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20 pages, 10700 KiB  
Article
A 2.4 GHz IEEE 802.15.4 Multi-Hop Network for Mountainous Forest and Watercourse Environments: Sensor Node Deployment and Performance Evaluation
by Apidet Booranawong, Puwasit Hirunkitrangsri, Dujdow Buranapanichkit, Charernkiat Pochaiya, Nattha Jindapetch and Hiroshi Saito
Signals 2024, 5(4), 774-793; https://doi.org/10.3390/signals5040043 - 20 Nov 2024
Viewed by 879
Abstract
In this paper, we demonstrate the realistic test of a 2.4 GHz multi-hop wireless network for mountainous forest and watercourse environments. A multi-hop network using IEEE 802.15.4 XBee3 micro-modules and a communication protocol among nodes were developed. A wireless node deployment solution was [...] Read more.
In this paper, we demonstrate the realistic test of a 2.4 GHz multi-hop wireless network for mountainous forest and watercourse environments. A multi-hop network using IEEE 802.15.4 XBee3 micro-modules and a communication protocol among nodes were developed. A wireless node deployment solution was introduced for practical testing. The proposed system’s communication reliability was tested in two different scenarios: a mountainous forest with sloping areas and trees and a watercourse, which referred to environmental and flooding monitoring applications. Wireless network performances were evaluated through the received signal strength indicator (RSSI) level of each wireless link, a packet delivery ratio (PDR), as the successful rate of packet transmission, and the end-to-end delay (ETED) of all data packets from the transmitter to the receiver. The experimental results demonstrate the success of the multi-hop WSN deployment and communication in both scenarios, where the RSSI of each link was kept at the accepted level and the PDR achieved the highest result. Furthermore, as a real-time response, the data from the source could be sent to the sink with a small ETED. Full article
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Figure 1
<p>A multi-hop WSN with the communication protocol among the nodes.</p>
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<p>Example of the proposed sensor node deployment process (before the data collection phase.</p>
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<p>Example of the proposed sensor node deployment process (before the data collection phase.</p>
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<p>Example of the proposed sensor node deployment process (before the data collection phase.</p>
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<p>Test scenarios #1 and #2.</p>
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<p>The test field layouts.</p>
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<p>Illustration of sensor node deployment and environments.</p>
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<p>An example of water flooding during the rainy season for field #2.</p>
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<p>Examples of raw RSSI signals collected from test scenario #1 (test times 5 and 15). The signals could be displayed in real time during the test in the GUI window. Note that the y-axis is the RSSI level in dBm, and RSSI B, C, and D refer to hops 3, 2, and 1.</p>
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<p>Average RSSIs of hops 1 to 3 for test scenarios #1 and #2.</p>
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<p>PDR results.</p>
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<p>ETED results.</p>
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<p>ETED results.</p>
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<p>The XBee3 micro-module with the GY-521 accelerometer/gyro sensor and 5 V battery.</p>
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<p>Examples of three-axis acceleration and gyro signals.</p>
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<p>Examples of three-axis acceleration and gyro signals.</p>
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<p>Examples of three-axis acceleration and gyro signals.</p>
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18 pages, 9899 KiB  
Article
Experimental Outdoor Vehicle Acoustic Testing Based on ISO-362 Pass-by-Noise and Tyre Noise Contribution for Electric Vehicles
by Daniel O’Boy, Simon Tuplin and Kambiz Ebrahimi
World Electr. Veh. J. 2024, 15(11), 485; https://doi.org/10.3390/wevj15110485 - 26 Oct 2024
Viewed by 1109
Abstract
This paper focuses on the novel and unique training provision of acoustics relevant for noise, vibration, and harshness (NVH), focused on the ISO-362 standard highlighting important design aspects for electric vehicles. A case study of the practical implementation of off-site vehicle testing supporting [...] Read more.
This paper focuses on the novel and unique training provision of acoustics relevant for noise, vibration, and harshness (NVH), focused on the ISO-362 standard highlighting important design aspects for electric vehicles. A case study of the practical implementation of off-site vehicle testing supporting an acoustics module is described, detailing a time-constrained test for automotive pass-by-noise and tyre-radiated noise with speed. Industrial test standards are discussed, with education as a primary motivation. The connections between low-cost, accessible equipment and future electric vehicle acoustics are made. The paper contains a full equipment breakdown to demonstrate the ability to link digital data transfer, analogue-to-digital communication, telemetry, and acquisition skills. The benchmark results of novel pass-by-noise and tyre testing are framed around discussion points for assessments. Inexpensive Arduino Uno boards provide data acquisition with class 1 sound pressure meters, XBee radios provide telemetry to a vehicle, and a vehicle datalogger provides GPS position with CANBUS data. Data acquisition is triggered through the implementation of light gate sensors on the test track, with the whole test lasting 90 minutes. Full article
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Figure 1
<p>Schematic of the test track with entry and exit lines. Inside this area, the track composition is prescribed to ISO-10844, and the vehicle travels along the centreline in both directions. The entry and exit to the test zone are denoted by the lines AA and BB.</p>
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<p>Bird’s eye view of the acoustics track at Horiba MIRA proving ground showing the turning facilities and acceleration and deceleration zones. The wider track area has no reflections, and some areas can be used for data recording and transport vehicles.</p>
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<p>Mean calculation of the sound pressure level from the pass-by-noise test. The table is a useful device to fill in during the test to compare the telemetry recorded and data processed results generated by examining sound pressure against time in Matlab.</p>
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<p>Vehicle data logger (ISAAC DRU916). Note complexity of wiring with power, GPS connections, CANBUS connections, and analogue inputs all required for understanding of experimental testing. Also shown is the real-time information screen available on a laptop inside the vehicle.</p>
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<p>Sound pressure meter and setup on location.</p>
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<p>Laser light proximity gates used to measure when the vehicle enters and leaves the test area (digital signal as open/closed).</p>
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<p>Telemetry box composed of Arduino, ADC board, and XBee transmitter. Power input is via a 12 V power pack.</p>
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<p>Connection detail for the on-track telemetry box. Major connections are shown, allowing students to visualise connections between main board components. The minor connection board is an analogue to digital converter (16-bit).</p>
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<p>Receiver telemetry box in the vehicle. This receives the message from the track and relays it to the vehicle data logger.</p>
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<p>Connection details for the in-vehicle telemetry receiver. The minor connection board is a digital-to-analogue converter (12-bit). Resolution is maintained through software amplification.</p>
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<p>Typical vehicle results for pass-by-driving through the test zone in second gear. The left corresponds to the left-hand side of the vehicle, while the right corresponds to the right-hand side.</p>
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<p>Typical vehicle results for pass-by-driving through the test zone in third gear. The left corresponds to the left-hand side of the vehicle, while the right corresponds to the right-hand side.</p>
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<p>Vehicle passing through the test zone at different constant speeds. The entry to the test zone is used as a trigger.</p>
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<p>Variation of peak sound pressure level for constant speed driving. Measured data points from the mean GPS speed in the test area together with a linear best-fit line.</p>
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<p>Examples of initial rapid experiments to gain confidence and understanding of the equipment. Background noise with the engine revving quickly before being switched off, the engine revving with the horn sounding, and calibrating the microphones with the pistonphone are shown.</p>
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<p>Matlab-generated data processing screen. The GPS position of the vehicle against time on the track, gate triggers (changes are when the vehicle blocks the signal), a linear potentiometer that the passenger can use to indicate the vehicle is in the test area and engine, and sound pressure level information are shown. The information is a holistic test plan, which is the intermediate step to obtain the main results in this paper. The script is provided in the data repository.</p>
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29 pages, 17407 KiB  
Article
Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
by Atsushi Suetsugu, Hirokazu Madokoro, Takeshi Nagayoshi, Takero Kikuchi, Shunsuke Watanabe, Makoto Inoue, Makoto Yoshida, Hitoshi Osawa, Nobumitsu Kurisawa and Osamu Kiguchi
Drones 2024, 8(2), 38; https://doi.org/10.3390/drones8020038 - 25 Jan 2024
Viewed by 2904
Abstract
Amphibious (air and water) drones, capable of both aerial and aquatic operations, have the potential to provide valuable drone applications in aquatic environments. However, the limited range of wireless data transmission caused by the low antenna height on water and reflection from the [...] Read more.
