Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Technique for Node Starvation Problem in Wireless Mesh Networks

Wireless Mesh Networks (WMNs) have emerged as a scalable, reliable, and agile wireless network that supports many types of innovative technologies such as the Internet of Things (IoT), Wireless Sensor Networks (WSN), and Internet of Vehicles (IoV). Due to the limited number of orthogonal channels, interference between channels adversely affects the fair distribution of bandwidth among mesh clients, causing node starvation in terms of insufficient bandwidth distribution, which impedes the adoption of WMN as an efficient access technology. Therefore, a fair channel assignment is crucial for the mesh clients to utilize the available resources. However, the node starvation problem due to unfair channel distribution has been vastly overlooked during channel assignment by the extant research. Instead, existing channel assignment algorithms equally distribute the interference reduction on the links to achieve fairness which neither guarantees a fair distribution of the network bandwidth nor eliminates node starvation. In addition, the metaheuristic-based solutions such as genetic algorithm, which is commonly used for WMN, use randomness in creating initial population and selecting the new generation which usually leads the search to local minima. To this end, this study proposes a Fairness-Oriented Semi-Chaotic Genetic Algorithm-Based Channel Assignment Technique (FA-SCGA-CAA) to solve Nodes Starvation Problem in Wireless Mesh Networks. FA-SCGA-CAA maximizes link fairness while minimizing link interference using a Genetic Algorithm (GA) with a novel nonlinear fairness-oriented fitness function. The primary chromosome with powerful genes is created based on multi-criterion links ranking channel assignment algorithm. Such a chromosome was used with a proposed semi-chaotic technique to create a strong population that directs the search towards the global minima effectively and efficiently. The proposed semi-chaotic was also used during the mutation and parent selection of the new genes. Extensive experiments were conducted to evaluate the proposed algorithm. Comparison with related work shows that the proposed FA_SCGA_CAA reduced the potential node starvation by 22% and improved network capacity utilization by 23%. It can be concluded that the proposed FA_SCGA_CAA is reliable to maintain high node-level fairness while maximizing the utilization of the network resources, which is the ultimate goal of many wireless networks

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

ReSCon '12, Research Student Conference: Book of Abstracts

The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

The Machine that Lives Forever

Design an intelligent micromachine that can self-power and sustain from environmental energy scavenging to achieve an autonomous device that can communicate at will with peers indefinitely. Explore sleep/wake hibernation strategies coupled with food scavenging off-grid traits to identify the tightest work to sleep efficiency schedule, incorporating adaptive reconfiguration to manage significant environmental impacts. Capture, store and manage background radiations and stray RF signals to feed on in a continued effort to make intelligent survival decisions and oversee management protocols. Ensure that every micro Watt of usable energy gets extracted from every part of the harvest and then forward-scheduled it for productive use. Finally, employ natures tricks and experience to introduce essential personality traits, pursuing maximising survival numbers and increasing dispersal target area sizes of large self-sufficient wireless sensor deployments. This research intends to provide a closely coupled software-hardware foundation that aids implementers in intelligently harnessing and using tiny amounts of ambient energy in a highly autonomous way. This platform then continues on to explore ways of maximising the efficient usage of the harvested energy using various hibernation/wake strategies and then making objective comparisons with proposed intelligent energy management protocols. Finally, the protocol extends to enable the device to manage its personal survival possibilities so the devices can use an evolutional personality-based approach to deal with the unknown environmental situations they will encounter. This work examines a machine that can self-power and sustain from environmental energy scavenging with the aim to live forever. Living forever implies a brain (microcontroller) that can manage energy and budget for continuous faculty. With these objectives, sleep/wake/hibernation and scavenging strategies are examined to efficiently schedule resources within a transient environment. Example harvesting includes induced and background radiation. Intelligent, biologically-inspired strategies are adopted in forward-scheduling strategies given temporal energy relative to the machine’s function (the Walton)