msub> <mi>p</mi> <mi mathvariant="normal">D</mi> </msub> </semantics> </math> versus total transmit power <math display="inline"> <semantics> <msub> <mi>P</mi> <mi>Total</mi> </msub> </semantics> </math> with different <math display="inline"> <semantics> <msub> <mi>r</mi> <mi>th</mi> </msub> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>γ</mi> <mi>com</mi> </msub> </semantics> </math> (<math display="inline"> <semantics> <mrow> <msub> <mi>δ</mi> <mi>FA</mi> </msub> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>-</mo> <mn>6</mn> </mrow> </msup> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>γ</mi> <mi>rad</mi> </msub> <mo>=</mo> <mo>-</mo> <mn>10</mn> <mspace width="3.33333pt"/> <mi>dB</mi> </mrow> </semantics> </math>).</p> Full article ">Figure 4
<p>Variation in probability of detection <math display="inline"> <semantics> <msub> <mi>p</mi> <mi mathvariant="normal">D</mi> </msub> </semantics> </math> with change in total transmit power <math display="inline"> <semantics> <msub> <mi>P</mi> <mi>Total</mi> </msub> </semantics> </math> and information rate <span class="html-italic">R</span> (<math display="inline"> <semantics> <mrow> <msub> <mi>δ</mi> <mi>FA</mi> </msub> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>-</mo> <mn>6</mn> </mrow> </msup> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>γ</mi> <mi>rad</mi> </msub> <mo>=</mo> <mo>-</mo> <mn>10</mn> <mspace width="3.33333pt"/> <mi>dB</mi> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>γ</mi> <mi>com</mi> </msub> <mo>=</mo> <mo>-</mo> <mn>5</mn> <mspace width="3.33333pt"/> <mi>dB</mi> </mrow> </semantics> </math>).</p> Full article ">Figure 5
<p>Variation in probability of detection <math display="inline"> <semantics> <msub> <mi>p</mi> <mi mathvariant="normal">D</mi> </msub> </semantics> </math> with change in <math display="inline"> <semantics> <msub> <mi>γ</mi> <mi>rad</mi> </msub> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>γ</mi> <mi>com</mi> </msub> </semantics> </math> (<math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>rad</mi> </msub> <mo>=</mo> <mn>500</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">W</mi> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>δ</mi> <mi>FA</mi> </msub> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>-</mo> <mn>6</mn> </mrow> </msup> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>r</mi> <mi>th</mi> </msub> <mo>=</mo> <mn>2</mn> <mspace width="3.33333pt"/> <mi>bpcu</mi> </mrow> </semantics> </math>).</p> Full article ">Figure 6
<p>Comparisons of transmit power for radar waveform and communication rate employing different algorithms (<math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>Total</mi> </msub> <mo>=</mo> <mn>1000</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">W</mi> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>δ</mi> <mi mathvariant="normal">D</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>9</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>δ</mi> <mi>FA</mi> </msub> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>-</mo> <mn>6</mn> </mrow> </msup> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>γ</mi> <mi>rad</mi> </msub> <mo>=</mo> <mo>-</mo> <mn>10</mn> <mspace width="3.33333pt"/> <mi>dB</mi> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>γ</mi> <mi>com</mi> </msub> <mo>=</mo> <mo>-</mo> <mn>5</mn> <mspace width="3.33333pt"/> <mi>dB</mi> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>r</mi> <mi>th</mi> </msub> <mo>=</mo> <mn>5</mn> <mspace width="3.33333pt"/> <mi>bpcu</mi> </mrow> </semantics> </math>).</p> Full article ">
1384 KiB  
Article
Reasoning and Knowledge Acquisition Framework for 5G Network Analytics
by Marco Antonio Sotelo Monge, Jorge Maestre Vidal and Luis Javier García Villalba
Sensors 2017, 17(10), 2405; https://doi.org/10.3390/s17102405 - 21 Oct 2017
Cited by 12 | Viewed by 5961
Abstract
Autonomic self-management is a key challenge for next-generation networks. This paper proposes an automated analysis framework to infer knowledge in 5G networks with the aim to understand the network status and to predict potential situations that might disrupt the network operability. The framework [...] Read more.
