Localized sensor management for multi-target tracking in wireless sensor networks

For a multi-hop wireless sensor network, the limited sensing and communication resources give rise to distinct challenges to the task of tracking mobile targets, which is traditionally treated primarily from the data fusion perspective. This paper investigates the impact of sensor management on data fusion in a resource-limited network. A localized multi-sensor multi-target tracking framework is presented, consisting of four intertwined modules: data acquisition, data fusion, information propagation, and sensor management. The sensor management module, which boils down to a constrained binary optimization problem, is emphasized for efficient sensing resource allocation. Given limited bandwidth and power in the network, a localized greedy-selection sensor management (GSSM) algorithm is proposed to dynamically select a subset of sensors that contributes most effectively to the tracking accuracy. Using only localized information propagation among one-hop neighbors, the proposed framework obviates the need for a fusion center or multi-hop relays, and thus improves network robustness and scalability.

Research highlights
► This paper presents a localized multi-sensor multi-target tracking framework.
► The sensor management module is emphasized for allocating network resources.
► A greedy-selection sensor management algorithm is proposed.
► The localized tracking framework improves robustness and scalability.