Amphibious (air and water) drones, capable of both aerial and aquatic operations, have the potential to provide valuable drone applications in aquatic environments. However, the limited range of wireless data transmission caused by the low antenna height on water and reflection from the water surface (e.g., 45 m for vertical half-wave dipole antennas with the XBee S2CTM, estimated using the two-ray ground reflection model) persists as a formidable challenge for amphibious systems. To overcome this difficulty, we developed a wireless data relay system for amphibious drones using the mesh-type networking functions of the XBeeTM. We then conducted field tests of the developed system in a large marsh pond to provide experimental evidence of the efficiency of the multiple-drone network in amphibious settings. In these tests, hovering relaying over water was attempted for extension and bypassing obstacles using the XBee S2CTM (6.3 mW, 2.4 GHz). During testing, the hovering drone (<10 m height from the drone controller) successfully relayed water quality data from the transmitter to the receiver located approximately 757 m away, but shoreline vegetation decreased the reachable distance. A bypassing relay test for vegetation indicated the need to confirm a connected path formed by pair(s) of mutually observable drones. Full article
(This article belongs to the Special Issue Wireless Networks and UAV)
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Figure 1
<p>A conceptual illustration of wireless data relay for water quality monitoring using multiple amphibious drones. Upper: no data relay, middle: data relay by a flying drone, lower: data relay with alternated roles of drones for large-scale applications with variable receiver locations. Drones A and B are identical but situated in different locations.</p>
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<p>Schematic representation of amphibious drone-mounted water quality monitoring system with wireless data relay.</p>
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<p>Water quality monitoring modules and output data. (<b>a</b>) Modules for the SplashDrone<sup>TM</sup> 3+ in a data stability test; (<b>b</b>) sensor electrodes for the SplashDrone<sup>TM</sup> 3+; (<b>c</b>) modules for the SplashDrone<sup>TM</sup> 4 in a data stability test; (<b>d</b>) received data displayed on a laptop PC. Its components have been numbered as follows: 1, sensor boards for the SplashDrone<sup>TM</sup> 3+ system; 2, sensor electrodes for the SplashDrone<sup>TM</sup> 3+ system; 3, controller for the SplashDrone<sup>TM</sup> 3+ system; 4, controller for reference temperature measurement with a continuous power source; 5, rechargeable batteries; 6, data display (laptop PC); 7, pH sensor; 8, EC sensor; 9, DO sensor; 10, temperature sensor (reference); 11, temperature sensor; 12, pH 4 buffer solution in a screw cap; 13, EC 1.41 dS m<sup>−1</sup> standard solution in a screw cap; 14, aerator; 15, controller for the SplashDrone 4 system; 16, XBee<sup>TM</sup> S2C board; 17, sensor boards for pH/EC/DO; 18, sensor board for temperature; 19, I<sup>2</sup>C connector hub; 20, rechargeable battery (for demonstration of a minimized installation into the system mounter).</p>
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<p>Slicer output images of the mounters for water quality monitoring tools with amphibious drones. (<b>a</b>) Mounters for the SplashDrone<sup>TM</sup> 3+ with vertical installation of sensor electrodes; (<b>b</b>) mounters for the SplashDrone<sup>TM</sup> 4 with horizontal installation of sensor electrodes (the holder is shown as a vertical image for 3D-printer output). GNSS-secure legs were used for sufficient space between the bottom of the upper mounter vessel and the GNSS antenna surface of each drone.</p>
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<p>Simplified side-view of the amphibious drone system with an upper mounter and additional floating materials. Some points are identified: M, metacenter; G, center of gravity; B, center of buoyancy. The floating materials are sufficiently narrow to release airflow and minimize the distance from G to B.</p>
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<p>Simplified top view (horizontal view) of the amphibious drone system with additional floating materials: (<b>a</b>) symmetric L-shaped polystyrene-foam bars (the concave of each L-shaped bar is fitted to each of the drone arms, approximately 60 g total weight) made by cutting a polystyrene foam box, (<b>b</b>) empty PET bottles (approximately 940 mL volume, approximately 136 g total weight), (<b>c</b>) coiled tubular polyethylene sponge foam discs (approximately 400 g total weight), and (<b>d</b>) the least amount of sponge-form arm-covers (approximately 85 g total weight) to balance the drones on still water.</p>
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<p>Flight stability test with the payload of the water quality monitoring system and reinforcing floats for aqueous operations. Upper: initial location of the drone system. Lower: on-flight location to be stabilized at ideally (initial location of each test). Lateral (<span class="html-italic">y</span>-axis) fluctuation was evaluated without aileron control. The threshold was set as 1.5 m by demarcating the test space with elastic strings. The time to deviate from the inner space (±1.5 m) was measured.</p>
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<p>Scheme of the wireless data relay tests. The transmitter drone measured surface water quality data and transmitted the data. The relayer drone relayed the data to a receiver. The receiver received the data.</p>
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<p>Components of the water quality monitoring system with amphibious drones for wireless data relay tests: 1, SplashDrone<sup>TM</sup> 3+; 2, SplashDrone<sup>TM</sup> 4; 3, upper mounter for SplashDrone<sup>TM</sup> 3+; 4, upper mounter for SplashDrone<sup>TM</sup> 4; 5, lower mounter for SplashDrone<sup>TM</sup> 4; 6, side mounter(s) for SplashDrone<sup>TM</sup> 3+; 7, drone controller for SplashDrone<sup>TM</sup> 4; 8, laptop computer for data display; 9, receiver; and 9’, contents included in the receiver.</p>
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<p>Schematic showing field tests at a pond for two-ray ground reflection model calculation of communicable distance. Each antenna height in this figure includes its own vertical length.</p>
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<p>Results of data stability testing of the system for the SplashDrone<sup>TM</sup> 3+ with rechargeable batteries: (<b>a</b>) pH values of a standard solution (potassium phthalate buffer, pH 4.01); (<b>b</b>) electrical conductivity values of a 1.41 dS m<sup>−1</sup> standard solution (141 mS m<sup>−1</sup>); (<b>c</b>) dissolved oxygen concentration values of the drawn tap water circulated continuously with air bubbling; (<b>d</b>) water temperature values of the tap water; (<b>e</b>) the ratio of water temperature and reference temperature values (the reference temperature values were obtained with stable power source). The EC values were corrected by the water temperature data to represent the values at 298 K. Temperature compensation for the DO measurement was conducted only for the membrane permeability fluctuation (not for standardization).</p>
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<p>Summarized result of the data stability test of the system for the SplashDrone<sup>TM</sup> 4 with rechargeable batteries. In the figure, tc represents temperature correction; std represents calculated standard data by the equations from Benson and Krause (1980). Also, EC_tc data are standardized values at 298 K (25 °C).</p>
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<p>Example of a time series dataset from the water quality monitoring system on an amphibious drone (Ogata Pond, 19 July 2023): EC, electrical conductivity; DO, dissolved oxygen; F1, first flight; L1, first landing on water; F2, second flight; L2, second landing on water; F3, third flight; L3, third landing on water; H, hovering; L4, fourth landing on water. Numbers on the data represent readings obtained at equilibrium (pH, EC, and water temperature) and immediately after each landing on water (DO). Exceptional treatment of the data on DO was conducted for oversaturated water by algal bloom, as detected by manual sampling measurements of nearshore water.</p>
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<p>Arrangement of wireless communication devices for quantifying the efficiency of drone hovering in data relaying. The test was conducted on 12 December, 2023 in a flat field at Akita Prefectural University (Akita Campus), Japan. The distance between the transmitter drone and the receiver (connected to a laptop computer) was approximately 100 m. The relayer drone was positioned at the center of the line between the transmitter drone and the receiver. No tall grasses were present in the field on the date of the test. Networking metrics (throughput, RSSI, and PDR) were measured both with and without hovering of the relayer drone. The hovering height was maintained at approximately 3 m.</p>
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<p>Results of throughput tests with hovering relayer drone positioned at the center of a 100 m line of sight. The test for each condition was repeated three times. The test duration (cutoff time) was set as 60 s for the rapid environmental monitoring with drones. The average throughput was 0.28 ± 0.07 kbps for the on-ground relayer and 0.77 ± 0.05 kbps for the hovering relayer,.</p>
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<p>Results of range tests with a hovering relayer drone positioned at the center of a 100 m line of sight. RSSI: Received Signal Strength Indicator. Success (%) on the right-side vertical axis represents the Packet Delivery Ratio (PDR, %). The test for each condition was repeated three times. Each packet was to be sent ideally at 1000 ms intervals, but the receiver timeout was set as 10 s for a measurement of water quality at 10 s intervals. The average PDR was 97 ± 2.7% for the on-ground relayer and 100 ± 0.3% for the hovering relayer. The sensitivity of the receiver (XBee<sup>TM</sup> S2C coordinator in the boost mode) was −102 dBm [<a href="#B17-drones-08-00038" class="html-bibr">17</a>].</p>
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<p>Locations of the wireless water quality monitoring system for extending relay test (Ogata Marsh Pond on 19 July 2023). Transmitter, transmitter drone (landed on the water and was fixed by mooring); 1–3, locations where the relayer drone was placed; Receiver, receiver on a tripod.</p>
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<p>Received water quality data from the extending relay test. The horizontal distance between the transmitter drone and the relayer drone at each landing site was the following: L1, 624 m; L2, 500 m; L3, 381 m; L4, 368 m. Each of the complete datasets (pH, EC, DO, and temperature) is surrounded by a dotted line.</p>
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<p>Locations of wireless water quality monitoring system for bypassing relay test (Ogata Pond on 27 September 2022): Transmitter, transmitter drone (landed on the water and was fixed by mooring from shore); 1–3, locations where the relayer drone was placed; Receiver, receiver on a tripod. Red arrow indicates the wireless communication path blocked by shoreline vegetation.</p>
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<p>Water quality data received from the bypassing relay test. Distances between the transmitter drone and the relayer drone at each landing site were the following: L1, 313 m; L2, 270 m; L3, 181 m.</p>
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<p>Photographs of the field test site. (<b>a</b>) Path to the pond shoreline with raised water (+0.3 m). (<b>b</b>) Overview from the shore in the extending relay test. (<b>c</b>) Overview from the shore in the bypassing relay test.</p>
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<p>Example of a wireless data relay application to amphibious drones. A tracking (‘follow me’) technology (3D Robotics Inc., Berkeley, CA, USA) enables large-scale water surveys with amphibious drones.</p>
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27 pages, 8494 KiB  
Article
A Comprehensive Analysis: Evaluating Security Characteristics of Xbee Devices against Zigbee Protocol
by Vlad Gavra, Ovidiu A. Pop and Ionut Dobra
Sensors 2023, 23(21), 8736; https://doi.org/10.3390/s23218736 - 26 Oct 2023
Cited by 3 | Viewed by 1768
Abstract
In recent times, the security of sensor networks, especially in the field of IoT, has become a priority. This article focuses on the security features of the Zigbee protocol in Xbee devices developed by Digi International, specifically in the Xbee 3 (XB3-24) devices. [...] Read more.
