Task Assignment in Extreme Edge Sensing: Balancing Response Time and Incentives

Extreme Edge Sensing (EES) offers an enhanced approach to efficient remote sensing by utilizing the computational capabilities of user devices for immediate data processing. In contrast to traditional Mobile Crowd Sensing (MCS), EES provides both data collection and local data processing to accelerate decision-making. However, due to the variability in participant capabilities and task requirements, the complexity of task assignments becomes challenging. This complexity necessitates a mechanism that balances incentives and response time, ensuring tasks are completed within predefined budget and time limits. This paper presents a new task assignment strategy that categorizes participants based on their capabilities and task needs. Using the Hungarian algorithm, our methodology optimizes task assignments with an objective function aiming to minimize both monetary and time costs. We then evaluate the minimum budget needed for successful task completion and its dependency on objective function parameters. A comparison of our method's performance against a standard greedy approach demonstrates its effectiveness. The results suggest that our method enhances the efficiency and reliability of task assignment in EES systems, with potential applications in smart cities, environmental monitoring, and other areas requiring efficient remote sensing.