Scheduling shared data acquisition for real-time decision making

This paper investigates scheduling policies for the acquisition of possibly overlapping sets of data items required to make multiple decisions by different deadlines. The work is motivated by military IoT applications in which a large number of sensors must collect intelligence data to make multiple decisions. For example, data from several cameras in a large city might be needed to decide where targets of interest are. We assume that network bandwidth is limited, creating a significant resource bottleneck (perhaps between sensors and the command center where decisions are made). This might be the case, for example, due to active interference by an adversary. We begin by introducing some of the observations and properties of the data acquisition scheduling problem followed by a heuristic algorithm based on the insights gained from these observations. Finally, we evaluate the new algorithm across multiple parameters and compare the results with previous heuristics, demonstrating improved performance of our solution.