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BOLD：用于多架无人机的生物启发式优化领导者选举。

BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones.

机构信息

Department of Information Technology, MIT campus, Anna University, Chennai 600 044, India.

Department of Electronics and Communication Engineering, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai 600 127, India.

出版信息

Sensors (Basel). 2020 Jun 1;20(11):3134. doi: 10.3390/s20113134.

DOI:10.3390/s20113134
PMID:32492971
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7308907/
Abstract

Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordinator to communicate, monitor, and control other drones. Hence, drones are energy-constrained; there is a need for effective coordination among them in terms of decision making and communication between drones and base stations during these critical situations. This paper focuses on providing an efficient approach for the election of the cluster head dynamically, which heads the other drones in the network. The main objective of the paper is to provide an effective solution to elect the cluster head among multi drones at different periods based on the various physical constraints of drones. The elected cluster head acts as the decision-maker and assigns tasks to other drones. In a case where the cluster head fails, then the next eligible drone is re-elected as the leader. Hence, an optimally distributed solution proposed is called Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD), which is based on two AI-based optimization techniques. The simulation results of BOLD compared with the existing Particle Swarm Optimization-Cluster head election (PSO-C) in terms of network lifetime and energy consumption, and from the results, it has been proven that the lifetime of drones with the BOLD algorithm is 15% higher than the drones with PSO-C algorithm.

摘要

在过去的几年中，无人机 (UAV) 或无人机已经被用于许多应用。在某些应用中，如监控和紧急救援行动，多个无人机作为一个网络工作，以实现目标，其中任何一个无人机将充当主或协调器，以进行通信、监控和控制其他无人机。因此，无人机的能量有限；在这些关键情况下，它们之间需要进行有效的协调，包括在无人机和基站之间进行决策和通信。本文重点介绍了一种动态选举簇头的有效方法，该方法由网络中的其他无人机担任。本文的主要目的是根据无人机的各种物理限制，为不同时期的多架无人机选举簇头提供有效的解决方案。当选的簇头充当决策者，并为其他无人机分配任务。如果簇头失败，则下一个有资格的无人机将重新当选为领导者。因此，提出了一种优化的分布式解决方案，称为基于生物启发的多无人机选举领导者算法 (BOLD)，它基于两种基于人工智能的优化技术。与现有的基于粒子群优化的簇头选举算法 (PSO-C) 相比，BOLD 的仿真结果在网络寿命和能量消耗方面，结果表明，使用 BOLD 算法的无人机的寿命比使用 PSO-C 算法的无人机高出 15%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20a2/7308907/660eec576980/sensors-20-03134-g013.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20a2/7308907/4f88e55ebf7c/sensors-20-03134-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20a2/7308907/91f98c3feb31/sensors-20-03134-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20a2/7308907/ebe9ea5305c6/sensors-20-03134-g010.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20a2/7308907/660eec576980/sensors-20-03134-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20a2/7308907/3d1cde765737/sensors-20-03134-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20a2/7308907/8a61040ba010/sensors-20-03134-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20a2/7308907/020dc343af50/sensors-20-03134-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20a2/7308907/790393eb2170/sensors-20-03134-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20a2/7308907/044f9b11dcb3/sensors-20-03134-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20a2/7308907/114d4d5cdef6/sensors-20-03134-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20a2/7308907/88b13f7f73f1/sensors-20-03134-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20a2/7308907/4f88e55ebf7c/sensors-20-03134-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20a2/7308907/91f98c3feb31/sensors-20-03134-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20a2/7308907/ebe9ea5305c6/sensors-20-03134-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20a2/7308907/94340ab7164d/sensors-20-03134-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20a2/7308907/cf2b4a27ba9c/sensors-20-03134-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20a2/7308907/660eec576980/sensors-20-03134-g013.jpg

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