Effects of population size for location-aware node placement in WMNs: evaluation by a genetic algorithm--based approach

Abstract

Wireless Mesh Networks (WMNs) are cost-efficient networks that have the potential to serve as an infrastructure for advanced location-based services. Location service is a desired feature for WMNs to support locationoriented applications. WMNs are also interesting infrastructures for supporting ubiquitous multimedia Internet access for mobile or fixed mesh clients. In order to efficiently support such services and offering QoS, the optimized placement of mesh router nodes is very important. Indeed, such optimized mesh placement, can support location service managed in the mesh and keep the rate of location updates low. This node location-based problem has been shown to be NP-hard and thus is unlikely to be solvable in reasonable amount of time. Therefore, heuristic methods, such as Genetic Algorithms are used as resolution methods. In this paper, we deal with the effect of population size for location-aware node placement in WMNs. Our WMN-GA system uses Genetic Algorithm (GA) to determine the positions of the mesh routers and mesh clients in the grid area. We used a location-aware node placement of mesh router in cells of considered grid area to maximize network connectivity and user coverage. We evaluate the performance of the proposed and implemented WMN-GA system for low and high density of clients considering different distributions and considering giant component and number of covered users parameters. The simulation results show that for low density networks, with the increasing of the population size, GA obtains better result. However, with the increase of the population size, the GA needs more computational time. The proposed system has better performance in dense networks like hotspots for Weibull distribution when the population size is big.

LoRaWAN for an IoT-based environmental monitoring application in Tirana city

“…The TTIG GW is positioned in the fourth floor of flat no 3 in Street Hekali', Tirana city, Albania. The positioning of the GW is random however, different positioning approaches can be used [28,29]. In this work, OpenStreetMaps [30] is used to create an editable map of Tirana where GW can precisely be positioned in Radio Mobile software.…”

Social-aware dynamic router node placement in wireless mesh networks

“…In WMNs, a bad-quality RNP could lead to unavoidable wireless interference as well as load unbalancing, in which some mesh routers have unexpected high service loading while the others have a lower utilization rate [16]. The objective of the WMN-RNP problem in this paper is to maximize the WMN connectively and the client coverage [17], which are size of the greatest topology subgraph component and number of the covered mesh clients, respectively.…”

Minimized Cost Gateway Deployment in Cyber-Physical Systems

“…Population size of client node affects the performance of gateway placement method. Reference [28] investigated the effect of population size for WMN genetic algorithm. This research is outstanding.…”