In recent times, the security of sensor networks, especially in the field of IoT, has become a priority. This article focuses on the security features of the Zigbee protocol in Xbee devices developed by Digi International, specifically in the Xbee 3 (XB3-24) devices. Using the TI LaunchXL-CC26X2R1 kit, we intercepted and analyzed packets in real-time using the Wireshark application. The study encompasses various stages of network formation, packet transmission and analysis of security key usage, considering scenarios as follows: without security, distributed security mode and centralized security mode. Our findings highlight the differences in security features of Xbee devices compared to the Zigbee protocol, validating and invalidating methods of establishing security keys, vulnerabilities, strengths, and recommended security measures. We also discovered that security features of the Xbee 3 devices are built around a global link key preconfigured therefore constituting a vulnerability, making those devices suitable for man-in-the-middle and reply attacks. This work not only elucidates the complexities of Zigbee security in Xbee devices but also provides direction for future research for authentication methods using asymmetric encryption algorithms such as digital signature based on RSA and ECDSA. Full article
(This article belongs to the Special Issue Communication, Security, and Privacy in IoT)
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<p>ZigBee Protocol Layers.</p>
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<p>ZigBee frame encapsulation according to protocol architecture.</p>
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<p>Secured frame format at Network layer.</p>
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<p>Application link key establishment method [<a href="#B23-sensors-23-08736" class="html-bibr">23</a>].</p>
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<p>Secured frame format at Application Support layer.</p>
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<p>Process of joining a network using a Preconfigure Link Key [<a href="#B23-sensors-23-08736" class="html-bibr">23</a>].</p>
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<p>Update the Trust Center Link Key after rejoining [<a href="#B23-sensors-23-08736" class="html-bibr">23</a>].</p>
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<p>Process of device to securely rejoin the network [<a href="#B23-sensors-23-08736" class="html-bibr">23</a>].</p>
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<p>Trust Center secured rejoining process [<a href="#B23-sensors-23-08736" class="html-bibr">23</a>].</p>
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<p>Decision-making process in a Trust Center formed network [<a href="#B23-sensors-23-08736" class="html-bibr">23</a>].</p>
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<p>DIGI XBee 3 devices.</p>
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<p>Texas Instruments LaunchXL-CC26X2R1 intercepting kit.</p>
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<p>Network formation message exchange—Unsecured Network.</p>
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<p>Association message exchange of a new device in the network [<a href="#B23-sensors-23-08736" class="html-bibr">23</a>].</p>
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<p>Unsecure message transmission frame format.</p>
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<p>Association sequence in a distributed security model network.</p>
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<p>Key transport frame format unencrypted.</p>
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<p>Encrypted Data Frame format.</p>
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<p>Transport Key frame encrypted with Link Key.</p>
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<p>Transport Key frame encrypted with Link Key—decrypted at destination.</p>
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<p>Data packet encrypted only with network key.</p>
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<p>End-to-end secured data frame format.</p>
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<p>Centralized network device association message sequence.</p>
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<p>Key transport frame format of link key with trust center.</p>
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<p>Verify Key Frame Format.</p>
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<p>Confirm Key Frame Format.</p>
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27 pages, 10559 KiB  
Article
Buildings’ Biaxial Tilt Assessment Using Inertial Wireless Sensors and a Parallel Training Model
by Luis Pastor Sánchez-Fernández, Luis Alejandro Sánchez-Pérez, José Juan Carbajal-Hernández, Mario Alberto Hernández-Guerrero and Lucrecia Pérez-Echazabal
Sensors 2023, 23(11), 5352; https://doi.org/10.3390/s23115352 - 5 Jun 2023
Cited by 2 | Viewed by 1490
Abstract
Applications of MEMS-based sensing technology are beneficial and versatile. If these electronic sensors integrate efficient processing methods, and if supervisory control and data acquisition (SCADA) software is also required, then mass networked real-time monitoring will be limited by cost, revealing a research gap [...] Read more.
Applications of MEMS-based sensing technology are beneficial and versatile. If these electronic sensors integrate efficient processing methods, and if supervisory control and data acquisition (SCADA) software is also required, then mass networked real-time monitoring will be limited by cost, revealing a research gap related to the specific processing of signals. Static and dynamic accelerations are very noisy, and small variations of correctly processed static accelerations can be used as measurements and patterns of the biaxial inclination of many structures. This paper presents a biaxial tilt assessment for buildings based on a parallel training model and real-time measurements using inertial sensors, Wi-Fi Xbee, and Internet connectivity. The specific structural inclinations of the four exterior walls and their severity of rectangular buildings in urban areas with differential soil settlements can be supervised simultaneously in a control center. Two algorithms, combined with a new procedure using successive numeric repetitions designed especially for this work, process the gravitational acceleration signals, improving the final result remarkably. Subsequently, the inclination patterns based on biaxial angles are generated computationally, considering differential settlements and seismic events. The two neural models recognize 18 inclination patterns and their severity using an approach in cascade with a parallel training model for the severity classification. Lastly, the algorithms are integrated into monitoring software with 0.1° resolution, and their performance is verified on a small-scale physical model for laboratory tests. The classifiers had a precision, recall, F1-score, and accuracy greater than 95%. Full article
(This article belongs to the Special Issue Application of MEMS/NEMS-Based Sensing Technology)
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Figure 1
<p>System overview. Sensor including IMU + the wireless communication module with the IEEE 802.15.4 protocol and a rechargeable battery. (<b>a</b>) Schematic of the four sensors installed on each of the walls of the rectangular building. (<b>b</b>) Local computer acquiring the sensors’ signals through a router. (<b>c</b>) Client–server architecture (TCP/IP protocol) of the system. (<b>d</b>) Monitoring of one or several buildings in a control room from any geographical location.</p>
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<p>An orientation of frame <span class="html-italic">B</span> relative to frame <span class="html-italic">A</span> can be attained through a rotation of the angle <span class="html-italic">θ</span> around an axis <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mover accent="true"> <mi>r</mi> <mo stretchy="false">^</mo> </mover> </msub> </mrow> </semantics></math> defined in frame <span class="html-italic">A</span>.</p>
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<p>Quaternion rotation method to obtain gravity acceleration.</p>
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<p>Algorithm block diagram of successive repetitions for a sampling instant and each of the eight biaxial tilt angles. The explanation of (<b>a</b>–<b>d</b>) may be read in the paragraph before <a href="#sensors-23-05352-f004" class="html-fig">Figure 4</a>.</p>
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<p>(<b>a</b>) Reference system for the sensors. (<b>b</b>) Example of pattern 0. (<b>c</b>) Angle rotation.</p>
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<p>The possible evolution of the tilt angles. (<b>a</b>) The wall is perpendicular to its base, and there are no tilts. (<b>b</b>) The wall has tilted over time; the Y-axis is tilted clockwise and is represented by a positive sign.</p>
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<p>A simplified scheme of the 18 base patterns proposed in this work.</p>
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<p>General diagram of the classification model. (<b>a</b>,<b>b</b>) Show the four sensors and the four biaxial tilts, respectively. (<b>c</b>) The classifier of base patterns. (<b>d</b>) The computer model for the pattern severity classification.</p>
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<p>General schema of the classifier of base biaxial tilt patterns (<math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">N</mi> <msub> <mi mathvariant="bold-italic">N</mi> <mn mathvariant="bold">1</mn> </msub> </mrow> </semantics></math>).</p>