Autonomic self-management is a key challenge for next-generation networks. This paper proposes an automated analysis framework to infer knowledge in 5G networks with the aim to understand the network status and to predict potential situations that might disrupt the network operability. The framework is based on the Endsley situational awareness model, and integrates automated capabilities for metrics discovery, pattern recognition, prediction techniques and rule-based reasoning to infer anomalous situations in the current operational context. Those situations should then be mitigated, either proactive or reactively, by a more complex decision-making process. The framework is driven by a use case methodology, where the network administrator is able to customize the knowledge inference rules and operational parameters. The proposal has also been instantiated to prove its adaptability to a real use case. To this end, a reference network traffic dataset was used to identify suspicious patterns and to predict the behavior of the monitored data volume. The preliminary results suggest a good level of accuracy on the inference of anomalous traffic volumes based on a simple configuration. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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<p>The SELFNET project architecture.</p> Full article ">Figure 2
<p>Knowledge acquisition framework for 5G environments.</p> Full article ">Figure 3
<p>Discordant observations at novelty detection for CAIDA’16-sample.</p> Full article ">Figure 4
<p>Metric variations on samples. (<b>a</b>) Euclidean; (<b>b</b>) Quadratic <math display="inline"> <semantics> <msup> <mi>X</mi> <mn>2</mn> </msup> </semantics> </math>; (<b>c</b>) Canberra; (<b>d</b>) Pearson; (<b>e</b>) Bhattacharyya; (<b>f</b>) Divergence.</p> Full article ">Figure 5
<p>Evolution of prediction and adaptive thresholding on CAIDA’16 sample. (<b>a</b>) Forecast <span class="html-italic">H</span> = 1; (<b>b</b>) Threhsolding <span class="html-italic">H</span> = 1; (<b>c</b>) Forecast <span class="html-italic">H</span> = 5; (<b>d</b>) Threhsolding <span class="html-italic">H</span> = 5; (<b>e</b>) Forecast <span class="html-italic">H</span> = 10; (<b>f</b>) Threhsolding <span class="html-italic">H</span> = 10.</p> Full article ">Figure 6
<p>Normal observation rate when varying <span class="html-italic">K</span>.</p> Full article ">Figure 7
<p>Thresholds and Unexpected traffic on CAIDA’16-sample. (<b>a</b>) Traffic volume variation on CAIDA’16-sample; (<b>b</b>) Unexpected observations on CAIDA’16-sample.</p> Full article ">Figure 8
<p>Evolution of observations, thresholding and novelty detection on CAIDA’16 monthly. (<b>a</b>) Observations (January); (<b>b</b>) Symptoms (January); (<b>c</b>) Observations (February); (<b>d</b>) Symptoms (February); (<b>e</b>) Observations (March); (<b>f</b>) Symptoms (March); (<b>g</b>) Observations (April); (<b>h</b>) Symptoms (April).</p> Full article ">
9132 KiB  
Article
Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks
by Ángel Leonardo Valdivieso Caraguay and Luis Javier García Villalba
Sensors 2017, 17(4), 731; https://doi.org/10.3390/s17040731 - 31 Mar 2017
Cited by 14 | Viewed by 8308
Abstract
This paper presents the Monitoring and Discovery Framework of the Self-Organized Network Management in Virtualized and Software Defined Networks SELFNET project. This design takes into account the scalability and flexibility requirements needed by 5G infrastructures. In this context, the present framework focuses on [...] Read more.
This paper presents the Monitoring and Discovery Framework of the Self-Organized Network Management in Virtualized and Software Defined Networks SELFNET project. This design takes into account the scalability and flexibility requirements needed by 5G infrastructures. In this context, the present framework focuses on gathering and storing the information (low-level metrics) related to physical and virtual devices, cloud environments, flow metrics, SDN traffic and sensors. Similarly, it provides the monitoring data as a generic information source in order to allow the correlation and aggregation tasks. Our design enables the collection and storing of information provided by all the underlying SELFNET sublayers, including the dynamically onboarded and instantiated SDN/NFV Apps, also known as SELFNET sensors. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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<p>SDN Architecture.</p> Full article ">Figure 2
<p>Traditional Network Functions vs. Virtual Network Functions.</p> Full article ">Figure 3
<p>NFV architecture.</p> Full article ">Figure 4
<p>SELFNET Architecture Overview.</p> Full article ">Figure 5
<p>Monitor and Analyzer Sublayer Interfaces.</p> Full article ">Figure 6
<p>Monitoring and Discovery Framework Architecture.</p> Full article ">Figure 7
<p>Data Source.</p> Full article ">Figure 8
<p>Sensor Onboarding Workflow.</p> Full article ">Figure 9
<p>Sensor Instantiation Workflow.</p> Full article ">Figure 10
<p>Sensor Monitoring—Metrics and/or Events Workflow.</p> Full article ">Figure 11
<p>Sensor Monitoring—Data Source Reconfiguration Workflow.</p> Full article ">Figure 12
<p>Sensor Instance Removal Workflow.</p> Full article ">Figure 13
<p>Ceilometer Architecture.</p> Full article ">Figure 14
<p>Mapping between Ceilosca and SELFNET Monitoring Framework.</p> Full article ">Figure 15
<p>Data Source Manager: Sensor Operations.</p> Full article ">Figure 16
<p>SELFNET Monitoring Framework Testbed.</p> Full article ">Figure 17
<p>SELFNET Monitoring GUI navigability.</p> Full article ">Figure 18
<p>List of SELFNET virtual elements on the Virtual Layer View.</p> Full article ">Figure 19
<p>Details of information gathered by a SELFNET Sensor.</p> Full article ">
19228 KiB  
Article
The Impact of 3D Stacking and Technology Scaling on the Power and Area of Stereo Matching Processors
by Seung-Ho Ok, Yong-Hwan Lee, Jae Hoon Shim, Sung Kyu Lim and Byungin Moon
Sensors 2017, 17(2), 426; https://doi.org/10.3390/s17020426 - 22 Feb 2017
Cited by 2 | Viewed by 5759
Abstract
Recently, stereo matching processors have been adopted in real-time embedded systems such as intelligent robots and autonomous vehicles, which require minimal hardware resources and low power consumption. Meanwhile, thanks to the through-silicon via (TSV), three-dimensional (3D) stacking technology has emerged as a practical [...] Read more.
