Completion time minimization for distributed feature extraction in a visual sensor network testbed

Abstract: Real-time detection and extraction of visual features in wireless sensor networks is a challenging task due to its computational complexity and the limited processing power of the nodes. A promising approach is to distribute the workload to other nodes of the network by delegating the processing of different regions of the image to different nodes. In this work a solution to optimally schedule the loads assigned to each node is implemented on a real visual sensor network testbed. To minimize the time required to process an image, the size of the subareas assigned to the cooperators are calculated by solving a linear programming problem taking into account the transmission and processing speed of the nodes and the spatial distribution of the visual features. In order to minimize the global workload, an optimal detection threshold is predicted such that only the most significant features are extracted. The solution is implemented on a visual sensor network testbed consisting of BeagleBone Black computers capable of communicating over IEEE 802.11. The capabilities of the testbed are also extended by adapting a reliable transmission protocol based on UDP capable of multicast transmission. The performance of the implemented algorithms is evaluated on the testbed.