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<p>Confusion matrix to evaluate <b><span class="html-italic">NN</span><sub>1.</sub></b> The Y-axis represents the accurate classification assigned to the patterns (true label), while the X-axis indicates the classification given by the network (predicted label). The diagonal values show the correctly labeled patterns; the neural network classified them as the same pattern to which they belong. <a href="#sensors-23-05352-t002" class="html-table">Table 2</a> shows the performance metrics of the multiclass classifier using macro-average, with values truncated to two decimal places.</p>
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<p>Multilayer perceptron neural network (<span class="html-italic">NN</span><sub>2</sub>) topology to classify biaxial tilt severity.</p>
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<p>Parallel recognition model training.</p>
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<p>The classifier of the base biaxial tilt pattern and parallel recognition model implementation to classify the tilt severity. (<b>a</b>) The input of the four biaxial tilts, illustrated initially in <a href="#sensors-23-05352-f008" class="html-fig">Figure 8</a>b. (<b>b</b>) The classifier of base biaxial tilt patterns of <a href="#sensors-23-05352-f008" class="html-fig">Figure 8</a>c. (<b>c</b>) This output allows the choice of a set of weights and biases (see <a href="#sensors-23-05352-f012" class="html-fig">Figure 12</a>c). (<b>d</b>) The classifier of the tilt severity (see <a href="#sensors-23-05352-f011" class="html-fig">Figure 11</a>).</p>
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<p>Most representative confusion matrix of <b><span class="html-italic">NN</span><sub>2</sub></b>.</p>
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<p>A small-scale physical model for laboratory tests in rectangular buildings.</p>
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<p>(<b>a</b>) IMU installation schematics. (<b>b</b>) Screws for tilt simulation. (<b>c</b>) Digital inclinometer.</p>
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<p>Clinic Prensa, which is a leaning building and is under supervision.</p>
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<p>The Palace of Fine Arts in Mexico City.</p>
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<p>(<b>a</b>) The main screen of the user interface. (<b>b</b>) A pop-up alarm screen.</p>
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16 pages, 5162 KiB  
Article
A Wearable-Sensor System with AI Technology for Real-Time Biomechanical Feedback Training in Hammer Throw
by Ye Wang, Gongbing Shan, Hua Li and Lin Wang
Sensors 2023, 23(1), 425; https://doi.org/10.3390/s23010425 - 30 Dec 2022
Cited by 7 | Viewed by 3827
Abstract
Developing real-time biomechanical feedback systems for in-field applications will transfer human motor skills’ learning/training from subjective (experience-based) to objective (science-based). The translation will greatly improve the efficiency of human motor skills’ learning and training. Such a translation is especially indispensable for the hammer-throw [...] Read more.
Developing real-time biomechanical feedback systems for in-field applications will transfer human motor skills’ learning/training from subjective (experience-based) to objective (science-based). The translation will greatly improve the efficiency of human motor skills’ learning and training. Such a translation is especially indispensable for the hammer-throw training which still relies on coaches’ experience/observation and has not seen a new world record since 1986. Therefore, we developed a wearable wireless sensor system combining with artificial intelligence for real-time biomechanical feedback training in hammer throw. A framework was devised for developing such practical wearable systems. A printed circuit board was designed to miniaturize the size of the wearable device, where an Arduino microcontroller, an XBee wireless communication module, an embedded load cell and two micro inertial measurement units (IMUs) could be inserted/connected onto the board. The load cell was for measuring the wire tension, while the two IMUs were for determining the vertical displacements of the wrists and the hip. After calibration, the device returned a mean relative error of 0.87% for the load cell and the accuracy of 6% for the IMUs. Further, two deep neural network models were built to estimate selected joint angles of upper and lower limbs related to limb coordination based on the IMUs’ measurements. The estimation errors for both models were within an acceptable range, i.e., approximately ±12° and ±4°, respectively, demonstrating strong correlation existed between the limb coordination and the IMUs’ measurements. The results of the current study suggest a remarkable novelty: the difficulty-to-measure human motor skills, especially in those sports involving high speed and complex motor skills, can be tracked by wearable sensors with neglect movement constraints to the athletes. Therefore, the application of artificial intelligence in a wearable system has shown great potential of establishing real-time biomechanical feedback training in various sports. To our best knowledge, this is the first practical research of combing wearables and machine learning to provide biomechanical feedback in hammer throw. Hopefully, more wearable biomechanical feedback systems integrating artificial intelligence would be developed in the future. Full article
(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Mechatronics Systems)
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<p>A reconfigurable WSN structure based on Arduino platform (The figure is adapted from the 1st author’s Ph.D. thesis [<a href="#B6-sensors-23-00425" class="html-bibr">6</a>]).</p>
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<p>The general modus operandi/framework for developing wearable-sensor systems to help establish real-time biomechanical feedback training in field (The figure is adapted from the 1st author’s Ph.D. thesis [<a href="#B6-sensors-23-00425" class="html-bibr">6</a>]).</p>
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<p>System architecture: the system consists of a sensor node and a receiver node. The sensor node device, including an Arduino microcontroller, an XBee wireless communication module, an on-board IMU (inserted onto the PCB inside the device), an attachable IMU (wrapped with red tape), an embedded micro load cell (along with a customized easy-to-release connector), etc., is worn on an athlete’s waist with a belt, which is used for collecting data in field tests and sending the data to the receiver node. The receiver node has another XBee module connected to an end device (e.g., laptop), which is used for receiving, processing, and analyzing data.</p>
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<p>A wearable-sensor system including its hardware and software development is designed for real-time biomechanical feedback training in hammer throw. AI technology is also applied to estimate selected joint angles based on a local motion feature existing among upper and lower limbs during the hammer-throw movements. An optoelectronic motion capture system is used as a referencing system to provide reliable kinematic data.</p>
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<p>The hardware design of the wearable device: (<b>a</b>) The layout of the PCB; (<b>b</b>) The logic diagram of the PCB design.</p>
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<p>The deep neural network structure of the simplified model.</p>
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<p>A typical result of the DNN modeling: (<b>a</b>) An example of training the complete model for 1000 epochs; (<b>b</b>) The simplified model’s prediction error; (<b>c</b>) The complete model’s prediction error; (<b>d</b>) The scatter plot of using the complete model to predict the left and right hip flexion/extension angles; (<b>e</b>) The scatter plot of using the complete model to predict the left and right knee flexion/extension angles; (<b>f</b>) The scatter plot of using the complete model to predict the left and right ankle flexion/extension angles.</p>
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<p>A typical result of the DNN modeling: (<b>a</b>) An example of training the complete model for 1000 epochs; (<b>b</b>) The simplified model’s prediction error; (<b>c</b>) The complete model’s prediction error; (<b>d</b>) The scatter plot of using the complete model to predict the left and right hip flexion/extension angles; (<b>e</b>) The scatter plot of using the complete model to predict the left and right knee flexion/extension angles; (<b>f</b>) The scatter plot of using the complete model to predict the left and right ankle flexion/extension angles.</p>
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<p>The two patterns of tension development during throwing: gradually increasing during body rotation (<b>left</b> in the figure, Peak 5–8) and suddenly increasing toward the end (<b>right</b> in the figure) (the figure is adapted from the 1st author’s PhD thesis [<a href="#B6-sensors-23-00425" class="html-bibr">6</a>]).</p>
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29 pages, 8539 KiB  
Article
Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network
by Saud Altaf, Shafiq Ahmad, Mazen Zaindin, Shamsul Huda, Sofia Iqbal and Muhammad Waseem Soomro
Sustainability 2022, 14(16), 10079; https://doi.org/10.3390/su141610079 - 15 Aug 2022
Cited by 2 | Viewed by 1754
Abstract
The voltage supply of induction motors of various sizes is typically provided by a shared power bus in an industrial production powerline network. A single motor’s dynamic behavior produces a signal that travels along the powerline. Powerline networks are efficient at transmitting and [...] Read more.