Recently, stereo matching processors have been adopted in real-time embedded systems such as intelligent robots and autonomous vehicles, which require minimal hardware resources and low power consumption. Meanwhile, thanks to the through-silicon via (TSV), three-dimensional (3D) stacking technology has emerged as a practical solution to achieving the desired requirements of a high-performance circuit. In this paper, we present the benefits of 3D stacking and process technology scaling on stereo matching processors. We implemented 2-tier 3D-stacked stereo matching processors with GlobalFoundries 130-nm and Nangate 45-nm process design kits and compare them with their two-dimensional (2D) counterparts to identify comprehensive design benefits. In addition, we examine the findings from various analyses to identify the power benefits of 3D-stacked integrated circuit (IC) and device technology advancements. From experiments, we observe that the proposed 3D-stacked ICs, compared to their 2D IC counterparts, obtain 43% area, 13% power, and 14% wire length reductions. In addition, we present a logic partitioning method suitable for a pipeline-based hardware architecture that minimizes the use of TSVs. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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<p>(<b>a</b>) Left image (R<sub>win</sub>: reference window); (<b>b</b>) right image (d<sub>x</sub>: disparity range, C<sub>win</sub>: candidate window); (<b>c</b>) dissimilarity between R<sub>win</sub> and C<sub>win</sub>; and (<b>d</b>) a depth map.</p> Full article ">Figure 2
<p>Flow diagram of the stereo matching processor.</p> Full article ">Figure 3
<p>Illustration of the multiple-read, single-write operation of the stereo matching algorithm.</p> Full article ">Figure 4
<p>Pipelined hardware architecture of our stereo matching processor.</p> Full article ">Figure 5
<p>Via-first bonding technology used in this paper: (<b>a</b>) Side view of via-first TSVs; and (<b>b</b>) top-down view of TSVs.</p> Full article ">Figure 6
<p>2D and 3D IC design flow.</p> Full article ">Figure 7
<p>(<b>a</b>) The conventional macro-level partitioning method; and (<b>b</b>) the proposed pipeline-level partitioning method.</p> Full article ">Figure 8
<p>An illustration of the proposed pipeline-level partitioning method: (<b>a</b>) Split the pipeline stages into two tiers, and (<b>b</b>) adjust the number of SRAMs in each tier.</p> Full article ">Figure 9
<p>Overall flow of the power and timing analyses for a 3D IC.</p> Full article ">Figure 10
<p>Comparisons between the normalized designs of 2D and 3D ICs: (<b>a</b>) 2D and 3D ICs designed in 130-nm process technology; and (<b>b</b>) 2D and 3D ICs designed in 45-nm process technology.</p> Full article ">Figure 11
<p>Layout snapshots of 2D and 3D ICs designed in 130-nm process technology: (<b>a</b>) 2D IC (2D-130); (<b>b</b>) the top and bottom tiers of a 3D IC using macro-level partitioning (3D-MP-130); and (<b>c</b>) the top and bottom tiers of a 3D IC using pipeline-level partitioning (3D-PP-130).</p> Full article ">Figure 12
<p>Layout snapshots of 2D and 3D ICs designed in 45-nm process technology: (<b>a</b>) 2D IC (2D-45); (<b>b</b>) the top and bottom tiers of a 3D IC using macro-level partitioning (3D-MP-45); and (<b>c</b>) the top and bottom tiers of a 3D IC using pipeline-level partitioning (3D-PP-45).</p> Full article ">Figure 13
<p>Normalized power comparisons of 2D and 3D ICs: (<b>a</b>) 130-nm process technology and (<b>b</b>) 45-nm process technology.</p> Full article ">Figure 14
<p>Normalized power comparisons of 2D and 3D ICs: (<b>a</b>) 130-nm process technology and (<b>b</b>) 45-nm process technology.</p> Full article ">Figure 15
<p>Comparisons of the normalized power of 2D and 3D ICs as a function of switching activity: (<b>a</b>) Total power; (<b>b</b>) net switching power; (<b>c</b>) cell internal power; (<b>d</b>) cell leakage power. Note that the power consumption of 2D-130 actually increases as the switching activity increases.</p> Full article ">Figure 16
<p>Comparisons of the normalized power of 2D and 3D ICs as a function of switching activity: (<b>a</b>) Total power; (<b>b</b>) net switching power; (<b>c</b>) cell internal power; (<b>d</b>) cell leakage power. Note that the power consumption of 2D-45 actually increases as the switching activity increases.</p> Full article ">
1358 KiB  
Article
Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network
by Sangil Choi and Jong Hyuk Park
Sensors 2016, 16(12), 2056; https://doi.org/10.3390/s16122056 - 2 Dec 2016
Cited by 5 | Viewed by 5081
Abstract
Wireless mesh networks (WMNs) have been considered as one of the key technologies for the configuration of wireless machines since they emerged. In a WMN, wireless routers provide multi-hop wireless connectivity between hosts in the network and also allow them to access the [...] Read more.
