Marine Observation Beacon Clustering and Recycling Technology Based on Wireless Sensor Networks.

Clicks: 333
ID: 60480
2019
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Abstract
Monitoring of marine polluted areas is an emergency task, where efficiency and low-power consumption are challenging for the recovery of marine monitoring equipment. Wireless sensor networks (WSNs) offer the potential for low-energy recovery of marine observation beacons. Reducing and balancing network energy consumption are major problems for this solution. This paper presents an energy-saving clustering algorithm for wireless sensor networks based on k-means algorithm and fuzzy logic system (KFNS). The algorithm is divided into three phases according to the different demands of each recovery phase. In the monitoring phase, a distributed method is used to select boundary nodes to reduce network energy consumption. The cluster routing phase solves the extreme imbalance of energy of nodes for clustering. In the recovery phase, the inter-node weights are obtained based on the fuzzy membership function. The Dijkstra algorithm is used to obtain the minimum weight path from the node to the base station, and the optimal recovery order of the nodes is obtained by using depth-first search (DFS). We compare the proposed algorithm with existing representative methods. Experimental results show that the algorithm has a longer life cycle and a more efficient recovery strategy.
Reference Key
zhang2019marinesensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhang, Zhenguo;Qi, Shengbo;Li, Shouzhe;
Journal sensors
Year 2019
DOI
E3726
URL
Keywords
Wireless sensor networks fuzzy logic system k-means algorithm network energy observation beacon doctor-patient communication self-efficacy health literacy older adults effectiveness narratives behavioral intention format preference photo stories

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