This paper discusses modelling and prediction of faults in cellular networks. We derive probabilistic models of the cellular network systems in which the independence relations between the variables of interest are represented explicitly. We use a directed graph in which two nodes are connected by an edge if one is a direct cause of the other. We present the process of construction of a structure of a Bayesian network. We explored the complexity and performance of fault prediction using Bayesian network.

Collaborative Recommender Systems suggest items to a user based on other users past behaviour (items they once bought, viewed or selected and/or ratings they gave to those items). They are very effective in generating meaningful recommendations to a group of users for products or items that might interest them. However, since Collaborative filtering techniques depend on outside sources of information they are susceptible to profile injection attacks popularly known as shilling attacks. Shilling is a process in which syndicating users can connive to promote or demote a certain item. These mischievous users can consciously inject shilling profiles in an effort to bias the recommender system to their advantage. In this paper we seek to understand the degree to which shilling attacks can harm recommender systems and how these attacks can be detected. Firstly, we evaluate the vulnerabilities of collaborative filtering techniques in providing reliable recommendations. We study various attack strategies that manipulators use to attack recommender systems. Secondly we investigate the most suitable features that can be used to adequately identify shilling attacks. We propose the combiner strategy that combines multiple classifiers in an effort to detect shilling attacks. The diversity measure is used to determine the most suitable combination of classifiers. In this paper, we made use k-Nearest Neighbour, Support Vector Machines and Bayesian Networks as the initial base classifiers. The Naïve Bayes was used as a Meta Classifier. The proposed Meta-Learning classifier gave an overall performance of 99% and was found to be more superior to Neural Networks and k-Nearest Neighbor.

The advancement of various wireless portable devices and wireless networks technologies increases the need for network services and applications, i.e., instant messaging, World Wide Web (WWW), e-Mail anywhere anytime. As a result, over the past years, we have seen an increasing number of users with the need and desire to be connected and reachable anywhere at any time. This is made possible by the existents of intersystem handoff/handover process between Heterogeneous Wireless Networks (HWNs). During the intersystem handoff process, certain applications and services are interrupted and ensuring efficiency of this process is challenging. In this paper, an Enhanced Handoff (EH) algorithm is designed by integrating enhanced fast roaming algorithm and Protocol for Carrying Authentication for Network Access (PANA) in order to lessen handoff delay in HWNs. As a result, the number of packet loss and handoff delay is minimized.

Wireless Backbone Networks (WBNs) equipped with Multi-Radio Multi-Channel (MRMC) configurations do experience power control problems such as the inter-channel and co-channel interference, high energy consumption at multiple queues and unscalable network connectivity. Such network problems can be conveniently modelled using the theory of queue perturbation in the multiple queue systems and also as a weak coupling in a multiple channel wireless network. Consequently, this paper proposes a queue perturbation and weakly coupled based power control approach forWBNs. The ultimate objectives are to increase energy efficiency and the overall network capacity. In order to achieve this objective, a Markov chain model is first presented to describe the behaviour of the steady state probability distribution of the queue energy and buffer states. The singular perturbation parameter is approximated from the coefficients of the Taylor series expansion of the probability distribution. The impact of such queue perturbations on the transmission probability, given some transmission power values, is also analysed. Secondly, the inter-channel interference is modelled as a weakly coupled wireless system. Thirdly, Nash differential games are applied to derive optimal power control signals for each user subject to power constraints at each node. Finally, analytical models and numerical examples show the efficacy of the proposed model in solving power control problems in WBNs.

The number of mobile users has exponentially grown over the past years, and these users have the desire of always being connected wirelessly at any time anywhere, to the best available network. The existence of Heterogeneous Wireless Networks (HWNs) allows mobile users to be always connected and have access to network services and applications. However, to provide seamless intersystem handover remains a challenge, since mobile users' still experience lengthy handover delay. This is mainly because, previously proposed handover algorithms fail to predict the future values of the received signal strength (RSS). This led to extensive handover delay due to lengthy handover discovery process. Furthermore, these previously proposed handover algorithms only expresses scales of relative comparison and do not deal with decision problems of fuzziness and uncertainty that comes with the use of multiple parameters. These handover algorithms have fixed weighting matrix and therefore, could not adapt to the change of the network conditions and user's preference. As a result, these handover algorithms still experience erroneous network selection. Consequently, in this paper, an intelligent Intersystem Handover (IH) algorithm was designed by integrating Grey Prediction Theory (GPT), Multiple-Attribute Decision Making (MADM), Fuzzy Analytic Hierarchy Process (FAHP) and Principal Component Analysis (PCA). Numerous computer simulation results confirmed that the proposed IH algorithm shortened handover delay as compared with Fuzzy Logic Based vertical handover (FLBVH) algorithm and Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm.