Vol. 76, n° 9-10, September-October 2021
Content available on Springerlink
Guest editors
Lyes Khoukhi, University of Technology of Troyes, Troyes, France
Hu Xiong, University of Electronic Science and Technology of China, Chengdu, China
Saru Kumari, Department of Mathematics, Ch. Charan Singh University, Meerut, India and
National Institute of Technology Karnataka, Mangalore, India
Editorial
The Internet of vehicles and smart cities
Lyes Khoukhi · Hu Xiong · Saru Kumari · Nicolas Puech
Challenges and limits of fractal and slot antennas for WLAN, LTE, ISM, and 5G communication: a review paper
Amer T. Abed1 · Mandeep S. Jit Singh2 · Vinesh Thiruchelvam3 · Shankar Duraikannan3 ·
Omar Almukhtar Tawfeeq1 · Bushra A. Tawfeeq4 · Mohammad Tariqul Islam4
(1) Department of Communication Engineering, AL-MAMON University College, Baghdad, Iraq
(2) Department of Electrical, Electronic and System Engineering, Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
(3) Asia Pacific University of Technology & Innovation, Kuala Lumpur, Malaysia
(4) AL-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq
Abstract Portable communication devices such as WLAN, WiMAX, LTE, ISM, and 5G utilize one or more of the triple bands at (2.3–2.7 GHz, 3.4–3.6 GHz, and 5–6 GHz) and suffer from the effect of multipath problems because they are used in urban regions. To date, no one has performed a review of the antennas used for these types of wireless communications. This study reviewed two types of microstrip antennas (slot and fractal) that have been reported by researchers (as a single element) using a survey that included the evaluation of several important specifications of the antennas in previous research, such as operating bandwidth, gain, efficiency, axial ratio bandwidth (ARBW), and size. The weaknesses in the design of all antennas were carefully identified to determine the most important challenges in the design of these antennas and name the most important limits in the design to be overcome in future research. This study also indicated some antennas that have circular polarization characteristics, the techniques used to generate the circular polarization characteristics, and the challenges. Finally, several suggestions as a guideline for antenna design and future work in designing antennas for Wi-Fi, LTE, and WiMAX communications, according to the market demands, and methods for overcoming the identified limits are presented.
Keywords Fractal · Slot · Circularly polarized · Linearly polarized
Joint mobile vehicle–UAV scheme for secure data collection
in a smart city
Shaobo Huang1 · Jinsong Gui1 · Tian Wang2 · Xiong Li3
(1) School of Computer Science and Engineering, Central South University, Changsha, China
(2) Department of Computer Science and Technology, Huaqiao University, Xiamen, Fujian Province, China
(3) Institute for Cyber Security, School of Computer Science and Engineering, University of Electronic Science and Technology of
China, Chengdu, China
Abstract A vehicular delay-tolerant network (VDTN) allows mobile vehicles (MVs) to collect data from widely deployed delay-tolerant sensors in a smart city through opportunistic routing, which has proven to be an efficient and low-cost data collection method. However, malicious MVs may report false data to obtain rewards, which will compromise applications. In this paper, the Active Trust Verification Data Collection (ATVDC) scheme is proposed for efficient, cheap, and secure data collection. In this scheme, an unmanned aerial vehicle (UAV) is adopted to collect baseline data from sensors to evaluate the trust of MVs, and a high-trust MV priority recruitment (HTMPR) strategy is proposed to recruit credible MVs at a low cost. In addition, a genetic-algorithm-based trajectory planning (GATP) algorithm is proposed to allow the UAV to collect more baseline data at the minimum flight cost. After sufficient experiments, the strategy proposed in this paper is seen to greatly improve performance in terms of the error-free ratio EF, the symbol error ratio ES, and the data coverage ratio ϑ.
