Multiple Roadside Units Research Articles

Mobility-Aware Edge Caching for Minimizing Latency in Vehicular Networks

This work proposes novel proactive caching schemesfor minimizing the communication latency in Vehicular <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ad Hoc</i> Networks (VANETs) under freeway and city mobility models. The main philosophy that underlies these schemes is to exploit information that may be available <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> for vehicles’ demands and mobility patterns. We consider two paradigms: cooperative, wherein multiple Roadside Units (RSUs) collaborate to expedite the transfer of information to the intended user, and non-cooperative, wherein each RSU operates independently of other RSUs in the network. To develop the proposed schemes, for each of the considered models we formulate optimization problems that expose the impact of velocity and demand profile of the vehicle on the optimal caching decision. Unfortunately, the developed formulations are NP-hard, and hence difficult to solve for moderate-to-large problems. To circumvent this difficulty, we use the insight developed through these formulations to develop practical caching algorithms based on the knapsack problem and suboptimal relaxations. These algorithms are shown to yield close-to-optimal solutions at much lower computation costs than the corresponding exhaustive search. Our numerical investigations suggest that the proposed proactive caching schemes yield substantial gains over the traditional caching policy, and their respective reactive baselines with no caching. Furthermore, the cooperative schemes are significantly more advantageous than their non-cooperative counterparts.

Deep Learning-Assisted Energy-Efficient Task Offloading in Vehicular Edge Computing Systems

In this paper, we study an energy-efficient computation offloading for vehicular edge computing systems, where multiple roadside units assist vehicular users to offload computation tasks to edge servers. Our goal is to minimize the users’ energy consumption by optimizing user association, data partition, transmit power, and computation resources, subject to the constraints of partial tasks offloading, user latency, maximum transmit power, outage performance, and computation capacity of edge servers. We utilize deep learning for user association to avoid combinatorial complexity, and develop an efficient optimization algorithm to optimize other variables. The resulting algorithm has scalable complexity with convergence guarantee, as confirmed by our theoretical analysis. Simulation results demonstrate that the introduced resource allocation algorithm can significantly reduce the total energy consumption of users.

Agile optimization for a real‐time facility location problem in Internet of Vehicles networks

AbstractThe uncapacitated facility location problem (UFLP) is a popular NP‐hard optimization problem that has been traditionally applied to logistics and supply networks, where decisions are difficult to reverse. However, over the years, many new application domains have emerged, in which real‐time optimization is needed, such as Internet of Vehicles (IoV), virtual network functions placement, and network controller placement. IoV scenarios take into account the presence of multiple roadside units (RSUs) that should be frequently assigned to operating vehicles. To ensure the desired quality of service level, the allocation process needs to be carried out frequently and efficiently, as vehicles' demands change. In this dynamic environment, the mapping of vehicles to RSUs needs to be reoptimized periodically over time. Thus, this article proposes an agile optimization algorithm, which is tested using existing benchmark instances. The experiments show that it can efficiently generate high‐quality and real‐time results in dynamic IoV scenarios.

Hybrid Task Coordination Using Multi-Hop Communication in Volunteer Computing-Based VANETs.

Computation offloading is a process that provides computing services to vehicles with computation sensitive jobs. Volunteer Computing-Based Vehicular Ad-hoc Networking (VCBV) is envisioned as a promising solution to perform task executions in vehicular networks using an emerging concept known as vehicle-as-a-resource (VaaR). In VCBV systems, offloading is the primary technique used for the execution of delay-sensitive applications which rely on surplus resource utilization. To leverage the surplus resources arising in periods of traffic congestion, we propose a hybrid VCBV task coordination model which performs the resource utilization for task execution in a multi-hop fashion. We propose an algorithm for the determination of boundary relay vehicles to minimize the requirement of placement for multiple road-side units (RSUs). We propose algorithms for primary and secondary task coordination using hybrid VCBV. Extensive simulations show that the hybrid technique for task coordination can increase the system utility, while the latency constraints are addressed.

