article

Decentralized service discovery and selection in Internet of Things applications based on artificial potential fields

Authors:

Anthony Karageorgos,

Catherine Houstis,

Elias HoustisAuthors Info & Claims

Service Oriented Computing and Applications, Volume 11, Issue 1

Pages 75 - 86

https://doi.org/10.1007/s11761-016-0198-1

Published: 01 March 2017 Publication History

Abstract

The Internet of Things (IoT) vision involves a future Internet integrated with real-world objects that can commonly offer their functionality trough services. In such pervasive environments of IoT networks, locating and invoking suitable services is quite challenging and traditional service discovery and selection approaches have been proven inadequate. In this paper, taking inspiration from natural metaphors, a decentralized service discovery and selection model is proposed. The model is based on artificial potential fields (APFs) which are formed upon each user service request and become active at points where services can be provided. Such points are termed as service provision nodes (SPNs). The strength of each APF depends on the percentage of requested services that can be provided by the respective SPN, as well as on SPN service load and availability with the aim to balance service load among SPNs. Service discovery and selection is then driven by artificial forces applied among user service requests and SPNs. Simulation results indicate that the proposed approach maintains satisfactory performance and scalability as the number of SPNs in an IoT network increase and efficient load balancing of the requested services among the SPNs in comparison with other approaches.

References

[1]

Zambonelli F, Viroli M (2011) A survey on nature-inspired metaphors for pervasive service ecosystems. Int J Pervasive Comput Commun 7(3):186---204

[2]

Christin D, Reinhardt A, Mogre PS, Steinmetz R (2009) Wireless sensor networks and the internet of things: selected challenges. In: Proceedings of the 8th GI/ITG KuVS Fachgespräch Drahtlose sensornetze, pp 31---34

[3]

Teixeira T, Hachem S, Issarny V, Georgantas N (2011) Service oriented middleware for the internet of things: a perspective. In: Abramowicz W, Llorente I, Surridge M, Zisman A, Vayssiere J (eds) Towards a service-based internet. Springer, Berlin, Heidelberg, pp 220---229

[4]

Ma HD (2011) Internet of things: objectives and scientific challenges. J Comput Sci Technol 26(6):919---924

Digital Library

[5]

Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787---2805

Digital Library

[6]

Uckelmann D, Harrison M, Michahelles F (2011) An architectural approach towards the future internet of things. Springer, Berlin

[7]

Ahmed T, Tripathi A, Srivastava A (2014) Rain4service: an approach towards decentralized web service composition. In: 2014 IEEE international conference on services computing (SCC). IEEE, pp 267---274

Digital Library

[8]

Spiess P, Karnouskos S, Guinard D, Savio D, Baecker O, De Souza LMS, Trifa V (2009) Soa-based integration of the internet of things in enterprise services. In: IEEE international conference on web services, 2009. ICWS 2009. IEEE, pp 968---975

Digital Library

[9]

Csorba MJ, Meling H, Heegaard PE (2011) A bio-inspired method for distributed deployment of services. New Gener Comput 29(2):185---222

[10]

Chan NN, Gaaloul W, Tata S (2012) A recommender system based on historical usage data for web service discovery. SOCA 6(1):51---63

Digital Library

[11]

Chen H, Li S (2010) Src: a service registry on cloud providing behavior-aware and qos-aware service discovery. In: 2010 IEEE international conference on service-oriented computing and applications (SOCA). IEEE, pp 1---4

[12]

Clement L, Hately A, von Riegen C, Rogers T et al (2004) Uddi version 3.0. 2, uddi spec technical committee draft. OASIS open standards consortium

[13]

Papadopoulos P, Tianfield H, Moffat D, Barrie P (2013) Decentralized multi-agent service composition. Multiagent Grid Syst 9(1):45---100

Digital Library

[14]

Al-Masri E, Mahmoud QH (2006) A context-aware mobile service discovery and selection mechanism using artificial neural networks. In: Proceedings of the 8th international conference on electronic commerce: the new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet. ACM, pp 594---598

[15]

Guinard D, Trifa V, Karnouskos S, Spiess P, Savio D (2010) Interacting with the soa-based internet of things: discovery, query, selection, and on-demand provisioning of web services. IEEE Trans Serv Comput 3(3):223---235

Digital Library

[16]

He Q, Yan J, Yang Y, Kowalczyk R, Jin H (2013) A decentralized service discovery approach on peer-to-peer networks. IEEE Trans Serv Comput 6(1):64---75

Digital Library

[17]

Sapkota B, Roman D, Kruk SR, Fensel D (2006) Distributed web service discovery architecture. In: Null. IEEE, p 136

[18]

Li Y, Zou F, Wu Z, Ma F (2004) Pwsd: a scalable web service discovery architecture based on peer-to-peer overlay network. In: Jeffrey Xu Yu, Xuemin Lin, Hongjun Lu, Yanchun Zhang (eds) Advanced web technologies and applications. Springer, Berlin, Heidelberg, pp 291---300

[19]

