Research Area:  Internet of Things
Study of pipeline networks which are used to transfer gas and oil from the production sites to consumers has widened all over the globe. On the other hand, there is a colossal loss of resources due to spills and leakages caused by natural disasters, human sabotage, and wear and tear of pipeline infrastructure. Serious economic losses can be faced in transportation of fluid through these anomalies that may incur additional costs for the final consumer. Nuclear fluids may also damage infrastructure and cause health risks to both humans and marine life. This issue is very critical to fulfill the energy demands of population in the entire world. For this purpose, a comprehensive study of recent pipeline anomalies detection techniques was performed. We proposed an effective solution to monitor pipelines and provided a framework for anomaly localization using Cooja simulator and geographical information systems that can also be used in pre-disaster management scenarios, i.e. pipelines can be maintained prior to actual leaks and spills. Timely precautionary measures can thus be taken during the pre-disaster, disaster and post disaster stages, thereby minimizing wastage of natural resources. We also compare localization accuracy with two detection and localization techniques namely: negative pressure wave and pressure point analysis.
Author(s) Name:  Sultan Anwar,Tarek Sheltami,Elhadi Shakshuki,Menshawi Khamis
Journal name:  Journal of Ambient Intelligence and Humanized Computing
Publisher name:  Springer
Volume Information:  volume 10, pages 2563–2575 (2019)
Paper Link:   https://link.springer.com/article/10.1007/s12652-018-0733-3