eCOTS: Efficient and Cooperative Task Sharing for Large-Scale Smart City Sensing Application

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 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 to . 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. 1. Introduction Recent years have witnessed a significant rise in the usage of smart phone as well as the associated applications. With the increasing number of mobile devices in network, people would share their data with mobile devices and make further collaborations with each other. Recently, crowdsourcing with participatory sensing schemes enables more unconscious and volunteering collaborations among sensing information shareholders. Actually, in such mobile computing environment, deeper collaborations are needed. Mobile users with different number of tasks will share and reassign tasks among them, taking advantages of computing and energy diversity. For example, users with low battery level will offload their tasks onto the contacted users in communication ranges with higher battery level. Even further, tasks should be assigned to nodes with higher computing ability. However, previous works [1–5] fail to achieve load balancing among users. Under these schemes, the queuing length may be extremely high in some particular nodes, which will inevitably lead to exhausted energy and long delay. The root reason is that these works are all focusing on the data sharing efficiency instead of the allocation equilibrium among diverse users. Moreover, computational capability and remaining energy levels are also vitally important for task