Rate-accuracy optimization in visual wireless sensor networks
We consider the problem of allocating the resources in a wireless sensor network, which is designed to perform visual analysis (e.g. object recognition). We depart from the traditional compress-then-analyze paradigm, in which nodes sense, compress and transmit visual data to a sink node. Instead, we study the case in which nodes extract and lossy code local features from pixel-domain representations of the sensed visual scene. The formulation of the allocation problem entails maximizing the lifetime of the visual sensor network subject to a target accuracy of the analysis task, together with energy, bandwidth and routing constraints. To this end, we contribute with the definition of a rate-accuracy model, which plays the role of the traditional rate-distortion model commonly adopted in visual communication. The proposed model captures the impact of: i) the number of selected local features; ii) the number of bits used for quantizing local features; iii) the criterion used to select the subset of local features to be transmitted. We verify the correctness of the models on two widely adopted visual dataset and we demonstrate the network lifetime gain that can be achieved by an optimal allocation of the resources.