TY - GEN
T1 - Data Sensing with Limited Mobile Sensors in Sweep Coverage
AU - Nie, Zixiong
AU - Liu, Chuang
AU - Du, Hongwei
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Sweep coverage has received great attention with the development of wireless sensor networks in the past few decades. Sweep coverage requires mobile sensors to cover and sense environmental information from Points Of Interests (POIs) in every sweep period. In some scenarios, due to the heterogeneity of POIs and a lack of mobile sensors, mobile sensors sense the different amounts of data from different POIs, and only part of POIs can be covered by mobile sensors. Therefore, how to schedule the mobile sensors to improve coverage efficiency is important. In this paper, we propose the optimization problem (MSDSC) to maximize sensed data with a limited number of mobile sensors in sweep coverage and prove it to be NP-hard. We then devise two algorithms named GD-MSDSC and MST-MSDSC for the problem. Our simulation results show that, with a limited number of mobile sensors, GD-MSDSC and MST-MSDSC are able to sense more data from POIs than algorithms from previous work. In addition, MST-MSDSC can sense more data while the time complexity of GD-MSDSC is better.
AB - Sweep coverage has received great attention with the development of wireless sensor networks in the past few decades. Sweep coverage requires mobile sensors to cover and sense environmental information from Points Of Interests (POIs) in every sweep period. In some scenarios, due to the heterogeneity of POIs and a lack of mobile sensors, mobile sensors sense the different amounts of data from different POIs, and only part of POIs can be covered by mobile sensors. Therefore, how to schedule the mobile sensors to improve coverage efficiency is important. In this paper, we propose the optimization problem (MSDSC) to maximize sensed data with a limited number of mobile sensors in sweep coverage and prove it to be NP-hard. We then devise two algorithms named GD-MSDSC and MST-MSDSC for the problem. Our simulation results show that, with a limited number of mobile sensors, GD-MSDSC and MST-MSDSC are able to sense more data from POIs than algorithms from previous work. In addition, MST-MSDSC can sense more data while the time complexity of GD-MSDSC is better.
KW - Data sensing
KW - Limited mobile sensors
KW - Sweep coverage
UR - https://www.scopus.com/pages/publications/85097849980
U2 - 10.1007/978-3-030-64843-5_45
DO - 10.1007/978-3-030-64843-5_45
M3 - 会议稿件
AN - SCOPUS:85097849980
SN - 9783030648428
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 669
EP - 680
BT - Combinatorial Optimization and Applications - 14th International Conference, COCOA 2020, Proceedings
A2 - Wu, Weili
A2 - Zhang, Zhongnan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th International Conference on Combinatorial Optimization and Applications, COCOA 2020
Y2 - 11 December 2020 through 13 December 2020
ER -