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.

在本文中，我们使用一组物理自主移动代理研究无线传感器网络（WSN）维护的问题。代理以这样一种方式部署在WSN的区域中，即可以最大程度地减少到达故障传感器并进行维修所需的时间。团队必须不断优化其集体部署，以解决占领的特工人员的问题。目的是定义最佳的部署和任务分配策略，以最小化解决方案成本。解决方案成本是加权传感器的停机时间，座席的行进距离以及在特定时限内未维修传感器导致的罚款的线性组合。

我们提出的解决方案算法受到计算几何学领域的研究的启发，我们的算法设计基于针对设施定位经典问题的最新近似算法。

我们根据经验比较和分析了几种建议算法的性能。分析了算法性能对以下参数的敏感性：代理与传感器的比率，传感器的稀疏性，故障的频率和分布，修复时间，修复能力以及通信限制。我们的结果表明：（i）合作将团队的绩效提高了几个数量级，（ii）$ķ$-基于中位数的部署算法最多可将停机时间缩短30％，（iii） $ķ$-基于中心的部署会减少10％的罚款，并且（iv） $ķ$-就最小化总体成本而言，基于中心的部署是最高效的，与次优算法相比，成本降低了21％。