The article presents a distributed algorithm, called Prediction-based Opportunistic Sensing for Resilient and Efficient Sensor Networks (POSE.R), where the sensor nodes utilize predictions of the targets' positions to probabilistically control their multi-modal operating states to track the targets. There are two desired features of the algorithm: energy efficiency and resilience. If the target is traveling through a high-node-density area, then an optimal sensor selection approach is employed that maximizes a joint cost function of remaining energy and geometric diversity around the target’s position. This provides energy efficiency and increases the network lifetime while preventing redundant nodes from tracking the target. However, if the target is traveling through a low-node-density area or in a coverage gap (e.g., formed by node failures or non-uniform node deployment), then a potential game is played amongst the surrounding nodes to optimally expand their sensing ranges via minimizing energy consumption and maximizing target coverage. This provides resilience, that is, the self-healing capability to track the target in the presence of low node densities and coverage gaps. The algorithm is comparatively evaluated against existing approaches through Monte Carlo simulations that demonstrate its superiority in terms of tracking performance, network-resilience, and network-lifetime.

中文翻译：

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姿势.R

本文提出了一种分布式算法，称为弹性和高效传感器网络的基于预测的机会传感 (POSE.R)，其中传感器节点利用目标位置的预测来概率控制其多模态操作状态以跟踪目标。该算法有两个期望的特性：能源效率和弹性。如果目标正在通过高节点密度区域，则采用最佳传感器选择方法，使目标位置周围剩余能量和几何多样性的联合成本函数最大化。这提供了能源效率并增加了网络寿命，同时防止冗余节点跟踪目标。但是，如果目标正在通过低节点密度区域或覆盖间隙（例如，由节点故障或非均匀节点部署形成），然后在周围节点之间进行潜在的博弈，以通过最小化能耗和最大化目标覆盖来优化扩展其感知范围。这提供了弹性，即在存在低节点密度和覆盖差距的情况下跟踪目标的自我修复能力。该算法通过蒙特卡罗模拟与现有方法进行了比较评估，证明了它在跟踪性能、网络弹性和网络寿命方面的优越性。在存在低节点密度和覆盖差距的情况下跟踪目标的自我修复能力。该算法通过蒙特卡罗模拟与现有方法进行了比较评估，证明了它在跟踪性能、网络弹性和网络寿命方面的优越性。在存在低节点密度和覆盖差距的情况下跟踪目标的自我修复能力。该算法通过蒙特卡罗模拟与现有方法进行了比较评估，证明了它在跟踪性能、网络弹性和网络寿命方面的优越性。