Abstract
In this study, a novel collaborative method is developed to optimize hybrid sensor networks (HSN) for environmental monitoring and anomaly search tasks. A weighted Gaussian coverage method hs been designed for static sensor allocation, and the Active Monitoring and Anomaly Search System method is adapted to mobile sensor path planning. To validate the network performance, a simulation environment has been developed for fire search and detection with dynamic temperature field and non-uniform fire probability distribution. The performance metrics adopted are the detection time lag, source localization uncertainty, and state estimation error. Computational experiments are conducted to evaluate the performance of HSNs. The results demonstrate that the optimal collaborative deployment strategy allocates static sensors at high-risk locations and directs mobile sensors to patrol the remaining low-risk areas. The results also identify the conditions under which HSNs significantly outperform either only static or only mobile sensor networks in terms of the monitoring performance metrics.
Highlights
Sensor networks are the focus of an increasingly vigorous research area with extensive applications in environmental monitoring and anomaly detection [1,2], in maritime search and rescue for example [3], in target tracking [4], and within the broad context of the Internet of Thing (IoT) [5,6,7,8]
With the recent development in low-cost robots, more applications consider the adoption of hybrid sensor networks (HSNs) which exploit mobile sensors in conjunction with static sensors that are smartly distributed across the environment to be monitored [9,10,11,12,13,14,15]
A collaborative static sensor allocation and mobile sensor path planning method was devised for the design and performance optimization of a hybrid sensor network (HSN)
Summary
Sensor networks are the focus of an increasingly vigorous research area with extensive applications in environmental monitoring and anomaly detection [1,2], in maritime search and rescue for example [3], in target tracking [4], and within the broad context of the Internet of Thing (IoT) [5,6,7,8]. There are three challenges in the current research on HSNs. First, the deployment strategy of HSN does not fully consider the collaboration between static and mobile sensors, and optimization has not yet been investigated for the portfolio mix of the two types of sensors under cost constraints. The results (1) demonstrate that the optimal collaborative deployment strategy allocates the static sensors at high-risk locations and directs the mobile sensors to patrol the remaining low-risk areas; (2) identify a set of conditions under which HSNs significantly outperform purely static and purely mobile sensor networks in terms of the ultimate monitoring performance; (3) establish how the cost constraints and mobile sensor speed affect the optimal sensor portfolio.
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