Swarm intelligence and its applications to wireless ad hoc and sensor networks.

Swarm intelligence, as inspired by natural biological swarms, has numerous powerful properties for distributed problem solving in complex real world applications such as optimisation and control. Swarm intelligence properties can be found in natural systems such as ants, bees and birds, whereby the collective behaviour of unsophisticated agents interact locally with their environment to explore collective problem solving without centralised control. Recent advances in wireless communication and digital electronics have instigated important changes in distributed computing. Pervasive computing environments have emerged, such as large scale communication networks and wireless ad hoc and sensor networks that are extremely dynamic and unreliable. The network management and control must be based on distributed principles where centralised approaches may not be suitable for exploiting the enormous potential of these environments. In this thesis, we focus on applying swarm intelligence to the wireless ad hoc and sensor networks optimisation and control problems. Firstly, an analysis of the recently proposed particle swarm optimisation, which is based on the swarm intelligence techniques, is presented. Previous stability analysis of the particle swarm optimisation was restricted to the assumption that all of the parameters are non random since the theoretical analysis with the random parameters is difficult. We analyse the stability of the particle dynamics without these restrictive assumptions using Lyapunov stability and passive systems concepts. The particle swarm optimisation is then used to solve the sink node placement problem in sensor networks. Secondly, swarm intelligence based routing methods for mobile ad hoc networks are investigated. Two protocols have been proposed based on the foraging behaviour of biological ants and implemented in the NS2 network simulator. The first protocol allows each node in the network to choose the next node for packets to be forwarded on the basis of mobility influenced routing table. Since mobility is one of the most important factors for route changes in mobile ad hoc networks, the mobility of the neighbour node using HELLO packets is predicted and then translated into a pheromone decay as found in natural biological systems. The second protocol uses the same mechanism as the first, but instead of mobility the neighbour node remaining energy level and its drain rate are used. The thesis clearly shows that swarm intelligence methods have a very useful role to play in the management and control iv problems associated with wireless ad hoc and sensor networks. This thesis has given a number of example applications and has demonstrated its usefulness in improving performance over other existing methods

On the use of autonomous unmanned vehicles in response to hazardous atmospheric release incidents

Recent events have induced a surge of interest in the methods of response to releases of hazardous materials or gases into the atmosphere. In the last decade there has been particular interest in mapping and quantifying emissions for regulatory purposes, emergency response, and environmental monitoring. Examples include: responding to events such as gas leaks, nuclear accidents or chemical, biological or radiological (CBR) accidents or attacks, and even exploring sources of methane emissions on the planet Mars. This thesis presents a review of the potential responses to hazardous releases, which includes source localisation, boundary tracking, mapping and source term estimation. [Continues.]</div

Digital Filter Design Using Improved Artificial Bee Colony Algorithms

Digital filters are often used in digital signal processing applications. The design objective of a digital filter is to find the optimal set of filter coefficients, which satisfies the desired specifications of magnitude and group delay responses. Evolutionary algorithms are population-based meta-heuristic algorithms inspired by the biological behaviors of species. Compared to gradient-based optimization algorithms such as steepest descent and Newton’s like methods, these bio-inspired algorithms have the advantages of not getting stuck at local optima and being independent of the starting point in the solution space. The limitations of evolutionary algorithms include the presence of control parameters, problem specific tuning procedure, premature convergence and slower convergence rate. The artificial bee colony (ABC) algorithm is a swarm-based search meta-heuristic algorithm inspired by the foraging behaviors of honey bee colonies, with the benefit of a relatively fewer control parameters. In its original form, the ABC algorithm has certain limitations such as low convergence rate, and insufficient balance between exploration and exploitation in the search equations. In this dissertation, an ABC-AMR algorithm is proposed by incorporating an adaptive modification rate (AMR) into the original ABC algorithm to increase convergence rate by adjusting the balance between exploration and exploitation in the search equations through an adaptive determination of the number of parameters to be updated in every iteration. A constrained ABC-AMR algorithm is also developed for solving constrained optimization problems.There are many real-world problems requiring simultaneous optimizations of more than one conflicting objectives. Multiobjective (MO) optimization produces a set of feasible solutions called the Pareto front instead of a single optimum solution. For multiobjective optimization, if a decision maker’s preferences can be incorporated during the optimization process, the search process can be confined to the region of interest instead of searching the entire region. In this dissertation, two algorithms are developed for such incorporation. The first one is a reference-point-based MOABC algorithm in which a decision maker’s preferences are included in the optimization process as the reference point. The second one is a physical-programming-based MOABC algorithm in which physical programming is used for setting the region of interest of a decision maker. In this dissertation, the four developed algorithms are applied to solve digital filter design problems. The ABC-AMR algorithm is used to design Types 3 and 4 linear phase FIR differentiators, and the results are compared to those obtained by the original ABC algorithm, three improved ABC algorithms, and the Parks-McClellan algorithm. The constrained ABC-AMR algorithm is applied to the design of sparse Type 1 linear phase FIR filters of filter orders 60, 70 and 80, and the results are compared to three state-of-the-art design methods. The reference-point-based multiobjective ABC algorithm is used to design of asymmetric lowpass, highpass, bandpass and bandstop FIR filters, and the results are compared to those obtained by the preference-based multiobjective differential evolution algorithm. The physical-programming-based multiobjective ABC algorithm is used to design IIR lowpass, highpass and bandpass filters, and the results are compared to three state-of-the-art design methods. Based on the obtained design results, the four design algorithms are shown to be competitive as compared to the state-of-the-art design methods

Positioning of a wireless relay node for useful cooperative communication

Given the exorbitant amount of data transmitted and the increasing demand for data connectivity in the 21st century, it has become imperative to search for pro-active and sustainable solutions to the effectively alleviate the overwhelming burden imposed on wireless networks. In this study a Decode and Forward cooperative relay channel is analyzed, with the employment of Maximal Ratio Combining at the destination node as the method of offering diversity combining. The system framework used is based on a three-node relay channel with a source node, relay node and a destination node. A model for the wireless communications channel is formulated in order for simulation to be carried out to investigate the impact on performance of relaying on a node placed at the edge of cell. Firstly, an AWGN channel is used before the effect of Rayleigh fading is taken into consideration. Result shows that performance of cooperative relaying performance is always superior or similar to conventional relaying. Additionally, relaying is beneficial when the relay is placed closer to the receiver