Bandwidth-Aware Data Filtering in Edge-Assisted Wireless Sensor Systems

By placing processing-capable devices at the edge of local wireless access networks, Edge Computing architectures have been recently proposed to connect mobile devices to computational power through a one-hop low-latency wireless link. In this paper, we propose a new design where edge assistance is used to control local data filtering at the mobile devices in bandwidth and energy constrained systems. We focus on real-time monitoring applications, where the video input from mobile devices is processed to centrally detect and recognize objects. The edge processor controls the activation and deactivation of local classifiers implemented by the mobile devices to remove useless portions of video frames. The objective is to adapt the video stream to time-varying bandwidth constraints, while minimizing the additional energy consumption introduced by local processing. To this end, an optimization problem is formulated for a loss function embodying the balance between the risk of violating the available bandwidth and the cost of overly-conservative data filtering. The edge assists the local decision by extracting parameters of the video, such as density of objects of interest in a frame, which influence the output of the sensor. Numerical results, obtained by performing a measurement campaign based on a real implementation, illustrate the tension between energy and bandwidth use for a Haar-feature based cascade classifier.