Coordinating Distributed Algorithms for Feature Extraction Offloading in Multi-Camera Visual Sensor Networks

Real-time visual analysis tasks, like tracking and recognition, require swift execution of computationally intensive algorithms. Visual sensor networks could be enabled to perform such tasks by allowing the camera nodes to offload their computational load to nearby processing nodes. In this paper, we address the problem of minimizing the completion time of multiple camera sensors that share the transmission and the processing resources of multiple processing nodes for computation offloading. We show that the problem is NP-hard, and propose a combination of central coordination and distributed optimization with limited signaling among the camera sensors as a solution. We analyze the existence of equilibrium allocations for the distributed algorithms, evaluate the effect of the network topology and of the video characteristics on the algorithms' performance, and assess the benefits of central coordination. Our results demonstrate that with sufficient information available, distributed optimization can provide low completion times, moreover predictable and stable performance can be achieved with additional, sparse central coordination.