Abstract
This paper proposes a distributed method for cooperative target tracking in hierarchical wireless sensor networks. The concept of leader-based information processing is conducted to achieve object positioning, considering a cluster-based network topology. Random timers and local information are applied to adaptively select a sub-cluster for the localization task. The proposed energy-efficient tracking algorithm allows each sub-cluster member to locally estimate the target position with a Bayesian filtering framework and a neural networking model, and further performs estimation fusion in the leader node with the covariance intersection algorithm. This paper evaluates the merits and trade-offs of the protocol design towards developing more efficient and practical algorithms for object position estimation.
Highlights
Giving the limited power and processing capability in a sensor mote, a critical challenge of target tracking is how to acquire suitable data and perform information processing at the local level through cooperative communication and networking in the vicinity of the target
The concept of leader-based information processing is conducted to automatically achieve cooperative sensor scheduling with multiple tasking sensors in a cluster-based network topology based on sensor residual energy level, target information, and estimation quality
The clusterhead and the cluster members refer to the original network topology, whereas the leader and the sub-cluster members refer to the sensor group for the tracking task
Summary
Giving the limited power and processing capability in a sensor mote, a critical challenge of target tracking is how to acquire suitable data and perform information processing at the local level through cooperative communication and networking in the vicinity of the target. The concept of leader-based information processing is conducted to automatically achieve cooperative sensor scheduling with multiple tasking sensors in a cluster-based network topology based on sensor residual energy level, target information, and estimation quality. The leader fuses the local estimates from the sub-cluster members and reports it to the clusterhead. Considering a cluster-based network topology, we introduce a distributed cooperative target tracking system, Two-level Clustering Approach via Timer (TCAT), which aims to improve the energy efficiency and provide good estimation accuracy. The TCAT scheme performs target localization in four phases: (I) Tasking Leader Selection; (II) Choosing the Sub-Cluster Members;. In Phases I and II, random timers and local information are applied to adaptively select a tasking leader and sub-cluster members for the localization task.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
