The Price of Fog: a Data-Driven Study on Caching Architectures in Vehicular Networks

Vehicular users are expected to consume large amounts of data, for both entertainment and navigation purposes. This will put a strain on cellular networks, which will be able to cope with such a load only if proper caching is in place, this in turn begs the question of which caching architecture is the best-suited to deal with vehicular content consumption. In this paper, we leverage a large-scale, crowd-collected trace to (i) characterize the vehicular traffic demand, in terms of overall magnitude and content breakup, (ii) assess how different caching approaches perform against such a real-world load, (iii) study the effect of recommendation systems and local contents. We define a price-of-fog metric, expressing the additional caching capacity to deploy when moving from traditional, centralized caching architectures to a "fog computing" approach, where caches are closer to the network edge. We find that for location-specific contents, such as the ones that vehicular users are most likely to request, such a price almost disappears. Vehicular networks thus make a strong case for the adoption of mobile-edge caching, as we are able to reap the benefit thereof -- including a reduction in the distance traveled by data, within the core network -- with little or no of the associated disadvantages.Comment: ACM IoV-VoI 2016 MobiHoc Workshop, The 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing: MobiHoc 2016-IoV-VoI Workshop, Paderborn, German

Vehicle to vehicle (V2V) wireless communications

This work focuses on the vehicle-to-vehicle (V2V) communication, its current challenges, future perspective and possible improvement.V2V communication is characterized by the dynamic environment, high mobility, nonpredective scenario, propagation effects, and also communicating antenna's positions. This peculiarity of V2V wireless communication makes channel modelling and the vehicular propagation quite challenging. In this work, firstly we studied the present context of V2V communication also known as Vehicular Ad-hoc Netwok (VANET) including ongoing researches and studies particularly related to Dedicated Short Range Communication (DSRC), specifically designed for automotive uses with corresponding set of protocols and standards. Secondly, we focused on communication models and improvement of these models to make them more suitable, reliable and efficient for the V2V environment. As specifies the standard, OFDM is used in V2V communication, Adaptable OFDM transceiver was designed. Some parameters as performance analytics are used to compare the improvement with the actual situation. For the enhancement of physical layer of V2V communication, this work is focused in the study of MIMO channel instead of SISO. In the designed transceiver both SISO and MIMO were implemented and studied successfully

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

Wi-Fi Offload: Tragedy of the Commons or Land of Milk and Honey?

Fueled by its recent success in provisioning on-site wireless Internet access, Wi-Fi is currently perceived as the best positioned technology for pervasive mobile macro network offloading. However, the broad transitions of multiple collocated operators towards this new paradigm may result in fierce competition for the common unlicensed spectrum at hand. In this light, our paper game-theoretically dissects market convergence scenarios by assessing the competition between providers in terms of network performance, capacity constraints, cost reductions, and revenue prospects. We will closely compare the prospects and strategic positioning of fixed line operators offering Wi-Fi services with respect to competing mobile network operators utilizing unlicensed spectrum. Our results highlight important dependencies upon inter-operator collaboration models, and more importantly, upon the ratio between backhaul and Wi-Fi access bit-rates. Furthermore, our investigation of medium- to long-term convergence scenarios indicates that a rethinking of control measures targeting the large-scale monetization of unlicensed spectrum may be required, as otherwise the used free bands may become subject to tragedy-of-commons type of problems.Comment: Workshop on Spectrum Sharing Strategies for Wireless Broadband Services, IEEE PIMRC'13, to appear 201

Relieving the Wireless Infrastructure: When Opportunistic Networks Meet Guaranteed Delays

Major wireless operators are nowadays facing network capacity issues in striving to meet the growing demands of mobile users. At the same time, 3G-enabled devices increasingly benefit from ad hoc radio connectivity (e.g., Wi-Fi). In this context of hybrid connectivity, we propose Push-and-track, a content dissemination framework that harnesses ad hoc communication opportunities to minimize the load on the wireless infrastructure while guaranteeing tight delivery delays. It achieves this through a control loop that collects user-sent acknowledgements to determine if new copies need to be reinjected into the network through the 3G interface. Push-and-Track includes multiple strategies to determine how many copies of the content should be injected, when, and to whom. The short delay-tolerance of common content, such as news or road traffic updates, make them suitable for such a system. Based on a realistic large-scale vehicular dataset from the city of Bologna composed of more than 10,000 vehicles, we demonstrate that Push-and-Track consistently meets its delivery objectives while reducing the use of the 3G network by over 90%.Comment: Accepted at IEEE WoWMoM 2011 conferenc

Why It Takes So Long to Connect to a WiFi Access Point

Today's WiFi networks deliver a large fraction of traffic. However, the performance and quality of WiFi networks are still far from satisfactory. Among many popular quality metrics (throughput, latency), the probability of successfully connecting to WiFi APs and the time cost of the WiFi connection set-up process are the two of the most critical metrics that affect WiFi users' experience. To understand the WiFi connection set-up process in real-world settings, we carry out measurement studies on $5$ million mobile users from $4$ representative cities associating with $7$ million APs in $0.4$ billion WiFi sessions, collected from a mobile "WiFi Manager" App that tops the Android/iOS App market. To the best of our knowledge, we are the first to do such large scale study on: how large the WiFi connection set-up time cost is, what factors affect the WiFi connection set-up process, and what can be done to reduce the WiFi connection set-up time cost. Based on the measurement analysis, we develop a machine learning based AP selection strategy that can significantly improve WiFi connection set-up performance, against the conventional strategy purely based on signal strength, by reducing the connection set-up failures from $33\%$ to $3.6\%$ and reducing $80\%$ time costs of the connection set-up processes by more than $10$ times.Comment: 11pages, conferenc