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• W2947033529 abstract "Abstract Sensor networks, as a promising network paradigm, have been widely applied in a great deal of critical real-world applications. A key challenge in sensor networks is how to improve and optimize coverage quality which is a fundamental metric to characterize how well a point or a region or a barrier can be sensed by the geographically deployed heterogeneous sensors. Because of the resource-limited, battery-powered and type-diverse features of the sensors, maintaining and optimizing coverage quality includes a significant amount of challenges in heterogeneous sensor networks. Many researchers from both academic and industrial communities have performed numerous significant works on coverage optimization problem in the past decades. Some of them also have surveyed the current models, theories and solutions on the problem of coverage optimization. However, most of the existing surveys and analytical studies ignore how to exploit data fusion and cooperation of the deployed sensors to enhance coverage performance. In this paper, we provide an insightful and comprehensive summarization and classification on the data fusion based coverage optimization problem and techniques. Aiming at overcoming the shortcomings existed in current solutions, we also discuss the future issues and challenges in this area and sketch a general research framework in the context of reinforcement learning." @default.
• W2947033529 created "2019-06-07" @default.
• W2947033529 creator A5001543147 @default.
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• W2947033529 creator A5044544898 @default.
• W2947033529 creator A5080751595 @default.
• W2947033529 date "2019-12-01" @default.
• W2947033529 modified "2023-10-17" @default.
• W2947033529 title "Data fusion based coverage optimization in heterogeneous sensor networks: A survey" @default.
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