A GA-based simulation system for WMNs: comparison analysis for different number of flows, client distributions, DCF and EDCA functions

In this paper, we compare the performance of Distributed Coordination Function (DCF) and Enhanced Distributed Channel Access (EDCA) for normal and uniform distributions of mesh clients considering two Wireless Mesh Network (WMN) architectures. As evaluation metrics, we consider throughput, delay, jitter and fairness index metrics. For simulations, we used WMN-GA simulation system, ns-3 and Optimized Link State Routing. The simulation results show that for normal distribution, the throughput of I/B WMN is higher than Hybrid WMN architecture. For uniform distribution, in case of I/B WMN, the throughput of EDCA is a little bit higher than Hybrid WMN. However, for Hybrid WMN, the throughput of DCF is higher than EDCA. For normal distribution, the delay and jitter of Hybrid WMN are lower compared with I/B WMN. For uniform distribution, the delay and jitter of both architectures are almost the same. However, in the case of DCF for 20 flows, the delay and jitter of I/B WMN are lower compared with Hybrid WMN. For I/B architecture, in case of normal distribution the fairness index of DCF is higher than EDCA. However, for Hybrid WMN, the fairness index of EDCA is higher than DCF. For uniform distribution, the fairness index of few flows is higher than others for both WMN architectures.Peer ReviewedPostprint (author's final draft

Genetic algorithms for efficient placement of router nodes in wireless mesh networks

In Wireless Mesh Networks (WMNs) the meshing architecture, consisting of a grid of mesh routers, provides connectivity services to different mesh client nodes. The good performance and operability of WMNs largely depends on placement of mesh routers nodes in the geographical area to achieve network connectivity and stability. Thus, finding optimal or near-optimal mesh router nodes placement is crucial to such networks. In this work we propose and evaluate Genetic Algorithms (GAs) for near-optimally solving the problem. In our approach we seek a two-fold optimization, namely, the maximization of the size of the giant component in the network and that of user coverage. The size of the giant component is considered here as a criteria for measuring network connectivity. GAs explore the solution space by means of a population of individuals, which are evaluated, selected, crossed and mutated to reproduce new individuals of better quality. The fitness of individuals is measured with respect to network connectivity and user coverage being the former a primary objective and the later a secondary one. Several genetic operators have been considered in implementing GAs in order to find the configuration that works best for the problem. We have experimentally evaluated the proposed GAs using a benchmark of generated instances varying from small to large size. In order to evaluate the quality of achieved solutions for different possible client distributions, instances have been generated using different distributions of mesh clients (Uniform, Normal, Exponential and Weibull). The experimental results showed the efficiency of the GAs for computing high quality solutions of mesh router nodes placement in WMNs.Peer ReviewedPostprint (published version

Channel parameter tuning in a hybrid Wi-Fi-Dynamic Spectrum Access Wireless Mesh Network