Wireless mesh networks (WMNs) have been considered as one of the key technologies for the configuration of wireless machines since they emerged. In a WMN, wireless routers provide multi-hop wireless connectivity between hosts in the network and also allow them to access the Internet via gateway devices. Wireless routers are typically equipped with multiple radios operating on different channels to increase network throughput. Multicast is a form of communication that delivers data from a source to a set of destinations simultaneously. It is used in a number of applications, such as distributed games, distance education, and video conferencing. In this study, we address a channel assignment problem for multicast in multi-radio multi-channel WMNs. In a multi-radio multi-channel WMN, two nearby nodes will interfere with each other and cause a throughput decrease when they transmit on the same channel. Thus, an important goal for multicast channel assignment is to reduce the interference among networked devices. We have developed a minimum interference channel assignment (MICA) algorithm for multicast that accurately models the interference relationship between pairs of multicast tree nodes using the concept of the interference factor and assigns channels to tree nodes to minimize interference within the multicast tree. Simulation results show that MICA achieves higher throughput and lower end-to-end packet delay compared with an existing channel assignment algorithm named multi-channel multicast (MCM). In addition, MICA achieves much lower throughput variation among the destination nodes than MCM. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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<p>An example of channel assignment by MCM.</p> Full article ">Figure 2
<p>Multicast tree and children of S and A.</p> Full article ">Figure 3
<p>Channel assignment example by MICA.</p> Full article ">Figure 4
<p>Average packets received by multi-receivers.</p> Full article ">Figure 5
<p>Standard deviation of all packets received by multi-receivers.</p> Full article ">Figure 6
<p>Impact of a different number of multi-receivers.</p> Full article ">Figure 7
<p>Comparison of end-to-end packet delay.</p> Full article ">
1572 KiB  
Article
A Reliable TTP-Based Infrastructure with Low Sensor Resource Consumption for the Smart Home Multi-Platform
by Jungho Kang, Mansik Kim and Jong Hyuk Park
Sensors 2016, 16(7), 1036; https://doi.org/10.3390/s16071036 - 5 Jul 2016
Cited by 7 | Viewed by 11857
Abstract
With the ICT technology making great progress in the smart home environment, the ubiquitous environment is rapidly emerging all over the world, but problems are also increasing proportionally to the rapid growth of the smart home market such as multiplatform heterogeneity and new [...] Read more.
With the ICT technology making great progress in the smart home environment, the ubiquitous environment is rapidly emerging all over the world, but problems are also increasing proportionally to the rapid growth of the smart home market such as multiplatform heterogeneity and new security threats. In addition, the smart home sensors have so low computing resources that they cannot process complicated computation tasks, which is required to create a proper security environment. A service provider also faces overhead in processing data from a rapidly increasing number of sensors. This paper aimed to propose a scheme to build infrastructure in which communication entities can securely authenticate and design security channel with physically unclonable PUFs and the TTP that smart home communication entities can rely on. In addition, we analyze and evaluate the proposed scheme for security and performance and prove that it can build secure channels with low resources. Finally, we expect that the proposed scheme can be helpful for secure communication with low resources in future smart home multiplatforms. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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<p>Smart home infrastructure.</p> Full article ">Figure 2
<p>Proposed smart home multiplatform infrastructure.</p> Full article ">Figure 3
<p>Provisioning phase: (<b>a</b>) sensor provisioning; (<b>b</b>) gateway provisioning.</p> Full article ">Figure 4
<p>Authentication phase.</p> Full article ">
10488 KiB  
Article
Computer-Aided Sensor Development Focused on Security Issues
by Andrzej Bialas
Sensors 2016, 16(6), 759; https://doi.org/10.3390/s16060759 - 26 May 2016
Cited by 8 | Viewed by 7458
Abstract
The paper examines intelligent sensor and sensor system development according to the Common Criteria methodology, which is the basic security assurance methodology for IT products and systems. The paper presents how the development process can be supported by software tools, design patterns and [...] Read more.
