Placement Routing Research Articles

Assessment of Radio Frequency Electromagnetic Field Exposure from Wi-Fi Routers in LAUTECH Campus, Ogbomoso, Nigeria

Wi-Fi routers play a crucial role in enabling internet connectivity within the academic environment. However, there are concerns that radiofrequency electromagnetic field exposure from Wi-Fi routers may be hazardous to human health. The main objective of the research is to assess the safety of RF-EMF exposure in educational environments. A total of 14 Wi-Fi routers were randomly selected and investigated across LAUTECH campus using stratified random sampling method. Measurement of the power density and the electric field strength from these routers were taken using a hand-held electrosmog, TES-593, radiofrequency meter. Measurement points were selected at various distances of 1.0, 2.0, 3.0, 4.0 and 5.0 m from the Wi-Fi routers, representing typical distances between users and routers. At each measurement point, the TES-593 meter was held 1 m above the ground at a fixed position for 30 seconds to record the E-field strength and the power density. The measured power density and the electric field strength, varied from routers to routers depending on their location and the distance of measurement from the routers. The power density recorded for all the Wi-Fi routers investigated ranged from 0.001 to 3.302 W/m2 with a mean of 0.390 W/m2. For all the Wi-Fi routers investigated. The overall exposure levels for the power density decreases with distances for all the routers. The measured electric field strength also decreases with distance ranging from 0.106 to 3.528 V/m with a mean of 0.981 V/m. The measured power densities and the electric field strengths in this research work were comparable and similar with reports from other research work from different environments including educational, residential, and urban settings. The power densities and the electric field strengths recorded were below the 10 W/m2 and 5 V/m recommended by various regulatory agencies respectively. All the Wi-Fi routers considered in this research work were within safety guidelines set by regulatory agencies for the power density and the electric field strength, thus emitting radiations at a level that are considered safe for human exposure within the academic communities. The operation of RF-EMF exposure from Wi-Fi routers in the academic environment of LAUTECH will no pose any potential health risks on staff and students of the institution. The evidence-based insights into potential health risks associated with prolonged exposure to electromagnetic fields from Wi-Fi routers in this research can guide policymakers in establishing safety regulations that will limit exposure levels. It can also inform guidelines on the optimal placement of routers, frequency of usage, and the implementation of periodic monitoring to ensure compliance with international safety standards.

A hound-inspired pre-hybridized genetic approach for router placement in wireless mesh networks

In the last years, wireless mesh networks (WMNs) have gained more and more popularity in many research and industrial applications thanks to their easy implementation, maintenance, and great reliability at a low cost. Nevertheless, for a large number of nodes, the performance of such networks is heavily influenced by the positioning of routers and gateways over the area to be covered. In this paper, we tackle the router placement problem, which is known to be NP-hard, and its approximate solution through a meta-heuristic approach. The proposed solution empowers the benefits offered by a genetic algorithm pre-hybridized with a local search approach inspired by the behavior of hound dogs. The basic idea is to exploit the dogs’ capabilities in moving throughout the solution space to effectively explore it by placing themselves in areas that are more favorable for achieving a high-quality approximate solution in a reasonable time. Experimental results on several benchmarking instances and comparisons with the most effective state-of-the-art algorithms have demonstrated the potential of the proposed approach. This is evidenced by very high connectivity and coverage, a low number of generations, and a small GA population required for convergence. This results in low computational effort and significant time savings, which are of paramount importance in IoT and edge scenarios. We remark that, although offering potential, at the current state, our proposal is not able to adapt to areas with obstacles or irregular shapes.

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.

Developing an Intelligent Decision Support System for large-scale smart grid communication network planning

The placement of routers and gateways in wireless communication networks for Smart Grid Advanced Metering Infrastructure (AMI) is a complex problem often addressed in the literature through heuristics, clustering, or linear programming approaches. In large-scale scenarios, the complexity is directly related to the region’s number of smart meters and poles. The terrain profile can introduce signal quality degradation between a smart meter and the routers and gateways, further complicating the problem. This study presents the AIDA-ML method, representing a preliminary step toward developing an Intelligent Decision Support System (IDSS) for an effective positioning strategy. AIDA-ML incorporates a feature engineering-based strategy and utilizes machine learning algorithms to learn from the results computed by a heuristic method called AIDA. The heuristic approach is too time-consuming, relying on external resources like numerous terrain profile API calls and a deep understanding of wireless communication loss models. To implement a machine learning-based and more straightforward approach, a feature engineering process is employed to capture the operational characteristics and results of the analytical method while requiring less processing time. Through experiments conducted with real-world data from 26 cities in the state of Paraná, Brazil, comprising 466,237 smart meters and 352,867 poles, the results obtained using machine learning algorithms suggest that AIDA-ML can ensure the connection coverage of smart meters while meeting the same minimum requirements established for the analytical method. Moreover, AIDA-ML offers the advantage of reducing the processing time by 87.60% and by 96.86% the overall search space size compared to the heuristic approach.

