移动人群感知中的分布式时间敏感任务选择
Distributed Time-Sensitive Task Selection in Mobile Crowdsensing
摘要：
随着智能手机中安装的丰富的嵌入式传感器和大量的移动用户，我们见证了许多创新的商业移动众测应用的出现，这些应用将移动技术的力量与众包相结合，为客户提供时间敏感和位置相关的信息。出于这些现实世界中的应用，我们考虑不同的初始位置，移动成本，移动速度和声誉水平的异构用户的任务选择问题。计算社会剩余最大化任务分配是一个NP难问题。因此，我们专注于分布式的情况下，并提出了一个异步和分布式的任务选择（ADTS）算法，以帮助用户规划自己的任务选择。我们证明了算法的收敛性，并进一步刻画了算法中用户更新的计算时间。仿真结果表明，ADTS方案实现了最高的Jain的公平性指数和覆盖率与几个基准算法相比，而产生类似的用户回报贪婪集中式基准。最后，我们说明了移动用户如何协调下的ADTS计划的基础上，一些实际的运动时间数据来自谷歌地图。
英文摘要：
With the rich set of embedded sensors installed in smartphones and the large number of mobile users, we witness the emergence of many innovative commercial mobile crowdsensing applications that combine the power of mobile technology with crowdsourcing to deliver time-sensitive and location-dependent information to their customers. Motivated by these real-world applications, we consider the task selection problem for heterogeneous users with different initial locations, movement costs, movement speeds, and reputation levels. Computing the social surplus maximization task allocation turns out to be an NP-hard problem. Hence we focus on the distributed case, and propose an asynchronous and distributed task selection (ADTS) algorithm to help the users plan their task selections on their own. We prove the convergence of the algorithm, and further characterize the computation time for users' updates in the algorithm. Simulation results suggest that the ADTS scheme achieves the highest Jain's fairness index and coverage comparing with several benchmark algorithms, while yielding similar user payoff to a greedy centralized benchmark. Finally, we illustrate how mobile users coordinate under the ADTS scheme based on some practical movement time data derived from Google Maps.
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