Keywords Vehicular delay-tolerant network · Smart city · Trust · Data collection · Mobile vehicles · Unmanned aerial vehicles
A review of clustering algorithms in VANETs
Mengying Ren1 · Jun Zhang1 · Lyes Khoukhi3 · Houda Labiod2 · Véronique Vèque4
(1) University of Technology of Troyes, Troyes, France
(2) Telecom Paris, Institut Polytechnique de Paris, Palaiseau, France
(3) Normandie Univ, UNICAEN, ENSICAEN, CNRS, GREYC, Caen, France
(4) Université Paris Saclay, CNRS Centrale Supélec, Laboratoire des Signaux et Systèmes, Gif-sur-Yvette, France
Abstract In vehicular ad hoc network (VANET), lots of information should be delivered on a large scale in a limited time. Meanwhile, vehicles are quite dynamic with high velocities, which causes a large number of vehicle disconnections. Both of these characteristics lead to unreliable information transmission in VANET. A vehicle clustering algorithm, which organizes vehicles in groups, is introduced in VANET to improve network scalability and connection reliability. However, different clustering techniques and algorithms are required for different scenarios, such as information transmission, routing, and accident detections. This paper explores the vehicle clustering techniques from the aspects of cluster head selection, cluster formation, and cluster maintenance procedures. Meanwhile, context-based clustering algorithms are summarized, and the hybrid-clustering algorithms are highlighted. The paper also summarizes the existing clustering performance metrics and performance evaluation approaches.
Keywords VANET · Cluster · Algorithm
Secure outsourced attribute-based signcryption for cloud-based
Internet of Vehicles in a smart city
Negalign Wake Hundera1 · Chuanjie Jin1 · Muhammad Umar Aftab2 · Dagmawit Mesfin3 · Sachin Kumar4
(1) School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
(2) Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot, Pakistan
(3) School of Information Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
(4) Department of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, India
Abstract The rapid growth of wireless communication and sensing technology will lead to an Internet of Vehicles (IoV) that builds a large network with interactions among the vehicles, roadside infrastructure, and the surrounding environment. This will allow a large amount of data to be gathered from vehicles through IoV nodes. On the other hand, the cloud provides on-demand computing resources and self-service. Therefore, the vast amounts of data collected by the IoV can be deployed to the cloud, and an authorized user can access it anytime from anywhere via the Internet. However, privacy and authorized access of data are the main issues for the data stored in the cloud. To deal with the security issues in a cloud environment, an attribute-based signcryption (ABSC) was presented that provides confidentiality, authenticity, and sender secrecy of the data stored in the cloud. However, an extensive amount of modular exponential operations and pairing incur an unfavorable computational overhead during the signcryption and key-distributing process in ABSC. To deal with the above challenges, we present a novel secure outsourced attribute-based signcryption for a cloud-based Internet of Vehicles in a smart city (SOABSC-IoV). The proposed scheme provides data confidentiality, data authentication, and key issuing and decreases the signcryption computational load in ABSC. Furthermore, this work reduces the computational load at the IoV node and the authorized user’s side by outsourcing the signcryption and unsigncryption algorithms. Finally, the experimental results demonstrate that our scheme is highly appropriate for cloud and IoV environments and has a lower computational complexity than the existing schemes.
Keywords Attribute-based signcryption · Signcryption · Unsigncryption · Cloud · Internet of Vehicles (IoV)
IEEE 802.11p performance enhancement based on Markov chain
and neural networks for safety applications
Fadlallah Chbib1,2 · Walid Fahs2 · Jamal Haydar2 · Lyes Khoukhi3 · Rida Khatoun4
(1) ICD/ERA Lab, Université de Technologie de Troyes, Troyes, France
(2) CCE/Faculty of Engineering, Islamic University of Lebanon, Khalde, Lebanon
(3) ENSICAEN, GREYC, Caen, France
(4) INFRES Department at Telecom Paristech, Paris, France
Abstract Vehicular communication is recently considered as one of the key future technology to improve the safety of vehicles, the efficiency of traffic and the comfort for both drivers and pedestrians. Vehicular communications, based on IEEE 802.11p, use the Enhanced Distributed Channel Access (EDCA) algorithm to support different levels of Quality of Service (QoS). In this paper, a machine learning neural network with Markov chain approach is proposed to ensure the delivery of urgent safety messages to the receiver whatever the situation of the network. We propose to control the rate of periodic messages in Control Channel (CCH), by modifying the back-off parameters according to the state of the buffer. We also use Radial Basis Function Neural Network (RBFNN) to adjust the EDCA back-off parameters, using the following parameters: the priority of message (P), the sensitivity of road (S), the threshold of buffer (T), and the type of vehicle (V). Our simulation is done using SUMO 0.22 simulator, NS 2.34 and awk scripts; the simulation was applied on Hamra area (Lebanon). The results show that our proposed models perform better compared to the IEEE 802.11p in terms of packet delivery ratio, throughput and end-to-end delay.
