Evaluation of the Potentials of the Business Case of Deploying Reconfigurable Segments in Wireless B3G Infrastructures

The B3G concept can be realized in two complementary ways. The first solution is the integration of the diverse radio access technologies into one composite radio environment. The alternative solution is provided by the concept of reconfigurable (adaptive) networks. Composite radio networks, sometimes also referred to as cooperative networks, jointly handle a difficult condition. Reconfigurable networks on the other hand, support B3G Systems by providing technologies that enable network elements and terminals to dynamically adapt to the environment requirements and conditions, in principle, by means of self-management. This paper provides proof on the business advantages of reconfigurable networks. In this context the paper performs an evaluation of the investment in both composite radio and reconfigurable networks, presenting a methodology that can be used for the financial assessment of such networks by applying investment appraisal techniques. Concrete results for both cases are presented and analyzed. The analysis clearly proves that reconfigurable networks can provide significant business benefits for network operators.     

Cognitive Management of Devices in the Wireless World

The success of mobile networks has been driven by the services offered, i.e. voice in second generation and multimedia services in third generation networks. Similarly, a key issue for the success of future generation networks is considered to be the provision of enhanced, always available, personalised services. At the same time, the complexity and heterogeneity of the infrastructure of mobile network operators increases as Radio Access Technologies continue to evolve and new ones emerge. All these issues call for self-management and learning capabilities in future generation network systems. Cognitive, reconfigurable systems encompassing self-management and learning capabilities have been devised as a solution in this direction. Cognitive systems determine their behaviour, in a self-managed way. This is done reactively or proactively, based on goals, policies, knowledge and experience obtained through learning. This paper focuses on the user device and presents a Cognitive Device Management System that comprises mechanisms for dynamically selecting the optimal device configuration, taking into account user preferences, device environment characteristics (context), policies, and knowledge established through machine learning functionality.     

User Profile Modeling in the context of web-based learning management systems

Over the past two decades, great research efforts have been made towards the personalization of e-learning platforms. This feature increases remarkably the quality of the provided learning services, since the users’ special needs and capabilities are respected. The idea of predicting the users’ preferences and adapting the e-learning platform accordingly is the focal point of this paper. In particular, this paper starts with the main requirements of an advanced e-learning system, explains the way a user navigates in such a system, presents the architecture of a corresponding e-learning system and describes its main components. Research is focused on the User Model component, its role in the e-learning system and the parameters that comprise it. In this context, Bayesian Networks are used as a tool for the encoding, learning and reasoning of probabilistic relationships, with the aim to effectively predict user preferences. In support of this vision, four different scenarios are presented, in order to test the way Bayesian Networks apply in the e-learning field.     

A management scheme for improving transportation efficiency and contributing to the enhancement of the social fabric

The continuously increasing need for mobility has brought about not only significant facilities in several aspects of human initiative, but also growing traffic congestions, a phenomenon that leads to unpleasant everyday situations at a short time level, but in the long run also to the degradation of the level of quality of living in large cities and the alienation between people. The management of traffic stands, thus, as a fundamental prerequisite for confronting those issues, enhancing transportation and improving the social fabric. This paper considers the concept of car pooling as a structured approach to this problem, by specifying, developing and validating a mobile-community-driven system for collaborative transportation, namely the “transportation management-car pooling system”. This system is capable of proposing optimal, reliable and secure community matches (taking into consideration personality features, talking interests, driving style, etc.), based on user profile and context information. The paper describes the transportation management-car pooling system, presenting its input parameters, decision making process and outcomes. Finally, indicative simulation results showcase its effectiveness.