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- W4381570483 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." @default.
- W4381570483 created "2023-06-22" @default.
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- W4381570483 date "2023-01-01" @default.
- W4381570483 modified "2023-09-23" @default.
- W4381570483 title "C-RPI: Cluster-Based Rendezvous Point Identification and Mobile Sink-Based Data Collection in LR-WPAN" @default.
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- W4381570483 doi "https://doi.org/10.1007/978-3-031-25194-8_18" @default.
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