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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. [ABSTRACT FROM AUTHOR]

The application of swarm intelligence algorithms to wireless sensor networks (WSNs) deployment has been the focus of research community for past few years. One such algorithm is ant colony optimization (ACO), whose application in reducing the cost of WSNs in terms of deployed sensor nodes has recently attracted attention of the researchers. In this letter, we propose an ACO-based framework for WSN deployment in a realistic 3-D environment, by making modifications to the standard ACO algorithm. The simulation results lead to the conclusion that the proposed framework achieves better performance compared with the state-of-the-art ACO-based algorithms in terms of size of the solution for node deployment. In addition, in a 3-D environment, time overhead problem arises in standard ACO-based algorithms since they require a large number of iterations to achieve better solutions. In contrast, the performance of the proposed approach does not degrade with reduction in number of iterations, which enables the algorithm to achieve quick convergence. [ABSTRACT FROM AUTHOR]

Wireless sensor networks on mountain roads will form winding and long transmission paths and accordingly result in centralized energy dissipation near the sink and large transmission delay. In this letter, we propose a node deployment scheme to settle the above-mentioned thorny issues. The core idea is to construct extra paths to the sink and achieve data diversion. Specifically, the places with relatively large transmission hops to the sink and also comparatively small distance away from it are selected as the starting points of the extra paths. This selection mechanism contributes to alleviating the energy holes and reducing the transmission delay. Finally, experimental simulations show the effectiveness and superiority of our deployment scheme. [ABSTRACT FROM PUBLISHER]

In this letter, a node deployment strategy is proposed for blindness avoiding in wireless sensor networks (WSNs) on the basis of ant colony optimization (ACO). Firstly, by group division of nodes, a group-based connection mechanism is designed to avoid blindness of connection and reduce deployment cost. Secondly, by actual load evaluation for a critical region, a load-balancing deployment mechanism is designed to prevent blindness of load-balancing deployment and realize on-demand load balancing in the true sense. Finally, simulations show the effectiveness and superiority of our findings. [ABSTRACT FROM PUBLISHER]