Ioannis V. Loumiotis

Dynamic Backhaul Resource Allocation: An Evolutionary Game Theoretic Approach

The recently deployed 4G access technology promises to satisfy the increasing demand of bandwidth consuming applications by providing high network capacity, low latency and seamless mobility. Towards this direction, concept solutions concerning the integration of wireless and optical networks have been proposed. However, the majority of these approaches assume the conventional fixed commitment of resources to the base stations, an inefficient and costly process, especially in case the Passive Optical Network (PON) belongs to a different operator. As a result, new, more dynamic backhaul resource allocation approaches are required. In this paper, the authors study the problem of committing resources of the backhaul network to a base station by employing evolutionary game theory in order to model the interactions between the subscribers and the base station. The asymptotic stability of the proposed scheme is proven under the replicator dynamics model. Finally, the impact of time delay in the proposed scheme is also investigated.

Providing recommendations on location-based social networks

During the last decade, in parallel with the rapid growth of mobile communications and devices, location-based social networks have met a tremendous growth with the acceptance of the public being constantly increasing. Users have access to a plethora of venues and points of interest, while they are able to share their visits to various locations along with comments and ratings about their experience (a process which is often referred to as “check-ins”). Location recommendations based on users’ needs have been a subject of interest for many researchers, while location prediction schemes have been developed in order to provide user’s possible future locations. In this paper, we present a novel method for predicting a user’s location based on machine learning techniques. In addition, following the incremental trend towards data accumulation in social networks, we introduce a clustering based prediction method in order to enhance the recommender system. For the prediction process we propose a probabilistic neural network and confirm its superior performance against two other types of neural networks, while for the clustering process we use a K-means clustering algorithm. The dataset we used was based on input from a well-known location-based social network. Prediction results can be used in order to make appropriate suggestions for venues or points of interests to users, based on their interests and social connections.

On the predictability of next generation mobile network traffic using artificial neural networks

Though the introduction of the new 4th Generation mobile access technologies promises to satisfy the increasing bandwidth demand of the end-users, it poses in parallel the need for novel resource management approaches at the side of the base station. To this end, schemes that try to predict the forthcoming bandwidth demand using supervised learning methods have been proposed in the literature. However, there are still open issues concerning the training phase of such methods. In the current work, the authors propose a novel scheme that dynamically selects a proper training set for artificial neural network prediction models, based on the statistical characteristics of the collected data. It is demonstrated that an initial statistical processing of the collected data and the subsequent selection of the training set can efficiently improve the performance of the prediction model. Finally, the proposed scheme is validated using network traffic collected by real, fully operational base stations. Copyright

Road to Next Generation Mobile Networks: An Evolutionary Dynamics Approach

One of the major challenges that mobile operators (MOs) are faced with nowadays is the transition to 4th Generation (4G) mobile communication technologies. The main reason for this lies on the reluctance of MOs to invest in a new technology without being sure about its success. The current paper investigates the decision-making procedures of a MO that wishes to migrate from its current technology type to 4G. Traditionally, the decision of deploying a new technology has been based on the analysis of similar implementations in other countries. However, such approaches can be inefficient and time consuming, as there are discrepancies concerning the technological progress among different countries. To this end, the authors employ evolutionary game theory to model the interactions of the MO’s decisions and the subscribers’ needs, and propose a practical and efficient qualitative model that identifies the circumstances under which the transition towards 4G networking can be facilitated. Specifically, the mathematical foundation of the decision making process is provided and the key role of the charging price and the quality of experience by the subscribers for using 4G connectivity is proven. With the process of 4G deployment still ongoing, this paper aims to present an analysis that can be used supplementary to the decision process of a MO that aims to evolve his network.

Artificial Neural Networks for Traffic Prediction in 4G Networks

The increasing proliferation of 4G mobile technologies is expected to satisfy the constantly growing demand for wireless broadband services. However, the high data rates provided by 4G networks at the air interface raise the need for more efficient management of the backhaul resources especially if the backhaul network has been leased by the mobile operator. In the present work, the authors investigate on the backhaul resource allocation problem at the side of the base station (BS) and a novel distributed scheme is proposed that can efficiently forecast the aggregated traffic demand at the BS using artificial neural networks. It is shown that the proposed scheme provides a mean absolute percentage error of about 10 % for the downlink traffic and about 19 % for the uplink traffic.

Dynamic Allocation of Backhaul Resources in Converged Wireless-Optical Networks

The market uptake of the 4th Generation networks is expected to support the increasing demand for wireless broadband services and ensure an enhanced mobile user experience. In this direction, the convergence of a wireless access network with an optical backhauling has been proposed. However, in such a converged architecture, the traditional fixed commitment of the backhaul resources does not prove to be as efficient, and novel dynamic schemes are required that consider both the needs of the base stations and the limitations of the passive optical network. This paper is concerned with the topic of resource allocation in two competing base stations that belong to different operators and share a common optical backhaul network infrastructure. An approach based on evolutionary game theory is proposed and employed, with a view to examining the interactions among the base stations and the passive optical network. Using the model of replicator dynamics, the proposed system design is proved to be asymptotically stable. In addition, this paper studies and reveals the extent to which time delay can have an impact on the proposed system design.

Optimal Backhaul Resource Management in Wireless-Optical Converged Networks

The introduction of the new 4G technologies promises to satisfy the increasing demands of the end-users for bandwidth consuming applications. However, the high data rates provided by 4G networks at the air interface raise the need for more efficient management of the backhaul resources. In the current work, the authors study the problem of the efficient management of the backhaul resources at the side of the base station. Specifically, a novel scheme is proposed that, initially, predicts the forthcoming demand using artificial neural networks and, then, based on the prediction results, it proactively requests the commitment of the appropriate resources using linear optimisation techniques. The experimental results show that the proposed scheme can efficiently and cost-effectively manage the backhaul resources, outperforming the traditional flat commitment approaches.

Introducing Community Awareness to Location-Based Social Networks

During the last years, Social Networks have been in the spotlight of many researchers, trying to enhance them with pervasive features that will simplify and facilitate users’ experience. One of the most innovative additions to social networks has been the introduction of communities in users’ lifecycle. However, there are still a lot of issues regarding the automation of this feature in order to minimize user’s effort to discover new communities and as a result, to improve his experience. In this paper, we introduce the use of communities in location-based social networks. We also present the proposed systems architecture including Processes and Services.

Intelligent and Efficient Car Management Application for Advanced Green Routing

The tremendous growth of the transportation systems during the last decades has created a significant environmental impact. As a result, in order to reduce the atmospheric pollution many attempts have been employed, including eco-driving systems. However, the scope of such systems is only to attempt to inform the user about his driving behaviour. In the current paper, a novel system targeted for supporting green daily commuting habits, with a particular focus on helping the user save on fuel expenses and time on a regular daily basis is proposed and its functional architecture is fully presented.