Simulation of visible light communications in vehicular networks

The rapid development in the field of lighting and illumination allows low energy consumption and a rapid growth in the use, and development of solid-state sources. As the efficiency of these devices increases and their cost decreases there are predictions that they will become the dominant source for general illumination in the short term. The objective of this thesis is to study, through extensive simulations in realistic scenarios, the feasibility and exploitation of visible light communication (VLC) for vehicular ad hoc networks (VANETs) applications. A brief introduction will introduce the new scenario of smart cities in which visible light communication will become a fundamental enabling technology for the future communication systems. Specifically, this thesis focus on the acquisition of several, frequent, and small data packets from vehicles, exploited as sensors of the environment. The use of vehicles as sensors is a new paradigm to enable an efficient environment monitoring and an improved traffic management. In most cases, the sensed information must be collected at a remote control centre and one of the most challenging aspects is the uplink acquisition of data from vehicles. My thesis discusses the opportunity to take advantage of short range vehicle-to-vehicle (V2V) and vehicle-to-roadside (V2R) communications to offload the cellular networks. More specifically, it discusses the system design and assesses the obtainable cellular resource saving, by considering the impact of the percentage of vehicles equipped with short range communication devices, of the number of deployed road side units, and of the adopted routing protocol. When short range communications are concerned, WAVE/IEEE 802.11p is considered as standard for VANETs. Its use together with VLC will be considered in urban vehicular scenarios to let vehicles communicate without involving the cellular network. The study is conducted by simulation, considering both a simulation platform (SHINE, simulation platform for heterogeneous interworking networks) developed within the Wireless communication Laboratory (Wilab) of the University of Bologna and CNR, and network simulator (NS3). trying to realistically represent all the wireless network communication aspects. Specifically, simulation of vehicular system was performed and introduced in ns-3, creating a new module for the simulator. This module will help to study VLC applications in VANETs. Final observations would enhance and encourage potential research in the area and optimize performance of VLC systems applications in the future.

Lightweight Internet Traffic Classification - A Subject Based Solution with Word Embeddings

Internet traffic classification is a relevant and mature research field, anyway of growing importance and with still open technical challenges, also due to the pervasive presence of Internet-connected devices into everyday life. We claim the need for innovative traffic classification solutions capable of being lightweight, of adopting a domain-based approach, of not only concentrating on application-level protocol categorization but also classifying Internet traffic by subject. To this purpose, this paper originally proposes a classification solution that leverages domain name information extracted from IPFIX summaries, DNS logs, and DHCP leases, with the possibility to be applied to any kind of traffic. Our proposed solution is based on an extension of Word2vec unsupervised learning techniques running on a specialized Apache Spark cluster. In particular, learning techniques are leveraged to generate word-embeddings from a mixed dataset composed by domain names and natural language corpuses in a lightweight way and with general applicability. The paper also reports lessons learnt from our implementation and deployment experience that demonstrates that our solution can process 5500 IPFIX summaries per second on an Apache Spark cluster with 1 slave instance in Amazon EC2 at a cost of $ 3860 year. Reported experimental results about Precision, Recall, F-Measure, Accuracy, and Cohen's Kappa show the feasibility and effectiveness of the proposal. The experiments prove that words contained in domain names do have a relation with the kind of traffic directed towards them, therefore using specifically trained word embeddings we are able to classify them in customizable categories. We also show that training word embeddings on larger natural language corpuses leads improvements in terms of precision up to 180%.