The voltage supply of induction motors of various sizes is typically provided by a shared power bus in an industrial production powerline network. A single motor’s dynamic behavior produces a signal that travels along the powerline. Powerline networks are efficient at transmitting and receiving signals. This could be an indication that there is a problem with the motor down immediately from its location. It is possible for the consolidated network signal to become confusing. A mathematical model is used to measure and determine the possible known routing of various signals in an electricity network based on attenuation and estimate the relationship between sensor signals and known fault patterns. A laboratory WSN based induction motors testbed setup was developed using Xbee devices and microcontroller along with the variety of different-sized motors to verify the progression of faulty signals and identify the type of fault. These motors were connected in parallel to the main powerline through this architecture, which provided an excellent concept for an industrial multi-motor network modeling lab setup. A method for the extraction of Xbee node-level features has been developed, and it can be applied to a variety of datasets. The accuracy of the real-time data capture is demonstrated to be very close data analyses between simulation and testbed measurements. Experimental results show a comparison between manual data gathering and capturing Xbee sensor nodes to validate the methodology’s applicability and accuracy in locating the faulty motor within the power network. Full article
(This article belongs to the Special Issue Electric Power Equipment Sustainable Development)
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<p>MCSA-based fault-diagnosis framework. Reprinted with permission from Ref. [<a href="#B12-sustainability-14-10079" class="html-bibr">12</a>]. Copyright 2003 American Pharmaceutical Association.</p>
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<p>A typical fault diagnosis process. Reprinted with permission from Ref. [<a href="#B4-sustainability-14-10079" class="html-bibr">4</a>]. Copyright 2003 American Pharmaceutical Association.</p>
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<p>Possible problems with an induction motor originally published by Sun et al. [<a href="#B14-sustainability-14-10079" class="html-bibr">14</a>].</p>
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<p>Different failure modes [<a href="#B15-sustainability-14-10079" class="html-bibr">15</a>].</p>
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<p>Model of the structure of a decentralized industrial induction machine network.</p>
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<p>Multi sensing points over a network to locate motors.</p>
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<p>Frequency diffusion for an improperly functioning rotor.</p>
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<p>Framework for motor-level fault diagnosis.</p>
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<p>Multi-motor industrial WSN fault detection framework.</p>
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<p>Laboratory-scale testbed to analyze motor fault signatures.</p>
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<p>Xbee-based AtmegA328P MC Device interconnection with motor sensing point.</p>
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<p>Proteus ISIS Professional test configuration of Arduino’s hardware and software schematics.</p>
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<p>(<b>a</b>) Configure XBee coordinator. (<b>b</b>) Configure end-node devices.</p>
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<p>Transmission mode for end nodes.</p>
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<p>The function of the coordinator as well as the end-node devices within a network.</p>
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<p>The framework for the data packet’s structure.</p>
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<p>Packet structuring, data fusion, and transmission instructions for microcontrollers.</p>
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<p>(<b>a</b>–<b>i</b>). Each electric motor (1–9) with no load has its own FFT spectrum.</p>
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<p>(<b>a</b>–<b>i</b>). Each electric motor (1–9) with no load has its own FFT spectrum.</p>
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<p>(<b>a</b>–<b>i</b>). Each electric motor (1–9) with load has its own FFT spectrum.</p>
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<p>(<b>a</b>–<b>i</b>). Each electric motor (1–9) with load has its own FFT spectrum.</p>
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<p>One faulty motor symptom within the motor network.</p>
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<p>All motors’ no-load, no-fault electric current spectrum estimation.</p>
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<p>All motors’ no-load, no-fault electric current spectrum estimation.</p>
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<p>BRB fault analysis at full load of all motors.</p>
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<p>BRB fault analysis at full load of all motors.</p>
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<p>Motor 3 fault propagation within network at full load.</p>
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<p>Significant sideband frequencies (<b>a</b>) 42 Hz; (<b>b</b>) 58 Hz; (<b>c</b>) 81 Hz, 85 Hz, and 97 Hz.</p>
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<p>Significant sideband frequencies (<b>a</b>) 42 Hz; (<b>b</b>) 58 Hz; (<b>c</b>) 81 Hz, 85 Hz, and 97 Hz.</p>
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<p>Faulty motors spread throughout various buses with faults.</p>
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<p>Multi-frequency fault propagation by electric motor.</p>
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<p>Multi-frequency fault analysis by motors 1, 5, and 9.</p>
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<p>Comparing outputs of motor conditions. (<b>a</b>) Simulation analysis; (<b>b</b>) Xbee based Experimental testbed results on terminal.</p>
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23 pages, 3506 KiB  
Article
Comprehensive Performance Analysis of Zigbee Communication: An Experimental Approach with XBee S2C Module
by Khandaker Foysal Haque, Ahmed Abdelgawad and Kumar Yelamarthi
Sensors 2022, 22(9), 3245; https://doi.org/10.3390/s22093245 - 23 Apr 2022
Cited by 22 | Viewed by 6765
Abstract
The recent development of wireless communications has prompted many diversified applications in both industrial and medical sectors. Zigbee is a short-range wireless communication standard that is based on IEEE 802.15.4 and is vastly used in both indoor and outdoor applications. Its performance depends [...] Read more.
The recent development of wireless communications has prompted many diversified applications in both industrial and medical sectors. Zigbee is a short-range wireless communication standard that is based on IEEE 802.15.4 and is vastly used in both indoor and outdoor applications. Its performance depends on networking parameters, such as baud rates, transmission power, data encryption, hopping, deployment environment, and transmission distances. For optimized network deployment, an extensive performance analysis is necessary. This would facilitate a clear understanding of the trade-offs of the network performance metrics, such as the packet delivery ratio (PDR), power consumption, network life, link quality, latency, and throughput. This work presents an extensive performance analysis of both the encrypted and unencrypted Zigbee with the stated metrics in a real-world testbed, deployed in both indoor and outdoor scenarios. The major contributions of this work include (i) evaluating the most optimized transmission power level of Zigbee, considering packet delivery ratio and network lifetime; (ii) formulating an algorithm to find the network lifetime from the measured current consumption of packet transmission; and (iii) identifying and quantizing the trade-offs of the multi-hop communication and data encryption with latency, transmission range, and throughput. Full article
(This article belongs to the Special Issue Reliability Analysis of Wireless Sensor Network)
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<p>Zigbee protocol stack.</p>
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<p>Different topologies of the Zigbee standard.</p>
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<p>Single hop and two-hop setup for QoS measurements.</p>
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<p>Location of the coordinator end nodes in the indoor lab environment.</p>
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<p>PDR vs. distances for different transmission power levels in (<b>a</b>) indoor and (<b>b</b>) outdoor environments.</p>
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<p>(<b>a</b>) Measurement setup and (<b>b</b>) current consumption capture of the successful reception of a packet.</p>
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<p>The current consumption captures at different P<sub>trans</sub> levels (<b>a</b>) without encryption and (<b>b</b>) with 128-AES encryption.</p>
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<p>End node lifetime estimation of XBee S2C with 5000 mAh battery at various packet intervals: (<b>a</b>) AES encrypted communication; (<b>b</b>) unencrypted communication.</p>
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<p>The link quality of transmission in terms of RSSI with different transmission power levels, distance, and deployment environment: (<b>a</b>) indoors and (<b>b</b>) outdoors.</p>
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<p>RSSI values of single-hop and two-hop communication with P<sub>Trans</sub> = 3 dBm for various transmission distances and deployment scenarios.</p>
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<p>RTL measurement setup for latency analysis.</p>
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<p>Zigbee round trip latency with variations of baud rates and packets size for (<b>a</b>) encrypted and (<b>b</b>) unencrypted communication.</p>
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<p>RTL performances of one-hop and two-hop communication with and without AES encryption.</p>
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<p>Throughput vs. packet size at three different baud rates for (<b>a</b>) indoor and (<b>b</b>) outdoor deployment.</p>
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26 pages, 7287 KiB  
Article
HVAC Control System Using Predicted Mean Vote Index for Energy Savings in Buildings
by Daniel Fernando Espejel-Blanco, José Antonio Hoyo-Montaño, Jaime Arau, Guillermo Valencia-Palomo, Abel García-Barrientos, Héctor Ricardo Hernández-De-León and Jorge Luis Camas-Anzueto
Buildings 2022, 12(1), 38; https://doi.org/10.3390/buildings12010038 - 3 Jan 2022
Cited by 18 | Viewed by 5200
Abstract
Nowadays, reducing energy consumption is the fastest way to reduce the use of fossil fuels and, therefore, greenhouse gas emissions. Heating, Ventilation, and Air Conditioning (HVAC) systems are used to maintain an indoor environment in comfortable conditions for its occupants. The combination of [...] Read more.