Keywords Vehicular Ad Hoc networks (VANETs) · Enhanced distributed channel Access (EDCA) · Control Channel(CCH) ·
Radial Basis Function Neural (RBFNN)
3DMAT: data dissemination for disaster management using
available technology in a smart city
Amira Ichrak Tei1 · Zouina Doukha1 · Youcef Zafoune1
(1) Faculty of Electronics and Computer Science, Laboratory for Research in Intelligent Computing, Mathematics and Applications (RIIMA), USTHB University, Bab Ezzouar, Algeria
Abstract Disaster management systems (DMSs) aim to mitigate the potential damage from disasters by ensuring immediate and suitable assistance to victims. Disaster management is a challenging problem because while information needs to be processed in real time, the damaged environment can prevent it from being disseminated to processing centres. Our goal is to exploit available technologies such as Wireless Sensor Network (WSNs) and Vehicular Ad hoc NETworks (VANETs) to forward alerts from victims to rescue services. We propose an opportunistic data dissemination protocol (3DMAT) for disaster management in which both WSNs and VANETs participate in decision-making for the dissemination process so that messages are delivered in a timely manner. The simulation results show that the proposed protocol performs data dissemination more efficiently than other protocols. 3DMAT calculates the quality of nodes and the link between them to select the most relevant relay. Our protocol is 47% faster and generates a 17% lower communication load and 14% fewer redundant messages. These improvements are due to a selection strategy that targets the most relevant nodes to relay information.
Keywords Disaster management system (DMS) · Heterogeneous network · Opportunistic communication ·
Multihop routing · VANETs (vehicular Ad hoc networks) · WSNs (Wireless sensor network) · Smart city · Data dissemination
Infrastructure localization service and tracking scheme
in uncovered areas for Internet of Vehicles
Chahrazed ksouri1,2 · Imen Jemili3 · Mohamed Mosbah1 · Abdelfettah Belghith4
(1) CNRS, University of Bordeaux, Bordeaux INP, LaBRI, UMR 5800, Bordeaux, France
(2) National School of Engineers of Sfax, University of Sfax, Sfax, Tunisia
(3) Faculty of Sciences of Bizerte, University of Carthage, Tunis, Tunisia
(4) College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
Abstract With the advent of Internet of Vehicles, overhead related to localization services and path discovery step can drain the network resources and overload the large-scale vehicular networks, thus slowing down the routing process, especially with the recent surge of time-sensitive and QoS (Quality of Service) greedy applications. Performing proactively these tasks contributes to reducing the associated overhead by exploiting the vehicular beaconing system, as related data can be piggybacked with beacon messages. In this context, we present ILTS, an Infrastructure Localization service and Tracking Scheme, designed to supply vehicles with contextual information about the infrastructure accessibility and the neighborhood, in order to track available paths and to forward promptly safety and non-safety-related information. Through extensive simulation experiments, we have proven the ability of ILTS to promptly supply vehicles with fresh information about the location of the infrastructure and the availability of paths to reach it. The obtained results show that the vehicles are able to maintain knowledge of full paths towards the infrastructure during an average of 90% of the time spent in an uncovered area.
