Distributed adaptive task allocation for energy conservation in camera sensor networks

Camera Sensor Networks (CSNs) have a large and diverse application spectrum ranging from security and safety-critical applications, to industrial monitoring, and augmented reality. Cameras in such networks are equipped with real-time multitasking processors and communication infrastructure, which enables them to perform various computer vision tasks in a distributed and collaborative manner. In many cases, the cameras in the network operate under limited or unreliable power sources. Therefore in order to extend the CSN lifetime it is important to manage the energy consumption of the cameras, which is related to the workload of the vision tasks they perform. Hence by managing and assigning vision tasks to cameras in an energy-aware manner it is possible to extend the network lifetime. In this paper we address this problem by proposing a distributed market-based solution where cameras bid for tasks using an energy-aware utility function. An additional novelty of the proposed solution is that as the cameras can adapt their bidding strategy based on their remaining energy levels. The results for different CSN configurations and setups show that the proposed methodology can increase network lifetime by 10%-30% while improving the number of dynamic and static tasks being monitored by 30-50%.