Hybrid Sensor Networks for Active Monitoring: Collaboration, Optimization, And Resilience

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Guo, Yanjie
Theodorou, Evangelos A.
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Hybrid sensor networks (HSN) consist of both static and mobile sensors deployed to fulfill a common monitoring task. The hybrid structure generalizes the network’s design problem and offers a rich set of possibilities for a host of environmental monitoring and anomaly detection applications. HSN also raise a new set of research questions. Their deployment and optimization provide unique opportunities to improve the network’s monitoring performance and resilience. This thesis addresses three challenges associated with HSN related to the collaboration, optimization, and resilience aspects of the network. Broadly speaking, these challenges revolve around the following questions: (1) how to collaboratively allocate the static sensors and devise the path planning of the mobile sensors to improve the monitoring performance? (2) how to select and optimize the sensor portfolio (the mix of each type of sensors) under given cost constraints? And (3) how to embed resilience in a HSN to sustain the monitoring performance in the face of sensor failures and disruptions? In part I, collaboration, this thesis develops a novel deployment strategy for HSN. The strategy solves the static sensor allocation problem, the mobile sensor path planning problem, and most importantly, the collaboration between these two types of sensors. Previous research in this area has addressed these problems separately in simplified environments. In this thesis, a collaborative deployment strategy of HSN is developed to improve the ultimate monitoring performance in complex environments with obstacles and non-uniform risk distribution. In part II, optimization, this thesis addresses the HSN sensor portfolio selection problem. It investigates the tradeoff between the static and mobile sensors to achieve the optimal monitoring performance under different cost constraints. Previous research in this area has studied the optimization problem for networks with a single type of sensor. In this thesis, a general optimization problem is formulated for HSN with static and mobile sensors and solved to identify the optimal portfolio mix and its main drivers. In part III, resilience, this thesis identifies monitoring resilience as a key feature enabled by HSN. This part focuses on the performance degradation of HSN in the presence of sensor failures and disruptions, and it identifies the means to embed resilience in a HSN to mitigate this performance degradation. Monitoring resilience is achieved by accounting for potential sensor failures in the deployment strategy of both static and mobile sensors through a novel, carefully designed probability sum technique. Previous research in this area has examined the reliability problem from a coverage point of view. This thesis extends the scope of investigation of HSN from reliability to resilience, and it shifts the focus from coverage considerations to the actual monitoring performance (e.g., detection time lag) and its resilience in the face of disruptions. To demonstrate and validate this novel perspective on HSN and the associated technical developments, this thesis focused on two examples of fire detection in a multi-room apartment using temperature sensors and CO leak detection in a 3D space station module with ventilation system. Three metrics are adopted as the ultimate monitoring performance, namely the detection time lag, the anomaly source localization uncertainty, and the state estimation error. A simulation environment based on the advection-conduction heat propagation model is developed for the computational experiments. 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 rest of the low-risk areas; (2) identify a set of conditions under which HSN significantly outperform purely static and purely mobile sensor networks across the three performance metrics here considered; and (3) establish that while sensor failures can considerably degrade the monitoring performance of traditional static sensor networks, the resilient deployment of HSN drastically reduces the performance degradation.
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