Nowadays, reducing energy consumption is the fastest way to reduce the use of fossil fuels and, therefore, greenhouse gas emissions. Heating, Ventilation, and Air Conditioning (HVAC) systems are used to maintain an indoor environment in comfortable conditions for its occupants. The combination of these two factors, energy efficiency and comfort, is a considerable challenge for building operations. This paper introduces a design approach to control an HVAC, focused on an energy consumption reduction in the operation of the HVAC system of a building. The architecture was developed using a Raspberry Pi as a coordinator node and wireless connection with sensor nodes for environmental variables and electrical measurement nodes. The data received by the coordinator node is sent to the cloud for storage and further processing. The control system manages the setpoint of the HVAC equipment, as well as the turning on and off the HVAC compressor using an XBee-based solid state relay. The HVAC temperature control system is based on the Predicted Mean Vote (PMV) index calculation, which is used by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) to find the appropriate setpoint to meet the thermal comfort of 80% of users. This method combines the values of humidity and temperature to define comfort zones. The coordinator node makes the compressor control decisions depending on the value obtained in the PMV index. The proposed PMV-based temperature control system for the HVAC equipment achieves energy savings ranging from 33% to 44% against the built-in control of the HVAC equipment, when operating with the same setpoint of 26.5 grades centigrade. Full article
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<p>Control system scheme diagram.</p>
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<p>Sensor circuit node.</p>
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<p>Electrical parameter meter circuit.</p>
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<p>Actuator, solid-state switch.</p>
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<p>Control system circuit.</p>
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<p>Comfort zones recommended by ASHRAE [<a href="#B26-buildings-12-00038" class="html-bibr">26</a>].</p>
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<p>Control zone of the proposed control system.</p>
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<p>Setpoint for HVAC compressor.</p>
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<p>Raspberry Pi, sensor node, and electrical parameter meter.</p>
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<p>Compressor actuator installed in HVAC equipment.</p>
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<p>Tests room schema.</p>
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<p>HVAC average power consumption with the built-in and proposed control systems.</p>
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<p>Average temperature with the built-in and proposed control systems.</p>
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<p>PMV index behavior.</p>
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<p>Power consumption and PMV index values comparison.</p>
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<p>Psychometric chart software output.</p>
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<p>HVAC power consumption with the built-in with 26.58 °C setpoint and the proposed control systems.</p>
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<p>Average temperature with the built-in with 26.58 °C and the proposed control systems.</p>
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20 pages, 2880 KiB  
Review
Mobile Detection and Alarming Systems for Hazardous Gases and Volatile Chemicals in Laboratories and Industrial Locations
by Mohammed Faeik Ruzaij Al-Okby, Sebastian Neubert, Thomas Roddelkopf and Kerstin Thurow
Sensors 2021, 21(23), 8128; https://doi.org/10.3390/s21238128 - 4 Dec 2021
Cited by 21 | Viewed by 8508
Abstract
The leakage of hazardous gases and chemical vapors is considered one of the dangerous accidents that can occur in laboratories, workshops, warehouses, and industrial sites that use or store these substances. The early detection and alarming of hazardous gases and volatile chemicals are [...] Read more.
The leakage of hazardous gases and chemical vapors is considered one of the dangerous accidents that can occur in laboratories, workshops, warehouses, and industrial sites that use or store these substances. The early detection and alarming of hazardous gases and volatile chemicals are significant to keep the safety conditions for the people and life forms who are work in and live around these places. In this paper, we investigate the available mobile detection and alarming systems for toxic, hazardous gases and volatile chemicals, especially in the laboratory environment. We included papers from January 2010 to August 2021 which may have the newest used sensors technologies and system components. We identified (236) papers from Clarivate Web of Science (WoS), IEEE, ACM Library, Scopus, and PubMed. Paper selection has been done based on a fast screening of the title and abstract, then a full-text reading was applied to filter the selected papers that resulted in (42) eligible papers. The main goal of this work is to discuss the available mobile hazardous gas detection and alarming systems based on several technical details such as the used gas detection technology (simple element, integrated, smart, etc.), sensor manufacturing technology (catalytic bead, MEMS, MOX, etc.) the sensor specifications (warm-up time, lifetime, response time, precision, etc.), processor type (microprocessor, microcontroller, PLC, etc.), and type of the used communication technology (Bluetooth/BLE, Wi-Fi/RF, ZigBee/XBee, LoRa, etc.). In this review, attention will be focused on the improvement of the detection and alarming system of hazardous gases with the latest invention in sensors, processors, communication, and battery technologies. Full article
(This article belongs to the Special Issue Intelligent IoT Platforms for Wellbeing)
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<p>Canaries as toxic gas detectors [<a href="#B4-sensors-21-08128" class="html-bibr">4</a>].</p>
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<p>Basic block diagram of mobile detecting and alarming system.</p>
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<p>Flowchart of the papers selection process.</p>
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<p>Distribution of the used communication technologies in the presented study.</p>
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<p>Distribution of the used data access technologies.</p>
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<p>Distribution of the mobility type of the proposed systems.</p>
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<p>Distribution of the used sensing elements technologies.</p>
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<p>Data access platforms for smartphone-based systems.</p>
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28 pages, 3740 KiB  
Article
Reliable Control Applications with Wireless Communication Technologies: Application to Robotic Systems
by Isidro Calvo, Eneko Villar, Cristian Napole, Aitor Fernández, Oscar Barambones and José Miguel Gil-García
Sensors 2021, 21(21), 7107; https://doi.org/10.3390/s21217107 - 26 Oct 2021
Cited by 9 | Viewed by 3286
Abstract
The nature of wireless propagation may reduce the QoS of the applications, such that some packages can be delayed or lost. For this reason, the design of wireless control applications must be faced in a holistic way to avoid degrading the performance of [...] Read more.
The nature of wireless propagation may reduce the QoS of the applications, such that some packages can be delayed or lost. For this reason, the design of wireless control applications must be faced in a holistic way to avoid degrading the performance of the control algorithms. This paper is aimed at improving the reliability of wireless control applications in the event of communication degradation or temporary loss at the wireless links. Two controller levels are used: sophisticated algorithms providing better performance are executed in a central node, whereas local independent controllers, implemented as back-up controllers, are executed next to the process in case of QoS degradation. This work presents a reliable strategy for switching between central and local controllers avoiding that plants may become uncontrolled. For validation purposes, the presented approach was used to control a planar robot. A Fuzzy Logic control algorithm was implemented as a main controller at a high performance computing platform. A back-up controller was implemented on an edge device. This approach avoids the robot becoming uncontrolled in case of communication failure. Although a planar robot was chosen in this work, the presented approach may be extended to other processes. XBee 900 MHz communication technology was selected for control tasks, leaving the 2.4 GHz band for integration with cloud services. Several experiments are presented to analyze the behavior of the control application under different circumstances. The results proved that our approach allows the use of wireless communications, even in critical control applications. Full article
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<p>Overall wireless architecture for reliable edge computing.</p>
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<p>Detail of the Edge Node prototype board.</p>
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<p>Operational states and transitions for the wireless control system.</p>
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<p>Control frame issued by the Wireless Controller to the Edge Nodes for a SISO control system.</p>
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<p>Measurement frame used by the microcontroller at the Edge Nodes with the process values for a SISO control system.</p>
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<p>Configuration frame sent by the Wireless Controller to one Edge Node for a SISO control system.</p>
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<p>Planar manipulator with two revolute joints.</p>
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<p>Fuzzy structure for PI gain scheduling.</p>
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<p>Fuzzy controller configuration within inputs and outputs.</p>
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<p>Response comparison to step reference of PID and Fuzzy Logic Controller over the robot HIL model.</p>
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<p>FLC for a cubic trajectory without communication errors.</p>
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<p>FLC for a cubic trajectory with sporadic communication errors.</p>
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<p>FLC for a cubic trajectory with burst of lost frames while in steady state.</p>
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<p>Burst of frames lost at start of the transitory state.</p>
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<p>Burst of frames lost at the middle of transitory state.</p>
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<p>Burst of frames lost at the end of transitory state.</p>
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<p>Communication recovery when a step reference is used.</p>
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23 pages, 4752 KiB  
Article
Design and Performance of a XBee 900 MHz Acquisition System Aimed at Industrial Applications
by Isidro Calvo, José Miguel Gil-García, Eneko Villar, Aitor Fernández, Javier Velasco, Oscar Barambones, Cristian Napole and Pablo Fernández-Bustamante
Appl. Sci. 2021, 11(17), 8174; https://doi.org/10.3390/app11178174 - 3 Sep 2021
Cited by 3 | Viewed by 2586
Abstract
Wireless technologies are being introduced in industrial applications since they provide certain benefits, such as the flexibility to modify the layout of the nodes, improving connectivity with monitoring and decision nodes, adapting to mobile devices and reducing or eliminating cabling. However, companies are [...] Read more.