He Q, Yan J, Yang Y, Kowalczyk R, Jin H (2008) Chord4s: a p2p-based decentralised service discovery approach. In: IEEE international conference on services computing, 2008. SCC'08, vol 1. IEEE, pp 221---228

Digital Library

[20]

Bicchi A, Marigo A, Pappas G, Pardini M, Parlangeli G, Tomlin C, Sastry S (1998) Decentralized airtraffic management systems: performance and fault tolerance. In: Proceedings of IFAC international workshop on motion control, Grenoble. Citeseer, pp 279---284

[21]

Stoica I, Morris R, Karger D, Kaashoek MF, Balakrishnan H (2001) Chord: a scalable peer-to-peer lookup service for internet applications. ACM SIGCOMM Comput Commun Rev 31(4):149---160

Digital Library

[22]

Gharzouli M, Boufaida M (2011) Pm4sws: a p2p model for semantic web services discovery and composition. J Adv Inf Technol 2(1):15---26

[23]

Ahmed T, Mrissa M, Srivastava A (2014) Magel: a magneto-electric effect-inspired approach for web service composition. In: 2014 IEEE international conference on web services (ICWS). IEEE, pp 455---462

Digital Library

[24]

Mamei M, Zambonelli F (2006) Field-based coordination for pervasive multiagent systems. Springer Science & Business Media, Berlin

[25]

Lenders V, May M, Plattner B (2005) Service discovery in mobile ad hoc networks: a field theoretic approach. Pervasive Mobile Comput 1(3):343---370

Digital Library

[26]

Adeli H, Tabrizi MHN, Mazloomian A, Hajipour E, Jahed M (2011) Path planning for mobile robots using iterative artificial potential field method. Int J Comput Sci Issues 8(4):28---32

[27]

Jaradat MAK, Garibeh MH, Feilat EA (2012) Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field. Soft Comput 16(1):153---164

Digital Library

[28]

Masoud AA (2007) Decentralized self-organizing potential field-based control for individually motivated mobile agents in a cluttered environment: a vector-harmonic potential field approach. IEEE Trans Syst Man Cybern Part A: Syst Hum 37(3):372---390

Digital Library

[29]

Jiang HF, Qian JS, Sun YJ (2011) Virtual electrostatic field based multi-sink routing algorithm in WSN. J China Univ Min Technol 40(2):321---326

[30]

Xu YS, Ren FY (2009) Potential field based multi-strategy routing protocol in WSN. ZTE Commun 15(6):32---36

[31]

Jiang H, Sun Y, Sun R, Chen W, Ma S, Gao J (2014) A distributed energy optimized routing using virtual potential field in wireless sensor networks. Int J Distrib Sens Netw 2014:10

[32]

Kalantari M, Shayman M (2006) Design optimization of multi-sink sensor networks by analogy to electrostatic theory. In: IEEE wireless communications and networking conference, 2006. WCNC 2006, vol 1. IEEE, pp 431---438

[33]

Ruchti J, Senkbeil R, Carroll J, Dickinson J, Holt J, Biaz S (2011) Uav collision avoidance using artificial potential fields. Tech. rep. CSSE11-03, Computer Science and Software Engineering Department, Auburn University

[34]

Rapti E, Karageorgos A, Gerogiannis VC (2015) Decentralised service composition using potential fields in internet of things applications. Proc Comput Sci 52:700---706

Digital Library

[35]

Gu X, Nahrstedt K, Yu B (2004) Spidernet: an integrated peer-to-peer service composition framework. In: 2004. Proceedings 13th IEEE international symposium on high performance distributed computing. IEEE, pp 110---119

[36]

Chakraborty D, Perich F, Joshi A, Finin T, Yesha Y (2003) A reactive service composition architecture for pervasive computing environments. In: Cambyse GO (ed) Mobile and wireless communications. Springer, Berlin, Heidelberg, pp 53---60

[37]

Yu T, Zhang Y, Lin K-J (2007) Efficient algorithms for web services selection with end-to-end qos constraints. ACM Trans Web (TWEB) 1(1):6

Digital Library

[38]

Luo J-Z, Zhou J-Y, Wu Z-A (2009) An adaptive algorithm for qos-aware service composition in grid environments. SOCA 3(3):217---226

[39]

Al-Oqily I, Karmouch A (2011) A decentralized self-organizing service composition for autonomic entities. ACM Trans Auton Adapt Syst (TAAS) 6(1):7

[40]

Sun microsystems, jini architecture specification version 2.0 (2003). http://www.sun.com/software/jini/specs/

[41]

Waldo J (1998) Javaspaces specification 1.0. Sun Microsyst 29 Technical report, p 30

[42]

Universal Plug (2003) Play (upnp) forum. Microsoft Corporation

[43]

Veizades J, Guttman E, Perkins CE, Kaplan S (1997) Service location protocol. Internet Eng Task Force: RFC 2165, p 72. https://www.ietf.org/rfc/rfc2165.txt

[44]

Czerwinski SE, Zhao BY, Hodes TD, Joseph AD, Katz RH (1999) An architecture for a secure service discovery service. In: Proceedings of the 5th annual ACM/IEEE international conference on mobile computing and networking. ACM, pp 24---35

