School of Electronic Engineering, Soongsil University, Room 1104, Huyngam Engineering Building 424, Sangdo-dong, Dongjak-Gu, Seoul 06978, Korea.
Sensors (Basel). 2020 Oct 29;20(21):6168. doi: 10.3390/s20216168.
Data collection is an important application of wireless sensor networks (WSNs) and Internet of Things (IoT). Current routing and addressing operations in WSNs are based on IP addresses, while data collection and data queries are normally information-centric. The current IP-based approach incurs significant management overheads and is inefficient for semantic data collection and queries. To address the above issue, this paper proposes a semantic data collection tree (sDCT) construction scheme to build up a semantic data collection tree for wireless sensor networks. The semantic tree is rooted at the edge/sink and supports data collection tasks, queries, and configurations efficiently. We implement the sDCT in Contiki and evaluate the performance of the sDCT in comparison with the state-of-the-art scheme, 6LoWPAN/RPL and L2RMR, using telosb sensors under various scenarios. The obtained results show that the sDCT achieves a significant improvement in terms of the energy efficiency and the packet transmissions required for data collection or a query task compared to 6LoWPAN/RPL and L2RMR.
数据收集是无线传感器网络(WSN)和物联网(IoT)的重要应用。当前 WSN 中的路由和寻址操作基于 IP 地址,而数据收集和数据查询通常是基于信息的。当前基于 IP 的方法会导致大量的管理开销,并且对于语义数据收集和查询效率低下。为了解决上述问题,本文提出了一种语义数据收集树(sDCT)构建方案,为无线传感器网络构建语义数据收集树。语义树以边缘/汇聚点为根,支持高效的数据收集任务、查询和配置。我们在 Contiki 中实现了 sDCT,并在各种场景下使用 telosb 传感器对 sDCT 与最先进的方案 6LoWPAN/RPL 和 L2RMR 进行了性能评估。获得的结果表明,与 6LoWPAN/RPL 和 L2RMR 相比,sDCT 在数据收集或查询任务所需的能量效率和数据包传输方面有显著的改进。