PIPENETa wireless sensor network for pipeline monitoring

• Published in: Proceedings of the 6th international conference on Information processing in sensor networks pp. 264 - 273
• Main Authors: Stoianov, Ivan, Nachman, Lama, Madden, Sam, Tokmouline, Timur
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 25.04.2007
• Series: ACM Conferences
• Subjects: Computer systems organization > Embedded and cyber-physical systems; Computer systems organization > Real-time systems; Hardware > Communication hardware, interfaces and storage > Signal processing systems; pipeline monitoring; Intel mote platforms; wireless sensor networks; water supply systems
• ISBN: 9781595936387; 1595936386
• DOI: 10.1145/1236360.1236396

US water utilities are faced with mounting operational and maintenance costs as a result of aging pipeline infrastructures. Leaks and ruptures in water supply pipelines and blockages and overflow events in sewer collectors cost millions of dollars a year, and monitoring and repairing this underground infrastructure presents a severe challenge. In this paper, we discuss how wireless sensor networks (WSNs) can increase the spatial and temporal resolution of operational data from pipeline infrastructures and thus address the challenge of near real-time monitoring and eventually control. We focus on the use of WSNs for monitoring large diameter bulk-water transmission pipelines. We outline a system, PipeNet, we have been developing for collecting hydraulic and acoustic/vibration data at high sampling rates as well as algorithms for analyzing this data to detect and locate leaks. Challenges include sampling at high data rates, maintaining aggressive duty cycles, and ensuring tightly time-synchronized data collection, all under a strict power budget. We have carried out an extensive field trial with Boston Water and Sewer Commission in order to evaluate some of the critical components of PipeNet. Along with the results of this preliminary trial, we describe the results of extensive laboratory experiments which are used to evaluate our analysis and data processing solutions. Our prototype deployment has led to the development of a reusable, field-reprogrammable software infrastructure for distributed high-rate signal processing in wireless sensor networks, which we also describe.