Mesh Router Placement Research Articles

Energy efficient optimal deployment of industrial wireless mesh networks using transient trigonometric Harris Hawks optimizer

Wireless mesh networks (WMNs) play a vital role in modern communication systems, and optimizing the placement of wireless mesh routers is crucial for achieving efficient network performance in terms of coverage and connectivity. However, network congestion caused by overlapping routers poses challenges in WMN optimization. To address these issues, researchers have explored metaheuristic algorithms to strike a balance between coverage and connectivity in WMNs. This study introduces a novel hybrid optimization algorithm, namely Transient Trigonometric Harris Hawks Optimizer (TTHHO), specifically designed to tackle the optimization problems in WMNs. The primary objective of TTHHO is to find an optimal placement of routers that maximizes network coverage and ensures full connectivity among mesh routers. Notably, TTHHO's unique advantage lies in its efficient utilization of residual energy, strategically placing the sink node in areas with higher energy levels. The effectiveness of TTHHO is demonstrated through a comprehensive comparison with seven well-known algorithms, including Harris Hawks optimization (HHO), Sine Cosine Algorithm (SCA), Gray Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Moth Flame Optimization (MFO), Equilibrium Optimizer (EO), and Transient Search Optimizer (TSO). The proposed algorithm is rigorously validated using 33 benchmark functions, and statistical analyses and simulation results confirm its superiority over other algorithms in terms of network connectivity, coverage, congestion reduction, and convergence. The simulation outcomes demonstrate the effectiveness and efficacy of the proposed TTHHO algorithm in optimizing WMNs, making it a promising approach for enhancing the performance of wireless communication systems.

An efficient mesh router nodes placement in wireless mesh networks based on moth‐flame optimization algorithm

SummaryOne of the most challenging tasks is deploying a wireless mesh network backbone to achieve optimum client coverage. Previous research proposed a bi‐objective function and used a hierarchical or aggregate weighted sum method to find the best mesh router placement. In this work, to avoid the fragmented network scenarios generated by previous formulations, we suggest and evaluate a new objective function to maximize client coverage while simultaneously optimizing and maximizing network connectivity for optimal efficiency without requiring knowledge of the aggregation coefficient. In addition, we compare the performance of several recent meta‐heuristic algorithms: Moth‐Flame Optimization (MFO), Marine Predators Algorithm (MPA), Multi‐Verse Optimizer (MVO), Improved Grey Wolf Optimizer (IGWO), Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Harris Hawks Optimization (HHO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Slime Mould Algorithm (SMA). We empirically examined the performance of the proposed function using different settings. The results show that our proposed function provides higher client coverage and optimal network connectivity with less computation power. Also, compared to other optimization algorithms, the MFO algorithm gives higher coverage to clients while maintaining a fully connected network.

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

Wireless Mesh Networks (WMNs) can build a communications infrastructure using only routers (called mesh routers), making it possible to form networks over a wide area at low cost. The mesh routers cover clients (called mesh clients), allowing mesh clients to communicate with different nodes. Since the communication performance of WMNs is affected by the position of mesh routers, the communication performance can be improved by optimizing the mesh router placement. In this paper, we present a Coverage Construction Method (CCM) that optimizes mesh router placement. In addition, we propose an integrated optimization approach that combine Simulated Annealing (SA) and Delaunay Edges (DE) in CCM to improve the performance of mesh router placement optimization. The proposed approach can build and provide a communication infrastructure by WMNs in disaster environments. We consider a real scenario for the placement of mesh clients in an evacuation area of Kurashiki City, Japan. From the simulation results, we found that the proposed approach can optimize the placement of mesh routers in order to cover all mesh clients in the evacuation area. Additionally, the DECCM-based SA approach covers more mesh clients than the CCM-based SA approach on average and can improve network connectivity of WMNs.

