Failure-Tolerant Monitoring Based on Spatial–Temporal Correlation via Mobile Sensors for Large-Scale Acyclic Flow Systems in Smart Cities

Abstract

Large-scale acyclic flow systems (LSAFSs) are models of pipeline networks that are used to transport important resources, such as water, oil, and natural gas in smart cities. LSAFSs have features of complex topology and deep-underground deployment, which would cause accidents, such as leakages and pollution that are difficult to be detected in time. Mobile sensors (MSs)-based monitoring schemes appear as an effective solution in recent years to handle this situation. These schemes drop MSs into an LSAFS from specified locations, and the MSs will move along with fluid inside the LSAFS to collect data. When the MSs pass through a predeployed and activated receiver node (RN), they will upload their data to the RN. However, how to decide the optimal locations and timings for dropping of the MSs and the best activation periods and deployment locations of the RNs, such that total length of monitored pipelines in the LSAFS is maximized, as well as energy consumption of the RNs is minimized, is a challenging problem. The problem is more challenging if potential uploading failures and undetermined movement of the MSs are considered. In this article, we first formalize the problem as a multiobjective optimization problem. Then, we decompose the problem into a submodular optimization problem and an union set optimization problem, and prove they are NP-hard. Next, an approximate algorithm based on the Pigeonhole principle is proposed to solve the first problem, and a heuristic algorithm based on the inclusion-exclusion principle is proposed to solve the second problem. Extensive theoretical analyses and simulations show that the proposed algorithms outperform state-of-the-art algorithms.