Design and Implementation of a Simulation System Based on Particle Swarm Optimization for Node Placement Problem in Wireless Mesh Networks

2015 ◽  
Author(s):  
Shinji Sakamoto ◽  
Tetsuya Oda ◽  
Makoto Ikeda ◽  
Leonard Barolli
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Mesh Networks ◽  
Mesh Networks ◽  
Particle Swarm ◽  
Simulation System ◽  
Placement Problem ◽  
Swarm Optimization ◽  
Node Placement ◽  
Design And Implementation ◽  
Wireless Mesh

Application of WMN-SA Simulation System for Node Placement in Wireless Mesh Networks

2014 ◽  
Vol 6 (2) ◽  
pp. 13-21 ◽  
Author(s):  
Shinji Sakamoto ◽  
Algenti Lala ◽  
Tetsuya Oda ◽  
Vladi Kolici ◽  
Leonard Barolli ◽  
...  
Keyword(s):  
Wireless Mesh Networks ◽  
Mesh Networks ◽  
Network Connectivity ◽  
Simulation System ◽  
Placement Problem ◽  
Node Placement ◽  
Wireless Mesh ◽  
User Coverage ◽  
Router Node Placement ◽  
Cost Efficient

One of the key advantages of Wireless Mesh Networks (WMNs) is their importance for providing cost-efficient broadband connectivity. In WMNs, there are issues for achieving the network connectivity and user coverage, which are related with the node placement problem. In this work, the authors consider the router node placement problem in WMNs. The objective is to find the optimal distribution of router nodes in order to provide the best network connectivity (the maximal number of connected routers) and coverage (maximal number of covered clients). The authors apply their proposed WMN-SA simulation system in a realistic scenario of the distribution of mesh clients considering Itoshima City, Fukuoka Prefecture, Japan. From simulation results, they found many insights that can be very important for real deployment of WMNs.

scholarly journals Improvement at Network Planning using Heuristic Algorithm to Minimize Cost of Distance between Nodes in Wireless Mesh Networks

2017 ◽  
Vol 7 (1) ◽  
pp. 309
Author(s):  
Shivan Qasim Ameen ◽  
Ravie Chandren Muniyandi
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Mesh Networks ◽  
Heuristic Algorithm ◽  
Network Performance ◽  
Mesh Networks ◽  
Cost Minimization ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Wireless Mesh ◽  
Different Levels

Wireless Mesh Networks (WMN) consists of wireless stations that are connected with each other in a semi-static configuration. Depending on the configuration of a WMN, different paths between nodes offer different levels of efficiency. One areas of research with regard to WMN is cost minimization. A Modified Binary Particle Swarm Optimization (MBPSO) approach was used to optimize cost. However, minimized cost does not guarantee network performance. This paper thus, modified the minimization function to take into consideration the distance between the different nodes so as to enable better performance while maintaining cost balance. The results were positive with the PDR showing an approximate increase of 17.83% whereas the E2E delay saw an approximate decrease of 8.33%.

scholarly journals Wireless Mesh Networks Based on MBPSO Algorithm to Improvement Throughput

2018 ◽  
Vol 8 (6) ◽  
pp. 4374 ◽  
Author(s):  
Shivan Qasim Ameen ◽  
Firas Layth Khaleel
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Mesh Networks ◽  
Communication Technology ◽  
Heuristic Algorithms ◽  
Mesh Networks ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Mesh Topology ◽  
Wireless Mesh ◽  
Different Types

Wireless Mesh Networks can be regarded as a type of communication technology in mesh topology in which wireless nodes interconnect with one another. Wireless Mesh Networks depending on the semi-static configuration in different paths among nodes such as PDR, E2E delay and throughput. This study summarized different types of previous heuristic algorithms in order to adapt with proper algorithm that could solve the issue. Therefore, the main objective of this study is to determine the proper methods, approaches or algorithms that should be adapted to improve the throughput. A Modified Binary Particle Swarm Optimization (MBPSO) approach was adapted to improvements the throughput. Finally, the finding shows that throughput increased by 5.79% from the previous study.

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

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Mariusz Wzorek ◽  
Cyrille Berger ◽  
Patrick Doherty
Keyword(s):  
Wireless Mesh Networks ◽  
Clustering Algorithm ◽  
Ad Hoc ◽  
Mesh Networks ◽  
Random Trees ◽  
Divide And Conquer ◽  
Placement Problem ◽  
Node Placement ◽  
Gateway Node ◽  
Wireless Mesh

AbstractThe 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.

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