An Optimal Routing Protocol Using a Multiverse Optimizer Algorithm for Wireless Mesh Network

Wireless networks, particularly Wireless Mesh Networks (WMNs), are undergoing a significant change as a result of wireless technology advancements and the Internet's rapid expansion. Mesh routers, which have limited mobility and serve as the foundation of WMN, are made up of mesh clients and form the core of WMNs. Mesh clients can with mesh routers to create a client mesh network. Mesh clients can be either stationary or mobile. To properly utilise the network resources of WMNs, a topology must be designed that provides the best client coverage and network connectivity. Finding the ideal answer to the WMN mesh router placement dilemma will resolve this issue MRP-WMN. Since the MRP-WMN is known to be NP-hard, approximation methods are frequently used to solve it. This is another reason we are carrying out this task. Using the Multi-Verse Optimizer algorithm, we provide a quick technique for resolving the MRP-WMN (MVO). It is also proposed to create a new objective function for the MRP-WMN that accounts for the connected client ratio and connected router ratio, two crucial performance indicators. The connected client ratio rises by an average of 16.1%, 12.5%, and 6.9% according to experiment data, when the MVO method is employed to solve the MRP-WMN problem, the path loss falls by 1.3, 0.9, and 0.6 dB when compared to the Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA), correspondingly.

An Efficient Method for Solving Router Placement Problem in Wireless Mesh Networks Using Multi-Verse Optimizer Algorithm.

Wireless Mesh Networks (WMNs) are increasingly being used in a variety of applications. To fully utilize the network resources of WMNs, it is critical to design a topology that provides the best client coverage and network connectivity. This issue is solved by determining the best solution for the mesh router placement problem in WMN (MRP-WMN). Because the MRP-WMN is known to be NP-hard, it is typically solved using approximation algorithms. This is also why we are conducting this work. We present an efficient method for solving the MRP-WMN using the Multi-Verse Optimizer algorithm (MVO). A new objective function for the MRP-WMN is also proposed, which takes into account two important performance metrics, connected client ratio and connected router ratio. Experiment results show that when the MVO algorithm is applied to the MRP-WMN problem, the connected client ratio increases by 15.1%, 11.5%, and 5.9% on average, and the path loss reduces by 1.3, 0.9, and 0.6 dB when compared to the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA), respectively.

Optimal Coverage and Connectivity in Industrial Wireless Mesh Networks Based on Harris’ Hawk Optimization Algorithm

The placement of wireless mesh routers is a significant matter in enhancing the performance of a wireless mesh network. Therefore, it is essential that the mesh routers are optimally placed to guarantee strong connectivity and coverage for ensuring the best network accessibility. The importance of this issue has motivated the researchers to pursue more investigation on optimizing the networks’ performance. Various optimization algorithms have been developed in the literature to identify the trade-offs between network connectivity and client coverage. This work is aimed at implementing a novel application of Harris Hawk’s Optimization (HHO) algorithm to optimally place mesh routers in a wireless mesh network so as to improve connectivity and coverage. The HHO seeks optimal routers placement that leads to maximizing the connection between routers and increasing the number of covered clients. In this study, three network configurations are used with sizes of 40, 60, and 100 mesh routers. The simulation results are statistically analyzed and compared to other population-based algorithms such as Sine Cosine Algorithm (SCA), Gray Wolf’s Optimization (GWO), and Particle Swarm Optimization (PSO). Moreover, the performance of HHO is compared with the state-of-the-art to validate the effectiveness of the proposed approach. The simulation findings and statistical analysis confirm that the HHO outperforms the other algorithms in terms of network coverage and connectivity. The outcomes demonstrate that HHO can be considered an efficacious means to enhance the performance of wireless mesh networks (WMNs).

Accelerated PSO algorithm applied to clients coverage and routers connectivity in wireless mesh networks

The deployment of wireless mesh routers is a crucial task for improving network performance. Therefore, it should be taken seriously to ensure the network accessibility in terms of coverage and connectivity. This placement problem of mesh routers in wireless mesh networks represents an instance of multi-objective optimization problems with huge searching space to explore. In the literature, various optimization algorithms have been applied to find a trade-off between client coverage and network connectivity. To find an optimal mesh router placement, in this paper, we consider Accelerated particle swarm optimizer (APSO) due to its rapid convergence and low computational complexity compared to other population-based algorithms. We have experimentally evaluated it using different generated benchmarks of multiple configurations. The experimental results show that APSO algorithm provides very promising results compared to linearly decreasing weight particle swarm optimizer (LDWPSO).

