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Yanjie Guo
(Advisor: Prof. Theodorou]
will propose a doctoral thesis entitled,
Hybrid Sensor Networks for Active Monitoring: Collaboration, Optimization, and Resilience
On
Friday, June 17 at 10:00 a.m.
[building & room] I will be proposing online
[Link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_YWU5OGQ5NzMtYWE2Yy00MmVlLThlZGUtMmM2OWU2MWM2ZWVk%40thread.v2/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%228658cf0b-46b9-4a2d-ac67-da704ec16e24%22%7d ]
Abstract
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 raise a new set of research questions. This proposal 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 this work, first, a collaborative deployment strategy of HSN is developed to improve the ultimate monitoring performance in complex environments with obstacles and non-uniform risk distribution. Second, 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. Third, potential sensor failures are accounted to embed resilience in a HSN to mitigate performance degradation when they occur. To demonstrate and validate the novel perspectives on HSN, a simulation environment of fire detection in a multi-room apartment using temperature sensors is developed for the computational experiments.
Committee