[45]

Ratnasamy S, Francis P, Handley M, Karp R, Shenker S (2001) A scalable content-addressable network, vol 31. ACM, New York

[46]

Rowstron A, Druschel P (2001) Pastry: scalable, decentralized object location, and routing for large-scale peer-to-peer systems. In: Rachid G (ed) Middleware 2001. Springer, Berlin, Heidelberg, pp 329---350

[47]

Khatib O (1986) Real-time obstacle avoidance for manipulators and mobile robots. Int J Robot Res 5(1):90---98

Digital Library

[48]

Kim DH, Wang H, Shin S (2006) Decentralized control of autonomous swarm systems using artificial potential functions: analytical design guidelines. J Intell Rob Syst 45(4):369---394

Digital Library

[49]

Vallée M, Ramparany F, Vercouter L (2005) A multi-agent system for dynamic service composition in ambient intelligence environments. Citeseer, Grenoble

[50]

Puliafito A, Cucinotta A, Minnolo AL, Zaia A (2010) Making the internet of things a reality: the wherex solution. In: Daniel G, Antonio I, Giacomo M, Luigi A (eds) The internet of things. Springer, New York, pp 99---108

[51]

Luke S, Cioffi-Revilla C, Panait L, Sullivan K, Balan G (2005) Mason: a multiagent simulation environment. Simulation 81(7):517---527

Digital Library

Cited By

Achir MAbdelli AMokdad LBenothman J(2022)Service discovery and selection in IoTJournal of Network and Computer Applications10.1016/j.jnca.2021.103331200:COnline publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1016/j.jnca.2021.103331
Pourghebleh BHayyolalam VAghaei Anvigh A(2020)Service discovery in the Internet of Things: review of current trends and research challengesWireless Networks10.1007/s11276-020-02405-026:7(5371-5391)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.1007/s11276-020-02405-0
Skiadopoulos KOikonomou KAvlonitis MGiannakis KKogias DStavrakakis I(2019)Multiple and replicated random walkers analysis for service discovery in fog computing IoT environmentsAd Hoc Networks10.1016/j.adhoc.2019.10189393:COnline publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1016/j.adhoc.2019.101893
Show More Cited By

Recommendations

Decentralised Service Composition using Potential Fields in Internet of Things Applications
Abstract
Traditional service composition approaches rely mostly on centralised architectures, which have been proven inadequate in pervasive Internet of Things (IoT)environments. In such settings, where decentralisation of decision-making is mandatory, ...
A hybrid service selection optimization algorithm in internet of things
Abstract
With the scale of Internet of Things (IoT) continues to increase, it brings big challenges for service selection in a large-scale IoT. For solving this problem, a service selection method based on the enhanced genetic algorithm is proposed in this ...
Service Selection Using Service Clusters
SCC '15: Proceedings of the 2015 IEEE International Conference on Services Computing

With the rapid development of cloud computing technologies, there are increasing number of Web services deployed by service providers on cloud platforms. Invoking Web services is in the form of given orders, by which the end-user can process business ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Service Oriented Computing and Applications

Service Oriented Computing and Applications Volume 11, Issue 1

March 2017

116 pages

ISSN:1863-2386

Issue’s Table of Contents

Copyright © Copyright © 2017 Springer-Verlag London.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 March 2017

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 19 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Achir MAbdelli AMokdad LBenothman J(2022)Service discovery and selection in IoTJournal of Network and Computer Applications10.1016/j.jnca.2021.103331200:COnline publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1016/j.jnca.2021.103331
Pourghebleh BHayyolalam VAghaei Anvigh A(2020)Service discovery in the Internet of Things: review of current trends and research challengesWireless Networks10.1007/s11276-020-02405-026:7(5371-5391)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.1007/s11276-020-02405-0
Skiadopoulos KOikonomou KAvlonitis MGiannakis KKogias DStavrakakis I(2019)Multiple and replicated random walkers analysis for service discovery in fog computing IoT environmentsAd Hoc Networks10.1016/j.adhoc.2019.10189393:COnline publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1016/j.adhoc.2019.101893
Li YHuang YZhang MRajabion L(2019)Service selection mechanisms in the Internet of Things (IoT): a systematic and comprehensive studyCluster Computing10.1007/s10586-019-02984-423:2(1163-1183)Online publication date: 19-Sep-2019
https://dl.acm.org/doi/10.1007/s10586-019-02984-4
Khansari MSharifian SMotamedi S(2018)Virtual sensor as a serviceThe Journal of Supercomputing10.5555/3288339.328836974:10(5485-5512)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.5555/3288339.3288369
Nallakaruppan MKumaran U(2018)Quick fix for obstacles emerging in management recruitment measure using IOT-based candidate selectionService Oriented Computing and Applications10.1007/s11761-018-0236-212:3-4(275-284)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s11761-018-0236-2
Sorouri MVyatkin V(2018)Intelligent product and mechatronic software components enabling mass customisation in advanced production systemsService Oriented Computing and Applications10.1007/s11761-018-0230-812:1(73-86)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s11761-018-0230-8

View Options

View options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents