International Journal of Vehicle Information and Communication Systems (33 papers in press)

Regular Issues

• End-to-end delay and backlog bound analysis for hybrid vehicular ad hoc networks: a stochastic network calculus approach  
  by Shivani Gupta, Vandana Khaitan 
  Abstract: This paper studies a hybrid Vehicular Ad-hoc Network (VANET) that incorporates two different technologies, i.e., IEEE 802.11p and the long-term evolution. End-to-end delay and backlog are acknowledged as major performance measures of vehicular networks that characterise its Quality of Service (QoS), therefore, in this paper we focus on obtaining some measures of these two attributes. We obtain the probabilistic upper bounds on the end-to-end delay and backlog instead of evaluating the delay and backlog in view of the fact that providing the probabilistic bounds is more reasonable as in some real-life scenarios it may be intricate to obtain the closed-form results. To obtain the probabilistic bounds on delay and backlog, a queueing network model is proposed that represents the message dissemination scheme used in the hybrid VANET architecture. The novelty of this paper lies in the fact that instead of considering a Markovian queueing network, the arrival and service processes in the proposed queueing network are assumed to be self-similar and heavy-tailed distributed, as such characteristics are extensively reported in communication networks. The mathematical analysis of the proposed queueing network follows the stochastic network calculus approach for the reason that it supports generally distributed arrival and service processes. Further, the probabilistic upper bounds on the end-to-end delay obtained using heavy-tailed arrival and service times are compared with the delay bounds obtained using exponential arrival and service times to validate the appropriateness of using heavy-tailed characteristics of the network traffic. In addition, a comparative study of hybrid VANET with the other two conventional architectures of VANET, i.e. ad-hoc network only and cellular network only, is also provided in the paper.
  Keywords: hybrid VANET; queueing network; stochastic network calculus; end-to-end delay; backlog; probabilistic bounds; heavy-tailed traffic.
  
  
• Camouflage-based location privacy preserving scheme in vehicular ad hoc networks  
  by Leila Benarous, Benamar Kadri, Saadi Boudjit 
  Abstract: Location privacy is critical and preserving it is essential. The tracking exposes the real time location, history of visited places and parsed trajectories. Metaphorically speaking, it is the cyber equivalent of physical stalking and as dangerous as it is. In vehicular networks in particular, this issue is serious because autonomous vehicles timely transmit their locations, headings, speed and identity to neighbouring vehicles and/or service infrastructures. To preserve the location privacy, various pseudonym-based approaches exist, mainly focusing on unlinkable pseudonym change strategies. In this paper, we propose a camouflage-based solution that prevents the linkability of pseudonyms even within low density roads where the tracking chances are high. The solution is simulated using NS2 against a global passive attacker that executes the semantic and syntactic linking attacks. The results demonstrate the effectiveness of the solution in protecting privacy.
  Keywords: autonomous vehicle; vehicular network; privacy; attacker; simulation; linkability.
  
  
• 3D object detection based on image and LIDAR fusion for autonomous driving  
  by Guoqiang Chen, Huailong Yi, Zhuangzhuang Mao 
  Abstract: 3D object detection is the fundamental task of autonomous driving. The existing approaches are very expensive in computation owing to the high dimensionality of point clouds. We use the 3D data more efficiently by representing the scene from the RGB image and the Birds Eye View (BEV). The whole network is composed of two parts: one is the 2D proposal network for 2D region proposal generation, and the other is the 3D region-based fusion network to predict the 2D locations, orientations, and 3D locations of the objects. First, we fuse the BEV feature map and the RGB image to enhance the input. Second, we adopt the 3D encoding form with 2D-3D bounding box consistency constraints and design ROI-wise feature fusion to predict location information. Our experimental evaluation on both the KITTI as well as a large scale 3D object detection benchmark shows significant improvements over the state of the art.
  Keywords: 3D object detection; image and LIDAR; deep learning; multisensory fusion; autonomous driving.
  
