Ion Turcanu

Heterogeneous cellular and DSRC networking for Floating Car Data collection in urban areas

Abstract Vehicular traffic monitoring is a major enabler for a whole range of Intelligent Transportation System services. Real time, high spatial and temporal resolution vehicular traffic monitoring is becoming a reality thanks to the variety of communication platforms that are being deployed. Dedicated Short Range Communications (DSRC) and cellular communications like Long Term Evolution (LTE) are the major technologies. The former is specifically tailored for Vehicular Ad-hoc Network, the second one is pervasive. We propose a fully distributed Floating Car Data (FCD) collection protocol that exploits the heterogeneous network provided by DSRC and LTE. The proposed approach adapts automatically to the penetration degree of DSRC, achieving the maximum possible LTE offloading, given the VANET connectivity achieved via DSRC. Extensive simulations in real urban scenarios are used to evaluate the protocol performance and LTE offloading, as compared to baseline and literature approaches.

An integrated VANET-based data dissemination and collection protocol for complex urban scenarios

Data dissemination and data collection to/from vehicles traveling on city roads are key features to fully enable the advent of Intelligent Transport Systems and Autonomous vehicles. Both Road Side Units and On Board Units need to disseminate different kind of data to vehicles or to collect data sensed by the vehicles themselves and transfer them to road monitoring and control centers. In this work we propose a protocol, named DISCOVER, that disseminates and collects the data of interest in a quite large city area efficiently and timely by using a single network structure, i.e., a multi-hop backbone made up only of vehicles nodes. DISCOVER is distributed and adaptive to the different traffic conditions, i.e., to the different levels of vehicular traffic density. Several numerical results show that it attains very good performance in different type of city maps (New York, Paris, Madrid and Rome) when compared with baseline approaches as well as when compared with a theoretical bound.

Pick the right guy: CQI-based LTE forwarder selection in VANETs

Periodical collection of data from vehicles inside a target area is of interest for many applications in the context of Intelligent Transportation Systems (ITS). Long Term Evolution (LTE) has been identified as a good candidate technology for supporting such type of applications — particularly for the non-safety domain. However, a high number of vehicles intermittently reporting their information via LTE can introduce a very high load on the LTE access network. In this context, the use of heterogeneous networking technologies can yield significant offloading of LTE — here, WLAN and Dedicated Short-Range Communication (DSRC) technology can support local data aggregation. In this paper, we propose an on-the-fly distributed clustering algorithm that uses both LTE and DSRC networks in the forwarder selection process. Our results clearly indicate that it is crucial to consider parameters drawn from both networking platforms for selecting the right forwarders. In particular, we show for the first time that relying on the Channel Quality Indicator (CQI) has a substantial impact. We demonstrate that our solution is able to significantly reduce the LTE channel utilization with respect to other state of the art approaches.

Traffic monitoring and incident detection using cellular and early stage VANET technology deployment

In the current Intelligent Transportation Systems (ITS) traffic monitoring and incident detection are usually supported with mostly traditional and relatively slow reactivity technologies. In this paper we propose a new service, namely THOR (Traffic monitoring Hybrid ORiented service), able to combine two different wireless technologies and to provide real time information about vehicular traffic monitoring and incident detection. THOR relies on LTE (Long Term Evolution) and Dedicated Short Range Communication based VANETs (Vehicular ad-hoc NETworks) in a hybrid approach, which is compliant with ITS standards. This hybrid networking approach can be deployed today and can be ready for tomorrow VANET technology. We test THOR by simulations in a scenario with vehicle flows synthesized from real measured vehicular traffic traces. We provide an LTE load analysis and an assessment of incident detection capabilities. Our results are promising in terms of reactivity, precision and network traffic load sustainability.

