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International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.14, No.2, Apr. 2022

Game Theory based Resource Identification Scheme for Wireless Sensor Networks

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Author(s)

Gururaj S. Kori, Mahabaleshwar S. Kakkasageri

Index Terms

Wireless Sensor Networks;Resource Identification;Bayesian Game;Nash Equilibrium

Abstract

In modern world of sensing and distributive systems, traditional Wireless Sensor Networks (WSN) has to deal with new challenges, such as multiple application requirements, dynamic and heterogeneous networks. Senor nodes in WSN are resource constrained in terms of energy, communication range, bandwidth, processing delay and memory. Numerous solutions are proposed to optimize the performance and to increase the lifetime of WSN by introducing new resource management principles. Effective and intelligent resource management in WSN involves in resource identification, resource scheduling, and resource utilization. This paper proposes a Bayesian Game Model (BGM) approach to efficiently identify the best node with the maximum resource in WSN for data transmission, considering energy, bandwidth, and computational delay. The scheme operates as follows: (1) Sensor nodes information such as residual energy, available bandwidth, and node ID, etc., is gathered (2) Energy and bandwidth of each node are used to generate the payoff matrix (3) Implementation of node identification scheme is based on payoff matrix, utilities assigned, strategies and reputation of each node (4) Find Bayesian Nash Equilibrium condition using Starring algorithm (5) Solving the Bayesian Nash Equilibrium using Law of Total Probability and identifying the best node with maximum resources (6) Adding/Subtracting reward (reputation factor) to winner/looser node. Simulation results show that the performance of the proposed Bayesian game model approach for resource identification in WSN is better as compared with the Efficient Neighbour Discovery Scheme for Mobile WSN (ENDWSN). The results indicate that the proposed scheme has up to 12% more resource identification accuracy rate, 10% increase in the average number of efficient resources discovered and 8% less computational delay as compared to ENDWSN.

Cite This Paper

Gururaj S. Kori, Mahabaleshwar S. Kakkasageri, "Game Theory based Resource Identification Scheme for Wireless Sensor Networks", International Journal of Intelligent Systems and Applications(IJISA), Vol.14, No.2, pp.54-73, 2022. DOI: 10.5815/ijisa.2022.02.05

Reference

[1]Lucia Keleadile Ketshabetswe, Adamu Murtala Zungeru, Mmoloki Mangwala, Joseph M. Chuma, Boyce Sigweni, “Communication Protocols for Wireless Sensor Networks: A survey and Comparison”, Journal of Heliyon, Elsevier, vol. 5, No. 5, pp. 01-43, 2019.

[2]Fatma Karray, Mohamed W. Jmal, Alberto Garcia-Ortiz, Mohamed Abid, Abdulfattah M. Obeid, “A Comprehensive Survey on Wireless Sensor Node Hardware Platforms”, Journal of Computer Networks, Elsevier, vol. 144, pp. 89-110, 2018.

[3]Mohammed Sulaiman, Ben Saleh, Raoudha Saida , Yessine Hadj Kacem Mohamed Abid, “Wireless Sensor Network Design Methodologies: A Survey”, Journal of Sensors, Hindwai, vol. 20, pp. 01-12, 2020.

[4]Murat Dener, “WiSeN: A New Sensor Node for Smart Applications with Wireless Sensor Networks”, Journal of Computers and Electrical Engineering, Elsevier, vol. 64, pp. 380-394, 2017.

[5]Giancarlo Fortino, Mert Bal, Wenfeng Li, Weiming Shen, “Collaborative Wireless Sensor Networks: Architectures, Algorithms and Applications”, Journal of Information Fusion, Elsevier, vol. 22, pp. 1-2, 2015.

[6]M.Siddappa, Channakrishna raju, "Survey on an Efficient Coverage and Connectivity of Wireless Sensor Networks using Intelligent Algorithms", International Journal of Information Technology and Computer Science(IJITCS), vol.4, no.5, pp.39-45, 2012. DOI: 10.5815/ijitcs.2012.05.06

[7]Yasir Arfat, Riaz Ahmed Shaikh,"A Survey on Secure Routing Protocols in Wireless Sensor Networks", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.6, No.3, pp.9-19, 2016.DOI: 10.5815/ijwmt.2016.03.02

[8]Bushra Rashid, Mubashir Husain Rehmani, “Applications of Wireless Sensor Networks for Urban Areas: A Survey”, Journal of Network and Computer Applications, Elsevier, vol. 60, pp. 192-219, 2016.

[9]Priyanka Rawat, Kamal Deep Singh, Hakima Chaouchi, Jean Marie Bonnin, “Wireless Sensor Networks: A Survey on Recent Developmentsand Potential Synergies” The Journal of Supercomputing, Springer, vol. 68, no. 01, pp. 1–48, April 2014.

[10]I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, “Wireless Sensor Networks: A Survey”, Journal of Computer Networks, Elsevier, pp. 393–422, 2013.

