An optimization of distributed Voronoi-based collaboration for energy-efficient geographic routing in wireless sensor ne
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An optimization of distributed Voronoi-based collaboration for energy-efficient geographic routing in wireless sensor networks M. Sridhar1 • P. B. Pankajavalli1 Received: 1 March 2020 / Revised: 26 March 2020 / Accepted: 5 May 2020 Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In energy-constrained wireless sensor networks (WSNs), geographic routing (GR) also known as location-aware routing has been developed because it uses neighbourhood locality data as an alternative of overall topology information for routing. But, this protocol frequently suffers from the energy holes in the data transmission resulting in path failure. To avoid this problem, energy-aware dual-path GR (EDGR) protocol has been suggested to improve the routing path from energy holes. However, it is unable to heal the energy holes since the node mobility can generate new energy holes. Also, higher mobility distance can cause high energy consumption (EC) and node failure. Hence in this article, an energyefficient distributed collaboration mechanism is proposed with EDGR for k-coverage energy holes detection and healing in WSN that minimizes the delivery delay (DD). In this protocol, the distributed Voronoi-based collaboration (DVOC) method is applied in which the nodes can cooperate in energy holes detection and recovery. The nodes are enabled to monitor each other node’s critical locations around themselves by constructing the local Voronoi diagrams (LVDs). Moreover, an optimized DVOC (ODVOC) is proposed with EDGR in which intelligent water drop (IWD) algorithm is used to find the globally optimized routes to minimize the DD. Finally, the simulation outcomes demonstrate the ODVOCEDGR accomplishes higher effectiveness than the EDGR and DVOC-EDGR in terms of different performance metrics. Keywords WSN Energy-aware geographic routing Energy holes Local voronoi diagram Intelligent water drop algorithm
1 Introduction Typically, WSNs are having more number of sensor nodes deployed in various applications like defense, agricultural activity monitoring, atmospheric conditions forecasting, healthcare systems, smart vehicles, and home automation. Once the nodes are deployed, each sensor node must have the capability to autonomously organize them into a wireless communication network. Also, they have the ability to accumulate and process the data as well as transmit the sensed data to one or more sink nodes via wireless link in multihop transmission manner. In these & M. Sridhar [email protected] P. B. Pankajavalli [email protected] 1
Department of Computer Science, Bharathiar University, Coimbatore, India
networks, each sensor node is equipped with the restricted power resources which are not easy to restore. In general, these types of networks are classified with denser levels of node’s functionality, unpredictability, constrained power, computation, and storage. These sole features and restrictions may cause several problems [1]. Specifically, the routin
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