Accurate per-link loss tomography in dynamic sensor networks

• Published in: Computer networks (Amsterdam, Netherlands : 1999) Netherlands : 1999), 2018-07, Vol.139, p.81-91
• Main Authors: Cao, Chenhong, Gao, Yi, Dong, Wei, Bu, Jiajun
• Format: Article
• Language: English
• Subjects: Accuracy; Arithmetic coding; Coding; Comparative analysis; Computer simulation; Loss tomography; Protocol (computers); Remote sensors; Sensors; Tomography; Wireless networks; Wireless sensor network; Wireless sensor networks
• ISSN: 1389-1286; 1872-7069
• DOI: 10.1016/j.comnet.2018.04.007

Wireless Sensor Networks (WSNs) have been successfully applied in many application areas. Understanding the wireless link performance is very helpful for both protocol designers and network managers to improve the network performance and prolong the network lifetime. Loss tomography is a popular approach to infer the per-link loss ratios from end-to-end delivery ratios. Previous studies, however, are usually targeted for networks with static or slowly changing routing paths. In this work, we propose Dophy, a Dynamic loss tomography approach specifically designed for dynamic WSNs where each node dynamically selects the forwarding nodes towards the sink. The key idea of Dophy is based on an observation that most existing protocols use retransmissions to achieve high data delivery ratio. Dophy employs arithmetic encoding to encode the number of retransmissions along the paths compactly. Dophy incorporates two mechanisms to optimize its performance. First, Dophy intelligently reduces the size of the symbol set by aggregating the number of retransmissions, reducing the encoding overhead significantly. Second, Dophy periodically updates the probability model to minimize the overall transmission overhead. We implement Dophy on the TinyOS platform and evaluate its performance extensively using large-scale simulations. Results show that Dophy achieves both high encoding efficiency and high estimation accuracy. Comparative studies show that Dophy significantly outperforms traditional loss tomography approaches in terms of accuracy.