Journal
INFORMATION FUSION
Volume 52, Issue -, Pages 90-105Publisher
ELSEVIER
DOI: 10.1016/j.inffus.2018.11.020
Keywords
Data fusion; Coverage optimization; Heterogeneous sensor networks; Survey; Reinforcement learning
Funding
- National Natural Science Foundation of Chinander [61871209, 61401193]
- Natural Science Foundation of Hunan Province [2017JJ3275, 2018JJ2333]
- State Scholarship Fund of the China Scholarship Council [201608430036, 201708430226]
- Hunan Province Key Laboratory for Ultra-Fast Micro/Nano Technology and Advanced Laser Manufacture [2018TP1041]
- Research Foundation of Education Bureau of Hunan [14A119, 16A181]
- NSERC
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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.
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