This work addresses Channel Assignment in a multi-radio multi-channel (MRMC) Wireless Mesh Network (WMN) using both Wi-Fi and Dynamic Spectrum Access (DSA) spectrum bands and standards. This scenario poses new challenges because nodes are spread out geographically so may have differing allowed channels and experience different levels of external interference in different channels. A solution must meet two conflicting requirements simultaneously: 1) avoid or minimise interference within the network and from external interference sources, and 2) maintain connectivity within the network. These two requirements must be met while staying within the link constraints and the radio interface constraints, such as only assigning as many channels to a node as it has radios. This work's original contribution to the field is a unified framework for channel optimisation and assignment in a WMN that uses both DSA and traditional Wi-Fi channels for interconnectivity. This contribution is realised by providing and analysing the performance of near-optimal Channel Assignment (CA) solutions using metaheuristic algorithms for the MRMC WMNs using DSA bands. We have created a simulation framework for evaluating the algorithms. The performance of Simulated Annealing, Genetic Algorithm, Differential Evolution, and Particle Swarm Optimisation algorithms have been analysed and compared for the CA optimisation problem. We introduce a novel algorithm, used alongside the metaheuristic optimisation algorithms, to generate feasible candidate CA solutions. Unlike previous studies, this sensing and CA work takes into account the requirement to use a Geolocation Spectrum Database (GLSD) to get the allowed channels, in addition to using spectrum sensing to identify and estimate the cumulative severity of both internal and external interference sources. External interference may be caused by other secondary users (SUs) in the vicinity or by primary transmitters of the DSA band whose emissions leak into adjacent channels, next-toadjacent, or even into further channels. We use signal-to-interference-plus-noise ratio (SINR) as the optimisation objective. This incorporates any possible source or type of interference and makes our method agnostic to the protocol or technology of the interfering devices while ensuring that the received signal level is high enough for connectivity to be maintained on as many links as possible. To support our assertion that SINR is a reasonable criterion on which to base the optimisation, we have carried out extensive outdoor measurements in both line-of-sight and wooded conditions in the television white space (TVWS) DSA band and the 5 GHz Wi-Fi band. These measurements show that SINR is useful as a performance measure, especially when the interference experienced on a link is high. Our statistical analysis shows that SINR effectively differentiates the performance of different channels and that SINR is well correlated with throughput and is thus a good predictor of end-user experience, despite varying conditions. We also identify and analyse the idle times created by Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) contention-based Medium Access Control (MAC) operations and propose the use of these idle times for spectrum sensing to measure the SINR on possible channels. This means we can perform spectrum sensing with zero spectrum sensing delay experienced by the end user. Unlike previous work, this spectrum sensing is transparent and can be performed without causing any disruption to the normal data transmission of the network. We conduct Markov chain analysis to find the expected length of time of a sensing window. We also derive an efficient minimum variance unbiased estimator of the interference plus noise and show how the SINR can be found using this estimate. Our estimation is more granular, accurate, and appropriate to the problem of Secondary User (SU)-SU coexistence than the binary hypothesis testing methods that are most common in the literature. Furthermore, we construct confidence intervals based on the probability density function derived for the observations. This leads to finding and showing the relationships between the number of sampling windows and sampling time, the interference power, and the achievable confidence interval width. While our results coincide with (and thus are confirmed by) some key previous recommendations, ours are more precise, granular, and accurate and allow for application to a wider range of operating conditions. Finally, we present alterations to the IEEE 802.11k protocol to enable the reporting of spectrum sensing results to the fusion or gateway node and algorithms for distributing the Channel Assignment once computed. We analyse the convergence rate of the proposed procedures and find that high network availability can be maintained despite the temporary loss of connectivity caused by the channel switching procedure. This dissertation consolidates the different activities required to improve the channel parameter settings of a multi-radio multi-channel DSA-WMN. The work facilitates the extension of Internet connectivity to the unconnected or unreliably connected in rural or peri-urban areas in a more cost-effective way, enabling more meaningful and affordable access technologies. It also empowers smaller players to construct better community networks for sharing local content. This technology can have knock-on effects of improved socio-economic conditions for the communities that use it

Optimizing infrastructure placement in Wireless Mesh Networks

Wireless Mesh Networks (WMNs) are a promising flexible and low cost technology to efficiently deliver broadband services to communities. In a WMN, a mesh router is deployed at each house, which acts both as a local access point and a relay to other nearby houses. Since mesh routers typically consist of off-the-shelf equipment, the major cost of the network is in the placement and management of Internet Transit Access Points (ITAP) which act as the connection to the internet. In designing a WMN, we therefore aimed to minimize the number of ITAPs required whilst maximizing the traffic that could be served to each house. We investigated heuristic and meta-heuristic approaches with an efficient combination of move operators to solve these placement problems by using single and multi-objective formulations. Many real-world optimisation problems involve dealing with multiple and sometimes conflicting objectives. A multi-objective approach to optimize WMN infrastructure placement design with three conflicting objectives is presented: it aims to minimize the number of ITAPs, maximize the fairness of bandwidth allocation and maximize the coverage to mesh clients. We discuss how such an approach could allow more effective ITAP deployment, enabling a greater number of consumers to obtain internet services. Two approaches are compared during our investigation of multi-objective optimization, namely the weighted sum approach and the use of an evolutionary algorithm. In this thesis we investigate a multi-objective optimization algorithm to solve the WMN infrastructure placement problem. The move operators demonstrate their efficiency when compared to simple Hill Climbing (HC) and Simulated Annealing (SA) for the single objective method

Intelligent MANET optimisation system

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In the literature, various Mobile Ad hoc NETwork (MANET) routing protocols proposed. Each performs the best under specific context conditions, for example under high mobility or less volatile topologies. In existing MANET, the degradation in the routing protocol performance is always associated with changes in the network context. To date, no MANET routing protocol is able to produce optimal performance under all possible conditions. The core aim of this thesis is to solve the routing problem in mobile Ad hoc networks by introducing an optimum system that is in charge of the selection of the running routing protocol at all times, the system proposed in this thesis aims to address the degradation mentioned above. This optimisation system is a novel approach that can cope with the network performance’s degradation problem by switching to other routing protocol. The optimisation system proposed for MANET in this thesis adaptively selects the best routing protocol using an Artificial Intelligence mechanism according to the network context. In this thesis, MANET modelling helps in understanding the network performance through different contexts, as well as the models’ support to the optimisation system. Therefore, one of the main contributions of this thesis is the utilisation and comparison of various modelling techniques to create representative MANET performance models. Moreover, the proposed system uses an optimisation method to select the optimal communication routing protocol for the network context. Therefore, to build the proposed system, different optimisation techniques were utilised and compared to identify the best optimisation technique for the MANET intelligent system, which is also an important contribution of this thesis. The parameters selected to describe the network context were the network size and average mobility. The proposed system then functions by varying the routing mechanism with the time to keep the network performance at the best level. The selected protocol has been shown to produce a combination of: higher throughput, lower delay, fewer retransmission attempts, less data drop, and lower load, and was thus chosen on this basis. Validation test results indicate that the identified protocol can achieve both a better network performance quality than other routing protocols and a minimum cost function of 4.4%. The Ad hoc On Demand Distance Vector (AODV) protocol comes in second with a cost minimisation function of 27.5%, and the Optimised Link State Routing (OLSR) algorithm comes in third with a cost minimisation function of 29.8%. Finally, The Dynamic Source Routing (DSR) algorithm comes in last with a cost minimisation function of 38.3%

