Name
Title | ||
|---|---|---|
A Spark-based genetic algorithm for sensor placement in large scale drinking water distribution systems. |
Abstract | ||
|---|---|---|
Water pollution incidents have occurred frequently in recent years, causing severe damages, economic loss and long-lasting society impact. A viable solution is to install water quality monitoring sensors in water supply networks (WSNs) for real-time pollution detection, thereby mitigating the risk of catastrophic contamination incidents. Given the significant cost of placing sensors at all locations in a network, a critical issue is where to deploy sensors within WSNs, while achieving rapid detection of contaminant events. Existing studies have mainly focused on sensor placement in water distribution systems (WDSs). However, the problem is still not adequately addressed, especially for large scale WSNs. In this paper, we investigate the sensor placement problem in large scale WDSs with the objective of minimizing the impact of contamination events. Specifically, we propose a two-phase Spark-based genetic algorithm (SGA). Experimental results show that SGA outperforms other traditional algorithms in both accuracy and efficiency, which validates the feasibility and effectiveness of our proposed approach. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s10586-017-0838-z | Cluster Computing |
Keywords | Field | DocType |
Sensor placement,Water distribution system,Genetic algorithm,Spark | Spark (mathematics),Damages,Computer science,Pollution,Water pollution,Genetic algorithm,Water quality,Contamination,Water supply,Distributed computing | Journal |
Volume | Issue | ISSN |
20 | 2 | 1386-7857 |
Citations | PageRank | References |
4 | 0.40 | 14 |
Authors | ||
5 | ||