Joint Computation Offloading and Sampling Interval Optimization for Accuracy-Guaranteed Surveillance

A key aspect to realize Internet of things applications such as industry automation and smart agriculture is to enable realtime and networked automatic monitoring via cloud computing and computer vision. However, to design a networked monitoring system, it is necessary to realize a balance between the monitoring accuracy and monitoring cost, for instance, between the network traffic to transmit images and the computation load. Although the monitoring cost can be decreased by increasing the sampling interval of cameras, it becomes more likely that informative images cannot be obtained; in other words, the monitoring accuracy decreases with a reduction in the amount of data. Moreover, although on-device image processing can decrease the network traffic, a large computation delay may be incurred, limiting the sampling rate of the monitoring system. The objective of this study was to examine the balance between the monitoring accuracy and cost and to develop a joint optimization technique for the sampling interval and computation offloading to minimize the monitoring cost in a networked monitoring system while ensuring a high monitoring accuracy. The main contributions of this paper are that we prove the joint optimization problem can be solved explicitly and to develop an algorithm to obtain the solution of the joint optimization problem. The simulation results demonstrated that the proposed algorithm can reduce the monitoring cost by 24-48% while maximizing the number of nodes ensured to achieve high monitoring accuracy.