Keywords Vehicular ad hoc networks · Internet of Vehicles · Intermittent infrastructure · Localization information service · Path tracking
Network slicing for vehicular communications: a multi-agent deep
reinforcement learning approach
Zoubeir Mlika1 · Soumaya Cherkaoui1
(1) Department of Electrical and Computer Engineering, Université de Sherbrooke, Sherbrooke, Canada
Abstract This paper studies the multi-agent resource allocation problem in vehicular networks using non-orthogonal multiple access (NOMA) and network slicing. Vehicles want to broadcast multiple packets with heterogeneous quality-of-service (QoS) requirements, such as safety-related packets (e.g., accident reports) that require very low latency communication, while raw sensor data sharing (e.g., high-definition map sharing) requires high-speed communication. To ensure heterogeneous service requirements for different packets, we propose a network slicing architecture. We focus on a non-cellular network scenario where vehicles communicate by the broadcast approach via the direct device-to-device interface (i.e., sidelink communication). In such a vehicular network, resource allocation among vehicles is very difficult, mainly due to (i) the rapid variation of wireless channels among highly mobile vehicles and (ii) the lack of a central coordination point. Thus, the possibility of acquiring instantaneous channel state information to perform centralized resource allocation is precluded. The resource allocation problem considered is therefore very complex. It includes not only the usual spectrum and power allocation, but also coverage selection (which target vehicles to broadcast to) and packet selection (which network slice to use). This problem must be solved jointly since selected packets can be overlaid using NOMA and therefore spectrum and power must be carefully allocated for better vehicle coverage. To do so, we first provide a mathematical programming formulation and a thorough NP-hardness analysis of the problem. Then, we model it as a multi-agent Markov decision process. Finally, to solve it efficiently, we use a deep reinforcement learning (DRL) approach and specifically propose a deep Q learning (DQL) algorithm. The proposed DQL algorithm is practical because it can be implemented in an online and distributed manner. It is based on a cooperative learning strategy in which all agents perceive a common reward and thus learn cooperatively and distributively to improve the resource allocation solution through offline training. We show that our approach is robust and efficient when faced with different variations of the network parameters and compared to centralized benchmarks.
Keywords Network slicing · Vehicle-to-vehicle · Non-orthogonal multiple access · Resource allocation · Deep Q learning ·
Deep reinforcement learning
Reinforcement learning‑based clustering scheme for the Internet
of Vehicles
Hayet Zerrouki1 · Samira Moussaoui1 · Abdessamed Derder1 · Zouina Doukha1
(1) RIIMA Lab. Computing Department, USTHB, Bab Ezzouar, Algeria
Abstract Clustering is an efficient technique for achieving high scalability on the Internet of Vehicles (IoV). However, the latency and overhead generated from forming and maintaining clusters are common barriers to the mass adoption of this technique. To this end, we propose an efficient clustering scheme for the IoV. Leveraging reinforcement learning, our scheme can quickly form network condition-aware clusters. In addition, our reinforcement learning-based clustering scheme (RLBC) assures dynamic and cooperative maintenance for clusters. The effectiveness of our scheme is evaluated through extensive simulations. The simulation results show that the RLBC outperforms a previously developed approach and allows for more persistent cluster heads with higher durations and stable connections with their members.
Keywords Internet of Vehicles · Clustering · Medium access control · Reinforcement learning · Mobility
A novel collision avoidance scheme for smart parking
Soumaya Dahi1 · Imen Jemili2
(1) Communication Engineering School (Sup’Com), LR11TIC05 MEDIATRON Lab, Faculty of Sciences of Bizerte, University
of Carthage, Tunis, Tunisia
(2) Faculty of Sciences of Bizerte, University of Carthage, Tunis, Tunisia
Abstract In overcrowded cities, parking space becomes a scarce resource especially during peak hours, when citizens are looking for a vacant parking spot near their work or their children’s school. The high demand for parking spaces near these locations leads to traffic jams and incidents due to drivers cruising the streets or around parking lots in search of an unoccupied space. Drivers feel even more frustrated when they are forced to give up a spot that is coveted by several other drivers at the same time, once they get there. Most of the proposed smart parking solutions do not address the problems that occur when multiple vehicles are chasing the same single space. In this context, we propose CAS-SP, a Collision-Avoidance Scheme for Smart Parking that aims to help drivers shorten their search time inside parking lots, reduce costs by consuming less fuel and feel less stressed during this daily routine task. Our scheme is inspired by the CSMA/CA method, widely deployed in wireless networks. It allows deferring the vehicle entry to the parking lot by applying a tuned backoff selection strategy. We investigate our proposed scheme under different scenarios related to the parking lot architecture (single level, multi-level), the parking lot occupancy and the number of incoming vehicles. The obtained results show the effectiveness of our scheme in resolving the contention, reducing the collision among vehicles by about 88%, shortening the wasted time in searching for a vacant place inside the parking lot and saving about 450 s for a multi-level parking lot with many entrances.