The paper examines intelligent sensor and sensor system development according to the Common Criteria methodology, which is the basic security assurance methodology for IT products and systems. The paper presents how the development process can be supported by software tools, design patterns and knowledge engineering. The automation of this process brings cost-, quality-, and time-related advantages, because the most difficult and most laborious activities are software-supported and the design reusability is growing. The paper includes a short introduction to the Common Criteria methodology and its sensor-related applications. In the experimental section the computer-supported and patterns-based IT security development process is presented using the example of an intelligent methane detection sensor. This process is supported by an ontology-based tool for security modeling and analyses. The verified and justified models are transferred straight to the security target specification representing security requirements for the IT product. The novelty of the paper is to provide a patterns-based and computer-aided methodology for the sensors development with a view to achieving their IT security assurance. The paper summarizes the validation experiment focused on this methodology adapted for the sensors system development, and presents directions of future research. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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<p>Block scheme of the CCMODE Tools suite.</p> Full article ">Figure 2
<p>Configuring the MethSens project—EAL2 and its evidences.</p> Full article ">Figure 3
<p>General view of the MethSens security model presented in the CCMODE EA-plugin—Security problem definition diagram.</p> Full article ">Figure 4
<p>Generics expressing an illegal access attack. (<b>A</b>) Threat <span class="html-italic">TDA.Access</span> generic and its property; (<b>B</b>) Properties of the subject <span class="html-italic">SNH.HighPotIntruder</span>; (<b>C</b>) Data asset <span class="html-italic">DTO.SensorData</span> and its property.</p> Full article ">Figure 5
<p>Generics expressing an attack against the sensor identifier. (<b>A</b>) Threat <span class="html-italic">TDA.ReplaceNode</span> generic and its property; (<b>B</b>) Properties of the subject <span class="html-italic">SNH.HighPotIntruder</span>; (<b>C</b>) Data asset <span class="html-italic">DTO.SensorID</span> and its property.</p> Full article ">Figure 6
<p>Security objectives selection based on the ontology-produced rank list.</p> Full article ">Figure 7
<p>General view of the MethSens security model presented in the CCMODE EA-plugin—a part of the security objectives diagram.</p> Full article ">Figure 8
<p>MethSens security model presented in the CCMODE EA-plugin—mapping security functional requirements to security objectives.</p> Full article ">Figure 9
<p>General view of the MethSens security model presented in the CCMODE EA-plugin—security functional requirements grouped by TOE security functions.</p> Full article ">Figure 10
<p>Security model transferred to the elaborated evidence presented in the CCMODE GenDoc application.</p> Full article ">Figure 11
<p>Part of the security target automatically elaborated on the basis of the security model with the use of the CCMODE GenDoc application.</p> Full article ">Figure 12
<p>Self-evaluation of the MethSens security target presented by the CCMODE GenDoc application.</p> Full article ">
3157 KiB  
Article
Implementation Strategies for a Universal Acquisition and Tracking Channel Applied to Real GNSS Signals
by Marc-Antoine Fortin and René Landry, Jr.
Sensors 2016, 16(5), 624; https://doi.org/10.3390/s16050624 - 2 May 2016
Cited by 6 | Viewed by 6741
Abstract
This paper presents a universal GNSS receiver channel capable of tracking any civil GNSS signal. This fundamentally differs from dedicated channels, each customized for a given signal. A mobile device could integrate fewer universal channels to harvest all available signals. This would allow [...] Read more.
This paper presents a universal GNSS receiver channel capable of tracking any civil GNSS signal. This fundamentally differs from dedicated channels, each customized for a given signal. A mobile device could integrate fewer universal channels to harvest all available signals. This would allow securing signal availability, while minimizing power consumption and chip size, thus maximizing battery lifetime. In fact, the universal channel allows sequential acquisition and tracking of any chipping rate, carrier frequency, FDMA channel, modulation, or constellation, and is totally configurable (any integration time, any discriminator, etc.). It can switch from one signal to another in 1.07 ms, making it possible for the receiver to rapidly adapt to its sensed environment. All this would consume 3.5 mW/channel in an ASIC implementation, i.e., with a slight overhead compared to the original GPS L1 C/A dedicated channel from which it was derived. After extensive surveys on GNSS signals and tracking channels, this paper details the implementation strategies that led to the proposed universal channel architecture. Validation is achieved using GNSS signals issued from different constellations, frequency bands, modulations and spreading code schemes. A discussion on acquisition approaches and conclusive remarks follow, which open up a new signal selection challenge, rather than satellite selection. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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<p>Tracking Channel Simplified Architecture.</p> Full article ">Figure 2
<p>Sub-Carriers and Spreading Codes Module (BPSK <span class="html-italic">vs.</span> MBOC).</p> Full article ">Figure 3
<p>MBOC Sub-Carriers Multi-Bit Simplification and Combination of both Data and Pilot Components (Differential = Early − Late; Prompt)</p> Full article ">Figure 4
