Subband and Sensing Task Allocation for Next-Generation Mobile Crowdsensing Networks: An Optimal Framework
Yaru Fu, Yue Zhang, Zheng Shi, Hong Wang, Yalin Liu · 2024
The growing reliance on mobile crowdsensing net-works for real-time data collection in various applications, from urban infrastructure monitoring to environmental sensing, ne-cessitates the reduction of latency for enhanced efficiency. In this work, we address this critical challenge by focusing on the joint subband and sensing task allocation for next-generation mobile crowdsensing networks, emphasizing the minimization of latency. To achieve this goal, an optimization problem is formulated to minimize the system's total latency, taking various practical constraints into account. Therein, the latency is comprised of sensing delay and transmission delay. The considered problem is a mixed-integer programming problem, which is also non-convex. To facilitate the analysis, we utilize the underlying structural properties of the problem and derive the optimal sensing task allocation strategy under a given sub band allocation scheme. With the discussions, we show that the original optimization problem can be transformed into a maximum weighted matching problem in a bipartite graph. This problem can be optimally solved by the Hungarian algorithm in a cubic time complexity. Building upon these analyses, we further approximate the optimal network delay in closed form under certain circumstances. Extensive simulation results validate that our proposed joint optimization method outperforms various benchmark strategies in terms of latency saving under comprehensive system settings.