An Optimization Framework for Mobile Data Collection in Energy-Harvesting Wireless Sensor Networks
An Optimization Framework for Mobile Data Collection in Energy-Harvesting Wireless Sensor Networks
复制标题
能量收集无线传感器网络中移动数据收集的优化框架
DOI：
10.1109/tmc.2016.2533390
复制
发表时间：
2016-12-01
影响因子：
7.9
通讯作者：
Yang, Yuanyuan
中科院分区：
文献类型：
--
作者：
Wang, Cong;Guo, Songtao;Yang, Yuanyuan
环境能量采集技术的最新进展为传统的电池供电传感器网络实现永久运行提供了巨大潜力。由于环境能源的时间分布动态性，到目前为止，大多数研究都集中在单个传感器节点的能量管理方案设计和优化上，而忽视了传感器在不同位置协同工作时能量分布的空间变化影响。为了设计一个稳健的传感器网络，在本文中，我们利用移动性来规避由空间能量变化导致的通信瓶颈。我们使用一种名为SenCar的移动收集器从指定的传感器收集数据并平衡网络中的能量消耗。为了展示时空能量变化，我们首先在一个太阳能供电网络中进行了一个案例研究，并分析了对网络性能可能产生的影响。接下来，我们提出了一种用于移动数据收集的两步方法。首先，我们自适应地选择传感器位置的一个子集，SenCar在这些位置以多跳方式停止收集数据包。我们开发了一种自适应算法，根据节点能量搜索节点，并保证数据收集路径长度有界。其次，我们专注于设计分布式算法，通过调整数据速率、链路调度和适应时空环境能量波动的流路由来实现最大网络效用。最后，我们的数值结果表明分布式算法能够非常快速地收敛到最优解，并在节点故障的情况下验证了其收敛性。我们还展示了我们的框架的优势，例如它能够适应时空能量变化，并证明了与具有静态数据汇聚节点的网络相比它的优越性。
Recent advances in environmental energy harvesting technologies have provided great potentials for traditional battery powered sensor networks to achieve perpetual operations. Due to dynamics from the temporal profiles of ambient energy sources, most of the studies so far have focused on designing and optimizing energy management schemes on single sensor node, but overlooked the impact of spatial variations of energy distribution when sensors work together at different locations. To design a robust sensor network, in this paper, we use mobility to circumvent communication bottlenecks caused by spatial energy variations. We employ a mobile collector, called SenCar, to collect data from designated sensors and balance energy consumptions in the network. To show spatial-temporal energy variations, we first conduct a case study in a solar-powered network and analyze possible impact on network performance. Next, we present a two-step approach for mobile data collection. First, we adaptively select a subset of sensor locations where the SenCar stops to collect data packets in a multi-hop fashion. We develop an adaptive algorithm to search for nodes based on their energy and guarantee data collection tour length is bounded. Second, we focus on designing distributed algorithms to achieve maximum network utility by adjusting data rates, link scheduling, and flow routing that adapts to the spatial-temporal environmental energy fluctuations. Finally, our numerical results indicate the distributed algorithms can converge to optimality very fast and validate its convergence in case of node failure. We also show advantages of our framework such as it can adapt to spatial-temporal energy variations and demonstrate its superiority compared to the network with static data sink.