Wireless technologies are being introduced in industrial applications since they provide certain benefits, such as the flexibility to modify the layout of the nodes, improving connectivity with monitoring and decision nodes, adapting to mobile devices and reducing or eliminating cabling. However, companies are still reluctant to use them in time-critical applications, and consequently, more research is needed in order to be massively deployed in industrial environments. This paper goes in this direction by presenting a novel wireless acquisition system aimed at industrial applications. This system embeds a low-cost technology, such as XBee, not frequently considered for deterministic applications, for deploying industrial applications that must fulfill certain QoS requirements. The use of XBee 900 MHz modules allows for the use of the 2.4 GHz band for other purposes, such as connecting to cloud services, without causing interferences with critical applications. The system implements a time-slotted media access (TDMA) approach with a timely transmission scheduling of the messages on top of the XBee 900 MHz technology. The paper discusses the details of the acquisition system, including the topology, the nodes involved, the so-called coordinator node and smart measuring nodes, and the design of the frames. Smart measuring nodes are implemented by an original PCB which were specifically designed and manufactured. This board eases the connection of the sensors to the acquisition system. Experimental tests were carried out to validate the presented wireless acquisition system. Its applicability is shown in an industrial scenario for monitoring the positioning of an aeronautical reconfigurable tooling prototype. Both wired and wireless technologies were used to compare the variables monitored. The results proved that the followed approach may be an alternative for monitoring big machinery in indoor industrial environments, becoming especially suitable for acquiring values from sensors located in mobile parts or difficult-to-reach places. Full article
(This article belongs to the Special Issue Secure and Intelligent Mobile Systems)
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<p>General architecture overview for the wireless monitoring system.</p>
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<p>Chronogram for simple sampling distributed monitoring operation. Processing of the nodes and exchanged messages.</p>
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<p>Broadcast command frame sent from the Coordinator Node to the Smart Measuring Nodes.</p>
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<p>Frame used by the microcontroller at the Smart Measuring Nodes to send the values acquired from the sensors.</p>
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<p>Configuration frame sent by the Coordinator Node to one Smart Measuring Node.</p>
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<p>Chronogram for distributed monitoring operation. Processing of the nodes and exchanged messages when multiple sampling operation is used.</p>
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<p>Hardware implementation of the monitoring system.</p>
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<p>Detail of the Smart Measuring Node prototype board.</p>
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<p>Scenario 1: distributed simple sampling of 5 SMNs with 4 connected sensors each.</p>
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<p>Scenario 2: combination of several SMNs using different sampling configurations.</p>
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<p>Minimum and maximum latency for a broadcast measurement command frame.</p>
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<p>Periodical sending of a broadcast measurement frame at 60 ms.</p>
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<p>Aeronautical reconfigurable tooling prototype based on bar-knot structure in which the monitoring system was tested.</p>
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<p>Comparison values acquired with sensors located at the bar-knot structure when an external disturbance is applied. Frequency: 10 Hz; Measuring/acquisition separation: 12.30 m. Test duration 3 min. Strain-gauge (top); LVDT (medium) and Load-cell (bottom). Wireless values shown in blue and wired values in red.</p>
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<p>Comparison values acquired with sensors located at the bar-knot structure when a force is applied. Frequency: 40 Hz; Measuring/acquisition separation: 12.30 m. Test duration 3 min. LVDT sensor. Wireless values shown in blue and wired values in red.</p>
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28 pages, 11833 KiB  
Article
Preliminary Results of a Hybrid Thermoelectric Propulsion System for a Multirotor UAS with Active Rectifying, Electronic Throttle Control and Supercapacitors
by Manés F. Cabanas, Salvador B. Duque, Juan D. González, Francisco P. González and María G. Fernández
Appl. Sci. 2021, 11(17), 7899; https://doi.org/10.3390/app11177899 - 27 Aug 2021
Cited by 4 | Viewed by 2882
Abstract
The main drawback of unmanned aerial systems (UAS) is that almost their entire field of application is autonomous in terms of energy. Flights beyond 50 min are nearly impossible when using conventional energy storage systems (lithium-ion polymer or lithium-ion batteries). Several commercial products [...] Read more.
The main drawback of unmanned aerial systems (UAS) is that almost their entire field of application is autonomous in terms of energy. Flights beyond 50 min are nearly impossible when using conventional energy storage systems (lithium-ion polymer or lithium-ion batteries). Several commercial products have been developed using hybrid systems (H-UAS). Although the improvement they have provided is undeniable, H-UAS in the present market are strongly limited by their low thrust vs. weight ratio, which is caused by limited electrical power generation and a non-optimal energy conversion with relatively low efficiencies. This paper reviews these systems to show the preliminary results of a prototype of hybrid generator which state-of-the-art electronics as well as a new approach using a supercapacitor (SC) array are used to save fuel, increase the thrust vs. weight ratio, optimize losses during conversion and prevent the overheating of the internal combustion unit (ICU). Whereas current generators mostly operate with the ICU at a constant speed, delivering maximum power, the presented prototype includes a throttle control system, and the engine works with a variable regime according to the power demand. Thus, fuel consumption is reduced, as well as heating and wear. The lifespan of the engine is also increased, and the time between maintenance operations is lengthened. The designed system provides almost twice the power of the hybrid current generators. The reduction in the RPM regime of the engine is achieved by means of a supercapacitor array that provides the necessary energy to keep the DC output power constant during the engine acceleration when the flight envelope experiences a perturbation or a sudden manoeuvre is performed by the pilot. To obtain maximum efficiency, the diode rectifiers and conventional converters used in the reviewed products are replaced by synchronous converters and rectifiers. The whole system is controlled by means of a FPGA where a specific control loop has been implemented for every device: ICU’s throttle, DC bus converter, charge and discharge of the SC’s array, cooling and monitoring of temperature for the cylinders heads, and on-line transmission, by means of a XBEE™ module, of all the monitored data to the flight ground station. Full article
(This article belongs to the Special Issue Autonomous Flying Robots: Recent Developments and Future Prospects)
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<p>Comparison between product specifications and measured data for a U8II kV 85.</p>
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<p>Inaccuracy in the U8II kV 85 specifications.</p>
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<p>Efficiency of an U812II according to product specifications. Oscillations and a maximum efficiency of nearly 94% at high speed appear.</p>
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<p>Block diagram of the NOVA2400-Foxtech™ generator.</p>
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<p>Prototype used as battery replacement in any UAS.</p>
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<p>Simplified architecture of the hybrid generator.</p>
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<p>Block diagram of the prototype.</p>
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<p>Discharge test of a third-generation GENSACE battery. Constant 90 A, 5 °C equivalent. The impossibility of this being used as a propulsion battery, even at 5 °C, is clear, although the manufacturer’s specifications indicate a maximum discharge of 15 °C.</p>
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<p>DLE-111™ ICU.</p>
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<p>Clutch and one-way bearing.</p>
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<p>ROTOMAX 150 cc BLD MOTOR™.</p>
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<p>Line voltages with the generator rotating at 6156 RPM. Frequency: 1026 Hz.</p>
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<p>Rectified voltage before filtering.</p>
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<p>Active or synchronous rectification.</p>
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<p>Altera<sup>®</sup> MAX 10 FPGA-10M08SAU169C8GES mounted in its development board.</p>
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<p>Advanced gate-driven signal for optimal MOSFET commutation.</p>
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<p>PCB test prototypes.</p>
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<p>SMD active rectifier: first SMD prototype.</p>
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<p>Voltage ripple of the 50 V DC bus with the first prototype of the converter.</p>
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<p>SC array: distribution and dimensions.</p>
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<p>Two-way DC/DC converter with its PID current controller.</p>
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<p>Internal structure of the two-way DC/DC converter.</p>
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<p>Example of the charge and discharge of the SC array.</p>
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<p>Present level of development of the monitoring system.</p>
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<p>Global view of the prototype.</p>
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<p>Flexible coupling properties (main features).</p>
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<p>Flexible coupling properties (detailed view).</p>
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<p>Throttle control.</p>
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28 pages, 12063 KiB  
Article
Wireless Sensor Networks for Smart Cities: Network Design, Implementation and Performance Evaluation
by Ala’ Khalifeh, Khalid A. Darabkh, Ahmad M. Khasawneh, Issa Alqaisieh, Mohammad Salameh, Ahmed AlAbdala, Shams Alrubaye, Anwar Alassaf, Samer Al-HajAli, Radi Al-Wardat, Novella Bartolini, Giancarlo Bongiovannim and Kishore Rajendiran
Electronics 2021, 10(2), 218; https://doi.org/10.3390/electronics10020218 - 19 Jan 2021
Cited by 71 | Viewed by 9680
Abstract
The advent of various wireless technologies has paved the way for the realization of new infrastructures and applications for smart cities. Wireless Sensor Networks (WSNs) are one of the most important among these technologies. WSNs are widely used in various applications in our [...] Read more.