A simulated annealing algorithm for the placement of dynamic mesh routers in a wireless mesh network with mobile clients

The placement of mesh routers in a mobile mesh network strongly influences the network performance. In particular, when the mesh clients are mobile, this deployment issue becomes more difficult to solve since mesh routers should adapt their locations to the dynamic network topology. In this letter, we propose to apply a simulated annealing algorithm to deal with the dynamic router node placement issue. The performance metrics considered when deploying these routers are: client coverage, network connectivity, and the average distance traveled by routers. Simulation results show a significant performance improvement in terms of client coverage and network connectivity and a considerable decrease in router movements.

An Electromagnetism-like mechanism algorithm for the router node placement in wireless mesh networks

In this paper, we consider the problem of mesh router placement in a wireless mesh network (WMN). The latter is an emerging networking technology consisting of three kinds of nodes: mesh clients, mesh routers and gateways. Mesh routers form a backbone to forward data between client nodes and the external network. Therefore, the optimization of mesh routers positions strongly influences the performance of the WMN. Since this issue has already been proved as being computationally NP-hard to solve, the use of non-exact methods (such as heuristics and metaheuristics) is indispensable. In this sense, our current work consists to apply and adapt the electromagnetism-like mechanism (EM) metaheuristic to solve the router node placement issue. The idea is to consider a population of solutions encoded as particles subject to attractions and repulsions as in electromagnetic systems. Finally, we have evaluated our proposed approach by simulating different scenarios under various settings. The obtained results indicate that the proposed EM algorithm outperforms the existing particle swarm intelligence algorithm and genetic algorithm in defining near optimal positions for mesh routers with regard to coverage and connectivity.

A Chemical Reaction Algorithm to Solve the Router Node Placement in Wireless Mesh Networks

This paper considers the problem of router node placement (WMN-RNP) in wireless mesh networks (WMN). A wireless mesh network consists of three kinds of nodes: mesh clients, mesh routers and gateways interconnected via radio links. The problem considered in this paper is the following: given a set of mesh clients deployed in a rectangular area, determine the best placement of mesh routers so that both client coverage and network connectivity are maximized. This issue is known to be NP-hard since it can be modeled as a facility location problem. To solve this issue, we propose to use a metaheuristic technique inspired from the interactions between molecules in chemical reactions to reach a low stable energy state, namely Chemical Reaction Optimization algorithm (CRO). A simulation tool has been developed to compare the performance of our CRO algorithm to the existing Genetic Algorithm (GA) and Simulated Annealing (SA). Simulation results show that our proposed algorithm can improve client coverage by 4.5% to 18% (3% to 17% respectively) and network connectivity by 5% to 61% (4.5% to 41% respectively) when compared to GA algorithm (SA algorithm respectively).

Implementation and evaluation of a simulation system based on particle swarm optimisation for node placement problem in wireless mesh networks

With the fast development of wireless technologies, wireless mesh networks WMNs are becoming an important networking infrastructure due to their low cost and increased high speed wireless internet connectivity. This paper implements a simulation system based on particle swarm optimisation PSO in order to solve the problem of mesh router placement in WMNs. Four replacement methods of mesh routers are considered: constriction method CM, random inertia weight method RIWM, linearly decreasing Vmax method LDVM and linearly decreasing inertia weight method LDIWM. Simulation results are provided, showing that the CM converges very fast, but has the worst performance among the methods. The considered performance metrics are the size of giant component SGC and the number of covered mesh clients NCMC. The RIWM converges fast and the performance is good. The LDIWM is a combination of RIWM and LDVM. The LDVM converges after 170 number of phases but has a good performance.