DISCOVER: A Unified Protocol for Data Dissemination and Collection in VANETs

Message dissemination and data collection from vehicles are two key enablers of Intelligent Transportation System services that can be offered by a Vehicular Ad-Hoc Network (VANET) technology. In this work we propose a fully distributed protocol for dissemination of query and collection of reply messages carrying information gathered from vehicles moving in a given target area, in an urban scenario. The key idea is to use the dissemination phase (forward wave) to create a network of relay nodes that are in charge of delivering reply messages back to the originating point (reverse wave). The proposed protocol is evaluated with reference to two real urban environments. Main parameters are dimensioned and an insight in the protocol working is given.

Traffic Management and Networking for Autonomous Vehicular Highway Systems

Abstract We develop traffic management and data networking mechanisms and study their integrated design for an autonomous transportation system. The traffic management model involves a multi-lane multi-segment highway. Ramp managers regulate admission of vehicles into the highway and their routing to designated lanes. Vehicles moving across each lane are organized into platoons. A Platoon Leader (PL) is elected in each platoon and is used to manage its members and their communications with the infrastructure and with vehicles in other platoons. We develop new methods that are employed to determine the structural formations of platoons and their mobility processes in each lane, aiming to maximize the realized flow rate under vehicular end-to-end delay constraints. We set a limit on the vehicular on-ramp queueing delay and on the (per unit distance) transit time incurred along the highway. We make use of the platoon formations to develop new Vehicle-to-Vehicle (V2V) wireless networking cross-layer schemes that are used to disseminate messages among vehicles traveling within a specified neighborhood. For this purpose, we develop algorithms that configure a hierarchical networking architecture for the autonomous system. Certain platoon leaders are dynamically assigned to act as Backbone Nodes (BNs). The latter are interconnected by communications links to form a Backbone Network (Bnet). Each BN serves as an access point for its Access Network (Anet), which consists of its mobile clients. We study the delay-throughput performance behavior of the network system and determine the optimal setting of its parameters, assuming both TDMA and IEEE 802.11p oriented wireless channel sharing (MAC) schemes. Integrating these traffic management and data networking mechanisms, we demonstrate the performance tradeoffs available to the system designer and manager when aiming to synthesize an autonomous transportation system operation that achieves targeted vehicular flow rates and transit delays while also setting the data communications network system to meet targeted message throughput and delay objectives.

Duplicate Suppression for Efficient Floating Car Data Collection in Heterogeneous LTE-DSRC Vehicular Networks

Abstract 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 is a major representative of this paradigm. Massive data collection from floating vehicles is the key to Intelligent Transportation Systems. We address the design and performance evaluation of a data collection protocol for the use case of periodic data collection. 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. From a system point of view, we believe that best solutions should jointly exploit the Long Term Evolution (LTE) cellular access network and the Dedicated Short-Range Communication (DSRC) based Vehicular Ad Hoc Network (VANET). Through a detailed comparative analysis, we show that such a hybrid approach offers superior performance, especially as for offloading the cellular radio access. A lightweight signaling procedure is designed, based on the DSRC VANET, which is able to avoid most of the duplicated data records, even if a distributed operation approach is pursued. The impact of the proposed protocol on the VANET load is evaluated and proved to be quite small, so that it does not interfere with other VANET-specific messages.

A Model for the Optimization of Beacon Message Age-of-Information in a VANET

Beaconing is a basic communication process taking place in Vehicular Ad Hoc Networks (VANETs) to achieve cooperative awareness among vehicles on the road. It is actually a paradigm of information spreading among peer-agents, where each node of a networks sends periodically broadcast messages containing information collected by the node itself. A trade-off arises between the update frequency of the broadcast information and the congestion induced in the wireless shared channel used to send the messages, which is based on the IEEE 802.11p standard in case of a VANET. For periodic updates, the primary metric is the Age-of-Information (AoI), i.e., the age of the latest update received by neighboring nodes. We define an analytical model to evaluate the AoI of a VANET, given the connectivity graph of the vehicles. Analytical results are compared to simulation to assess the accuracy of the model. The model provides a handy tool to optimize the AoI trade-off.