Prediction-based Opportunistic Sensing for Energy-Efficiency and Reliability in Distributed Sensor Networks
Digital Document
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Handle
http://hdl.handle.net/11134/20002:860653504
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Persons |
Persons
Creator (cre): Hare, James Z.
Major Advisor (mja): Gupta, Shalabh
Associate Advisor (asa): Luh, Peter
Associate Advisor (asa): Dani, Ashwin
Associate Advisor (asa): Wettergren, Thomas
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Title |
Title
Title
Prediction-based Opportunistic Sensing for Energy-Efficiency and Reliability in Distributed Sensor Networks
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Origin Information
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Parent Item
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Resource Type
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Digital Origin |
Digital Origin
born digital
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Description |
Description
Distributed Sensor Networks (DSN) containing a large number of sensors nodes are rapidly advancing to perform automated tasks for a variety of applications (e.g. Target Tracking). One of the main problems studied in DSN is the Target Coverage problem, where the objective is to maximize the network lifetime while ensuring that all targets are covered during all times. Therefore, the main challenges in developing a DSN are network control strategies for energy-efficiency and resilience in the event of sensor failures. This thesis presents the emph{Prediction-based Opportunistic Sensing for Energy-efficiency} (POSE) algorithm, which aims to address these challenges by designing a DSN that minimizes energy consumption while ensuring target coverage. The first algorithm presents a distributed node-level supervisor that controls each sensor node to allow the network to self-adapt to the targets' trajectory via opportunistic sensing. This approach minimizes the network energy consumption by only enabling high power consuming devices when a target is predicted to travel within the sensor nodes coverage area, while using low power consuming devices when a target is absent. The second algorithm, emph{POSE using Distributed Classification, Clustering and Control} (POSE.3C), extends the POSE algorithm to include classification in the control loop to opportunistically observe targets of interest, while minimizing energy consumption via distributed clustering. Finally, the third algorithm, emph{POSE for Resilience} (POSE.R), extends the POSE.3C algorithm to incorporate Resilient Target Coverage into the DSN. This approach incorporates target and network density predictions to adapt the distributed clustering method to ensure that a target is covered in the event of sensor failures. The compilation of the three algorithms presents a distributed control strategy for DSN that performs energy-efficient and resilient target coverage.
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Genre
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Organizations
Degree granting institution (dgg): University of Connecticut
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Rights Statement
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Use and Reproduction |
Use and Reproduction
These materials are provided for educational and research purposes only.
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Local Identifier |
Local Identifier
OC_d_1758
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