Study on real time data collection and distribution method in sensor networks

在大型传感器网络中，为了覆盖更大的监测区域，传感器节点之间需要进行多跳通信。此外，传感器节点应分组成集群，以提高可扩展性和鲁棒性。我们研究了大规模传感器网络中集群间多跳通信的特点，并将其与其他路由方法进行了比较。由于只需要邻近传感器节点的信息，我们也应用了CSMA/CA。因此，使用CSMA/CA与使用基于所有传感器节点位置信息的TDMA相比，功耗增加了12%，数据包收集时间增加了约4倍。此外，为了验证我们的自主室内定位系统在实际环境中传感器网络的有效性，我们在基于ZigBee标准的无线传感器网络中实现了它。系统通过在适当数量的传感器节点上测量接收信号强度指标RSSI (received signal strength indicator)，自动估算传感器节点之间的距离。通过实验，我们证明了我们的数据收集和位置估计技术的有效性。结果表明，位置估计误差减小到1.5 ~ 2 m。此外，我们还提出了一种受群体智能启发的多sink传感器网络数据采集新方案。在该方案中，每个传感器节点通过与邻居和环境的反复交互和反馈来决定自己的下一步动作。这些行为在网络层面产生了有利的集群和路由。仿真结果表明，该方案能够可靠地将事件信息传递到汇聚节点，在极低质量的无线信道上具有鲁棒性，并具有自恢复处理传感器节点故障和汇聚节点故障的能力。

In large-scale sensor networks, multi-hop communication between sensor nodes is necessary to cover a large monitoring region. Moreover, sensor nodes should be grouped into clusters to enhance scalability and robustness. We examine the characteristics of multi-hop communication between clusters in large-scale sensor networks and compare them with other routing methods. We also apply CSMA/CA since the information of only neighboring sensor nodes is necessary. As a result, using CSMA/CA, power consumption increases by 12% and the packet collection time becomes about four times longer in comparison to using TDMA based on location information of all sensor nodes.Furthermore, to verify the validity of our autonomous indoor localization system for sensor networks in an actual environment, we implemented it in a wireless sensor network based on the ZigBee standard. The system automatically estimates the distance between sensor nodes by measuring the RSSI (received signal strength indicator) at an appropriate number of sensor nodes. Through experiments, we clarified the validity of our data collection and position estimation techniques. The results show that the position estimation error was reduced to 1.5-2 m.Moreover, we propose a new scheme of gathering data from sensor networks with multi-sink configurations inspired by the swarm intelligence. Each sensor node determines its next action through repeated interaction and feedback from its neighbors and environments in this scheme. Advantageous clustering and routing emerge in network level from these actions. Our simulation results revealed that the proposed scheme can reliably deliver event information to the sink nodes, is robust over very-poor-quality wireless channels, and has self-recovery capability to deal with sensor-node failure and even that in sink nodes.