Ribal Atallah

Vehicular networking: A survey on spectrum access technologies and persisting challenges

Advanced wireless technologies are, nowadays, being exploited as means for intelligent transportation management and on-the-road driving assistance. However, the recent synergic efforts in both industry and academia are symptomatic of a paradigm shift in Intelligent Transportation Systems. Todays vehicles, being equipped with computerized modules and wireless devices, characterize themselves by a revolutionary smart personality. In particular they can carry and distribute information, they inter-communicate and are capable of communicating with other stationary units deployed along roadways. Most importantly, they can sense. Ultimately, the transportation infrastructure embraces versatile wireless communication systems with the objective of provisioning Wireless Access in Vehicular Environments (WAVE). In order to catalyze the realization of this objective, the U.S. Federal Communications Commission (FCC) has allocated 75 MHz of spectrum for Dedicated Short Range Communications (DSRC). The Institute of Electrical and Electronics Engineers (IEEE) has specifically tailored the 802.11p standard to regulate access to this spectrum. The IEEE 1609.4 protocol allows for multi-channel access and is currently being standardized. Alternatively, researchers have engaged in the design of scheduling policies for the purpose of increasing the spectrum access efficiency and optimizing the performance of vehicular networks in terms of several classical metrics. The recently emerging spectrum scarcity problem has led numerous investigations on the potential exploitation of cognitive radios as well as the feasibility of Vehicular Dynamic Spectrum Access (VDSA) schemes with the objective of: a) enhancing spectrum utilization and b) improving the networks throughput and response time. This paper sheds the light over the latest advancements in each of the above-mentioned research sectors and highlights pending open issues in each of them.

Energy harvesting in vehicular networks: a contemporary survey

Vehicular networks have recently been witnessing an upsurge of interest in energy consumption control. Precisely, in the majority of vehicular networking scenarios, roadside units are deployed along roadways in rural areas as well as on the sides of long highways where a direct connection to the electric grid is merely available. In such situations, these roadside units will be equipped with rechargeable batteries with maintenance requiring costly human intervention. Thus far, the literature offers several proposals of efficient operation schemes for roadside units aiming at optimizing their energy consumption, hence, elongating their duration of availability and participation in the network. Energy harvesting presents itself as an appealing alternative to power/recharge nodal batteries in wireless networks. This article starts by presenting a concise general overview of energy harvesting sources, techniques, and applications. Second, it investigates the feasibility of energy harvesting in vehicular networks, specifically the different challenges confronting its applicability in vehicular environments. Finally, pending related open research problems and directions are presented.

Unmanned Aerial Vehicles as Store-Carry-Forward Nodes for Vehicular Networks

A fully connected vehicular ad hoc network (VANET) establishes a strong foundation for the development of smart cities, where one of the main objectives is the improvement of the welfare of commuting passengers. The availability of a multi-hop path across a VANET system, through vehicle-to-vehicle communication, depends mainly on the vehicular density and the willingness of vehicles to cooperate with one another. This paper proposes to minimize the path availability’s dependence on vehicular density and cooperation, by utilizing unmanned aerial vehicles (UAVs). Particularly, this paper explores, both mathematically as well as through an extensive simulation study, the advantages of exploiting UAVs as store-carry-forward nodes so as to enhance the availability of a connectivity path as well as to reduce the end-to-end packet delivery delay. The obtained results shed clear light on the benefits emanating from the coupling of UAVs with vehicles in the context of a highly promising, innovative, and hybrid vehicular networking architecture.

A Reinforcement Learning Technique for Optimizing Downlink Scheduling in an Energy-Limited Vehicular Network

In a vehicular network where roadside units (RSUs) are deprived from a permanent grid-power connection, vehicle-to-infrastructure (V2I) communications are disrupted once the RSUs battery is completely drained. These batteries are recharged regularly either by human intervention or using energy harvesting techniques, such as solar or wind energy. As such, it becomes particularly crucial to conserve battery power until the next recharge cycle in order to maintain network operation and connectivity. This paper examines a vehicular network whose RSU dispossesses a permanent power source but is instead equipped with a large battery, which is periodically recharged. In what follows, a reinforcement learning technique, i.e., protocol for energy-efficient adaptive scheduling using reinforcement learning (PEARL), is proposed for the purpose of optimizing the RSUs downlink traffic scheduling during a discharge period. PEARLs objective is to equip the RSU with the required artificial intelligence to realize and, hence, exploit an optimal scheduling policy that will guarantee the operation of the vehicular network during the discharge cycle while fulfilling the largest number of service requests. The simulation input parameters were chosen in a way that guarantees the convergence of PEARL, whose exploitation showed better results when compared with three heuristic benchmark scheduling algorithms in terms of a vehicles quality of experience and the RSUs throughput. For instance, the deployment of the well-trained PEARL agent resulted in at least 50% improved performance over the best heuristic algorithm in terms of the percentage of vehicles departing with incomplete service requests.

