This thesis focuses on the enhancement of the IEEE 802.15.4 beaconless-enabled MAC protocol as a solution to overcome the network bottleneck, less flexible nodes, and more energy waste at the centralised wireless sensor networks (WSN). These problems are triggered by mechanism of choosing a centralised WSN coordinator to start communication and manage the resources. Unlike IEEE 802.11 standard, the IEEE 802.15.4 MAC protocol does not include method to overcome hidden nodes problem. Moreover, understanding the behaviour and performance of a large-scale WSN is a very challenging task. A comparative study is conducted to investigate the performance of the proposed ad hoc WSN both over the low data rate IEEE 802.15.4 and the high data rate IEEE 802.11 standards. Simulation results show that, in small-scale networks, ad hoc WSN over 802.15.4 outperforms the WSN where it improves 4-key performance indicators such as throughput, PDR, packet loss, and energy consumption by up to 22.4%, 17.1%, 34.1%, and 43.2%, respectively. Nevertheless, WSN achieves less end-to-end delay; in this study, it introduces by up to 2.0 ms less delay than that of ad hoc WSN. Furthermore, the ad hoc wireless sensor networks work well both over IEEE 802.15.4 and IEEE 802.11 protocols in small-scale networks with low traffic loads. The performance of IEEE 802.15.4 declines for the higher payload size since this standard is dedicated to low rate wireless personal access networks. A deep performance investigation of the IEEE 802.15.4 beaconless-enabled wireless sensor network (BeWSN) in hidden nodes environment has been conducted and followed by an investigation of network overhead on ad hoc networks over IEEE 802.11 protocol. The result of investigation evinces that the performance of beaconless-enabled ad hoc wireless sensor networks deteriorates as indicated by the degradation of throughput and packet reception by up to 72.66 kbps and 35.2%, respectively. In relation to end-to-end delay, however, there is no significant performance deviation caused by hidden nodes appearance. On the other hand, preventing hidden node effect by implementing RTS/CTS mechanism introduces significant overhead on the network that applies low packet size. Therefore, this handshaking method is not suitable for low rate communications protocol such as IEEE 802.15.4 standard. An evaluation study of a 101-node large-scale beaconless-enabled wireless sensor networks over IEEE 802.15.4 protocol has been carried out after the nodes deployment model was arranged. From the experiment, when the number of connection densely increases, then the probability of packet delivery decreases by up to 40.5% for the low payload size and not less than of 44.5% for the upper payload size. Meanwhile, for all sizes of payload applied to the large-scale ad hoc wireless sensor network, it points out an increasing throughput whilst the network handles more connections among sensor nodes. In term of dropped packet, it confirms that a fewer data drops at smaller number of connecting nodes on the network where the protocol outperforms not less than of 34% for low payload size of 30 Bytes. The similar trend obviously happens on packet loss. In addition, the simulation results show that the smaller payload size performs better than the bigger one in term of network latency, where the payload size of 30 Bytes contributes by up to 41.7% less delay compared with the contribution of the payload size of 90 Bytes.

The current large concentration of wireless networks brings new horizons, but also new concerns. Failure to follow basic rules may produce far-reaching problems that could make more wrinkles to all affected managers and administrators. The aim of this thesis was to measure and compare the radio parameters of selected products and how they behave in neighboring interference and the speed they have while there are hidden nodes. The resulting values were measured in the laboratory network Wificolab and compared with the various support protocols. Possible effects on the specific situation are also analyzed in this thesis.

In this thesis the two disciplines of Statistics and Artificial Neural Networks are combined into an integrated study of a data set of a weather modification Experiment. An extensive literature study on artificial neural network methodology has revealed the strongly interdisciplinary nature of the research and the applications in this field. An artificial neural networks are becoming increasingly popular with data analysts, statisticians are becoming more involved in the field. A recursive algoritlun is developed to optimize the number of hidden nodes in a feedforward artificial neural network to demonstrate how existing statistical techniques such as nonlinear regression and the likelihood-ratio test can be applied in innovative ways to develop and refine neural network methodology. This pruning algorithm is an original contribution to the field of artificial neural network methodology that simplifies the process of architecture selection, thereby reducing the number of training sessions that is needed to find a model that fits the data adequately. [n addition, a statistical model to classify weather modification data is developed using both a feedforward multilayer perceptron artificial neural network and a discriminant analysis. The two models are compared and the effectiveness of applying an artificial neural network model to a relatively small data set assessed. The formulation of the problem, the approach that has been followed to solve it and the novel modelling application all combine to make an original contribution to the interdisciplinary fields of Statistics and Artificial Neural Networks as well as to the discipline of meteorology. / Mathematical Sciences / D. Phil. (Statistics)

In this thesis the two disciplines of Statistics and Artificial Neural Networks are combined into an integrated study of a data set of a weather modification Experiment. An extensive literature study on artificial neural network methodology has revealed the strongly interdisciplinary nature of the research and the applications in this field. An artificial neural networks are becoming increasingly popular with data analysts, statisticians are becoming more involved in the field. A recursive algoritlun is developed to optimize the number of hidden nodes in a feedforward artificial neural network to demonstrate how existing statistical techniques such as nonlinear regression and the likelihood-ratio test can be applied in innovative ways to develop and refine neural network methodology. This pruning algorithm is an original contribution to the field of artificial neural network methodology that simplifies the process of architecture selection, thereby reducing the number of training sessions that is needed to find a model that fits the data adequately. [n addition, a statistical model to classify weather modification data is developed using both a feedforward multilayer perceptron artificial neural network and a discriminant analysis. The two models are compared and the effectiveness of applying an artificial neural network model to a relatively small data set assessed. The formulation of the problem, the approach that has been followed to solve it and the novel modelling application all combine to make an original contribution to the interdisciplinary fields of Statistics and Artificial Neural Networks as well as to the discipline of meteorology. / Mathematical Sciences / D. Phil. (Statistics)