Abstract
In a wireless visual sensor network consisting of wireless, battery-powered, and field-of-view (FoV) overlapping and stationary visual sensors, trade-offs exist between extending network lifetime and enhancing its sensing accuracy. Moreover, aggregating individual inferences from each sensor is essential to generate a globally consistent inference, because these individual inferences can be biased by noise or other unexpected conditions. Those challenges can be addressed by reducing the amount of data transmission among the sensors and by activating, in a timely manner, only a desirable camera subset for given targets. In this paper, we initialize an optimal data transmission path among visual sensors using the inference tree method, which is vital for collecting individual inferences and building a global inference. Based on the optimal data transmission path, we model the camera selection problem in a cooperative bargaining game. In this game, based on the serial dictatorial rule, camera sensors cooperatively attempt to raise the overall sensing accuracy by sequentially deciding their own mode between “sleep” and “active” in descending order of their bargaining power. Simulated results demonstrate that our proposed approach outperforms other alternatives, resulting in reduced resource overhead and improved network lifetime and sensing accuracy.
Highlights
There has been an increasing necessity to extract relevant information for multiple targets moving around inside wide areas for surveillance purposes
It will be necessary to initialize an optimal data transmission path to reduce the amount of data transmission among the sensors for a global inference and to efficiently activate only selected cameras, which optimizes their collective coverage of given targets in a timely manner
We addressed trade-offs between extending network lifetime and enhancing its sensing accuracy
Summary
There has been an increasing necessity to extract relevant information for multiple targets moving around inside wide areas for surveillance purposes. A visual sensor is primarily equipped with an image sensing device, several processing units, communication facilities, and a set of batteries This composition is very suitable for surveillance, because of the advantageous characteristics such as a wide monitoring area, rich visual information, and humanfriendly data. It will be necessary to initialize an optimal data transmission path to reduce the amount of data transmission among the sensors for a global inference and to efficiently activate only selected cameras, which optimizes their collective coverage of given targets in a timely manner. Each sensor can autonomously switch its mode between “sleep” (in sleep mode, the sensor stops capturing data; it will continue to transmit data) and “active” only with local knowledge, during advanced target analysis beyond basic tracking This local rationale can be feasible under the practical assumption that FoV overlapping cameras can directly communicate with each other; the view
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