Design and Analysis of Four Port Router for Network-On-Chip Applications

System-On-Chip (SoC) is a popular VLSI technology that integrates multiple devices onto a single chip. The Network-On-Chip (NOC) concept, which is a novel paradigm in sophisticated System-On-Chip designs that offers efficient communication throughout the on-chip networks, is used to communicate amongst various devices. The router is the main component in an NoC architecture that is responsible for routing information. Packets are sent through a network by an NoC made up of routers. A routing algorithm divides packets into flow control digits (flits) and determines their paths. The placement of routers is determined by the network topology. In the present work, the main target is to design and analyse the router, which is primarily based on a number of critical factors, including topology, routing algorithm, and packet format. The performance of the suggested router is evaluated and compared to that of the Conventional Wormhole Router (CWHR) with respect to power and slack time (ST). When compared to a traditional wormhole router, the proposed router architecture has 18% less slack. As a result, total power consumption is reduced by 29.3% as compared to traditional wormhole architecture. The architecture is synthesized using Cadence Encounter RTL Compiler using standard 65nm technology.

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.

OPTIMAL WIFI POSITION DETECTION USING ARTIFICIAL INTELLIGENCE

The placement of WI-FI routers in the network is an intensive problem concerning connectivity and coverage.It directly affects the transmission loss, installation cost, operational complexity, wi-fi network coverage, etc.However, optimizing the location of the routers can resolve these issues and increase network performance. Thus,using major deep-learning models the problem is resolved. The proposed model concentrates on the optimization of the objective function in terms of the empty spaces, hindrances such as concrete walls, metallic objects, etc. in the area, maximum client coverage in the location, and the network connectivity. It is an initial step to ensure the desired network performance such as throughput, connectivity, and coverage of the network.. Furthermore, a Wi-Fi analyzing system for generating the results based on the observations of the Wi-Fi router network is implemented. It analyzes the wireless network, devices in the network, and the connected users. The model also gives a WLAN report of the Wi-Fi router

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.

Hybrid Harris Hawks With Sine Cosine for Optimal Node Placement and Congestion Reduction in an Industrial Wireless Mesh Network

The optimal performance of a wireless mesh network (WMN) can be greatly improved by strategically placing wireless mesh routers. As a result, it is crucial to optimally locate the WMN routers for better coverage and connectivity. Besides the optimal placement, the network congestion due to overlaying routers has to be taken into consideration. These issues have become a motivation for researchers to identify a variety of approaches to optimize WMN performance. Multiple metaheuristic algorithms have been employed for identifying the trade-offs between coverage and connectivity in WMN. Consequently, a novel hybrid Harris Hawks optimization with the sine cosine algorithm (HHOSCA) is presented in this work to tackle the aforementioned WMN optimization problems. The proposed HHOSCA seeks optimal router placement that leads to significantly increased network coverage and achieves full connectivity between the mesh routers. In addition, the proposed HHOSCA produces a cost-effective WMN by reducing the congestion in the network to the minimum number of routers whilst ensuring maximum coverage and connectivity. The superiority of the proposed HHOSCA in comparison to the other algorithm was validated by using 33 benchmark functions. It was compared against four well-known algorithms including Sine Cosine Algorithm (SCA), Harris Hawks optimization (HHO), Gray Wolf Optimization (GWO), and Particle Swarm Optimization (PSO). These algorithms are statistically analyzed and compared to the simulated results of the proposed method. In addition, the performance of HHOSCA is compared to the state-of-the-art to highlight the efficacy of the proposed algorithm. The statistical analyses and simulation findings confirm that the HHOSCA outperforms the other algorithms in terms of network connectivity, coverage, network reduction, and convergence. The experimental results reveal that the proposed HHOSCA method achieves favourable optimization results compared with other relevant methods.