  
• Efficient clustering for wireless sensor networks using modified bacterial foraging algorithm  
  by Dharmraj Biradar, Dharmpal D. Doye, Kulbhushan A. Choure 
  Abstract: The energy efficiency and clustering are directly related to each other in Wireless Sensor Networks (WSNs). A significant number of methods have been introduced for energy-efficient clustering in the last couple of decades. To limit energy use and improve network throughput, various methods for the clustering algorithm were introduced using an optimisation algorithm, fuzzy logic, and thresholding techniques. The optimisation algorithms such as Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), Ant Colony Optimisation (ACO) and their variants were presented, but the challenge of selecting the efficient Cluster Head (CH) and cluster formation around it with minimum overhead and energy consumption is unresolved. In this paper, energy proficient and lightweight clustering algorithm for WSNs is proposed using the Modified Bacterial Foraging optimisation Algorithm (MBFA). The aim of designing the MBFA is to limit energy use, control overhead, and improve network throughput in this paper. The process of CH selection using MBFA is performed via a novel fitness function. The wellness capacity is planned using key parameters, for example, remaining energy, node degree, and geographical distance between sensors to base station. The MBFA selects the sensor node as CH using the fitness value. The proposed clustering protocol is simulated and evaluated with state-of-art protocols to justify efficiency.
  Keywords: bacterial foraging optimisation; clustering; cluster head selection; energy efficiency; particle swarm optimisation.
  
  
• Enhanced video-based traffic management application with virtual multi-loop crate  
  by Manipriya Sankaranarayanan, Mala Chelliah, Samson Mathew 
  Abstract: The growth in urban population leads to gridlock of vehicles in city roads. The quality of transportation is improved by the latest technologies of Intelligent Transportation Systems (ITS) applications. Any ITS application relies heavily on sensors for data collection for efficient management, control and planning of transportation. In this paper, the video-based traffic data collection systems and their techniques are improved by using the proposed Virtual Multi-Loop Crate (VMLC) framework. VMLC uses the all the spatial colour information for image processing without losing information. The results of the proposed framework are used to estimate traffic statistics and parameters that are essential for ITS applications. The parameter values obtained from VMLC are analysed for accuracy and efficiency using Congestion Level (CoLe) estimation application. The results show that the VMLC framework improves the quality of data collection for any video-based ITS applications.
  Keywords: vehicle detection; traffic statistics and parameters; spatial colour information; image processing data management; video-based traffic data.
  
  
• Improved stability through self-localisation scheme in heterogeneous vehicular clustering  
  by Iftikhar Ahmad, Zaheed Ahmed, Muhammad Ahmad Al-Rashid 
  Abstract: Intelligent local data processing within vehicular ad hoc networks (VANET) may increase the capabilities of the Internet of Vehicles (IoV). To share data effectively, vehicular clusters should be synchronised and stable. A vehicle needs an uninterrupted Global Positioning System (GPS) signal for synchronisation purposes, especially in the urban environment. GPS interruption leads to an unstable connection that is a big hurdle in developing cost-effective solutions for navigation and route planning applications. To solve this problem, a self-location calculation scheme within the vehicular clustering process is proposed. The proposed self-location calculation algorithm enables vehicles to calculate their coordinates in the absence of GPS signals. A clustering mechanism is developed for sharing traffic information system (TIS) data among multiple vehicles over a particular road segment. Sharing of vehicular data in real-time helps vehicles to synchronise well. The developed scheme is simulated and compared with existing known approaches. The results show the better stability of our proposed mechanism over others.
  Keywords: VANET; stability; vehicular clustering; synchronisation; localisation.
  
  
• Detection and prevention of various routing attacks in RPL for a smart vehicle environment using an enhanced privacy secure RPL routing protocol  
  by Paganraj Deepavathi, Chelliah Mala 
  Abstract: The recent advancements in the Internet of Things (IoT) play a significantly important role in the day-to-day activities of human beings to connect things-to-things; it reduces the efforts of an individual as well as increases the safety and smartness of people through smart phones. With the increase in population, there is a tremendous increase in the number of vehicles, which leads to more possibilities of road accidents. In a smart vehicle environment, various sensors are used to collect vehicle-related information from different locations and alert the driver about road traffic conditions to avoid accidents. This paper proposes an Enhanced Privacy Secure-Routing Protocols for Low Power and Lossy Networks (EnPS-RPL) routing protocol to detect various attacks and protect a vehicle from accidents. The proposed work is simulated in Cooja Platform Simulator, which is in Contiki Operating System. The simulation results show that the proposed algorithm improves networks' lifetime and throughput, and decreases the packet loss ratio and end-to-end delay compared with the existing RPL-based protocols.
  Keywords: EnPS-RPL; smart vehicle environment; road traffic; packet loss; RPL-based protocols.
  