Keywords Smart parking · Driver assistance · Collision-avoidance
Named data networking architecture for internet of vehicles
in the era of 5G
Abdellah Kaci1 · Abderrezak Rachedi2
(1) Ecole Nationale Superieure de Technologie (ENST), Algiers, Algeria
(2) Universite Paris-Est, Champs sur Marne, France
Abstract Things are interconnected using information and communication technologies in smart cities, forming Internet of Things (IoT). The Internet of Vehicles (IoV) refers to an IoT application, where the urban vehicle fleet forms a worldwide network, using V2X (Vehicle-to-Everything) communications. The 5G is the new generation of cellular networks that will eliminate the bounds of bandwidth, performance, and latency limitations. IoV is one of the high-priority application domains for 5G. Among the under development IEEE Standard regarding 5G, the IEEE P1931.1 standard (named also Real-time Onsite Operations Facilitation (ROOF) Standard) seems to be very promising for IoV requirements. This paper proposes ROOF-based Named Data Vehicular Networking (RND − Vn), a named data networking (NDN) architecture for IoV. In addition to the proposal, we provide SeCrNDn (Searchable Encryption for Content Retrieval in NDN), a searchable encryption technique for NDN content retrieval. Furthermore, we propose the intelligent Named Data Caching (iNDC), a machine-learning–based data caching technique for ROOF-based named data networking. The iNDC predicts the number of content requests, such that popular contents are kept as long as possible on roadside units. The proposed iNDC is also used to predict the storage capacity required by each roadside unit. A performance study was conducted to evaluate the performance of machine learning algorithms applied to iNDC. The results show that linear and ridge regressions are the most efficient in terms of content popularity prediction. To predict the capacity of new roadside units, iNDC provides better accuracy using k-Nearest Neighbors.
Keywords Internet of vehicles · Named data networking · ROOF standard · Machine learning · Searchable encryption ·
Smart cities · Intelligent transportation systems
Testbed of V2X infrastructure for autonomous vehicles
Naila Bouchemal1 · Sondes Kallel1
(1) Centre de Recherche de L’ECE Paris, 37 Quai de Grenelle, Paris, France
(2) Laboratoire DAVID 45 Avenue des États Unis, 78000 Versailles, France
Abstract In recent years, research concerning autonomous driving has gained momentum to enhance road safety and traffic efficiency. Relevant concepts are being applied to the fields of perception, planning, and control of automated vehicles, to leverage the advantages offered by vehicle-to-everything (V2X) communication technology. The emergence of V2X communications increases the level of certainty regarding a vehicle’s surroundings and serves as an enabler for autonomous driving. V2X is essential for safe and efficient autonomous driving. Therefore, we need to study the performance of V2X communications to better perform its integration in future autonomous vehicles. In this paper, we describe a testbed V2X infrastructure with components to test the real effectiveness of V2X for autonomous driving using dedicated short-range communications (DSRC) technology. We present our developed intelligent transportation system (ITS) applications, and then, we show their integration within the autonomous vehicle on the one hand and within the infrastructure module on the other hand. Finally, to show the efficiency of this development, we conduct several real-world tests on an end-to-end communication architecture.
Keywords V2X · Autonomous driving levels 3 and 4 · ITS applications · Testbed · IoV
ICN clustering-based approach for VANETs
Lamia Chaari Fourati1 · Samiha Ayed2 · Mohamed Ali Ben Rejeb1
(1) Digital Research Center of Sfax-SM@RTS, University of Sfax, Sfax, Tunisia
(2) University of Technology of Troyes, LIST3N, Troyes, France
Abstract In recent years, the use of information-centric networks (ICNs) within vehicular ad hoc networks (VANETs) has been shown to be a favourable and deployable future Internet paradigm. Within the context of VANETs, the use of ICNs has many advantages. Indeed, ICNs offer the benefit of in-network content caching and strategies. In addition, ICNs accelerate content retrieval based on the caching of different nodes. Cache deployment within VANETs can be performed at low cost and with low energy consumption. However, the use of ICNs within the VANET context may be challenging. In fact, it may impact network mobility and continuity. Thus, traditional clustering algorithms are not suitable in the context of ICN-based VANETs. In this paper, we propose a new clustering-based approach integrating ICN, called VC-ICN. The aim of our approach is to guarantee network continuity without impacting its mobility when the ICN paradigm is used. We show through simulation, considering scenarios in real-world contexts, that VC-ICN allows vehicles to enhance their content delivery ratio, the average delay for content delivery and the transmission overhead.
