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KFOA: K-mean Clustering, Firefly Based Data Rate Optimization and ACO Routing for Congestion Control in WSN

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Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Wireless sensor network (WSN) is assortment of sensor nodes proficient in environmental information sensing, refining it and transmitting it to base station in sovereign manner. The minute sensors communicate themselves to sense and monitor the environment. The main challenges are limited power, short communication range, low bandwidth and limited processing. The power source of these sensor nodes are the main hurdle in design of energy efficient network. The main objective of the proposed clustering and data transmission algorithm is to augment network performance by using swarm intelligence approach. This technique is based on K-mean based clustering, data rate optimization using firefly optimization algorithm and Ant colony optimization based data forwarding. The KFOA is divided in three parts: (1) Clustering of sensor nodes using K-mean technique and (2) data rate optimization for controlling congestion and (3) using shortest path for data transmission based on Ant colony optimization (ACO) technique. The performance is analyzed based on two scenarios as with rate optimization and without rate optimization. The first scenario consists of two operations as k-mean clustering and ACO based routing. The second scenario consists of three operations as mentioned in KFOA. The performance is evaluated in terms of throughput, packet delivery ratio, energy dissipation and residual energy analysis. The simulation results show improvement in performance by using with rate optimization technique.
Słowa kluczowe
Rocznik
Strony
659--665
Opis fizyczny
Bibliogr. 19 poz., rys., tab., wykr.
Twórcy
  • Patil Institute of Technology, Pimpri, Pune, India
  • Army Institute of Technology, Dighi Hills, Pune, India
Bibliografia
  • [1] Vikas Srivastava, Sachin Tripathi, Karan Singh, Le Hoang Son, (2019), “Energy efficient optimized rate based congestion control routing in wireless sensor network”, Journal of Ambient Intelligence and Humanized Computing, Springer, Vol.11, pp 1325-1338.
  • [2] Shelke MP, Malhotra A, Mahalle P. (2017), “A packet priority intimation based data transmission for congestion free traffic management in wireless sensor networks”, Computers & Electrical Engineering, Vol.64, pp. 248-261.
  • [3] Abdulrauf Montaser Ahmed, Rajeev Paulus, (2017), “Congestion detection technique for multipath routing and load balancing in WSN”, Wireless Networks, Vol. 23, pp 881-888.
  • [4] Hossein Dabbagh Nikokheslat, Ali Ghaffari, (2017), “Protocol for Controlling Congestion in Wireless Sensor Networks”, Wireless Personal Communications, vol. 95, pp. 3233-3251.
  • [5] Chen W, Niu Y, Zou Y, (2016), “Congestion control and energy-balanced scheme based on the hierarchy for WSNs”, IET wireless sensor systems 7(1):1-8.
  • [6] Jan MA, Nanda P, He X, Liu RP, (2014), “PASCCC: priority-based application-specific congestion control clustering protocol”. Comput Netw 74:92-102.
  • [7] C. J. Raman, Visumathi James, (2019) “FCC: Fast congestion control scheme for wireless sensor networks using hybrid optimal routing algorithm”, Cluster Computing, Springer, vol. 22, pp. 12701-1271.
  • [8] Ding, W., Tang, L. & Ji, S.(2016), Optimizing routing based on congestion control for wireless sensor networks. Wireless Netw 22, 915-925.
  • [9] Jeyasekar A, SV KR, Uthra A, (2017) Congestion avoidance algorithm using ARIMA (2, 1, 1) model-based RTT estimation and RSS in heterogeneous wired-wireless networks. J Netw Comput Appl 93:91-109.
  • [10] Karishma Singh, Karan Singh, Le Hoang Son , Ahmed Aziz,(2018) “Congestion Control in Wireless Sensor Networks by Hybrid Multi-Objective Optimization Algorithm”, Computer Networks, Vol. 138, 19, Pages 90-107.
  • [11] Mohamed Amine Kafi, Jalel Ben-Othman, Abdelraouf Ouadjaout, Miloud Bagaa, Nadjib Badache,(2016) “REFIACC: Reliable, efficient, fair and interference-aware congestion control protocol for wireless sensor networks”, Computer Communications , Elsevier B.V., vol. 101, pp. 1-11.
  • [12] Majid Gholipour, Abolfazl Toroghi Haghighat, Mohammad Reza Meybodi, (2017)“Hop-by-Hop Congestion Avoidance in wireless sensor networks based on genetic support vector machine”, Neuro Computing, Vol. 223, pp 63-76.
  • [13] Kakelli Anil Kumar, Addepalli V.N. Krishna, K. Shahu Chatrapati, (2016), “Congestion Control in Heterogeneous Wireless Sensor Networks for High-Quality Data Transmission” Proceedings of the International Congress on Information and Communication Technology, Advances in Intelligent Systems and Computing, Springer, Singapore, vol. 439, pp 429-437.
  • [14] Chia-Hsu Kuo, Tzung-Shi Chen, Ying-Hung Lo, (2015), “Efficient traffic load reduction algorithms for mitigating query hotspots for wireless sensor networks”, International Journal of Ad Hoc and Ubiquitous Computing, Vol. 18, No. 3.
  • [15] M. J. A. Jude and V. C. Diniesh, (2017), “DACC: Dynamic agile congestion control scheme for effective multiple traffic wireless sensor networks," International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, IEEE, pp. 1329-1333.
  • [16] Sagar B. Tambe, Suhas S. Gajre, (2017),“Novel Strategy for Fairness-Aware Congestion Control and Power Consumption Speed with Mobile Node in Wireless Sensor Networks”, Smart Trends in Systems, Security and Sustainability, Lecture Notes in Networks and Systems, Springer, vol. 18.
  • [17] Sankalap Arora, Satvir Singh, (2013), “The Firefly Optimization Algorithm: Convergence Analysis and Parameter Selection”, International Journal of Computer Applications (0975 - 8887) Volume 69- No.3.
  • [18] Yang XS., “Firefly Algorithms for Multimodal Optimization”, In: Watanabe O., Zeugmann T. (eds) Stochastic Algorithms: Foundations and Applications. SAGA 2009. Lecture Notes in Computer Science, vol 5792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04944-6_14.
  • [19] Almajidi A.M., Pawar V.P., Alammari A., (2019) K-Means-Based Method for Clustering and Validating Wireless Sensor Network., International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 55. Springer, Singapore. https://doi.org/10.1007/978-981-13-2324-9_25.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e0161fcb-1ddd-4f4e-bc04-807c960050ce
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