  
• Performance analysis of a fog-computing-based vehicular communication  
  by Manoj Kumar, Pratik Gupta, Ankur Kumar 
  Abstract: Vehicular networking is an emerging research area. It enables vehicles equipped with sensing capacities to establish communication vehicle-to-vehicle or with available roadside infrastructure. It has significant applications in terms of information and traffic management and road safety. To attain effective communication in vehicular networking, a hybrid technological solution is proposed in terms of blockchain in vehicular communication using vehicular resources, cloud or fog computing environment. Blockchain-based fog computation is an alternative solution to get both security and privacy, especially in vehicular communication. As security and privacy are the key challenges in cloud-assisted vehicular communications owing to the adoption of the internet of things environment, a secure and efficient communication framework is required. This paper presents a model based on blockchain technology i.e. blockchain in fog nodes. This framework for vehicular communication ensures security and privacy. The presented frameworks are efficient being based on new blockchain design rather than the general cloud and fog-computing framework. This scheme has capabilities to establish an authenticated connection among the user, the fog node and the cloud server, where cloud services can respond to users treating fog node as a semi-trusted party. The effect of the proposed scheme was investigated and the obtained results are explained in the form of tables and graphs.
  Keywords: blockchain; vehicular communication; elliptic curve cryptography, authentication key agreement.
  
  
• Route selection strategy with minimised mobile charger in wireless rechargeable sensor networks  
  by Cong-Xiang Wang, Chih-Min Chao 
  Abstract: How to design an efficient route for a mobile charger is an important issue in rechargeable sensor networks (WRSNs). Mobile chargers are costly and may take a lot of time to reach and charge sensor nodes. Existing route-planning algorithms either fail to minimise the number of mobile chargers or do not consider the actual traffic conditions. In this paper, we propose a Hybrid-Clustering-Minimum-Mobile (HCMM) charger route-planning algorithm for WRSNs. The charging route selected by the HCMM protocol prolongs the network lifetime by using a minimised number of mobile chargers. Simulation results verify that the charging efficiency achieved by HCMM is better than that of the m-MTS protocol, which is currently the best charging route-planning protocol for dispatching the least number of mobile chargers.
  Keywords: wireless charging; mobile charger; wireless rechargeable sensor networks; route planning.
  
  
• A proactive load-balanced handoff scheme for vehicular ad-hoc networks  
  by Rajeshwari Madli, G. Varaprasad 
  Abstract: The embracement of the wireless technology by the automobile industry has led to novel research interests in the field of Vehicular Ad-hoc Networks (VANETs). In addition to the ad-hoc mode, a VANET supports an infrastructure mode of communication, which provides access to internet services such as Hyper Text Transfer Protocol (HTTP), Voice over Internet Protocol (VoIP) and File Transfer Protocol (FTP), through access points known as Road Side Units (RSU). Vehicles can benefit from these services when associated with an RSU. However, because of uneven distribution and high mobility of vehicles, some RSUs are overloaded, resulting in network congestion and affecting the packet delivery ratio and throughput. This paper proposes a proactive load-balanced handoff scheme, in which vehicles evade overloaded RSUs and choose an alternative RSU based on signal strength, network load and service reliability. Simulation results indicate that vehicles using the load-balanced handoff method, experience a higher packet delivery ratio and throughput.
  Keywords: VANET; handoff; load balance; RSU reliability; packet delivery ratio.
  
  
• Data synchronisation method of intelligent transportation system based on internet of vehicles technology  
  by Fangling  Zhang 
  Abstract: In order to overcome the problems of large error of traffic data synchronisation and high traffic congestion rate in traditional methods, a data synchronisation method of intelligent transportation system based on the internet of vehicles technology is proposed, using the car networking technology. The collected data is used to extract the road traffic flow, velocity and density online traffic flow characteristics. Combined with the data acquisition and traffic flow feature extraction result of intelligent transportation system for scheduling task, according to the result of task scheduling based on training symbol synchronisation method to construct scheduling task timing metric function. The function is used to calculate the data synchronisation estimation points of the intelligent transportation system and realise data synchronisation. The simulation results show that the traffic data synchronisation error rate and traffic congestion rate of the proposed method are low, and the practical application effect is good.
  Keywords: internet of vehicles technology; intelligent transportation; data synchronisation; training symbol.
  