Novel Roadside Unit Positioning Framework in the Context of the Vehicle-to-Infrastructure Communication System Based on AHP — Entropy for Weighting and Borda — VIKOR for Uniform Ranking

The positioning of roadside units (RSUs) in a vehicle-to-infrastructure (V2I) communication system may have an impact on network performance. Optimal RSU positioning is required to reduce cost and maintain the quality of service. However, RSU positioning is considered a difficult task because numerous criteria, such as the cost of RSUs, the intersection area and communication strength, affect the positioning process and must be considered. Furthermore, the conflict and trade-off amongst these criteria and the significance of each criterion are reflected on the RSU positioning process. Thus, this work proposes a new RSU positioning framework based on multicriteria decision-making (MCDM) in the context of the V2I communication system. Three stages are completed for this purpose. First, a real-time V2I hardware is developed to collect data. The developed hardware consists of multiple mobile nodes (i.e., cars with sending–receiving hardware devices) and physical RSUs. The RSUs and the devices in the cars are connected via the nRF24L01[Formula: see text]PA/LNA transceiver module with Arduino Uno. Second, seven testing scenarios are identified toward acquiring the required data upon the connection of the V2I devices. Moreover, three evaluation attributes (i.e., number of packet losses [PKL], cost and ratio of intersection area [RIA]) are used to evaluate each scenario. A decision matrix is constructed on the basis of the crossover between ‘RSU positioning scenarios’ and ‘multi-evaluation attributes (i.e., PKL, cost and RIA)’. Third, the RSU positioning scenarios are ranked using MCDM techniques, such as the integrated analytic hierarchy process (AHP), entropy and group Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Furthermore, the Borda voting approach is used to aggregate multiple individual rankings into a uniform and final rank. Results indicate the following: (1) integrating AHP, entropy and VIKOR is effective for solving RSU positioning problems; (2) the VIKOR ranking results for individuals vary; (3) the rank of scenarios obtained from the group-VIKOR-based Borda voting context shows that the second scenario, which consists of four RSUs distributed along the street with a maximum distance of 200[Formula: see text]m between them and 2-m high antennas, is the best in terms of optimally placing the RSUs; and (4) significant differences are observed amongst the scores of the groups, indicating that the ranking results are valid.

Dynamic Digital Twin and Distributed Incentives for Resource Allocation in Aerial-Assisted Internet of Vehicles

Internet of Vehicles (IoV), when empowered by aerial communications, provides vehicles with seamless connections and proximate computing services. The unpredictable network dynamics of aerial-assisted IoV pose challenges to the resource allocation. In this article, dynamic digital twin (DT) of aerial-assisted IoV is established to capture the time-varying resource supply and demands, so that unified resource scheduling and allocation can be performed. We design a two-stage incentive mechanism for resource allocation based on Stackelberg game where DT of vehicles or road side units (RSUs) is deemed as the leader, and the RSUs who provide computing services are as the follower. In the first stage incentive, we determine the computing resources that RSUs are willing to offer according to vehicles&#x2019; preferences. To further maximize the satisfaction of vehicles and the overall energy efficiency, a distributed incentive mechanism based on alternating direction method of multipliers (ADMMs) is then designed, in which the resource allocation policy for each vehicle is optimized. Thanks to ADMM, the incentive mechanism can be run at multiple RSUs in parallel to reduce delay and relieve the computational burden of UAVs. Simulation results show that the proposed scheme can improve the satisfaction of vehicles and the energy efficiency at the same time.

Vehicular cloud computing networks: availability modelling and sensitivity analysis

Vehicle ad hoc networks (VANETs) have emerged to make traffic more efficient and intelligent. Road side units (RSUs) can act as sensors and as a provider of route information for vehicles. RSUs have processing, storage, and communication capabilities. However, RSUs can suffer from peak requests, non-functional data demands and unavailability. To overcome this deficiency, cloud computing can act as an additional resource, processing part of the requests, named vehicular cloud computing (VCC). This paper uses stochastic Petri nets (SPNs) and reliability block diagrams (RBD) to assess a VCC architecture's availability and reliability with multiple RSUs. Two sensitivity analyses were performed which have identified the model's components that have the most significant impact. In addition to a base model, extended models with greater redundancy were also proposed. The base model has obtained A = 97.68%, and the extended model obtained A = 99.19%. Therefore, the models aim to help network administrators plan more optimised VANET architectures, reducing failures.