Social-aware dynamic router node placement in wireless mesh networks

The problem of dynamic router node placement (dynRNP) in wireless mesh networks (WMNs) is concerned with determining a dynamic geographical placement of mesh routers to serve mobile mesh clients at different times, so that both network connectivity (i.e., the greatest topology subgraph component size) and client coverage (i.e., the number of the served mesh clients) are maximized. Mesh clients are wireless devises associated with users, and in real world, the users with same interests or some social relationship have higher chance to gather and move together geographically, i.e., they form a community, and the WMN with multiple communities can be regarded as a social network. Therefore, this paper investigates the so-called social-aware WMN-dynRNP problem assuming that mesh routers should be aware of the social community structure of mesh clients to dynamically adjust their placement to improve network performance. To cope with this problem, this paper proposes a social-based particle swarm optimization approach, which additionally includes a social-supporting vector to direct low-loading mesh routers to support the heavy-loading mesh routers in the same topology subgraph component (community), so as to dynamically adopt to the social community behavior of mesh clients. As compared with the previous approach, our experimental results show that the proposed approach is capable of effectively reducing number of the unserved mesh clients and increasing network connectivity in dynamic social scenarios.

Analysis of mesh router placement in wireless mesh networks using Friedman test considering different meta-heuristics

In this paper, we deal with connectivity and coverage problem in wireless mesh networks WMNs. We used Friedman test to compare genetic algorithm GA, tabu search TS, hill climbing HC and simulated annealing SA. We found out that GA, TS, HC and SA have differences in their performance. Then, we used the implemented systems WMN-GA, WMN-TS, WMN-HC and WMN-SA to evaluate and compare the performance of the systems for different distributions of mesh clients in terms of size of giant component SGC and number of covered mesh clients NCMC. The simulation results show that for uniform distribution the WMN-HC and WMN-SA perform better than WMNGA and WMN-TS. However, for small radius of communication distance, the SGC of WMN-TS is better than other systems. For normal distribution, for big radius of communication distance, the WMN-GA has the best performance. For exponential distribution, the WMN-HC and WMN-SA perform better than WMN-GA for all communication distances. For Weibull distribution, the WMN-TS has a good performance for small radius of communication distance, but for big radius of communication distances the WMN-GA, WMN-HC and WMN-SA perform better.

Dynamic router node placement in wireless mesh networks: A PSO approach with constriction coefficient and its convergence analysis

Different from previous works, this paper considers the router node placement of wireless mesh networks (WMNs) in a dynamic network scenario in which both mesh clients and mesh routers have mobility, and mesh clients can switch on or off their network access at different times. We investigate how to determine the dynamic placement of mesh routers in a geographical area to adapt to the network topology changes at different times while maximizing two main network performance measures: network connectivity and client coverage, i.e., the size of the greatest component of the WMN topology and the number of the clients within radio coverage of mesh routers, respectively. In general, it is computationally intractable to solve the optimization problem for the above two performance measures. As a result, this paper first models a mathematical form for our concerned problem, then proposes a particle swarm optimization (PSO) approach, and, from a theoretical aspect, provides the convergence and stability analysis of the PSO with constriction coefficient, which is much simpler than the previous analysis. Experimental results show the quality of the proposed approach through sensitivity analysis, as well as the adaptability to the topology changes at different times.

IMeX: Intergateway Cross-Layer Handoffs in Internet-Based Infrastructure Wireless Mesh Networks

Wireless mesh networks (WMNs) have recently emerged to be a cost-effective solution to support large-scale wireless Internet access. Handoff management plays an important role in WMNs in delivering Quality of Service to mobile users. In this paper, the handoff support in Internet-based infrastructure WMNs is investigated. Previous solutions on handoff management in infrastructure WMNs mainly focus on intragateway mobility (e.g., single gateway is assumed in IEEE 802.11s WMNs). Furthermore, new handoff issues involved in intergateway mobility in WMNs (e.g., the network-layer handoff detection issue) have not been properly addressed. In this paper, a new architectural design, IMeX, is proposed to facilitate intergateway handoff management in Internet-based infrastructure WMNs. The proposed IMeX architecture is designed to specifically address the special handoff challenge in Internet-based WMNs. It can facilitate parallel executions of handoffs from multilayers, in conjunction with a data caching mechanism which guarantees minimum packet loss during handoffs. The required number and optimal placement of special mesh routers that form the IMeX architecture are modeled as a set covering problem which is solved based on a greedy algorithm. A comprehensive simulation study shows that the IMeX architecture enables fast handoffs and reestablishment of session communications in the intergateway mobility environment. Performance results demonstrate that the proposed IMeX architecture, with both the parallel handoff execution and data caching mechanism, outperforms the conventional WMN handoff schemes in terms of reducing handoff delays caused in multilayers and improving end-to-end performance of applications in WMNs.