A cost effective approach to enhance surface integrity and fatigue life of precision milled forming and forging dies

Previously held under moratorium from 8 August 2019 until 19 January 2022The machining process determines the overall quality of produced forming and forging dies, including surface integrity. Previous research found that surface integrity has a significant influence on the fatigue life of the dies. This thesis aims to establish a cost-effective approach for precision milling to obtain forming and forging dies with good surface integrity and long fatigue life. It combined experimental study accompanied by Finite Element Modelling and Artificial Intelligence soft modelling to predict and enhance forming and forging die life. Four machining parameters, namely Surface Speed, Depth of cut, Feed Rate and Tool Lead Angle, each with five levels, were investigated experimentally using Design of Experiment. An ANOVA analysis was carried out to identify the key factor for every Surface Integrity (SI) parameter and the interaction of every factor. It was found that the cutting force was mostly influenced by the tool lead angle. The residual stress and microhardness were both significantly influenced by the surface speed. However, on the surface roughness it was found that the feed rate had the most influence. After the machining experiments, four-point bending fatigue tests were carried out to evaluate the fatigue life of precision milled parts at an elevated temperature in a low cycle fatigue set-up imitated for the forming and forging production. It was found that surface roughness and hardness were the most influential factors for fatigue life. A 3D-FE-Modelling framework including a new material model subroutine was developed; this led to a more comprehensive material model. A fractional factorial simulation with over 180 simulations was carried out and validated with the machining experiment. Based on the experimental and simulation results, a soft prediction model for surface integrity was established by using Artificial Neural Networks (ANN) approach. These predictions for SI were then used in a Genetic Algorithm model to optimise the SI. The confirmation tests showed that the machining strategy was successfully optimised and the average fatigue duration was increased by at least a factor of two. It was found that a surface speed of 270 m/min, a feed rate of 0.0589 mm/tooth, a depth of cut of 0.39 mm and a tool lead angle of 16.045° provided the good surface integrity and increased fatigue performance. Overall, these findings conclude that the fundamentals and methodology utilised have developed a further understanding between machining and forming/forging process, resulting in a good foundation for a framework to generate FE and soft prediction models which can be used to in optimisation of precision milling strategy for different materials.The machining process determines the overall quality of produced forming and forging dies, including surface integrity. Previous research found that surface integrity has a significant influence on the fatigue life of the dies. This thesis aims to establish a cost-effective approach for precision milling to obtain forming and forging dies with good surface integrity and long fatigue life. It combined experimental study accompanied by Finite Element Modelling and Artificial Intelligence soft modelling to predict and enhance forming and forging die life. Four machining parameters, namely Surface Speed, Depth of cut, Feed Rate and Tool Lead Angle, each with five levels, were investigated experimentally using Design of Experiment. An ANOVA analysis was carried out to identify the key factor for every Surface Integrity (SI) parameter and the interaction of every factor. It was found that the cutting force was mostly influenced by the tool lead angle. The residual stress and microhardness were both significantly influenced by the surface speed. However, on the surface roughness it was found that the feed rate had the most influence. After the machining experiments, four-point bending fatigue tests were carried out to evaluate the fatigue life of precision milled parts at an elevated temperature in a low cycle fatigue set-up imitated for the forming and forging production. It was found that surface roughness and hardness were the most influential factors for fatigue life. A 3D-FE-Modelling framework including a new material model subroutine was developed; this led to a more comprehensive material model. A fractional factorial simulation with over 180 simulations was carried out and validated with the machining experiment. Based on the experimental and simulation results, a soft prediction model for surface integrity was established by using Artificial Neural Networks (ANN) approach. These predictions for SI were then used in a Genetic Algorithm model to optimise the SI. The confirmation tests showed that the machining strategy was successfully optimised and the average fatigue duration was increased by at least a factor of two. It was found that a surface speed of 270 m/min, a feed rate of 0.0589 mm/tooth, a depth of cut of 0.39 mm and a tool lead angle of 16.045° provided the good surface integrity and increased fatigue performance. Overall, these findings conclude that the fundamentals and methodology utilised have developed a further understanding between machining and forming/forging process, resulting in a good foundation for a framework to generate FE and soft prediction models which can be used to in optimisation of precision milling strategy for different materials