Router and gateway node placement in wireless mesh networks for emergency rescue scenarios

Abstract

The focus of this paper is on base functionalities required for UAV-based rapid deployment of an ad hoc communication infrastructure in the initial phases of rescue operations. The main idea is to use heterogeneous teams of UAVs to deploy communication kits that include routers, and are used in the generation of ad hoc Wireless Mesh Networks (WMN). Several fundamental problems are considered and algorithms are proposed to solve these problems. The Router Node Placement problem (RNP) and a generalization of it that takes into account additional constraints arising in actual field usage is considered first. The RNP problem tries to determine how to optimally place routers in a WMN. A new algorithm, the RRT-WMN algorithm, is proposed to solve this problem. It is based in part on a novel use of the Rapidly Exploring Random Trees (RRT) algorithm used in motion planning. A comparative empirical evaluation between the RRT-WMN algorithm and existing techniques such as the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Particle Swarm Optimization (PSO), shows that the RRT-WMN algorithm has far better performance both in amount of time taken and regional coverage as the generalized RNP problem scales to realistic scenarios. The Gateway Node Placement Problem (GNP) tries to determine how to locate a minimal number of gateway nodes in a WMN backbone network while satisfying a number of Quality of Service (QoS) constraints.Two alternatives are proposed for solving the combined RNP-GNP problem. The first approach combines the RRT-WMN algorithm with a preexisting graph clustering algorithm. The second approach, WMNbyAreaDecomposition, proposes a novel divide-and-conquer algorithm that recursively partitions a target deployment area into a set of disjoint regions, thus creating a number of simpler RNP problems that are then solved concurrently. Both algorithms are evaluated on real-world GIS models of different size and complexity. WMNbyAreaDecomposition is shown to outperform existing algorithms using 73% to 92% fewer router nodes while at the same time satisfying all QoS requirements.

A Delaunay Edges and Simulated Annealing-Based Integrated Approach for Mesh Router Placement Optimization in Wireless Mesh Networks

“…They proposed a mesh router placement optimization method for Wanglang National Nature Reserve in Sichuan province, China. In [ 29 ], the authors considered the placement optimization of Unmanned Aerial Vehicles (UAVs) equipped with ad hoc communication capabilities for providing WMN services. The UAV placement optimization system was evaluated using a model based on GIS data from the Swedish Government Agency.…”

Gateway selection and placement for Multi-radio multi-channel Wireless Mesh Networks with Garter Snake Optimization algorithm

“…Wireless mesh networks have a wide variety of applications, including but not limited to community networks, smart homes, outdoor wireless networks, wireless sensor networks, and Internet of Things (IoT) deployments. They provide a versatile and adaptable alternative to traditional infrastructure-based networks in scenarios when doing so would be impractical or prohibitively expensive, such as with wireless communication [31,32].…”

“…The objective of this study is to analyze the average queue length, denoted as Q, at the gateway of the mesh network over an extended period of time. This analysis allows us to assess the level of tra c congestion and overall load experienced by the gateway [30][31][32].…”

Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning

“…The concept of gateway node placement problem (GNP) was explored by [ 25 ] to establish the smallest possible number of gateways to satisfy QoS requirements in search-and-rescue environments in a wireless mesh network (WMN). The authors treat the gateway placement problem (GNP) in combination with the router placement problem (RNP).…”