A Privacy-Preserving and Scalable Authentication Protocol for the Internet of Vehicles

One of the most important and critical requirements for the Internet of Vehicles (IoV) is security under strict latency. Typically, authentication protocols for vehicular ad hoc networks need to authenticate themselves frequently. This results in reduced application traffic and increased overhead. Moreover, the mobile nature of vehicles makes them a prime target for physical, side channel, and cloning attacks. To address these issues, this article presents an efficient protocol for authentication in the IoV. The proposed protocol uses physical unclonable functions to provide the desired security characteristics. To reduce the overhead of authentication and improve the throughput of application layer packets, the proposed protocol uses a three-layered infrastructure architecture for IoVs, i.e., roadside units (RSUs), RSU gateways, and trusted authority. A vehicle needs to authenticate only once when it enters the area of an RSU gateway which may engulf multiple RSUs. A performance analysis of the protocol shows that the proposed strategy significantly reduces the number of authentication packets and MAC/PHY overhead while the security analysis demonstrates its robustness against various types of attacks.

Detecting Sybil Attacks Using Proofs of Work and Location in VANETs

Vehicular Ad Hoc Networks (VANETs) have the potential to enable the next-generation Intelligent Transportation Systems (ITS). In ITS, data contributed by vehicles can build a spatio-temporal view of traffic statistics, which can improve road safety and reduce slow traffic and jams. To preserve drivers&#x2019; privacy, vehicles should use multiple pseudonyms instead of only one identity. However, vehicles may exploit this abundance of pseudonyms and launch Sybil attacks by pretending to be multiple vehicles. Then, these Sybil (or fake) vehicles report false data, e.g., to create fake congestion or pollute traffic management data. In this article, we propose a Sybil attack detection scheme using proofs of work and location. The idea is that each road side unit (RSU) issues a signed time-stamped tag as a proof for the vehicle&#x2019;s anonymous location. Proofs sent from multiple consecutive RSUs are used to create a trajectory which is used as vehicle anonymous identity. Also, contributions from one RSU are not enough to create trajectories, rather the contributions of several RSUs are needed. By this way, attackers need to compromise an infeasible number of RSUs to create fake trajectories. Moreover, upon receiving the proof of location from an RSU, the vehicle should solve a computational puzzle by running proof of work (PoW) algorithm. Then, it should provide a valid solution (proof of work) to the next RSU before it can obtain a proof of location. Using the PoW can prevent the vehicles from creating multiple trajectories in case of low-dense RSUs. To report an event, the vehicle has to send the latest trajectory to an event manager. Then, the event manager uses a matching technique to identify the trajectories sent from Sybil vehicles. The scheme depends on the fact that the Sybil trajectories are bounded physically to one vehicle, and therefore, their trajectories should overlap. Extensive experiments and simulations demonstrate that our scheme achieves high detection rate of Sybil attacks with low false negative and acceptable communication and computation overhead.

Semi-Persistent Resource Allocation Based on Traffic Prediction for Vehicular Communications

In cellular vehicular communications, high density and mobility of vehicles require frequent resource allocation, which can cause network congestion and large signalling and processing delay. To overcome this problem, we propose a novel semi-persistent resource allocation scheme based on a two-tier heterogeneous network architecture. The architecture includes a central macro base station (MBS) and multiple roadside units (RSU). In the proposed semi-persistent scheme, the MBS pre-allocates persistent resource to RSUs based on predicted traffic, and then allocates dynamic resource upon real-time requests from RSUs while vehicles simultaneously communicate using the pre-allocated resource. A simple Space-Time k-Nearest Neighbour (ST-kNN) method is developed for short-term traffic prediction, and a geometric water-filling algorithm is developed for minimizing the relative latency. Simulation results validate the effectiveness of the proposed semi-persistent scheme in comparison with two benchmark schemes.