Keywords Clustering · Information-centric network · Vehicular networks
Enhancing video dissemination over urban VANETs using line
of sight and QoE awareness mechanisms
Lazhar Khamer1,2,3 · Nabila Labraoui1 · Abdelhak Mourad Gueroui3 · Ado Adamou Abba Ari3,4
(1) Department of Computer Science, University Abou Bekr Belkaid Tlemcen, P.O. Box 230, Chetouane, Tlemcen, Algeria
(2) Department of Mathematics and Computer Science, Mohamed-Cherif Messaadia University, Souk Ahras, Algeria
(3) LI-PaRAD Lab, Université Paris Saclay, University of Versailles Saint-Quentin-en-Yvelines, 45 Avenue des Etats-Unis,
Versailles, cedex, France
(4) LaRI Lab, University of Maroua, P.O. Box 814 Maroua, Cameroon
Abstract Video broadcasting in Vehicular Ad Hoc Networks (VANETs) is beneficial for traffic management, entertainment, and advertising services because video notifications in active safety applications provide more information regarding accident scenarios than simple text messages. However, broadcasting videos over urban VANETs is challenging because of specificities, e.g., dynamic topology, shadowing phenomena, node mobility, and network partition. Moreover, the delay, jitter, and packet loss ratio associated with video streaming should not exceed strict thresholds for an acceptable quality of experience. To meet video streaming requirements, we propose a receiver-based, line-of-sight-aware and reliable bi-directional broadcasting protocol that obtains a tradeoff between broadcast reliability and coverage capabilities. The road network is segmented into a set of straight sections and the bi-directional broadcast method is applied to each section to address the obstructed line of sight problem and the coverage capacity simultaneously. Our protocol selects a sub-set of forwarders likely to have the best line of sight in a fully distributed manner. Furthermore, we overcame packet loss by designing an enhanced version of the store-carry-and-forward method that prioritizes the retransmission of packets containing more important video blocks. The simulation shows that our solution outperforms two innovative video broadcasting protocols in terms of frame loss, peak signal-to-noise ratio, and mean opinion score while keeping the end-to-end delay within the video streaming requirement range.
Keywords V2V communication type · Video streaming · Intelligent transportation system · Smart City ·
Broadcasting protocol · Line of sight · Quality of experience
Junction-based stable clustering algorithm for vehicular ad
hoc network
Mohammad Mukhtaruzzaman1 · Mohammed Atiquzzaman1
(1) School of Computer Science, University of Oklahoma, Norman OK-73019, USA
Abstract Vehicular communication is an essential part of a smart city. Scalability is a major issue for vehicular communication. Clustering can solve the issues of vehicular ad hoc network (VANET); however, due to the high mobility of the vehicles, clustering in VANET suffers stability issue. Previously proposed clustering algorithms for VANET are optimized for either cluster head or cluster member duration. Moreover, the absence of the intelligent use of mobility parameters, such as direction, movement, position, and velocity, results in cluster stability issues. A dynamic clustering algorithm considering the efficient use of mobility parameters can solve the stability problem in VANET. To achieve higher stability for VANET, a new robust and dynamic mobility-based clustering algorithm junction-based clustering for VANET (JCV) is proposed in this paper. In contrast to previous studies, transmission range, moving direction of the vehicle at the next junction, and vehicle density are considered in the creation of a cluster, whereas relative position, movement at the junction, degree of a node, and time spent on the road are considered to select the cluster head. The performance of JCV is compared with two existing VANET clustering algorithms in terms of the average cluster head duration, the average cluster member duration, the average number of cluster head change, and the percentage of vehicles participating in the clustering process. The simulation result shows JCV outperforms the existing algorithms and achieved better stability.
Keywords VANET clustering . Junction-based clustering . Intersection-based clustering . Efficient stable clustering