Modelling of multi-hop inter-vehicular path formation for connecting far vehicles to RSUs

Vehicular Ad hoc Networks have been receiving significant interest during the past years as they support both safety and non-safety applications for passengers commuting onboard smart vehicles. Vehicles may communicate with each others for the purpose of sharing information. Moreover, they may be privileged by Broadband Internet access as well as other services provisioned by stationary Roadside Units (RSUs) deployed along the roadways. When a vehicle leaves the coverage range of an RSU, it enters a dark area. However, it may still maintain connectivity with the RSU through multi-hop communication with other cooperative vehicles serving as intermediate relays. In this paper, we study the probability of establishing a connectivity path between a far away vehicle residing in a dark area and an RSU deployed along a roadway experiencing free-flow traffic conditions. For this purpose, we establish a stochastic mathematical framework which jointly considers the availability of intermediate relay vehicles as well as their ability to capture the communication channel in a contention-based MAC environment. Extensive simulations were conducted for the purpose of validating the derived expressions and examining the throughput performance of the system.

Deep reinforcement learning-based scheduling for roadside communication networks

The proper design of a vehicular network is the key expeditor for establishing an efficient Intelligent Transportation System, which enables diverse applications associated with traffic safety, traffic efficiency, and the entertainment of commuting passengers. In this paper, we address both safety and Quality-of-Service (QoS) concerns in a green Vehicle-to-Infrastructure communication scenario. Using the recent advances in training deep neural networks, we present a deep reinforcement learning model, namely deep Q-network, that learns an energy-efficient scheduling policy from high-dimensional inputs corresponding to the characteristics and requirements of vehicles residing within a RoadSide Units (RSU) communication range. The realized policy serves to extend the lifetime of the battery-powered RSU while promoting a safe environment that meets acceptable QoS levels. Our presented deep reinforcement learning model is found to outperform both random and greedy scheduling benchmarks.

A Novel Drive-Thru Internet Channel Access Scheme

This letter proposes a novel and complexity minimal random vehicle selection (RVS) scheme for drive-thru Internet systems. A mathematical framework is established with the objective of modeling a vehicles onboard units buffer and evaluating its performance under RVS in terms of several quality-of-service metrics. Extensive simulations are conducted to verify the proposed models validity and accuracy.

A First Step Towards the Resolution of the Starvation Problem in Multi-Point-to-Point ICRCNs

This letter revolves around an Intermittently Connected Roadside Communication Network (ICRCN) scenario consisting of isolated source Stationary Roadside Units (SRUs) exploiting mobile smart vehicles as store-carry-forward data relays to a destination SRU. In this case, it is observed that a subset of these source SRUs may suffer from a significant starvation problem. In this letter, first, an Markov Decision Process (MDP) framework is established for the purpose of identifying a suitable Bulk Release Decision Policy (BRDP). Second, BRDP is implemented within a Starvation Mitigation and Delay-Minimal (SMDM) bundle delivery scheme. Extensive simulations are conducted for the purpose of: a) quantifying the severity of the starvation experienced by the downstream SRUs and b) gauging the merit of the proposed SMDM scheme through its ability to jointly mitigate starvation and achieve end-to-end delay minimal bundle delivery to the destination SRU.

Throughout analysis of IEEE 802.11p-based multi-hop V2I communications

This paper revolves around the evaluation of the achievable throughput in the context of an IEEE 802.11p-based vehicular subnetwork scenario where a completely isolated source vehicle, S, desires to communicate with a distant stationary Roadside Internet Gateway (RIG), D. Multi-hop inter-vehicular communication is exploited for the purpose of establishing a path between S and D along which downstream cooperative vehicles serve intermediate packet relays. The formation of such a path is governed by the vehicular traffic behaviour as well as the per-hop contention-oriented data forwarding process. Following the formation of a continuous chain of in-range cooperative vehicles between an arbitrary source-destination pair (S,D), a stochastic analytical framework is developed with the objective of determining the probability of successful data transfer from S to D taking into account the per-hop vehicle contentions for channel access. Then, theoretical expressions for the achievable per-hop as well as the end-to-end throughput are presented. Simulations are conducted for purpose of validating the presented analysis and evaluating the considered subnetworks performance.

Impact of information availability on starvation mitigation and delay minimal delivery in ICRCNs

This paper looks into an Intermittently Connected Roadside Communication Network (ICRCN) scenario comprising two isolated source Stationary Roadside Units (SRUs) relying on mobile smart vehicles to relay data to a destination SRU. In this case, it was shown in [1] that the downstream source SRU may suffer from a significant starvation problem. As such, a Markov decision process framework was established therein to identify a suitable Bulk Release Decision Policy (BRDP). BRDP was then implemented within a Starvation Mitigation and Delay-Minimal (SMDM) delivery scheme. In this paper, we investigate the impact of the level of information availability at the upstream non-starving node on the performance of the SMDM scheme. In particular, extensive simulations are conducted for the purpose of quantifying the ability of SMDM to jointly mitigate starvation and achieve minimal end-to-end bundle delivery delay under conditions of perfect, imperfect, and no information availability at the nonstarving node.