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Top1. Introduction
Since the last decade there is a communication problem in wireless sensor networks in reaching the destination for disseminating the data due to the energy holes. Researchers concentrate more on the prediction of energy holes. It consists of two main tasks: prediction of energy holes and shifting routing paths. Fig.1. depicts the energy hole problem in Wireless Multimedia Sensor (WMS) Networks. The WMS Networks face energy holes due to power depletion, atmospheric conditions, natural disasters such as volcanoes, earthquakes, deep oceans, and the surrounding effects.
Network topology is affected by the occurrence of energy holes, which causes the failure of the data delivery in the path. As a result, the sensor node's energy exhaustion is quickly achieved in the multipath. Hence, the sensor nodes are not able to determine and select the neighborhood to forward the packets. This term is called an empty space or referred to as an energy hole in WMS networks. The classical approaches to deal the energy hole are the Voronoi diagram (Marbate & Jaini, 2013) (Benavides et al., 2011) and a heuristics approach. Ant Colony Optimization (ACO) (Habib et al., 2015) is also used to find energy holes and the shortest path. A meta-heuristic ACO algorithm and probabilistic technique are used to provide the optimized route from the source to destination. It is used in various research filed in networking like vehicle routing, asymmetric traveling salesman problem, WSN routing, etc.
Whenever problems arise for selecting the neighborhood, the Voronoi diagram is used to overcome the issue by selecting the neighborhood in the closed region. The roadmap method of the Voronoi diagram is beneficial for tracking and monitoring the targets (Chen et al., 2004), balancing the network (Byers et al., 2004), and conserve energy (Zhou et al., 2009).Ant Colony Optimization with heuristic information (Shi, 2014) supports inherent parallelism, flexible, and adaptive nature, and works with static and dynamic environment (Thenmozhi et al., 2020). This hybrid method, such as Voronoi Diagram (Kolahdouzan & Shahabi, 2004) and ACO, allows the sensor node to mark paths, guide other nodes, and find shortest paths from the network's overall status.
The paper's remaining part is organized as follows: the related works of energy hole prediction and routing are described in section 2; Section 3 deliberates the problem formulation. Section 4 discusses the proposed method. The experimental setup is given in section 5. The experimental results and the performance analysis of the proposed and existing algorithms are discussed in section 6. Finally, Section7 concludes the paper with the scope for further study.