面向移动群智感知的多任务分发算法

针对在移动群智感知中基于机会通信完成数据传输会消耗大量时间成本的问题，提出了一种基于中枢节点的多任务分发（HTA）算法。该算法利用节点在移动网络中社交关系属性不同的特点，通过中枢节点选择算法将部分节点作为中枢节点，并将其用于协助任务请求节点分发任务。在任务请求节点与中枢节点相遇时，同时给中枢节点本身和它的从属节点分配任务，并由中枢节点负责向从属节点分发任务与回收任务结果。基于The ONE模拟器进行实验，与在线任务分配（NTA）算法相比，HTA算法时间成本平均降低了24.9%，同时任务完成率平均提高150%。实验结果表明，HTA算法能够提高任务的完成速度，降低时间成本消耗。

Multi-task assignment algorithm for mobile crowdsensing

Data transmission based on opportunistic communication in mobile crowdsensing may take a long period of time. To address this issue, a new Hub-based multi-Task Assignment (HTA) algorithm was proposed. In this algorithm, some nodes were selected to perform as the hubs which could help the requester node to deliver the tasks, according to the different characteristics of the social relationship of the nodes in mobile networks. When the task requester encountered a hub node, the hub node itself and its slave nodes were assigned tasks. After that, the hub node would distribute the tasks to the salve nodes, and received the results from them. Simulations were conducted on The ONE simulator. Compared with the oNline Task Assignment (NTA) algorithm, HTA algorithm reduced the time cost by 24.9% on average and improved the task completion ratio by 150% on average. The experimental results demonstrate that HTA algorithm can accelerate the accomplishment speed of the task and reduce the time cost.