Multi-Objective Mapping Method for 3D Environmental Sensor Network Deployment

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.

[1]  Abbas Al-Refaie,et al.  An Effective Approach for Solving The Multi-Response Problem in Taguchi Method , 2010 .

[2]  Lajos Hanzo,et al.  A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems , 2016, IEEE Communications Surveys & Tutorials.

[3]  Teresa Riesgo,et al.  Modelling and planning reliable wireless sensor networks based on multi-objective optimization genetic algorithm with changeable length , 2015, J. Heuristics.

[4]  Greg P. Timms,et al.  Optimisation in the Design of Environmental Sensor Networks with Robustness Consideration , 2015, Sensors.

[5]  Mohamed Elhoseny,et al.  Balancing Energy Consumption in Heterogeneous Wireless Sensor Networks Using Genetic Algorithm , 2015, IEEE Communications Letters.

[6]  B. Vural,et al.  A dynamic lithium-ion battery model considering the effects of temperature and capacity fading , 2009, 2009 International Conference on Clean Electrical Power.

[7]  Anh Tuan Nguyen,et al.  A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems , 2016 .

[8]  Nga T. T. Mai,et al.  TEMPERATURE MAPPING OF FRESH FISH SUPPLY CHAINS – AIR AND SEA TRANSPORT , 2012 .

[9]  Ying Lin,et al.  A novel genetic algorithm for lifetime maximization of wireless sensor networks with adjustable sensing range , 2018, GECCO.

[10]  Kai Leung Yung,et al.  A Smart Bat Algorithm for Wireless Sensor Network Deployment in 3-D Environment , 2018, IEEE Communications Letters.

[11]  Ujjwal Maulik,et al.  An evolutionary technique based on K-Means algorithm for optimal clustering in RN , 2002, Inf. Sci..

[12]  George Q. Huang,et al.  A NSGA-II based memetic algorithm for multiobjective parallel flowshop scheduling problem , 2017, Comput. Ind. Eng..

[13]  Harun Resit Yazgan,et al.  Genetic algorithm parameter optimisation using Taguchi method for a flexible manufacturing system scheduling problem , 2015 .