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Red deer optimization algorithm inspired clustering-based routing protocol for reliable data dissemination in FANETs
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DOI：10.1016/j.matpr.2021.12.527.png)
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Abstract
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Flying ad hoc Networks (FANETs) are considered to possess dynamic topology due to the uncertain and rapidly changing mobility of Unmanned Aerial Vehicles (UAVs). The routing in FANET is determined to be unstable due to the restricted battery resources and the constantly changing mobility characteristics of UAVs. In this situation, topology table maintenance and iterative estimation of new routes in this dynamic environment incurs high delays and communication overhead. This dynamically changing topology also wastes the restricted battery source and reduces the network throughput of UAVs. Clustering is a type of routing, which is identified as the suitable candidate ideal for resolving the issues for limited resources in order to enhance network performance with improved throughput, scalability and minimized communication overhead. In this paper, Red Deer Optimization Algorithm Inspired Clustering-based Routing Protocol (RDOAICRP) for minimizing the issue of instability and mobility during the process of reliable data dissemination in FANETs. This proposed RDOAICRP included the benefits of a Red Deer Optimization Algorithm for cluster head selection and energy-aware cluster formation. It utilized a function for path detection based on the distance between the UAVs, number of neighbors and weighted residual energy for achieving efficient communication. It inherits significant cluster maintenance by a continuous monitoring process that estimates the energy level associated with each individual UAVs in a cluster. The potential of the proposed RDOAICRP is explored based on the cluster lifetime, packet delivery success probability, energy consumptions and cluster construction time with the benchmarked clustering-based routing protocols. Copyright (C) 2022 Elsevier Ltd. All rights reserved.
Keywords:
Flying ad hoc Networks (FANETs)
Clustering
Red Deer Optimization Algorithm (RDOA)
Unmanned Aerial Vehicles (UAVs)
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