[11]Kgotlaetsile Mathews Modieginyane, Babedi Betty Letswamotse, Reza Malekian, Adnan M. Abu-Mahfouz, “Software Defined Wireless Sensor Networks Application Opportunities for Efficient Network Management: A Survey”, Journal of Computers and Electrical Engineering, Elsevier, vol. 66, pp. 274-287, 2018.

[12]Muhammad Noman Riaz, " Clustering Algorithms of Wireless Sensor Networks: A Survey", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.8, No.4, pp. 40-53, 2018.DOI: 10.5815/ijwmt.2018.04.03

[13]Veena I Puranikmath, Sunil S Harakannanavar, Satyendra Kumar, Dattaprasad Torse,"Comprehensive Study of Data Aggregation Models, Challenges and Security Issues in Wireless Sensor Networks", International Journal of Computer Network and Information Security(IJCNIS), Vol.11, No.3, pp.30-39, 2019.DOI: 10.5815/ijcnis.2019.03.05 

[14]Elhadi M. Shakshuki, Stephen Isiuwe, “Resource Management Approach to an Efficient Wireless Sensor Network”, Proc. of the 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, Elsevier, Procedia Computer Science, pp. 190–198, 2018.

[15]Anand Rakshe, B. Prasad, V. Akshay and C. Channaveer, “Resource Management in Wireless Sensor Network”, International Journal on Emerging Technologies, vol. 07, no. 02, pp. 293-298, 2016.

[16]Edoardo Regini, Daeseob Lim, and Tajana Simunic Rosing, “Resource Management in Heterogeneous Wireless Sensor Networks”, Journal of Low Power Electronics, American Scientific Publishers, vol. 07, 01–18, 2011.

[17]Valerie Galluzzi, Ted Heman, “Survey: Discovery in Wireless Sensor Networks”, International Journal of Distributed Sensor Networks, vol. 08, no. 01, pp. 12-24, 2012.

[18]B. Senthil Murugan, Daphne Lopez, “A Survey of Resource Discovery Approaches in Distributed Computing Environment”, International Journal of Computer Applications, vol. 22, no.0 9, pp. 44-46, 2011.

[19]Jie Lu, Vahid Behbood, Peng Hao, Hua Zuo, Shan Xue, Guangquan Zhang, “Transfer Learning using Computational Intelligence: A Survey”, Journal of Knowledge Based Systems, Elsevier, vol. 80, pp. 14–23, 2018.

[20]Artificial Intelligence, Computational Intelligence, Soft Computing, Natural Computation - What's the Difference? www.andata.at., 2015.

[21]Gururaj S. Kori, Mahabaleshwar S. Kakkasageri, Sunilkumar S. Manvi, “Computational Intelligent Techniques for Resource Management Schemes in Wireless Sensor Networks”, Recent Trends in Computational Intelligence Enabled Research, Elsevier chapter no. 03, pp. 41-59, 2021.

[22]Nicolas Primeau, Rafael Falcon, Rami Abielmona, Emil M. Petriu, “A Review of Computational Intelligence Techniques in Wireless Sensor and Actuator Networks”, IEEE Communications Surveys and Tutorials, vol. 20, no. 04, pp. 2822 – 2854, 2018.

[23]Sohail Jabbar, Rabia Iram, Abid Ali Minhas, Imran Shafi, Shehzad Khalid, “Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing: Survey and Future Directions”, International Journal of Distributed Sensor Networks, vol. 10, pp. 13- 25, 2013.

[24]Sandeep Kumar, Medha Sharma, “Convergence of Artificial Intelligence, Emotional Intelligence, Neural Network and Evolutionary Computing”, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 02, no. 03, 2012.

[25]Raghavendra V. Kulkarni, Anna Förster, Ganesh Kumar Venayagamoorthy, “Computational Intelligence in Wireless Sensor Networks: A Survey”, IEEE Communications Surveys and Tutorials, vol. 13, no. 01, pp. 68-96, 2011.

[26]Dmitry Smirnov, Alessandro Golkar, “Design Optimization Using Game Theory”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 01 – 11, 2019.

[27]Jose Moura, David Hutchison, “Game Theory for Multi-Access Edge Computing: Survey, Use Cases, and Future Trends”, IEEE Communications Surveys and Tutorials, vol. 21, No. 01, pp. 260 – 288, 2019.

[28]M. Shoukath Ali, R. P. Singh, “A Study on Game Theory Approaches for Wireless Sensor Networks”, International Journal of Engineering and Advanced Technology, vol. 06, no. 03, pp. 05–07, 2017.

[29]Mohamed S. Abdalzaher, Karim Seddik, Maha Elsabrouty, Osamu Muta, Hiroshi Furukawa, “Game Theory Meets Wireless Sensor Networks Security Requirements and Threats Mitigation: A Survey”, Sensors Journal, MDPI Publications, vol. 16, no. 07, pp. 01–27, 2016.

[30]Rui Zhang, “MP2P Resource Node Selection Strategy based on Reputation and Bayesian Game”, Proc. of the 5th IEEE International Conference on Information Technology and Mechatronics Engineering Conference, 2020.

[31]Gururaj S. Kori, Mahabaleshwar S. Kakkasageri, “Intelligent Resource Identification Scheme for Wireless Sensor Networks”, Proc. of the International Conference on Recent Trends in Machine Learning, IOT, Smart Cities & Applications, Springer, 2020.

