Efficient Data Collection in Widely Distributed Wireless Sensor Networks with Time Window and Precedence Constraints.
Efficient Data Collection in Widely Distributed Wireless Sensor Networks with Time Window and Precedence Constraints.
复制标题
具有时间窗和优先级约束的广泛分布式无线传感器网络中的高效数据收集
DOI：
10.3390/s17020421
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发表时间：
2017-02-22
期刊：
影响因子：
--
通讯作者：
Ding Y
中科院分区：
文献类型：
--
作者：
Liu P;Fu T;Xu J;Ding Y
除了传统的密集部署情况外，广泛分布的无线传感器网络（WDWSN）也开始出现。在这些网络中，传感器彼此距离很远，并且没有网络连接。在本文中，考虑了 WWSN 数据收集的特殊应用，其中每个传感器（无人地面车辆，UGV）在具有许多障碍物的危险且复杂的地形中移动。它们有自己的工作周期，并且只能在少数几个位置访问。移动接收器在地面上巡航，收集从这些 UGV 收集的数据。如果无人车和移动终端错过了会面窗口或在会面地点闲置，将不可避免地造成相当大的延迟。这里的独特挑战是，对于 UGV 的每个周期，它出现在移动接收器前面的时间窗口有限。因此，我们提出对单个移动宿的路径进行调度，目标是根据 UGV 周期约束的时序约束，以最短路径及时访问最大数量的 UGV。然后，我们提出了一种基于二分匹配的算法来减少移动接收器的数量。仿真结果表明，所提出的算法可以实现接近由占空比实例确定的理论最大值的性能。
In addition to the traditional densely deployed cases, widely distributed wireless sensor networks (WDWSNs) have begun to emerge. In these networks, sensors are far away from each other and have no network connections. In this paper, a special application of data collection for WDWSNs is considered where each sensor (Unmanned Ground Vehicle, UGV) moves in a hazardous and complex terrain with many obstacles. They have their own work cycles and can be accessed only at a few locations. A mobile sink cruises on the ground to collect data gathered from these UGVs. Considerable delay is inevitable if the UGV and the mobile sink miss the meeting window or wait idly at the meeting spot. The unique challenge here is that, for each cycle of an UGV, there is only a limited time window for it to appear in front of the mobile sink. Therefore, we propose scheduling the path of a single mobile sink, targeted at visiting a maximum number of UGVs in a timely manner with the shortest path, according to the timing constraints bound by the cycles of UGVs. We then propose a bipartite matching based algorithm to reduce the number of mobile sinks. Simulation results show that the proposed algorithm can achieve performance close to the theoretical maximum determined by the duty cycle instance.