多Sink群智感知网络数据收集方法

随着Android和iPhones等移动设备的广泛普及，群智感知网络成为研究热点.人们携带这些移动设备在日常生活中收集环境感知数据.人们的移动具有社会性，其移动轨迹难以预测，如何设计一种有效的数据收集算法是一个值得研究的问题.针对多Sink群智感知网络，提出一种基于地点的感知数据收集方法.首先采用多目标决策的层次分析法，以移动设备与Sink节点之间的距离、连接时间和相遇概率为性能指标，提出最优Sink的选择机制，确定感知数据的目标传输节点；受PeopleRank启发，提出基于地点的数据转发方法，以优化下一跳选择策略.最后，通过一系列实验对该方法的可行性和有效性进行验证，实验结果表明，该方法不仅大大提高了感知数据的传输成功率，而且转发开销和延迟有了明显的降低.

Data Gathering Scheme for Multi-Sink Crowd Sensing Networks

With the widespread popularity of sensor-rich mobile devices, such as Android phones and iPhones, crowdsensing networks have attracted enormous attentions from researchers recently. Mobile devices, carried by people in their daily life, move randomly in monitoring areas and acquire environmental sensing data. However, it is an arduous task to design an efficient data gathering approach for crowdsensing since the movements of mobile devices are difficult to predict. In this paper, a location based data gathering scheme for multi-sink crowdsening networks is proposed. First, a selection method based on multi-objective decision-making is presented to find the optimal sink according to the distances, connection durations and encounter probabilities between mobile devices and sinks. Then, inspired by PeopleRank algorithm, a location based sensing data forwarding algorithm is presented to optimize the data forwarding strategy. Furthermore, the feasibility and effectiveness of the proposed scheme are validated through a series of experiments employing opportunistic network environment simulator. The results show that the proposed scheme not only improves the transmission rate of perception data but also has lower cost and forwarding delay than the existing methods.