Error Correction CP-BZD Storage Codes for Content Delivery in Drive-Thru Internet

In drive-thru internet, road side units (RSUs) are deployed along the road, which facilitate content dissemination to the vehicles on the go through vehicle to infrastructure (V2I) communications. Due to many factors including short and intermittent connections, the fading nature of wireless channel, and relatively fast speed of the vehicle, the vehicles might not be able to receive the complete content file successfully. Therefore, caching a file at multiple RSUs along the road in a collaborative manner is needed. To this end, the combination property (CP) is desired for caching the content: if $k$ source packets are mapped into $n \geq k$ packets and with any $k$ out of these $n$ packets are able to recover all the information. Reed-Solomon (RS) codes possess CP and have been widely adopted in distributed storage (DS) systems. RS codes operating within a large size finite field have high encoding/decoding complexity, which dramatically increase the computation burden and prolong the processing delay. By introducing several overhead bits and by smart design, binary zigzag decoding (BZD) can significantly reduce the decoding complexity, and CP-BZD codes that possess both CP and BZD have been proposed recently. For CP-BZD structured drive-thru internet system, packets delivered over the air might encounter errors in certain bits. In this work, without adding extra checking bits, existing overhead bits in CP-BZD is used instead, and a novel decoding method that reaps error correction ability is proposed. In other words, our method has self-error correction ability. Complexity analysis of this proposed method is performed and a low complexity algorithm is designed. This error correction module is completely optional, adaptable, and flexible to be deployed to various environments. Numerical studies show that the proposed method can achieve CP with both low decoding complexity and self-error correction.

OptDynLim: An Optimal Algorithm for the One-Dimensional RSU Deployment Problem With Nonuniform Profit Density

Proper deployment of roadside units (RSUs) is of crucial importance to vehicular ad hoc networks (VANETs). However, our understandings to the simple one-dimensional RSU Deployment (D1RD) problem with nonuniform profit density is still seriously limited. In this paper, we analyze the D1RD problem and try to design optimal algorithms for it. We first analyze the properties of the optimal solutions of the D1RD problem involving a single RSU, and then extend to multiple RSUs. Next, we propose an efficient technique named Dynamic Limiting (DynLim), which reduces the solution search space size considerably by adjusting search space limits dynamically. Finally, an optimal algorithm named OptDynLim is proposed based on the DynLim technique, and its optimality is proved. Numerical simulations validate the correctness of our analyzes and show that DynLim can usually reduce solution search space size by more than 99%.

Connectivity Analysis of a Highway With One Entry/Exit and Multiple Roadside Units

This paper studies the connectivity problem of vehicles moving on a highway road and considers a one-way road scenario where there exist one entry and exit, and multiple roadside units (RSUs) equidistantly deployed along the road. A connectivity analytical model is derived for analyzing the connectivity probability, taking into account the existence of the entry and exit and the deployment of multiple RSUs. In the considered road scenario, the entry and exit is uniformly distributed on the highway road. At the entry and exit, a vehicle may enter or exit from the road, or drive through the entry and exit with a certain probability. In deriving the connectivity analytical model, the highway road is divided into several segments and the connectivity probability of each segment is derived based on probability distribution theory. The connectivity probability of the highway road is then derived based on the connectivity probability on each sub-segment. Moreover, several parameters that may affect the connectivity of the road are taken into account, including a vehicle's arrival rate and moving speed, the number of RSUs deployed on the road, and a vehicle's driving-through probability at the entry and exit. The derived analytical model is justified based on simulation results. It can be used to investigate the effects of major parameters on the connectivity probability of a highway road, which is useful for the deployment of RSUs in such scenarios.

