Location-Dependent Task Assignment For Opportunistic Mobile Crowdsensing

In mobile crowdsensing applications that rely on opportunistic sensing and communication, efficient task assignment strategies are needed to ensure that the tasks are completed before their expiration time. This requires to optimize the tradeoff between high task completion ratio and cost-efficiency by assigning tasks only to a small group of users who are expected to be of most assistance to task owners. To address this issue, in this paper, we propose two new task assignment protocols based on a new metric that accurately measures the utility of users to each other in performing tasks in specific regions. Through simulations we show that the proposed protocols not only provide a high task completion ratio, but also utilize the network resources efficiently by assigning tasks to as few users as possible, hence they perform better than the previous work.