The problem of node placement in a rural wireless mesh network (RWMN) consists of determining router placement which minimizes the number of routers while providing good coverage of the area of interest. This problem is NP-hard with a factorial complexity. This article introduces a new approach, called the simulated annealing-based centre of mass (SAC) for solving this placement problem. The intent of this approach is to improve the robustness and the quality of solution, and to minimize the convergence time of a simulated annealing (SA) approach in solving the same problem in small and large scale. SAC is compared to the centre of mass (CM) and simulated annealing (SA) approaches. The performances of these algorithms were evaluated on a set of 24 instances. The experimental results show that the SAC approach provides the best robustness and solution quality, while decreasing by half the convergence time of the SA algorithm.

Drones are used increasingly for agricultural surveillance. The limited flight range of drones poses a problem for surveillance of large, inaccessible areas. One possible solution is to place autonomous, solar-powered charging stations within the area of interest, where the drone can recharge during its mission. This paper designs and implements a software system for planning low-cost drone coverage of large areas. The software produces a feasible, cost-minimizing charging station placement, as well as a drone path specification. Multiple optimizations are required, which are formulated as integer linear programs. In extensive simulations, the resulting drone paths achieved 70–90 percent of theoretical optimal performance in terms of minimizing mission time for a given number of charging stations, for a variety of field configurations.

No abstract

Wireless mesh networks are presented as an attractive solution to reduce the digital divide between rural and developed areas. In a multi-hop fashion, they can cover larger spaces. However, their planning is subject to many constraints including robustness. In fact, the failure of a node may result in the partitioning of the network. The robustness of the network is therefore achieved by carefully placing additional nodes. This work tackles the problem of additional nodes minimization when planning bi and tri-connectivity from a given network. We propose a vertex augmentation approach inspired by the placement of Steiner points. The idea is to incrementally determine cut vertices and bridges in the network and to carefully place additional nodes to ensure connectivity, bi and tri-connectivity. The approach relies on an algorithm using the centre of mass of the blocks derived after the partitioning of the network. The proposed approach has been compared to a modified version of a former approach based on the Minimum Steiner Tree. The different experiments carried out show the competitiveness of the proposed approach to connect, bi-connect, and tri-connect the wireless mesh networks.

No abstract