Engineering a Game Theoretic Access for Urban Vehicular Networks

Drive-thru Internet has emerged as a fundamental approach for information/content distribution to vehicles on the go. In the drive-thru Internet, one vehicle may connect to multiple roadside units (RSUs) along its trip, and in the meantime, multiple vehicles may compete one RSU at the same time for transmissions. Therefore, both vehicles and RSUs need to optimize their connections to achieve their best utilities. In the perspective of RSUs, it is important to select optimal vehicles to transmit so as to maximize the global RSU system's utilization. For individual vehicles, it needs to wisely select RSUs along its trip to connect so as to minimize its download cost, e.g., energy consumption and bandwidth cost. This paper targets to address the two design goals in one framework using a game theoretic model. Specifically, we model the two-dimensional drive-thru Internet as a second-price sealed-bid auction. In each RSU cell, an adaptive reserve price scheme is designed such that the RSU is allowed to selectively provide connections to vehicles based on the network size, vehicle's transmission rate, urgency of download, and content popularity; the RSU finally obtains the optimal utility on a Bayesian Nash equilibrium of the auction. For individual vehicles, a finite-horizon Markov decision process has been developed to guide vehicles to optimally select RSUs to connect along their road trips. Using extensive simulations, we demonstrate that the proposed framework can achieve the highest utility for the RSUs compared with existing proposals. It can also help vehicles keep a higher utility and transmission ratio when going through a single RSU or multiple RSUs than the conventional schemes.

A multiple RSU collaborative scheduling scheme for data services in vehicular ad hoc networks

As studies on vehicular ad hoc networks have been conducted actively in recent years, convenient and reliable services can be provided to vehicles through traffic information, surrounding information, and file sharing. To provide services for multiple requests, road side units (RSUs) should receive requests from vehicles and provide a scheduling scheme for data transfer according to priority. In this paper, we propose a new scheduling scheme by which multiple RSUs are connected through wired networks and data is transferred through the collaboration of RSUs. The proposed scheme transfers safety and non-safety data by employing a collaborative strategy of multiple RSUs as well as reducing the deadline miss ratio and average response time. When safety data is generated, data is transferred from the previous RSU in advance, while priority is assigned considering the deadline and reception rate. Since non-safety data is an on-demand data processed by user requests, the proposed scheme provides a method that reduces the deadline miss ratio upon loads generated in RSUs. To prove the superiority of the proposed scheme, we perform a performance evaluation in which the number and velocities of vehicles were changed. It is shown through the performance evaluation that the proposed scheme has better deadline miss ratios and faster response time than the existing schemes.

Collaborative Vehicle Location Management Service for Enhanced Hybrid Reactive and Proactive Multicast in VANETs

Delivery of information to highly dynamic nodes is a big challenge in vehicular ad hoc networks (VANETs). Existing location management protocols for VANET maintain locations of mobile nodes, but they exploit mobile vehicles as location servers, which increases location server changes and reduces stability. In this paper collaborative vehicle location management service (CVLMS) for an enhanced hybrid reactive and proactive multicast (EHRPM) facilitates knowing the location information of the moving destinations. EHRPM uses speeds, length, and densities of roads to choose the least delivery delay path. Existing information dissemination in VANETs does not discuss how location servers are used in localization. CVLMS exploits roadside units (RSUs) in cashing and dissemination of locations and dissemination of multicast messages. CVLMS guarantees stability using static RSUs that maintain locations to overcome multiple location server change problem. Location tables are stored in multiple RSUs which maintain load distribution. CVLMS provides an infrastructure platform which enables collaboration of RSUs to exchange locations. Scalability of the CVLMS is provided by increasing the number of vehicles and still gives high-performance results. Results demonstrate that CVLMS outperforms grid location service, intersection location service, Dynamic MANET On-demand with RSUs, and time-based vehicle-to-roadside unit communication in terms of packet delivery ratio, packet loss ratio, end-to-end delay, and overhead.

Reliable Adaptive Resource Management for Cognitive Cloud Vehicular Networks

In this paper, we design and test the performance of a distributed and adaptive resource management controller, which allows the optimal exploitation of cognitive radio and soft-input/soft-output data fusion in vehicular access networks. The goal is to allow energy and computing-limited car smartphones to utilize the available vehicle-to-infrastructure (V2I) WiFi connections for performing traffic offloading toward local or remote clouds by opportunistically acceding to a spectral-limited wireless backbone built up by multiple roadside units (i.e., cloudlets). We cast the resource management problem into a suitable constrained stochastic network utility maximization problem and derive the optimal cognitive resource manager that dynamically allocates the access time windows at the serving roadside units (i.e., the access points), together with the access rates and traffic flows at the served vehicular clients (i.e., the secondary users of the wireless backbone). The developed controller provides hard reliability guarantees to the cloud service provider (i.e., the primary user of the wireless backbone) on a per-slot basis. Furthermore, it is able to acquire context information about the currently available bandwidth–energy resources to quickly adapt to the mobility-induced abrupt changes in the state of the vehicular network.