[32]Sharifzadeh Manaf, Payam Porker, Mehdi Gheisari, “New Algorithm for Resource Discovery in Sensor Networks based on Neural Network”, International Journal of Biology, Pharmacy and Allied Sciences, vol. 04, no. 12, pp. 125-140, 2015.

[33]Mahantesh G. Kambalimath, Mahabaleshwar S. Kakkasageri, "Dynamic Resource Discovery Scheme for Vehicular Cloud Networks", International Journal of Information Technology and Computer Science(IJITCS), Vol.11, No.12, pp.38-49, 2019. DOI: 10.5815/ijitcs.2019.12.04

[34]Absalom E. Ezugwu, Aderemi O. Adewumi, “Soft Sets based Symbiotic Organisms Search Algorithm for Resource Discovery in Cloud Computing Environment”, Future Generation Computer Systems, Elsevier, vol. 76, pp. 33-50, 2017.

[35]Thanikaivel B., Venkatalakshmi K., Kannan A., “Optimized Mobile Cloud Resource Discovery Architecture based on Dynamic Cognitive and Intelligent Technique”, Journal of Microprocessors and Microsystems, Elsevier, vol. 81, no. 2, pp. 1-24, 2020.

[36]Mekhla Sharma, Jaiteg Singh, Ankur Gupta, “Intelligent Resource Discovery in Inter-cloud using Blockchain”, Proc. of the IEEE International Conference Smart World, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation, pp. 1333 – 1338, 2019.

[37]Rodolfo I. Meneguette, Azzedine Boukerche, “Vehicular Clouds Leveraging Mobile Urban Computing Through Resource Discovery”, IEEE Transactions on Intelligent Transportation Systems, vol.  21, no. 6, pp. 2640 – 2647, 2019.

[38]Priya Dongarwar, Purnima Soni, “A Survey on Energy Efficient Node Discovery in Wireless Sensor Network”, International Journal of Computer Science and Network, vol. 04, no. 01, pp. 83- 85, 2015.

[39]Marija Tutunovic, Pongpisit Wuttidittachotti, “Discovery of Suitable Node Number for Wireless Sensor Networks Based on Energy Consumption using Cooja”, Proc. of the IEEE International Conference on Advanced Communications Technology (ICACT), pp. 168 - 172, 2019.

[40]L. Gandhimathi, G. Murugaboopathi, “Mobile Malicious Node Detection Using Mobile Agent in Cluster-Based Wireless Sensor Networks”, Wireless Personal Communications, Springer, vol. 117, pp. 1209–1222, 2020.

[41]Omar A. Saraereh, Imran Khan, Byung Moo Lee, “An Efficient Neighbor Discovery Scheme for Mobile WSN”, IEEE Access, vol. 07, pp. 4843-4855, 2018. 

[42]S. M. Chithra, S. Sridevi, M. Kavitha, “A Performance on Repeated Bayesian Game Theory in Wireless Sensor Networks”, International Journal of Engineering and Advanced Technology, vol. 09, no. 01, pp.  2553 – 2558, 2019.

[43]K. C. Lalropuia, Vandana Gupta, “A Bayesian Game Model and Network Availability Model for Small Cells Under Denial of Service (Dos) Attack in 5G Wireless Communication Network”, Wireless Networks, Springer, vol. 26, pp. 557- 572, 2019.

[44]S. Gheisari, M.R. Meybodi, “A New Reasoning and Learning Model for Cognitive Wireless Sensor Networks Based on Bayesian Networks and Learning Automata Cooperation”, Computer Networks, Elseiver, vol. 124, pp. 11 – 26, 2017.

[45]R. Latha, P. Vetrivelan, M. Jagannath, “Balancing Emergency Message Dissemination and Network Lifetime in Wireless Body Area Network using Ant Colony Optimization and Bayesian Game Formulation”, Informatics in Medicine Unlocked, Elsevier, vol. 8, pp. 60-65, 2017.

[46]N. Abuzainab, Walid Saad, “A Graphical Bayesian Game for Secure Sensor Activation in Internet of Battlefield Things”, Ad Hoc Networks, Elseiver, vol. 85, pp. 103 – 109, 2019.

[47]David Rios Insua, Fabrizio Ruggeri, Refik Soyer, Simon Wilson, “Advances in Bayesian decision making in reliability”, European Journal of Operational Research, Elseiver, vol. 282, no. 01, pp. 1 – 18, 2020.

[48]Evangelos D. Spyrou, Dimitrios K. Mitrakos, “Approximating Nash Equilibrium Uniqueness of Power Control in Practical WSNs”, International Journal of Computer Networks & Communications, Hindawi, vol. 07, no. 06, pp. 53- 68, 2015.

[49]Le Nguyen Hoang, Francois Soumis, Georges Zaccour, “The Return Function: A New Computable Perspective on Bayesian–Nash Equilibria, European Journal of Operational Research, Elseiver, vol. 279, no. 02, pp. 471 – 485, 2019.