<p>Spreading Codes Module (BPSK <span class="html-italic">vs.</span> TMBPSK Overhead).</p> Full article ">Figure 5
<p>Single-Component Products and Correlation (BPSK <span class="html-italic">vs.</span> MBOC Overhead).</p> Full article ">Figure 6
<p>Infinite Bandwidth BPSK (1) and BOC(1,1) − n = 2 − Coherent and Non-Coherent Normalized Correlation Functions.</p> Full article ">Figure 7
<p>Effect of Correlator Spacing (<math display="inline"> <semantics> <mstyle mathvariant="bold" mathsize="normal"> <mi>Δ</mi> </mstyle> </semantics> </math> (chip)) on a BOC(1,1) Coherent EML Discriminator (assuming an infinite front-end bandwidth).</p> Full article ">Figure 8
<p><span class="html-italic">b</span> and <math display="inline"> <semantics> <mrow> <msub> <mi>δ</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics> </math> <span class="html-italic">vs.</span> <math display="inline"> <semantics> <mrow> <mn>1</mn> <mo>/</mo> <msub> <mi>T</mi> <mi>s</mi> </msub> </mrow> </semantics> </math> with a 22.3 MHz Front-end Bandwidth at 60 MHz.</p> Full article ">Figure 9
<p>Proposed Universal Channel High Level Architecture.</p> Full article ">Figure 10
<p>GPS L1 C/A LoS Variation (s) Observed every 1 ms Epoch, Based on 10 ms Non-Coherent Integration Time DLL Feedback Commands.</p> Full article ">Figure 11
<p>Propagation Time Noise During 40 sLegend: (signal type) <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <mrow> <mrow> <mo>〈</mo> <mrow> <mi mathvariant="bold-italic">C</mi> <mo>/</mo> <msub> <mi mathvariant="bold-italic">N</mi> <mn>0</mn> </msub> </mrow> <mo>〉</mo> </mrow> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> : (pseudo-range noise standard deviation).</p> Full article ">Figure 12
<p>Different Correlator Spacing (Through Different Signals) Impact on DLL Noise <span class="html-italic">vs.</span> <math display="inline"> <semantics> <mrow> <mi>C</mi> <mo>/</mo> <msub> <mi>N</mi> <mn>0</mn> </msub> </mrow> </semantics> </math>.</p> Full article ">Figure 13
<p>Relative 2D error of a GPS L1 C/A WAAS Augmented Solution.</p> Full article ">
10383 KiB  
Article
Robust Stability of Scaled-Four-Channel Teleoperation with Internet Time-Varying Delays
by Emma Delgado, Antonio Barreiro, Pablo Falcón and Miguel Díaz-Cacho
Sensors 2016, 16(5), 593; https://doi.org/10.3390/s16050593 - 26 Apr 2016
Cited by 8 | Viewed by 5364
Abstract
We describe the application of a generic stability framework for a teleoperation system under time-varying delay conditions, as addressed in a previous work, to a scaled-four-channel (γ-4C) control scheme. Described is how varying delays are dealt with by means of dynamic encapsulation, giving [...] Read more.
We describe the application of a generic stability framework for a teleoperation system under time-varying delay conditions, as addressed in a previous work, to a scaled-four-channel (γ-4C) control scheme. Described is how varying delays are dealt with by means of dynamic encapsulation, giving rise to mu-test conditions for robust stability and offering an appealing frequency technique to deal with the stability robustness of the architecture. We discuss ideal transparency problems and we adapt classical solutions so that controllers are proper, without single or double differentiators, and thus avoid the negative effects of noise. The control scheme was fine-tuned and tested for complete stability to zero of the whole state, while seeking a practical solution to the trade-off between stability and transparency in the Internet-based teleoperation. These ideas were tested on an Internet-based application with two Omni devices at remote laboratory locations via simulations and real remote experiments that achieved robust stability, while performing well in terms of position synchronization and force transparency. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Generic teleoperation system.</p> Full article ">Figure 2
<p>Local controllers in the 4C control scheme.</p> Full article ">Figure 3
<p>Teleoperation loop.</p> Full article ">Figure 4
<p>(<b>a</b>) LTI system with time-varying delay in the feedback loop; (<b>b</b>) Loop transformation.</p> Full article ">Figure 5
<p>Simulated sine reference force: PE control scheme with hmax = 0, d = 0. (<b>a</b>) Positions; (<b>b</b>) Forces.</p> Full article ">Figure 6
<p>Simulated sine reference force: 3C-i control scheme with <span class="html-italic">h<sub>max</sub></span> = 0, <span class="html-italic">d</span> = 0. (<b>a</b>) Positions; (<b>b</b>) Forces.</p> Full article ">Figure 7
<p>Simulated sine reference force: 3C-ii control scheme with <span class="html-italic">h<sub>max</sub></span> = 0, <span class="html-italic">d</span> = 0. (<b>a</b>) Positions; (<b>b</b>) Forces.</p> Full article ">Figure 8
<p>Simulated sine reference force: γ-4C control scheme with <span class="html-italic">h<sub>max</sub></span> = 0, <span class="html-italic">d</span> = 0. (<b>a</b>) Positions; (<b>b</b>) Forces.</p> Full article ">Figure 9
<p>Simulated f<sub>h</sub> = f<sub>e</sub> = 0 with <span class="html-italic">h<sub>max</sub></span> = 0, <span class="html-italic">d</span> = 0. Positions. (<b>a</b>) PE; (<b>b</b>) γ-4C.</p> Full article ">Figure 10
<p>Simulated ramp-step reference force: PE control scheme with <span class="html-italic">h<sub>max</sub></span> = 0, <span class="html-italic">d</span> = 0. (<b>a</b>) Positions; (<b>b</b>) Forces.</p> Full article ">Figure 11
<p>Simulated ramp-step reference force: γ-4C control scheme with <span class="html-italic">h<sub>max</sub></span> = 0, <span class="html-italic">d</span> = 0. (<b>a</b>) Positions; (<b>b</b>) Forces.</p> Full article ">Figure 12
<p>Simulated sine reference force: γ-4C control scheme, Ps ≠ P, with <span class="html-italic">h<sub>max</sub></span> = 0, <span class="html-italic">d</span> = 0. (<b>a</b>) Positions; (<b>b</b>) Forces.</p> Full article ">Figure 13
