.png){kind=icon}
.png){kind=icon}
Return
Efficient Cluster-Based Tracking Mechanisms for Camera-Based Wireless Sensor Networks
.png){kind=icon}
PRE
AI
DOI：10.1109/TMC.2014.2374164.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes mechanisms to efficiently address critical tasks in the operation of cluster-based target tracking, namely: (1) measurement integration, (2) inclusion/exclusion in the cluster, and (3) cluster head rotation. They all employ distributed probabilistic tools designed to take into account wireless camera networks (WCNs) capabilities and constraints. They use efficient and distribution-friendly representations and metrics in which each node contributes to the computation in each mechanism without requiring any prior knowledge of the rest of the nodes. These mechanisms are integrated in two different distributed schemes so that they can be implemented in constant time regardless of the cluster size. Their experimental validation showed that the proposed mechanisms and schemes significantly reduce energy consumption (>55 percent) and computational burden with respect to existing methods.
Keywords:
Wireless sensor networks
tracking
sensor fusion
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.2
Papers:
5.6K
Citations:
1.8W