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Modelling of ill-defined or unpredictable systems can be very challenging. Most models have relied on conventional mathematical models which does not adequately track some of the multifaceted challenges of such a system. Load balancing, which is a self-optimization operation of Self-Organizing Networks (SON), aims at ensuring an equitable distribution of users in the network. This translates into better user satisfaction and a more efficient use of network resources. Several methods for load balancing have been proposed. While some of them have a very buoyant theoretical basis, they are not practical. Furthermore, most of the techniques proposed the use of an iterative algorithm, which in itself is not computationally efficient as it does not take the unpredictable fluctuation of network load into consideration. This chapter proposes the use of soft computing, precisely Adaptive Neuro-Fuzzy Inference System (ANFIS) model, for dynamic QoS aware load balancing in 3GPP LTE. The use of ANFIS offers learning capability of neural network and knowledge representation of fuzzy logic for a load balancing solution that is cost effective and closer to human intuition. Three key load parameters (number of satisfied user in the network, virtual load of the serving eNodeB, and the overall state of the target eNodeB) are used to adjust the hysteresis value for load balancing.

Application of the term video streaming in contemporary usage denotes compression techniques and data buffering, which can transmit video in real time over the network. There is currently a rapid growth and development of technologies using wireless broadband technology as a transport, which is a seri- ous alternative to cellular communication systems. Adverse effect of the aggressive environment used in wireless networks transmission results in data packets undergoing serious distortions and often get- ting lost in transit. All existing research in this area investigate the known types of errors separately. At present there are no standard approaches to determining the effect of errors on transmission quality of services. Besides, the spate in popularity of multimedia applications has led to the need for optimization of bandwidth allocation and usage in telecommunication networks. Modern telecommunication networks should by their definition be able to maintain the quality of different applications with different Quality of Service (QoS) levels. QoS requirements are generally dependent on the parameters of network and application layers of the OSI model. At the application layer QoS depends on factors such as resolution, bit rate, frame rate, video type, audio codecs, and so on. At the network layer, distortions (such as delay, jitter, packet loss, etc.) are introduced.

The authors present in this entry chapter the basic rubrics of models, modeling, and simulation, an understanding of which is indispensible for the comprehension of subsequent chapters of this text on the all-important topic of modeling and simulation in Information Communication Systems and Networks (ICSN). A good example is the case of analyzing simulation results of traffic models as a tool for investigating network behavioral pattarns as it affects the transmitted content (Atayero, et al., 2013). The various classifications of models are discussed, for example classification based on the degree of semblance to the original object (i.e. isomorphism). Various fundamental terminologies without the knowledge of which the concepts and models and modeling cannot be properly understood are explained. Model stuctures are highlighted and discussed. The methodological basis of formalizing complex system structures is presented. The concept of componential approach to modeling is presented and the necessary stages of mathematical model formation are examined and explained. The chapter concludes with a presentation of the concept of simulation vis-à-vis information communication systems and networks.

Government establishments are most times highly involved in different reorganization programs. The processes in e-Government are diversified and complex, hence the need for an appropriate training and learning strategy for governmental employees. Changing business processes and organizational structures always mean that the personnel have to be familiar with the changed procedures. Consequently, the employees need to be trained to develop capacity for new responsibilities. Existing methods of learning and training do not make provision for certain category of employees such as the visually impaired. They do not provide an alternative learning platform for government of employees that are not physically challenged. Many studies have demonstrated the value of several learning platforms, including mobile learning (m-Learning) but with the problems of access barriers and streamlined participation of most learners. The purpose of this chapter is to propose a voice-based e-Learning system, also known as voice-learning (v-Learning) as a variant of the m-Learning with particular relevance for the visually and mobility impaired learners. V-Learning makes possible ubiquitous learning in e-Government and provides additional capacity and speed of response to help facilitate change. Cost reduction is also achieved and there is no shortage of teachers.

This chapter examines the learning environment of visually impaired students in the school for the blind. The level of Information and Communication Technology (ICT) utilization and adoption is reported with specific interest in VoiceXML and its application areas. As a case study, a prototype voice-based e-Learning application for course registration and examination was developed and reported. The system was evaluated using ISO 9241-11 usability criteria. The outcome of the usability evaluation is also presented. The voice-based e-Learning technology described in this chapter will improve accessibility to education, including distance learning for learners who are visually impaired in the school for the blind.