<p>Upper bound on <span class="html-italic">μ</span> for <span class="html-italic">d</span>, <span class="html-italic">h</span>, |<span class="html-italic">η</span>(t)| ≤ 0.01.</p> Full article ">Figure 14
<p>γ<sub>min</sub> (<span class="html-italic">z</span> axis) needed to ensure stability given the delay bounds.</p> Full article ">Figure 15
<p>Local teleoperation using linear models of master and slave with <span class="html-italic">h<sub>max</sub></span> = 0.5 s and <span class="html-italic">d</span> = 0.45. (<b>a</b>) Positions; (<b>b</b>) Forces.</p> Full article ">Figure 16
<p>Local teleoperation using haptic devices with <span class="html-italic">h<sub>max</sub></span> = 0.5 s and <span class="html-italic">d</span> = 0.45. (<b>a</b>) Positions; (<b>b</b>) Forces.</p> Full article ">Figure 17
<p>Remote teleoperation between Vigo and Manchester using linear models of master and slave. (<b>a</b>) Positions; (<b>b</b>) Forces.</p> Full article ">Figure 18
<p>Real remote teleoperation between Vigo and Manchester using haptic devices. (<b>a</b>) Positions; (<b>b</b>) Forces.</p> Full article ">Figure 19
<p>Experimental delay bounds between Vigo and Manchester. (<b>Top</b>) Magnitude; (<b>Bottom</b>) Variation.</p> Full article ">
2940 KiB  
Article
A Comparison of Alternative Distributed Dynamic Cluster Formation Techniques for Industrial Wireless Sensor Networks
by Mohammad Gholami and Robert W. Brennan
Sensors 2016, 16(1), 65; https://doi.org/10.3390/s16010065 - 6 Jan 2016
Cited by 18 | Viewed by 7008
Abstract
In this paper, we investigate alternative distributed clustering techniques for wireless sensor node tracking in an industrial environment. The research builds on extant work on wireless sensor node clustering by reporting on: (1) the development of a novel distributed management approach for tracking [...] Read more.
In this paper, we investigate alternative distributed clustering techniques for wireless sensor node tracking in an industrial environment. The research builds on extant work on wireless sensor node clustering by reporting on: (1) the development of a novel distributed management approach for tracking mobile nodes in an industrial wireless sensor network; and (2) an objective comparison of alternative cluster management approaches for wireless sensor networks. To perform this comparison, we focus on two main clustering approaches proposed in the literature: pre-defined clusters and ad hoc clusters. These approaches are compared in the context of their reconfigurability: more specifically, we investigate the trade-off between the cost and the effectiveness of competing strategies aimed at adapting to changes in the sensing environment. To support this work, we introduce three new metrics: a cost/efficiency measure, a performance measure, and a resource consumption measure. The results of our experiments show that ad hoc clusters adapt more readily to changes in the sensing environment, but this higher level of adaptability is at the cost of overall efficiency. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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Figure 1

Figure 1
<p>Mobile Node tracking in a factory wireless sensor network (WSN).</p> Full article ">Figure 2
<p>Adaptability of the auction-based cluster formation technique in response to signal blockage.</p> Full article ">Figure 3
<p>Clusters formed by the <span class="html-italic">Ad Hoc</span> technique.</p> Full article ">Figure 4
<p>Clusters formed by the Dynamic technique.</p> Full article ">Figure 5
<p>Clusters formed by the Static technique.</p> Full article ">Figure 6
<p>Main effect plot for load distribution. <span class="html-italic">n</span> = 160, ANOVA, 1 <span class="html-italic">df</span>, <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 7
<p>Interval plot for load distribution. <span class="html-italic">n</span> = 160, ANOVA, 1 <span class="html-italic">df</span>, <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 8
<p>Main effect plot for effectiveness. <span class="html-italic">n</span> = 160, ANOVA, 1 <span class="html-italic">df</span>, <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 9
<p>Interval plot for effectiveness. <span class="html-italic">n</span> = 160, ANOVA, 1 <span class="html-italic">df</span>, <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 10
<p>Main effect plot for resource consumption. <span class="html-italic">n</span> = 160, ANOVA, 1 <span class="html-italic">df</span>, <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 11
<p>Interval plot for resource consumption. <span class="html-italic">n</span> = 160, ANOVA, 1 <span class="html-italic">df</span>, <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 12
<p>Interval plot for tracking accuracy. <span class="html-italic">n</span> = 160, ANOVA, 1 <span class="html-italic">df</span>, <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 13
<p>Agent interaction diagram showing distributed cluster formation using the static technique.</p> Full article ">Figure 14
<p>Agent interaction diagram showing distributed cluster formation using the dynamic technique.</p> Full article ">
2431 KiB  
Article
An Enhanced Error Model for EKF-Based Tightly-Coupled Integration of GPS and Land Vehicle’s Motion Sensors
by Tashfeen B. Karamat, Mohamed M. Atia and Aboelmagd Noureldin
Sensors 2015, 15(9), 24269-24296; https://doi.org/10.3390/s150924269 - 22 Sep 2015
Cited by 17 | Viewed by 6920
Abstract
Reduced inertial sensor systems (RISS) have been introduced by many researchers as a low-cost, low-complexity sensor assembly that can be integrated with GPS to provide a robust integrated navigation system for land vehicles. In earlier works, the developed error models were simplified based [...] Read more.
Reduced inertial sensor systems (RISS) have been introduced by many researchers as a low-cost, low-complexity sensor assembly that can be integrated with GPS to provide a robust integrated navigation system for land vehicles. In earlier works, the developed error models were simplified based on the assumption that the vehicle is mostly moving on a flat horizontal plane. Another limitation is the simplified estimation of the horizontal tilt angles, which is based on simple averaging of the accelerometers’ measurements without modelling their errors or tilt angle errors. In this paper, a new error model is developed for RISS that accounts for the effect of tilt angle errors and the accelerometer’s errors. Additionally, it also includes important terms in the system dynamic error model, which were ignored during the linearization process in earlier works. An augmented extended Kalman filter (EKF) is designed to incorporate tilt angle errors and transversal accelerometer errors. The new error model and the augmented EKF design are developed in a tightly-coupled RISS/GPS integrated navigation system. The proposed system was tested on real trajectories’ data under degraded GPS environments, and the results were compared to earlier works on RISS/GPS systems. The findings demonstrated that the proposed enhanced system introduced significant improvements in navigational performance. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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Figure 1

Figure 1
<p>High level block diagram of system integration through estimation techniques.</p> Full article ">Figure 2
<p>Arrangement of 3D reduced inertial sensor system (RISS) sensors with respect to the body frame.</p> Full article ">Figure 3
<p>3D RISS mechanization block diagram.</p> Full article ">Figure 4
<p>EKF-based RISS/GPS tightly-coupled integrated navigation system.</p> Full article ">Figure 5
<p>Navigation equipment and supporting hardware placed in a van for trajectory data collection.</p> Full article ">Figure 6
<p>Test trajectory in Kingston downtown, ON, Canada.</p> Full article ">Figure 7
<p>Maximum 2D positional errors for GPS outages of the Kingston downtown trajectory.</p> Full article ">Figure 8
<p>A comparison of tightly-coupled (TC-EKF), TC-linearized KF (LKF) and particle filter (PF) for the average maximum 2D position error for the Kingston downtown trajectory.</p> Full article ">Figure 9
<p>A comparison of TC-EKF with TC-LKF for the average RMS 2D position error for the Kingston downtown trajectory.</p> Full article ">Figure 10
<p>Noisy GPS during the Kingston downtown trajectory.</p> Full article ">Figure 11
<p>(Top) Performance of estimated pitch angle <span class="html-italic">versus</span> the reference (NovAtel) for the Kingston trajectory. The bottom plot shows the difference between the two systems.</p> Full article ">Figure 12
<p>(Top) Performance of estimated roll angle <span class="html-italic">versus</span> the reference (NovAtel) for the Kingston trajectory. The bottom plot shows the difference between the two systems.</p> Full article ">Figure 13
<p>Gyroscope bias estimation convergence in the Kingston downtown trajectory.</p> Full article ">Figure 14
<p>Main section of the test trajectory in downtown Toronto, ON, Canada.</p> Full article ">Figure 15
<p>Availability of the satellites during the Toronto downtown trajectory.</p> Full article ">Figure 16
<p>Maximum 2D positional errors of TC-EKF, the LC-LKF of [<a href="#B28-sensors-15-24269" class="html-bibr">28</a>], the TC-LKF of [<a href="#B14-sensors-15-24269" class="html-bibr">14</a>] and the PF of [<a href="#B31-sensors-15-24269" class="html-bibr">31</a>].</p> Full article ">Figure 17
<p>Performance in noisy GPS signal conditions during outage # 2, 3 and 4.</p> Full article ">Figure 18
<p>Performance in noisy and jeopardized GPS signal conditions during outage #7.</p> Full article ">Figure 19
<p>(Top) Performance of estimated pitch angle <span class="html-italic">versus</span> the reference for the Toronto trajectory. The bottom plot shows the difference between the two systems.</p> Full article ">Figure 20
<p>(Top) Performance of estimated roll angle <span class="html-italic">versus</span> the reference for the Toronto trajectory. The bottom plot shows the difference between the two systems.</p> Full article ">Figure 21
<p>Convergence of gyroscope bias during the Toronto downtown trajectory.</p> Full article ">
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