This paper presents a distributed algorithm, called prediction-based opportunistic sensing using distributed classification, clustering, and control (POSE.3C), for self adaptation of sensor networks for energy management. The underlying 3C network autonomy concept enables utilization of the target classification information to form dynamic clusters around the predicted target position via selection of sensor nodes with the highest energies and maximum geometric diversity. Furthermore, the nodes can probabilistically control their heterogeneous devices to track targets of interest and minimize energy consumption in a completely distributed manner. Theoretical properties of the POSE.3C network are established and derived in terms of the network lifetime and missed detection characteristics. The algorithm is validated through extensive simulations that demonstrate a significant increase in the network lifetime as compared to other network control approaches, while providing high tracking accuracy and low missed detection rates.

中文翻译：

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POSE.3C：在异构传感器网络中使用分布式分类，聚类和控制的基于预测的机会感知

本文提出了一种分布式算法，称为使用分布式分类，聚类和控制（POSE.3C）的基于预测的机会传感，用于传感器网络的自适应能量管理。潜在的3C网络自治概念可通过选择具有最高能量和最大几何多样性的传感器节点，利用目标分类信息在预测目标位置周围形成动态簇。此外，节点可以概率性地控制其异构设备以跟踪目标目标，并以完全分布式的方式将能耗降至最低。建立并根据网络寿命和错过的检测特性得出POSE.3C网络的理论特性。