Collecting data from a large number of agents scattered over a region of interest is becoming an increasingly appealing paradigm to feed big data archives that lay the ground for a vast array of applications. Vehicular Floating Car Data (FCD) collection, a major representative of this paradigm, is a key enabler for a wide range of Intelligent Transportation Systems (ITS) services and applications aiming at enhancing safety, efficiency and sustainability. Obtaining real time, high spacial and temporal resolution vehicular FCD information is becoming a reality thanks to the variety of communication platforms that are being deployed. Dedicated Short-Range Communication (DSRC) and Long Term Evolution (LTE) are the most prominent communication technologies able to support periodic and persistent FCD collection. DSRC technology was mainly proposed for safety applications and is specifically tailored for Vehicular Ad Hoc Networks (VANETs). The first parts of this work are dedicated to assessing the suitability of DSRC to support FCD collection in real urban scenarios. We first study the basic communication paradigm that takes place in VANETs to populate vehicles’ local data bases with FCD information, named beaconing, and the trade-off between the beaconing frequency and the congestion induced in the wireless shared channel used to exchange these beacons. The primary metric to measure the information freshness inside every vehicle’s local data base is the Age-of-Information (AoI). We define an analytical model to evaluate the AoI of a VANET, given the connectivity graph of the vehicles, and validate the model by comparing it with realistic simulations of an urban area. Then, we propose an integrated DSRC-based protocol that disseminates queries and collects FCD messages from vehicles roaming in a quite large city area efficiently and timely by using a single network structure, i.e., a multi-hop backbone network made up of only vehicle nodes. The proposed solution is distributed and adaptive to different traffic conditions, i.e., to different levels of vehicular traffic density. One of the main protocol advantages is that for the dissemination of queries it exploits an existing standardized data dissemination algorithm, namely the GeoNetworking Contention-Based Forwarding (CBF). The proposed protocol is evaluated with reference to a real urban environment. The main parameters are dimensioned and an insight into the protocol operation is given. One of the main outcomes of this part of the thesis is the confirmation of the fact that DSRC is suitable to support not only safety applications, but also periodic FCD collection. The main issue with DSRC is the low penetration rate. LTE on the other hand is pervasive and has been identified as a good candidate technology for non-safety applications. However, a high number of vehicles intermittently reporting their information via LTE can introduce a very high load on the LTE access network. The second part of this work addresses the design and performance evaluation of heterogeneous LTE-DSRC networking solutions to yield significant offloading of LTE – here, DSRC technology can support local data aggregation. We propose distributed clustering algorithms that use both LTE and DSRC networks in the cluster head selection process. We target robustness, optimizing the amount of data and the value of the collection period, keeping in mind the goals of autonomous node operation and minimal coordination effort. Our results clearly indicate that it is crucial to consider parameters drawn from both networking platforms for selecting the right forwarders. We demonstrate that our solutions are able to significantly reduce the LTE channel utilization with respect to other state-of-the-art approaches. The impact of the proposed protocols on the DSRC channels’ load is evaluated and proved to be quite small, so that it does not interfere with other VANET-specific messages.
|Titolo:||Integrated wireless access and networking to support floating car data collection in vehicular networks|
|Data di discussione:||22-feb-2018|
|Appartiene alla tipologia:||07a Tesi di Dottorato|