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Collective multi agent deployment for wireless sensor network maintenance

机译:用于无线传感器网络维护的集体多代理部署

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In this paper, we study the problem of wireless sensor network (WSN) maintenance using a team of physical autonomous mobile agents. The agents are deployed in the area of the WSN in such a way that would minimize the time it takes them to reach a failed sensor and repair it. The team must constantly optimize its collective deployment to account for occupied agents. The objective is to define the optimal deployment and task allocation strategy, that minimize the solution cost. The solution cost is a linear combination of the weighted sensors' downtime, the agents' traveling distance, and penalties incurred due to unrepaired sensors within a certain time limit.Our proposed solution algorithms are inspired by research in the field of computational geometry and the design of our algorithms is based on state of the art approximation algorithms for the classical problem of facility location.We empirically compare and analyze the performance of several proposed algorithms. The sensitivity of the algorithms' performance to the following parameters is analyzed: agents to sensors ratio, sensors' sparsity, frequency and distribution of failures, repair duration, repair capacity, and communication limitations. Our results demonstrate that: (i) cooperation enhances the team's performance by orders of magnitude, (ⅱ) k-Median based deployment algorithm provides up to 30% improvement in downtime, (ⅲ) k-Center based deployment incurs 10% fewest penalties, and (ⅳ) k-Centroid based deployment is most efficient in terms of minimizing the overall costs, with up to 21% lower cost than the next best algorithm.
机译:在本文中,我们研究了使用物理自主移动代理团队的无线传感器网络(WSN)维护问题。该代理以这样的方式部署在WSN的区域中,这种方式将最小化其达到失败传感器并修复其所需的时间。该团队必须不断优化其集体部署,以考虑被占用者。目标是定义最佳部署和任务分配策略,从而最大限度地降低解决方案成本。解决方案成本是加权传感器停机的线性组合,代理的行进距离,以及由于一定时间限制内未完成的传感器而产生的惩罚。我们所提出的解决方案算法是通过计算几何和设计领域的研究启发我们的算法基于用于设施位置的经典问题的最新的近似算法。我们经验比较并分析了几种提出算法的性能。分析了算法对以下参数的性能的灵敏度:代理传感器比,传感器的稀疏,频率和故障分布,修复持续时间,修复能力和通信限制。我们的结果表明:(i)合作通过数量级来提高团队的表现,(Ⅱ)基于k中位数的部署算法在停机时间内提供高达30%的改进,(Ⅲ)基于K-Center基于刑罚,最少的罚款(ⅳ)基于K-Ciredroid的部署在最小化整体成本方面最有效,比下一个最佳算法降低了高达21%。

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