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With the pervasive use of mobile devices and increasingly computational ability, more concrete and deeper collaborations among mobile users are becoming possible and needed. However, most of the studies fail to consider load balancing requirement among mobile users. When tasks are unevenly distributed, the processing time as well as energy consumption will be extremely high on some devices, which will inevitably counterweight the benefits from incentive mechanism and task scheduling scheme. In this work, we propose eCOTS (Efficient and Cooperative Task Sharing for Large-scale Smart City Sensing Application). We leverage the “balls and bins” theory for task assignment, where d mobile users in contact range are investigated, and select the least loaded one among the d users. It has been proved that such simple case can effectively reduce the largest queueing length from θ ( log n / log log n ) to θ ( log log n / log d ) . Simulation and real-trace driven studies have shown that, eCOTS can effectively improve the balancing effects in typical network scenarios, even the energy level and computational capability are diverse. In simulation study, eCOTS can reduce the gap between the maximum and minimum queueing lengths up to 5× and over 2× in real trace data evaluations.