Enabling visual analysis in wireless sensor networks

This demo showcases some of the results obtained by the GreenEyes project, whose main objective is to enable visual analysis on resource-constrained multimedia sensor networks. The demo features a multi-hop visual sensor network operated by BeagleBones Linux computers with IEEE 802.15.4 communication capabilities, and capable of recognizing and tracking objects according to two different visual paradigms. In the traditional compress-then-analyze (CTA) paradigm, JPEG compressed images are transmitted through the network from a camera node to a central controller, where the analysis takes place. In the alternative analyze-then-compress (ATC) paradigm, the camera node extracts and compresses local binary visual features from the acquired images (either locally or in a distributed fashion) and transmits them to the central controller, where they are used to perform object recognition/tracking. We show that, in a bandwidth constrained scenario, the latter paradigm allows to reach better results in terms of application frame rates, still ensuring excellent analysis performance

Analytical framework for Adaptive Compressive Sensing for Target Detection within Wireless Visual Sensor Networks

Wireless visual sensor networks (WVSNs) are composed of a large number of visual sensor nodes covering a specifc geographical region. This paper addresses the target detection problem within WVSNs where visual sensor nodes are left unattended for long-term deployment. As battery energy is a critical issue it is always challenging to maximize the network's lifetime. In order to reduce energy consumption, nodes undergo cycles of active-sleep periods that save their battery energy by switching sensor nodes ON and OFF, according to predefined duty cycles. Moreover, adaptive compressive sensing is expected to dynamically reduce the size of transmitted data through the wireless channel, saving communication bandwidth and consequently saving energy. This paper derives for the first time an analytical framework for selecting node's duty cycles and dynamically choosing the appropriate compression rates for the captured images and videos based on their sparsity nature. This reduces energy waste by reaching the maximum compression rate for each dataset without compromising the probability of detection. Experiments were conducted on different standard datasets resembling different scenes; indoor and outdoor, for single and multiple targets detection. Moreover, datasets were chosen with different sparsity levels to investigate the effect of sparsity on the compression rates. Results showed that by selecting duty cycles and dynamically choosing the appropriate compression rates, the desired performanc

Energy-accuracy trade-offs for hybrid localization using RSS and inertial measurements in wireless sensor networks

This paper presents a framework for optimizing the trade-off between energy consumption and localization accuracy in hybrid localization systems combining Received Signal Strength (RSS) measurements with inertial ones. The proposed framework aims at finding the optimal operation point that minimizes the radio energy consumption for a desired target accuracy, or equivalently, the one that maximizes the localization accuracy for a given energy budget. To this end, the proposed approach considers the joint optimization of the localization frequency and number of RSS measurements used at each localization round and leverages practical models to predict the energy consumption and the localization accuracy for combined RSS-inertial localization systems. Simulations and real-field experiments are used to demonstrate that, for a given target accuracy, the proposed strategy entails a lower energy consumption than state-of-the-art methods available in the literature