The advent of various wireless technologies has paved the way for the realization of new infrastructures and applications for smart cities. Wireless Sensor Networks (WSNs) are one of the most important among these technologies. WSNs are widely used in various applications in our daily lives. Due to their cost effectiveness and rapid deployment, WSNs can be used for securing smart cities by providing remote monitoring and sensing for many critical scenarios including hostile environments, battlefields, or areas subject to natural disasters such as earthquakes, volcano eruptions, and floods or to large-scale accidents such as nuclear plants explosions or chemical plumes. The purpose of this paper is to propose a new framework where WSNs are adopted for remote sensing and monitoring in smart city applications. We propose using Unmanned Aerial Vehicles to act as a data mule to offload the sensor nodes and transfer the monitoring data securely to the remote control center for further analysis and decision making. Furthermore, the paper provides insight about implementation challenges in the realization of the proposed framework. In addition, the paper provides an experimental evaluation of the proposed design in outdoor environments, in the presence of different types of obstacles, common to typical outdoor fields. The experimental evaluation revealed several inconsistencies between the performance metrics advertised in the hardware-specific data-sheets. In particular, we found mismatches between the advertised coverage distance and signal strength with our experimental measurements. Therefore, it is crucial that network designers and developers conduct field tests and device performance assessment before designing and implementing the WSN for application in a real field setting. Full article
(This article belongs to the Section Networks)
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<p>Different ZigBee network topologies (<b>a</b>) star, (<b>b</b>) cluster tree and (<b>c</b>) mesh.</p>
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<p>DigiMesh network.</p>
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<p>WSNs node architecture and components.</p>
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<p>(<b>a</b>) XBee S2 wireless module, (<b>b</b>) shows how it can be connected to the microcontroller via a shield mounted on top of the microcontroller (on the middle left) or using a gateway (on the right).</p>
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<p>XBee-PRO wireless module.</p>
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<p>Various sensors used in this work (<b>a</b>) Gas (<b>b</b>) Temperature and humidity, (<b>c</b>) PIR motion, and (<b>d</b>) Ultrasonic sensors.</p>
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<p>(<b>a</b>) the first and (<b>b</b>) the second implementations for the wireless sensor nodes using a PIR motion sensor as an example.</p>
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<p>WSNs architecture.</p>
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<p>The wiring diagrams for the HC-12 and APC220 wireless modules with the (<b>a</b>) Waspmot (<b>b</b>) Arduino UART interfacing sockets</p>
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<p>The sink node with two ZigBee modules mounted on Waspmote Microcontroller. The first one operates at 2.4 GHz and is mounted on socket 0, while the other one operates at 868 MHz and is mounted on socket 1 using the wireless expansion board.</p>
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<p>A flow diagram for the (<b>a</b>) UAV, and (<b>b</b>) sink nodes during the handshaking and data transmission processes. A code implementation of the proposed protocol is published and can be found in this URL (<a href="https://github.com/WSNProject" target="_blank">https://github.com/WSNProject</a>).</p>
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<p>The sink to UAV data flow diagram.</p>
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<p>(<b>a</b>) wireless sensor nodes spread over line of sight area with small obstacles, (<b>b</b>) nodes’ alignments for different distance ranges.</p>
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<p>Performance evaluation and comparison between the first and second WSNs implementations depicting (<b>a</b>) RSSI, (<b>b</b>) Packets received percentage.</p>
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<p>Wireless sensor nodes spread over non-line of sight area with observable obstacles at the German Jordanian University campus.</p>
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<p>Performance evaluation and comparison between the first and second WSNs implementations for non-line of sight area with observable obstacles depicting (<b>a</b>) RSSI, (<b>b</b>) Packets received percentage.</p>
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<p>Performance evaluation and comparison between the first and second WSNs implementations for non-line of sight area with observable obstacles depicting (<b>a</b>) RSSI, (<b>b</b>) Packets received percentage.</p>
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<p>Wireless sensor nodes spread over non-line of sight with wall blocked obstacles at the German Jordanian University campus.</p>
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<p>Performance evaluation and comparison between the first and second WSNs implementations for non-line of sight with wall blocked obstacles (<b>a</b>) RSSI, (<b>b</b>) Packets received percentage.</p>
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<p>The UAV communication node mounted at the bottom of the DJI F550 Hex-rotor drone. (<b>a</b>,<b>b</b>) show the bottom and side views, respectively.</p>
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21 pages, 7758 KiB  
Article
Context-Aware Wireless Sensor Networks for Smart Building Energy Management System
by Najem Naji, Mohamed Riduan Abid, Driss Benhaddou and Nissrine Krami
Information 2020, 11(11), 530; https://doi.org/10.3390/info11110530 - 15 Nov 2020
Cited by 19 | Viewed by 4778
Abstract
Energy Management Systems (EMS) are indispensable for Smart Energy-Efficient Buildings (SEEB). This paper proposes a Wireless Sensor Network (WSN)-based EMS deployed and tested in a real-world smart building on a university campus. The at-scale implementation enabled the deployment of a WSN mesh topology [...] Read more.
Energy Management Systems (EMS) are indispensable for Smart Energy-Efficient Buildings (SEEB). This paper proposes a Wireless Sensor Network (WSN)-based EMS deployed and tested in a real-world smart building on a university campus. The at-scale implementation enabled the deployment of a WSN mesh topology to evaluate performance in terms of routing capabilities, data collection, and throughput. The proposed EMS uses the Context-Based Reasoning (CBR) Model to represent different types of buildings and offices. We implemented a new energy-efficient policy for electrical heaters control based on a Finite State Machine (FSM) leveraging on context-related events. This demonstrated significant effectiveness in minimizing the processing load, especially when adopting multithreading in data acquisition and control. To optimize sensors’ battery lifetime, we deployed a new Energy Aware Context Recognition Algorithm (EACRA) that dynamically configures sensors to send data under specific conditions and at particular times to avoid redundant data transmissions. EACRA increases the sensors’ battery lifetime by optimizing the number of samples, used modules, and transmissions. Our proposed EMS design can be used as a model to retrofit other kinds of buildings, such as residential and industrial, and thus converting them to SEEBs. Full article
(This article belongs to the Special Issue Data Processing in the Internet of Things)
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<p>General architecture (<b>A</b>) and communication process (<b>B</b>) of the proposed Energy Management Systems (EMS).</p>
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<p>Information and Communication Technology (ICT) components for data acquisition in Smart Energy-Efficient Buildings (SEEB).</p>
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<p>Components of the Wireless Sensor Network (WSN) nodes in the data acquisition (<b>A</b>) Arduino nano and its components, (<b>B</b>) the installed actuator with the electric heater, (<b>C</b>–<b>E</b>) gateway devices.</p>
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<p>WSN architecture deployment at the university campus building.</p>
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<p>(<b>A</b>) Energy consumption of different sensor nodes configurations, (<b>B</b>) Energy consumption of XBee RF module under different operating modes.</p>
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<p>Packet arrival rate using the different gateways under full mesh topology and cluster tree mesh topology.</p>
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<p>Link quality between WSN nodes.</p>
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<p>Local and remote Remote Signal Strength Indicator (RSSI) of WSN nodes.</p>
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<p>RSSI value between sensor nodes and gateway.</p>
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<p>Knowledge types used in enabling and disabling the heating process.</p>
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<p>Groups in the Context-Based Reasoning (CBR) model.</p>
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<p>Finite State Machine (FSM) State diagram that controls Linux Laboratory heater.</p>
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<p>Energy Aware Context Recognition Algorithm (EACRA) Client algorithm.</p>
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<p>WSN nodes displayed on the web application.</p>
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<p>List of buildings in the EMS.</p>
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<p>Rooms of building 7 where the EMS was implemented.</p>
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<p>The application getting an update about the status of the heater.</p>
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