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).

Optimizing Router Placement of Indoor Wireless Sensor Networks in Smart Buildings for IoT Applications

Internet of Things (IoT) is characterized by a system of interconnected devices capable of communicating with each other to carry out specific useful tasks. The connection between these devices is ensured by routers distributed in a network. Optimizing the placement of these routers in a distributed wireless sensor network (WSN) in a smart building is a tedious task. Computer-Aided Design (CAD) programs and software can simplify this task since they provide a robust and efficient tool. At the same time, experienced engineers from different backgrounds must play a prominent role in the abovementioned task. Therefore, specialized companies rely on both; a useful CAD tool along with the experience and the flair of a sound expert/engineer to optimally place routers in a WSN. This paper aims to develop a new approach based on the interaction between an efficient CAD tool and an experienced engineer for the optimal placement of routers in smart buildings for IoT applications. The approach follows a step-by-step procedure to weave an optimal network infrastructure, having both automatic and designer-intervention modes. Several case studies have been investigated, and the obtained results show that the developed approach produces a synthesized network with full coverage and a reduced number of routers.

A Simulated Annealing Based Centre of Mass (SAC) Approach for Mesh Routers Placement in Rural Areas

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.

Genetic algorithms and greedy-randomized adaptive search procedure for router placement problem in wireless networks

Genetic algorithms and greedy-randomized adaptive search procedure for router placement problem in wireless networks

A generalized service deployment framework for OSPF networks using multi‐topology routing: Modeling and methodology

SummaryThe exponential growth of various applications requires deploying an ever‐growing number of network services. A generalized service deployment framework for Open Shortest Path First (OSPF) networks is proposed in this paper. The framework includes placing programmable routers, distributing different types of services on these routers, and leading traffic flow through them according to the predetermined sequence order requirement. However, it is not possible to direct all the traffic flows through the required service nodes along the shortest path with a single and suitable set of link weights. To address the issue, multiple topology routing (MTR) technique is incorporated to have various logical topologies with multiple sets of link weights. Correspondingly, the problem of jointly optimizing Placement of programmable routers, Distribution of different types of services among these routers, and Link Weights setting based on MTR (shortened to PD‐LW‐MTR) and its mixed integer linear programming formulation are presented in this paper. A novel decomposition algorithm is also proposed to address this problem efficiently. Experiment results validate the correctness and feasibility of our algorithm. It is also shown that the optimization algorithm can obtain near‐optimal solution and just only a few logical topologies over multiple sets of link weights are necessary for traffic flows to guarantee service order requirements.

Cascaded cuckoo search optimization of router placement in signal attenuation minimization for a wireless sensor network in an indoor environment

ABSTRACTOptimization of the energy required during data transmission in a wireless indoor area network can be achieved through intelligent router placements to keep the network active for longer and improve the packet delivery ratio. In this work, a cascaded cuckoo search algorithm (C-CSA) approach is implemented for optimal router placement in a wireless indoor area network based on minimization of signal attenuation during data packet transmission through a novel mathematical formulation. The transmission energy for each packet, signal-to-noise ratio and packet error ratio are studied over 50 independent runs of the algorithm. The results are presented with statistical confidence to prove the efficiency of the algorithm. C-CSA provides superior results for data transmission energy and the packet delivery ratio compared to existing algorithms. Physical placement of wireless nodes in a building further establishes the reduction in energy requirement and data packet loss through this optimal router placement strategy.

Compressive Traffic Monitoring in Hybrid SDN

Hybrid software-defined network (SDN) is a network where legacy routers and SDN routers coexist during the incremental deployment of SDNs. The existing SDN router placement methods mainly focus on maximizing the traffic engineering performance under a limited budget. Traffic engineering requires real-time link-load information. However, the latency for collecting the global link-load information can be prohibitively long in a wide area network due to the long interior gateway protocol convergence time. Inspired by the sparsity of the link load, we propose a novel compressive traffic monitoring method for collecting real-time load information of all links. In this method, the controller only needs to collect the load of a small subset of important links and then estimates the link load of the rest. The minimal number of SDN routers is placed to cover these important links. We use real-world topologies and traffic matrices to evaluate our method. Experiment results show that our method can quickly estimate the global link load at an error rate of 5% within sub-seconds. Compared with the state-of-the-art methods, our method has better adaptability to the dynamic traffic changes and can reduce the maximal link usage by 39%.

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.