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C-RPI: Cluster-Based Rendezvous Point Identification and Mobile Sink-Based Data Collection in LR-WPAN

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Applications of Computational Intelligence in Management & Mathematics (ICCM 2022)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 417))

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Abstract

The emerging Internet of Things (IoT) and wireless sensor network (WSN) applications require low-powered devices having sensing, communicating, and computing capabilities that can monitor various environmental conditions. The demand for low-complexity, low-cost wireless links is due to the exponential growth of data in these networks. This leads to the development of low-rate wireless personal area networks (LR-WPAN) and its operations as defined by the IEEE 802.15.4 standard. In this chapter, a data collection strategy is proposed that uses mobile sink (MS) within a network consisting of sensors as LR-WPAN devices. Initially, MS moves in the deployment area and records the information of static, randomly deployed low-power devices. Afterward, the sink node identifies the rendezvous points (RPs) and collects data periodically from the low-energy devices surrounding the RP. The number and position of the RP are determined using C-RPI (cluster-based RP identification). After finding RPs, traveling salesman problem (TSP) is applied to discover the optimal sink movement track. The proposed method is compared with max–min and min–max algorithms. The implementation is done in NS-3 using LR-WPAN module. The simulation result shows that C-RPI outperforms the other two algorithms in terms of the number of RPs, average path length, and data collection delay.

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Author information

Authors and Affiliations

  1. NIT Tiruchirappalli, Computer Science and Engineering, Tiruchirappalli, India

    S. Jayalekshmi & R. Leela Velusamy

Authors
  1. S. Jayalekshmi
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  2. R. Leela Velusamy
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Editor information

Editors and Affiliations

  1. Electronics & Communication Engineering, North Eastern Regional Institute of Science and Technology (NERIST), Itanagar, Arunachal Pradesh, India

    Madhusudhan Mishra

  2. Department of Computer Science, Central University of Rajasthan, Ajmer, Rajasthan, India

    Nishtha Kesswani

  3. EMLYON Business School, Écully, France

    Imene Brigui

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Jayalekshmi, S., Velusamy, R.L. (2023). C-RPI: Cluster-Based Rendezvous Point Identification and Mobile Sink-Based Data Collection in LR-WPAN. In: Mishra, M., Kesswani, N., Brigui, I. (eds) Applications of Computational Intelligence in Management & Mathematics. ICCM 2022. Springer Proceedings in Mathematics & Statistics, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-031-25194-8_18

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