A novel cross-layer mesh router placement scheme for wireless mesh networks

Wireless mesh networks (WMNs) offer a great promise in supporting ubiquitous multimedia Internet access for mobile or fixed mesh clients (MCs). In WMNs, Internet traffic from MCs is aggregated by serving mesh router (MR) and forwarded hop-by-hop by MRs to an internet gateway (IGW) or vice versa. While deploying MRs and IGWs, intricate relationships among antenna types, wireless links with adaptive modulation and coding, MAC scheduling, routing, and equipment cost render the network planning an extremely complex problem. This article presents a novel cross-layer MR placement (CMRP) scheme that can cope up with this issue. CMRP encapsulates the cross-layer metrics into three underlying attributes: Local Coverage (LC), Backbone Residual Capacity (BRC), and Deployment Cost (DC), and are used to minimize the network deployment cost. Coupled with our proposed novel tree-based minimal cost routing scheme and weight-based link assignment for user coverage, we are able to plan the design of WMNs efficiently. Extensive simulations have been performed to examine the performance and feasibility of CMRP and compared with existing design schemes based on coverage, connectivity, and combination of the two. The result demonstrates that our approach outperforms existing schemes both in the cost performance ratio and potential implementation feasibility.

A simulated annealing algorithm for router nodes placement problem in Wireless Mesh Networks

Mesh router nodes placement is a central problem in Wireless Mesh Networks (WMNs). An efficient placement of mesh router nodes is indispensable for achieving network performance in terms of both network connectivity and user coverage. Unfortunately the problem is computationally hard to solve to optimality even for small deployment areas and a small number of mesh router nodes. As WMNs are becoming an important networking infrastructure for providing cost-efficient broadband wireless connectivity, researchers are paying attention to the resolution of the mesh router placement problem through heuristic approaches in order to achieve near optimal, yet high quality solutions in reasonable time. In this work we propose and evaluate a simulated annealing (SA) approach to placement of mesh router nodes in WMNs. The optimization model uses two maximization objectives, namely, the size of the giant component in the network and user coverage. Both objectives are important to deployment of WMNs; the former is crucial to achieve network connectivity while the later is an indicator of the QoS in WMNs. The SA approach distinguishes for its simplicity yet its policy of neighborhood exploration allows to reach promising areas of the solution space where quality solutions could be found. We have experimentally evaluated the SA algorithm through a benchmark of generated instances, varying from small to large size, and capturing different characteristics of WMNs such as topological placements of mesh clients. The experimental results showed the efficiency of the annealing approach for the placement of mesh router nodes in WMNs.

Local search methods for efficient router nodes placement in wireless mesh networks

Wireless Mesh Networks (WMNs) are an important networking infrastructure for providing cost-efficient broadband wireless connectivity to a group of users. WMNs are increasingly being used in urban, metropolitan and municipal area networks for deployment of medical, transport, surveillance systems, etc. The performance and operability of WMNs largely depends on placement of mesh routers nodes in the geographical area to achieve network connectivity and stability. The objective is to find an optimal and robust topology of the mesh router network to support intelligent telecommunication services to clients such as adaptive and flexible wireless Internet access, mobile data, voice, video in addition to supporting other preferred client services. In this work, we propose and evaluate local search methods for intelligent placement of mesh routers in WMNs with a two fold objective: maximizing the size of the giant component in the network and user coverage. Given a grid area where to distribute a given number of mesh router nodes, which can have different radio coverage, and a number of fixed clients a priori distributed in the given area, local search methods explore different local movements and incrementally improve the quality of the router nodes placement in terms of network connectivity and user coverage. We have experimentally evaluated the proposed local search methods through a benchmark of generated instances of varying sizes. Moreover, different distributions of mesh clients (Uniform, Normal, Exponential and Weibull) are used. The experimental evaluation showed the good performance of local search methods for optimizing network connectivity and user coverage in WMNs.