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Assessing the efficacy of a novel adaptive fuzzy c-means (AFCM) based clustering algorithm for mobile agent itinerary planning in wireless sensor networks using validity indices

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Abstract

Wireless Sensor Networks (WSN) are composed of small sensor nodes that either transmit their sensed data to the sink node directly or transmit it to its respective cluster head, which then transmits it to the sink node. However, this consumes a lot of network bandwidth and energy from the constrained sensor nodes. To address these constraints, Mobile Agents (MA) paradigm can be used in WSNs, which may lead to better energy and bandwidth conservation. When a single mobile agent is insufficient to complete a task, multiple mobile agents can be deployed to perform in parallel and reduce network latency. The set of sensor nodes and their sequence that MAs must migrate to complete a task is called an itinerary. The planning of the itinerary is the most prominent and significant issue related to the MA-based system, including the determination of an appropriate number of MAs to be dispatched, determining the set of sensor nodes and their sequence to be visited by MAs. This paper proposes a fuzzy-based algorithm to partition Wireless Sensor Networks into a set of sensor nodes, called domains, for enhancing the efficiency of the WSN in terms of its prolonged operation. Experimental evaluations are conducted to compare the proposed algorithm with benchmarked algorithms. The paper suggests that the proposed algorithm's integration with MA-based systems can enhance their performance and prolong the WSN's lifetime.

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References

  1. Bendjima M, Feham M, Lehsaini M (2021) Directional itinerary planning for multiple mobile agents. Wirel Sens Netw. https://doi.org/10.1155/2021/5584581

    Article  Google Scholar 

  2. Chen M, Gonzalez S, Leung VCM (2007) Applications and design issues for mobile agents in wireless sensor networks. IEEE Wirel Commun 14(6):20–26. https://doi.org/10.1109/MWC.2007.4407223

    Article  Google Scholar 

  3. El Fissaoui M, Beni-hssane A, Saadi M (2018) Multi-mobile agent itinerary planning-based energy and fault aware data aggregation in wireless sensor networks. EURASIP J Wirel Commun Network 92. https://doi.org/10.1186/s13638-018-1099-0

  4. Pourroostaei Ardakani S (2021) MINDS: mobile agent itinerary planning using named data networking in wireless sensor networks. J Sens Actuat Netw 10(2):28. https://doi.org/10.3390/jsan10020028

    Article  Google Scholar 

  5. Lohani D, Varma S (2013) Itinerary planning using grey relational analysis for mobile agent based wireless sensor networks. Sixth International Conference Contemporary Computing (IC3), Noida, India, pp 29–34. https://doi.org/10.1109/IC3.2013.6612215

    Book  Google Scholar 

  6. Daramola OA, Fakoya JT, Danjuma HI, Egwuche OS (2021) Towards clustering technique for a fault tolerance mobile agent-based system in wireless sensor networks. Int J Comput Sci Inf Secur 19(1):48–58. https://doi.org/10.5281/zenodo.4533402

    Article  Google Scholar 

  7. Prapulla SB, Chandra J, Mudakavi MB, Shobha G, Thanuja TC (2016) Multi mobile agent itinerary planning using farthest node first nearest node next (FNFNNN) technique. 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS). IEEE, pp 105–111. https://doi.org/10.1109/CSITSS.2016.7779404

    Chapter  Google Scholar 

  8. Chen M, Leung V, Mao S, Kwon T, Li M (2009) Energy-efficient itinerary planning for mobile agents in wireless sensor networks. IEEE International Conference on Communications, Dresden, Germany, pp 1–5. https://doi.org/10.1109/ICC.2009.5198997

    Book  Google Scholar 

  9. Gavalas D, Venetis IE, Konstantopoulos C, Pantziou G (2016) Energy-efficient multiple itinerary planning for mobile agents-based data aggregation in WSNs. Telecommun Syst 63(4):531–545. https://doi.org/10.1007/s11235-016-0140-z

    Article  Google Scholar 

  10. Hong W, Liu Z, Chen Y, Guo W (2016) Energy-efficient mobile agent communications for maximizing lifetime of wireless sensor networks. Wireless communications, networking and applications. Springer, New Delhi, pp 305–317. https://doi.org/10.1007/978-81-322-2580-5_29

    Chapter  Google Scholar 

  11. Makki S, Wunnava SV (2007) Application of mobile agents in managing the traffic in the network and improving the reliability and quality of service. Int J Comput Sci 33(1):135

    Google Scholar 

  12. Nidhi, Upadhyaya S (2022) Fuzzy C-means clustering of network for multi mobile agent itinerary planning. Smart trends in computing and communications, vol 396. Springer, Warsaw, Poland, pp 589–598

    Chapter  Google Scholar 

  13. Aloui I, Kazar O, Kahloul L, Servigne S (2015) A new itinerary planning approach among multiple mobile agents in wireless sensor networks (WSN) to reduce energy consumption. Int J Commun Netw Inf Secur 7(2):116–122. https://doi.org/10.17762/ijcnis.v7i2.1276