Energy-Efficient Scheduling in Green Vehicular Infrastructure With Multiple Roadside Units

In this paper, we propose low-complexity algorithms for downlink traffic scheduling in green vehicular roadside infrastructure. In multiple roadside unit (RSU) deployments, the energy provisioning of the RSUs may differ, and it is therefore desirable to balance RSU usage from a normalized min-max energy viewpoint. This paper considers both splittable RSU assignment (SRA) and unsplittable RSU asssignment (URA) scheduling. An offline integer linear programming bound is first derived for normalized min-max RSU energy usage. We then show that in the SRA case, there is a polynomial complexity 2-approximation bound for the normalized min-max energy schedule. This paper then proposes several online scheduling algorithms. The first is a greedy online algorithm that makes simple RSU selections, followed by minimum-energy time slot assignments. A normalized min-max algorithm is then proposed [2-approximation online algorithm (TOAA)], which is an online version of the 2-approximation bound. Two algorithms are then introduced based on a potential function scheduling approach. The 1-objective algorithm uses an objective based on normalized min-max energy, and we show that it has an upper bounded worst-case competitive ratio performance. The 2-objective algorithm uses the same approach but incorporates a total-energy secondary objective as well. Results from a variety of experiments show that the proposed scheduling algorithms perform well. In particular, we find that in the SRA case, the TOAA algorithm performs very close to the lower bound but at the expense of having to reassign time slots whenever a new vehicle arrives. In the URA case, our low-complexity 1-objective algorithm performs better than the others over a wide range of traffic conditions.

Distributed and adaptive resource management in Cloud-assisted Cognitive Radio Vehicular Networks with hard reliability guarantees

In this contribution, we design and test the performance of a distributed and adaptive resource management controller, which allows the optimal exploitation of Cognitive Radio and soft-input/soft-output data fusion in Vehicular Access Networks. The ultimate goal is to allow energy and computing-limited car smartphones to utilize the available Vehicular-to-Infrastructure WiFi connections for performing traffic offloading towards local or remote Clouds by opportunistically acceding to a spectral-limited wireless backbone built up by multiple Roadside Units. For this purpose, we recast the afforded resource management problem into a suitable constrained stochastic Network Utility Maximization problem. Afterwards, we derive the optimal cognitive resource management controller, which dynamically allocates the access time-windows at the serving Roadside Units (i.e., the access points) together with the access rates and traffic flows at the served Vehicular Clients (i.e., the secondary users of the wireless backbone). Interestingly, the developed controller provides hard reliability guarantees to the Cloud Service Provider (i.e., the primary user of the wireless backbone) on a per-slot basis. Furthermore, it is also capable to self-acquire context information about the currently available bandwidth-energy resources, so as to quickly adapt to the mobility-induced abrupt changes of the state of the vehicular network, even in the presence of fadings, imperfect context information and intermittent Vehicular-to-Infrastructure connectivity. Finally, we develop a related access protocol, which supports a fully distributed and scalable implementation of the optimal controller.

Supporting real-time multiple data items query in multi-RSU vehicular ad hoc networks (VANETs)

There has been increasing interest in the issue of multi-item queries in wireless broadcasting systems recently. Query starvation and bandwidth utilization have been identified as key issues for improved performance. In this paper, we examine this problem in the context of VANETs with multiple cooperating Road Side Units (RSUs). We characterize a query with two deadlines: Query Total Deadline (QTD) which is the actual deadline of a query and Query Local Deadline (QLD) which is the duration a query is valid for serving in an RSU. By considering these two deadlines together with vehicle speed, RSU range and inter-RSU distance, we formulate a Cooperative Query Serving (CQS) scheme which allows multiple RSUs to share residual bandwidth and effectively address the query starvation as well as the bandwidth utilization problems, hence maximizing the chance of serving multiple items queries. Extensive simulation results confirm that CQS outperforms other existing scheduling algorithms.