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A Predictive Duty Cycle Adaptation Framework Using Augmented Sensing for Wireless Camera Networks
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DOI：10.1145/2530280.png)
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Abstract
En 中文
Energy efficiency dominates practically every aspect of the design of a wireless sensor network and duty cycling is an important tool for achieving high energy efficiencies. Duty cycling for wireless camera networks meant for tracking objects is made complex by the nodes having to anticipate the arrival of the objects in their field-of-view. The consequences of an object arriving in the view-region of a camera when it is sleeping need no elaboration. Our work presents a predictive framework to provide nodes with an ability to anticipate the arrival of objects in the field-of-view of their cameras. Our predictive framework differs from others in that the nodes whose duty cycles are increased are at least one step removed from the immediate neighborhood of the nodes where the objects are currently visible. By eliminating the need for the currently busiest nodes to also be in charge of informing their nonbusy immediate neighbors to get ready for object arrival, we end up with a more robust strategy for updating the duty cycle at the nodes where the objects are highly likely to appear soon. The proposed scheme works by using an existing MAC header bit that is already in the 802.15.4 protocol and, in that sense, our anticipatory approach for notifying the nodes about the current state of the object location entails no additional expenditure of energy. Our contribution includes evaluations based on large-scale simulations as well as real experiments with an Imote2-based wireless camera network.
Keywords:
Design
Algorithms
Performance
Wireless sensor networks
wireless camera networks
cross-layer optimization
predictive network adaptation
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