    Article  Google Scholar 

  14. Aloui I, Kazar O, Kahloul L, Aissaoui A, Servigne S (2016) A new “data size” based algorithm for itinerary planning among mobile agents in wireless sensor networks, vol 36. Proceedings of the International Conference on Big Data and Advanced Wireless Technologies, pp 1–9. https://doi.org/10.1145/3010089.3010121

    Book  Google Scholar 

  15. Wu C, Yan B, Yu R, Yu B, Zhou X, Yu Y, Chen N (2021) k-means clustering algorithm and its simulation based on distributed computing platform. Complexity. https://doi.org/10.1155/2021/9446653

    Article  Google Scholar 

  16. Qadori HQ, Zulkarnain ZA, Hanapi ZM, Subramaniam S (2017) A spawn mobile agent itinerary planning approach for energy-efficient data gathering in wireless sensor networks. Sensors 17(6):1280–1296. https://doi.org/10.3390/s17061280

    Article  Google Scholar 

  17. Chen M, Cai W, Gonzalez S, Leung VC (2010) Balanced itinerary planning for multiple mobile agents in wireless sensor networks. In: Zheng J, Simplot-Ryl D, Leung VCM (eds) Ad hoc networks. ADHOCNETS 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17994-5_28

    Chapter  Google Scholar 

  18. Konstantopoulos C, Mpitziopoulos A, Gavalas D, Pantziou G (2009) Effective determination of mobile agent itineraries for data aggregation on sensor networks. IEEE Trans Knowl Data Eng 22(12):1679–1693. https://doi.org/10.1109/TKDE.2009.2032009

    Article  Google Scholar 

  19. Mpitziopoulos A, Gavalas D, Konstantopoulos C, Pantziou G (2007) Deriving efficient mobile agent routes in wireless sensor networks with NOID algorithm. IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, pp 1–5. https://doi.org/10.1109/PIMRC.2007.4394337

    Book  Google Scholar 

  20. Liu B, Cao J, Yin J, Yu W, Liu B, Fu X (2016) Disjoint multi mobile agent itinerary planning for big data analytics. J Wirel Commun Netw 1:1–12. https://doi.org/10.1186/s13638-016-0607-3

    Article  Google Scholar 

  21. Mpitziopoulos A, Gavalas D, Konstantopoulos C, Pantziou G (2010) CBID: a scalable method for distributed data aggregation in WSNs. Int J Distrib Sens Netw 6(1):206517. https://doi.org/10.1155/2010/206517

    Article  Google Scholar 

  22. Gavalas D, Pantziou G, Konstantopoulos C, Mamalis B (2006) New techniques for incremental data fusion in distributed sensor networks. Proceedings of the 11th Panhellenic Conference on Informatics, pp 599–608

    Google Scholar 

  23. Chou YC, Nakajima M (2018) A clonal selection algorithm for energy-efficient mobile agent itinerary planning in wireless sensor networks. Mob Netw Appl 23(5):1233–1246. https://doi.org/10.1007/s11036-017-0814-0

    Article  Google Scholar 

  24. Kuila P, Gupta SK, Jana PK (2013) A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm Evol Comput 12:48–56. https://doi.org/10.1016/j.swevo.2013.04.002

    Article  Google Scholar 

  25. Mahmoudi M, Avokh A, Barekatain B (2022) SDN-DVFS: an enhanced QoS-aware load-balancing method in software defined networks. Cluster Comput 25:1237–1262. https://doi.org/10.1007/s10586-021-03522-x

    Article  Google Scholar 

  26. Wu Q, Rao NS, Barhen J, Iyenger SS, Vaishnavi VK, Qi H, Chakrabarty K (2004) On computing mobile agent routes for data fusion in distributed sensor networks. IEEE Trans Knowl Data Eng 16(6):740–753. https://doi.org/10.1109/TKDE.2004.12

    Article  Google Scholar 

  27. Sadrishojaei M, Navimipour NJ, Reshadi M et al (2022) A new clustering-based routing method in the mobile internet of things using a krill herd algorithm. Cluster Comput 25:351–361. https://doi.org/10.1007/s10586-021-03394-1

    Article  Google Scholar 

  28. Rajagopalan R, Mohan CK, Varshney P, Mehrotra K (2005) Multi-objective mobile agent routing in wireless sensor networks, vol 2. 2005 IEEE Congress on Evolutionary Computation, pp 1730–1737. https://doi.org/10.1109/CEC.2005.1554897

    Book  Google Scholar 

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Correspondence to Nidhi Kashyap.

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Kashyap, N., Upadhyaya, S., Poriye, M. et al. Assessing the efficacy of a novel adaptive fuzzy c-means (AFCM) based clustering algorithm for mobile agent itinerary planning in wireless sensor networks using validity indices. Peer-to-Peer Netw. Appl. (2024). https://doi.org/10.1007/s12083-024-01695-x

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