TY - JOUR
T1 - Multi-Objective Mapping Method for 3D Environmental Sensor Network Deployment
AU - Tsang, Yung Po
AU - Choy, King Lun
AU - Wu, Chun Ho
AU - Ho, George To Sum
N1 - Funding Information:
Manuscript received April 14, 2019; accepted April 25, 2019. Date of publication May 3, 2019; date of current version July 10, 2019. The authors would like to thank the Research Office of the Hong Kong Polytechnic University for supporting the project (Project Code: RUDV). Gratitude is also extended to the Big Data and Artificial Intelligence Group of The Hang Seng University of Hong Kong for their support in this work. The associate editor coordinating the review of this letter and approving it for publication was N. I. Miridakis. (Corresponding author: King Lun Choy.) Y. P. Tsang and K. L. Choy are with the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Effective deployment of the emerging environmental sensor network in environmental mapping has become essential in numerous industrial applications. The essential factors for deployment include cost, coverage, connectivity, airflow of heating, ventilation, and air conditioning, system lifetime, and fault tolerance. In this letter, a three-stage deployment scheme is proposed to formulate the above-mentioned considerations, and the fuzzy temperature window is established to adjust sensor activation times over various ambient temperatures. To optimize the deployment effectively, a multi-response Taguchi-guided k-means clustering is proposed to embed in the genetic algorithm, where an improved set of the initial population is formulated and system parameters are optimized. Therefore, the computational time for repeated deployment is shortened, while the solution convergence can be improved.
AB - Effective deployment of the emerging environmental sensor network in environmental mapping has become essential in numerous industrial applications. The essential factors for deployment include cost, coverage, connectivity, airflow of heating, ventilation, and air conditioning, system lifetime, and fault tolerance. In this letter, a three-stage deployment scheme is proposed to formulate the above-mentioned considerations, and the fuzzy temperature window is established to adjust sensor activation times over various ambient temperatures. To optimize the deployment effectively, a multi-response Taguchi-guided k-means clustering is proposed to embed in the genetic algorithm, where an improved set of the initial population is formulated and system parameters are optimized. Therefore, the computational time for repeated deployment is shortened, while the solution convergence can be improved.
KW - Environmental sensor network
KW - genetic algorithm
KW - mapping method
KW - multi-objective optimization
KW - multi-responses Taguchi method
UR - http://www.scopus.com/inward/record.url?scp=85068832992&partnerID=8YFLogxK
U2 - 10.1109/LCOMM.2019.2914440
DO - 10.1109/LCOMM.2019.2914440
M3 - Journal article
AN - SCOPUS:85068832992
SN - 1089-7798
VL - 23
SP - 1231
EP - 1235
JO - IEEE Communications Letters
JF - IEEE Communications Letters
IS - 7
ER -