Abstract
Urban water distribution networks (WDNs) are usually threatened by leakage, reflux, infiltration and internal pollution. To ensure the safety of water supply, it is essential to properly monitor the WDNs. However, it is not easy to design a fast, fine-grained and comprehensive monitoring solution under limited cost, due to the large scale and high complexity of WDNs. In recent years, mobile sensor networks have been widely utilized to monitor WDNs with data uploaded by static nodes or receiver nodes. However, existing solutions haven't addressed the impact of duplicated uploading caused by the topology of WDNs. Hence, the coverage rate is unsatisfactory. In addition, the mobile sensor nodes could be potentially far away from the receiver nodes, which might introduce long data acquisition delay. In this work, we propose a low delay and high coverage WDNs monitoring solution, with the constraint of limited number of mobile and receiver nodes. The proposed solution can find the optimal deployment strategy of mobile and receiver nodes by constructing a probability dependent model between mobile and receiver nodes, and coordinating the trade-off between low-delay and high-coverage under multi-objective optimization model. We also analyzed the theoretical lower bound of the algorithm, by utilizing the submodular function characteristics of the objective. Extensive simulation proved that the proposed algorithm can obtain higher coverage with lower delay.
Highlights
Monitoring urban Water Distribution Networks (WDNs) is vital to people’s daily lives
This paper proposes a low delay and high coverage WDNs monitoring approach with mobile sensors
We use the simulation program to test the performance of Bidirectional Greedy Iterative algorithm (BGI) on a large-scale WDN
Summary
Monitoring urban Water Distribution Networks (WDNs) is vital to people’s daily lives. The neglect of this has led to a long delay in the existing solutions [12] To solve these issues, this paper proposes a low delay and high coverage WDNs monitoring approach with mobile sensors. The proposed solution formulates WDNs monitoring problem with forbearing stratified multi-objective optimization problem [13]. It balances the trade-off between high coverage and low delay. The contribution of this paper are as follows: 1) We formulate the mobile sensor and receiver nodes deployment problem as monitoring coverage maximization and expected monitoring delay minimization problems, and combine them together into a forbearing stratified multi-objective optimization problem, in which MCM is the primary problem and EMDM is the secondary. The results validate that the proposed approach can obtain better coverage rate while keeping lower monitoring delay
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