Cell Selection in Wireless Two-Tier Networks: A Context-Aware Matching Game

The deployment of small cell networks is seen as a major feature of the next generation of wireless networks. In this paper, a novel approach for cell association in small cell networks is proposed. The proposed approach exploits new types of information extracted from the users' devices and environment to improve the way in which users are assigned to their serving base stations. Examples of such context information include the devices' screen size and the users' trajectory. The problem is formulated as a matching game with externalities and a new, distributed algorithm is proposed to solve this game. The proposed algorithm is shown to reach a stable matching whose properties are studied. Simulation results show that the proposed context-aware matching approach yields significant performance gains, in terms of the average utility per user, when compared with a classical max-SINR approach.Comment: 11 pages, 11 figures, Journal article in ICST Wireless Spectrum, 201

Heterogeneous V2V Communications in Multi-Link and Multi-RAT Vehicular Networks

Connected and automated vehicles will enable advanced traffic safety and efficiency applications thanks to the dynamic exchange of information between vehicles, and between vehicles and infrastructure nodes. Connected vehicles can utilize IEEE 802.11p for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. However, a widespread deployment of connected vehicles and the introduction of connected automated driving applications will notably increase the bandwidth and scalability requirements of vehicular networks. This paper proposes to address these challenges through the adoption of heterogeneous V2V communications in multi-link and multi-RAT vehicular networks. In particular, the paper proposes the first distributed (and decentralized) context-aware heterogeneous V2V communications algorithm that is technology and application agnostic, and that allows each vehicle to autonomously and dynamically select its communications technology taking into account its application requirements and the communication context conditions. This study demonstrates the potential of heterogeneous V2V communications, and the capability of the proposed algorithm to satisfy the vehicles' application requirements while approaching the estimated upper bound network capacity

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Cloud-Assisted Device Clustering for Lifetime Prolongation in Wireless IoT Networks

One of the crucial challenges in the recently emerging Internet of Things (IoT) applications is how to handle the massive heterogeneous data generated from a large number of resource-constrained sensors. In this context, cloud computing has emerged as a promising paradigm due to its enormous storage and computing capabilities, thus leading to the IoT-Cloud convergence. In such a framework, IoT devices can be grouped into several clusters and each cluster head can send the aggregated information to the cloud via a gateway for further processing. Although a number of clustering methods have been proposed for the conventional Wireless Sensor Networks (WSNs), it is important to consider specific IoT characteristics while adapting these techniques for wireless IoT networks. One of the important features of IoT networks that can be exploited while developing clustering techniques is the collaborations among heterogeneous IoT devices. In this regard, the network-wide knowledge at the cloud center can be useful to provide information about the device relations to the IoT gateway. Motivated by this, we propose and evaluate a cloud-assisted device interaction-aware clustering scheme for heterogeneous IoT networks. The proposed method considers the joint impact of residual energy and device closeness factor for the effective selection of cluster heads. Our results show that the proposed clustering scheme can significantly prolong the network lifetime, and enhance the overall throughput of a wireless IoT network

EVEREST IST - 2002 - 00185 : D23 : final report

Deliverable públic del projecte europeu EVERESTThis deliverable constitutes the final report of the project IST-2002-001858 EVEREST. After its successful completion, the project presents this document that firstly summarizes the context, goal and the approach objective of the project. Then it presents a concise summary of the major goals and results, as well as highlights the most valuable lessons derived form the project work. A list of deliverables and publications is included in the annex.Postprint (published version

Smart PIN: utility-based replication and delivery of multimedia content to mobile users in wireless networks

Next generation wireless networks rely on heterogeneous connectivity technologies to support various rich media services such as personal information storage, file sharing and multimedia streaming. Due to users’ mobility and dynamic characteristics of wireless networks, data availability in collaborating devices is a critical issue. In this context Smart PIN was proposed as a personal information network which focuses on performance of delivery and cost efficiency. Smart PIN uses a novel data replication scheme based on individual and overall system utility to best balance the requirements for static data and multimedia content delivery with variable device availability due to user mobility. Simulations show improved results in comparison with other general purpose data replication schemes in terms of data availability