Andreas S. Pomportsis

Optical switching: switch fabrics, techniques, and architectures

The switching speeds of electronics cannot keep up with the transmission capacity offered by optics. All-optical switch fabrics play a central role in the effort to migrate the switching functions to the optical layer. Optical packet switching provides an almost arbitrary fine granularity but faces significant challenges in the processing and buffering of bits at high speeds. Generalized multiprotocol label switching seeks to eliminate the asynchronous transfer mode and synchronous optical network layers, thus implementing Internet protocol over wavelength-division multiplexing. Optical burst switching attempts to minimize the need for processing and buffering by aggregating flows of data packets into bursts. In this paper, we present an extensive overview of the current technologies and techniques concerning optical switching.

The design and the formative evaluation of an adaptive educational system based on cognitive styles

Adaptive Hypermedia Systems (AHS) can be developed to accommodate a variety of individual differences, including learning style and cognitive style. The current research is an attempt to examine some of the critical variables, which may be important in the design of an Adaptive Educational System (AES) based on students cognitive style. Moreover, this paper describes the design issues that were considered for the development of the system that are reported in the relevant literature Throughout the development of the system, formative evaluation was an integral part of the design methodology. The results of the formative evaluation were used to improve our system in order to make the instruction more effective and efficient. Furthermore, the recommendations resulted from the formative evaluation could be seen as some points worth considering from designers of AHS.

Fostering collaborative learning in Second Life: Metaphors and affordances

In this paper we examine the transferability of the Jigsaw and Fishbowl collaborative learning techniques to the Second Life platform. Our aim is to assess the applicability of Second Life for collaborative learning by developing virtual tools and metaphors and exploiting the representational richness of this novel medium. In order to enhance the existing metaphors and affordances of SL, our research team implemented educational spaces, avatar clothing, and tools for non-verbal communication and visualisation. By implementing a blended learning evaluation approach we attempted to answer three research questions focusing on student collaboration, avatar representation and learning space awareness. We can conclude that SL can supplement and/or augment face to face interactions, improving upon previous approaches in distance collaboration and communication. Furthermore, although our team augmented SLs ability to support collaborative learning, avatar representation does not seem to scale well. Finally, the majority of the implemented affordances and metaphors seem to have enhanced collaboration and learning space awareness.

Using learning automata for adaptive push-based data broadcasting in asymmetric wireless environments

Push systems are not suitable for applications with a priori unknown, dynamic client demands. This paper proposes an adaptive push-based system. It suggests the use of a learning automaton at the broadcast server to provide adaptivity to an existing push system while maintaining its computational complexity. Using simple feedback from the clients, the automaton continuously adapts to the client population demands so as to reflect the overall popularity of each data item. Simulation results are presented that reveal the superior performance of the proposed approach in environments with a priori unknown, dynamic client demands.

Learning automata-based polling protocols for wireless LANs

A learning automata-based polling (LEAP) protocol for wireless LANs, capable of operating efficiently under bursty traffic conditions, is introduced. We consider an infrastructure wireless LAN, where the access point (AP) is located at the center of a cell which comprises a number of mobile stations. According to the proposed protocol, the mobile station that is granted permission to transmit is selected by the AP by means of a learning automaton. The learning automaton takes into account the network feedback information in order to update the choice probability of each mobile station. It is proved that the learning algorithm asymptotically tends to assign to each station a portion of the bandwidth proportional to the stations needs. LEAP is compared to the randomly addressed polling and group randomly addressed polling protocols and is shown to exhibit superior performance under bursty traffic.

Guest editorial learning automata: theory, paradigms, and applications

L EARNING automata [1] have attracted a considerable interest in the last three decades. They are adaptive decision making devices that operate in unknown stochastic environments and progressively improve their performance via a learning process. They have been initially used by psychologists and biologists to describe the human behavior from both psychological and biological viewpoints. Learning automata have made a significant impact on all areas of engineering. They can be applied to a broad range of modeling and control problems, which are characterized by nonlinearity and a high degree of uncertainty. Learning automata have some key features, which make them applicable to a broad range of applications: they combine rapid and accurate convergence with a low computational complexity. Learning is defined as any permanent change in behavior as a result of past experience, and a learning system should therefore have the ability to improve its behavior with time, toward a final goal. In a purely mathematical context, the goal of a learning system is the optimization of a function not known explicitly [2]. Thirty years ago, Tsypkin [3] introduced a method to reduce the problem to the determination of an optimal set of parameters and then applied stochastic hill-climbing techniques. Tsetlin [4] started the work on learning automata during the same period. An alternative approach to applying stochastic hillclimbing techniques, introduced by Narendra and Viswanathan [5], is to regard the problem as one of finding an optimal action out of a set of allowable actions and to achieve this using stochastic automata. The difference between the two approaches is that the former updates the parameter space at each iteration while the latter updates the probability space. The stochastic automaton attempts a solution of the problem without any information on the optimal action. One action is selected at random, the response from the environment is observed, action probabilities are updated based on that response,

The value of adaptivity based on cognitive style : an empirical study

Adaptive Hypermedia Systems can be developed to accommodate a variety of individual differences, including learning style and cognitive style. This study investigates the hypothesis that adaptive hypermedia accommodating cognitive styles can be beneficial for the observed learning outcomes. A prototype system, designed to be adapted to individual cognitive styles, was developed as a case study. In order to evaluate the effectiveness of the prototype system, an empirical study was conducted. This paper presents the results of the summative evaluation of the system. Statistical analyses indicated that students in the experimental group performed significantly better than students in a control group. These findings indicate that student performance is mainly affected by adaptivity based on individual cognitive styles.

Learning-automata-based TDMA protocols for broadcast communication systems with bursty traffic

A learning automata-based time-division multiple-access protocol for broadcast networks, which is capable of operating efficiently under bursty traffic conditions, is introduced. According to the proposed protocol, the station which grants permission to transmit at each time slot is selected by means of learning automata. The learning automata update the choice probability of each station according to the network feedback information in such a way that it asymptotically tends to be proportional to the probability that this station is ready. In this manner, the number of idle slots is minimized and the network performance is significantly improved. Furthermore, the portion of the bandwidth assigned to each station is dynamically adapted to the stations needs.

Self-adaptive TDMA protocols for WDM star networks: a learning-automata-based approach

A learning-automata-based protocol for WDM passive star networks, which is capable of operating efficiently under bursty and correlated traffic, is introduced. According to the proposed protocol, the stations which grant permission to transmit at each time slot, are selected by means of learning automata. The choice probabilities of the selected stations are updated by taking into account the network feedback information. The probability updating scheme is designed in such a way, that the number of idle slots tends to be minimized, while the bandwidth of each wavelength Is allocated to the stations according to their needs.

Adaptive wireless networks using learning automata

Wireless networks operate in environments with unknown and time-varying characteristics. The changing nature of many of these characteristics will significantly affect network performance. This fact has a profound impact on the design of efficient protocols for wireless networks and as a result adaptivity arises as one of the most important properties of these protocols. Learning automata are artificial intelligence tools that have been used in many areas where adaptivity to the characteristics of the wireless environment can result in a significant increase in network performance. This article reviews state of the art approaches in using learning automata to provide adaptivity to wireless networking.