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Wireless sensor network deployment in a 3-D environment with an optimal balance of system lifetime, cost, coverage, connectivity, and fault tolerance is a challenging task. Current studies on this task are still very few. In this letter, we propose a two-stage deployment strategy for this task. To implement the strategy, we propose a Bat Algorithm-based meta-heuristic algorithm named Smart Bat Algorithm (SBA). The SBA enhances the searching behaviors of artificial bats to be smarter and more efficient by leveraging decision theory and fuzzy logic techniques. The simulation results indicate that the SBA delivers better and more robust solutions compared with three other popular meta-heuristic algorithms.

Internet of Things (IoT) has gradually become one of the most popular topics among both academia and industry, and it is considered as a crucial part of future Internet. However, very few o...

Network topology management is one of the critical concerns when designing a Wireless Sensor Network (WSN). In this research, four basic factors including the total production cost, sensing coverage, network connectivity and fault tolerance are considered. A mathematical model is proposed to optimize four optimization metrics corresponding to the four design factors. This approach attaches a weighting coefficient to each optimization metric to adjust their importance in the optimization model. To solve the proposed model, an Ant Colony Optimization (ACO) based metaheuristics method, called MAX–MIN Ant System (MMAS) is used. In the experiment, Greedy algorithm (Greedy) and Genetic Algorithm (GA) are also adopted to solve the proposed model. The results indicate that MMAS shows a satisfactory performance on solving the proposed model, which there is an improvement on the number of sensor nodes comparing to the result of Greedy, and a better fitness value than the result of GA.