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Energy and bandwidth are limited resources in wireless sensor networks, and communication consumes significant amount of energy. When wireless vision sensors are used to capture and transfer image and video data, the problems of limited energy and bandwidth become even more pronounced. Thus, message traffic should be decreased to reduce the communication cost. In many applications, the interest is to detect composite and semantically higher-level events based on information from multiple sensors. Rather than sending all the information to the sinks and performing composite event detection at the sinks or control-center, it is much more efficient to push the detection of semantically high-level events within the network, and perform composite event detection in a peer-to-peer and energy-efficient manner across embedded smart cameras. In this paper, three different operation scenarios are analyzed for a wireless vision sensor network. A detailed quantitative comparison of these operation scenarios are presented in terms of energy consumption and latency. This quantitative analysis provides the motivation for, and emphasizes (1) the importance of performing high-level local processing and decision making at the embedded sensor level and (2) need for peer-to-peer communication solutions for wireless multimedia sensor networks.

Abstract-An embedded smart camera is a stand-alone unit that not only captures images, but also includes a processor, memory and communication interface. With battery-powered and embedded smart cameras, it has become viable to install many spatially-distributed cameras interconnected by wireless links. Not requiring to have access to electrical outlets and have wired links increase system flexibility. However, wireless and battery-powered smart-camera networks introduce many additional challenges since they have very limited resources, such as power, memory and bandwidth. The algorithms running on the camera boards should be lightweight and efficient. In addition, the frequency of communication between camera nodes, and the content of the message packets should be carefully designed, since communication consumes power. In this paper, we present a wireless embedded smart-camera system that performs peer-topeer object tracking and event detection. We analyze the power consumption and performance of this system during different parts of the algorithm execution and for different message exchanges between camera nodes. We also present a graph of the energy consumption for different tasks performed in a camera's processor. The number of instructions are also presented. The results demonstrate the importance of the careful choice of when and what data to transfer between cameras, and also the necessity of having lightweight algorithms in these resource-constrained systems.

Wireless embedded smart cameras provide flexibility in camera deployment in terms of the locations and number of the cameras. However, these battery-powered embedded vision sensors have very limited energy, memory, and processing power. Energy consumption and latency are two major concerns in wireless embedded camera networks. In multi-camera tracking applications, the amount of data exchanged between cameras has an effect on the tracking accuracy, the energy consumption of the camera nodes and the latency. In this paper, we provide a detailed quantitative analysis of the accuracy-latency-energy tradeoff for overlapping and non-overlapping camera setups when different-sized data packets are transferred in a wireless manner. The experiments have been performed with an actual wireless embedded smart camera network employing CITRIC motes, and performing tracking of objects.

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