  
• Compromised-resilient counter-measures against black-hole and worm-hole attacks in edge-based internet of vehicles  
  by Oladayo Olakanmi, Kehinde Odeyemi 
  Abstract: The Internet of Vehicles (IoV) is a distributed network that supports the use of data created by connected cars and vehicular ad-hoc networks (VANETs) for real-time communication among the vehicles and other infrastructures in the network. Although IoV increases safety and efficient information exchange in transportation, its inter-connectivity exposes the vehicles and the people to different cyber-attacks such as black-hole and worm-hole which are capable of disrupting the network. In this paper, we identify the black-hole and worm-hole attacks as the major security threats to the IoV network. We then propose periodic-time slicing and trust factor approaches to detect and prevent a black-hole attack, a cryptography procedure to detect and prevent worm-hole attacks and enforce integrity in the IoV networks.
  Keywords: car connectivity; cyber-security ; cloud and fog-assisted; Internet of things; autonomous vehicles.
  
  
• Machine learning for efficient link adaptation strategy in VANETs  
  by Etienne Alain Feukeu, Lucas W. Snyman 
  Abstract: The benefit brought by Vehicular Ad Hoc Networks (VANETs) can only be gained if the successful Road State Information (RSI) message notifications are exchanged between the mobiles involved. Moreover, a successful exchange is only possible with a well-integrated Link Adaptation (LA) mechanism. Furthermore, the higher mobility induces Doppler Shift (DS) in the carrier frequency component of the transmitter node, which corrupts the transmitted signal and makes decoding difficult at the receiver end. Several authors have addressed the LA in VANETs, but almost all of them have done so without incorporating an effective DS mitigation strategy. The current study presents a Machine Learning (ML) approach for an efficient LA strategy in VANETs. The simulation results demonstrated that the ML outperformed AMC, ARF, and Cte in threefold, with an improvement level of 212% in terms of throughput, 86.5% in terms of transmission duration, and 39% in terms of model efficiency.
  Keywords: VANET; Doppler shift; machine learning; link adaptation; WAVE; DSRC; V2V; V2I; IEEE802.11p.
  
  
• Efficient resource allocation scheme using PSO-based scheme of D2D communications for overlay networks  
  by Yogesh Kumar Sharma, Bharat Ghanta, Pavan Mishra, Shailesh Tiwari 
  Abstract: Device-to-Device communication (D2D) is an essential technology in cellular networks which enables direct communication between devices and supports the high data rate compared with cellular communication. To improve the system capacity, multiple D2D uses are allowed to share the same resource block. With the limited number of available resource blocks, it is very challenging to assign a resource block for newly formed D2D pairs. Furthermore, to solve the aforementioned problem, an effective resource allocation scheme is proposed that gives the minimum number of required resource blocks for a given link. The proposed scheme is based on particle swarm optimisation (PSO). The proposed scheme reduces the number of resource blocks for a given D2D link and improves the network throughput. Moreover, compared with greedy and LIFA schemes, the proposed scheme could set aside to 26.69% resource blocks around and enhance the throughput per resource block by up to 34.4%.
  Keywords: device-to-device communication; interference; overlay network; PSO; resource block.
  
  
• Modified stable weighted clustering scheme for secured V2V communications  
  by Emmanuel Siman, Isnin Fauzi, Mohamad Murtadha 
  Abstract: Designing a medium access control (MAC) protocol for a vehicle ad hoc network is challenging owing to the high node speed, frequent topology changes, lack of infrastructure, and varying quality-of-service requirements. When the clusters are designed, the vehicles must be evenly distributed based on vehicle-specific metrics and cluster head (CH) selection. Therefore, we propose an enhanced cluster multiple access (ECWA) protocol that supports multiple access mechanisms (i.e., random access, on-demand access time-slot reservation, and pooling access) and employs a modified stable weighted clustering algorithm to ensure the cluster head selection and stability. Owing to the rapid changes in the cluster network, a Chain Markov model is used to improve the rapid transition from one frame slot reservation to the next on different access mechanisms based on the node's threshold value for a unique stable cluster network. We evaluated the proposed protocol's sensitivity using four weighting factors, including network throughput, end-to-end delay, and packet delivery ratio, so as to identify the most stable group.
  Keywords: cluster-based; IEEE 802.11p; time division multiple access; threshold; stable weighted clustering; vehicular ad hoc network.
  
  
• Route forecasting-based authentication scheme using A* algorithm in vehicular communication network  
  by Vartika Agarwal, Sachin Sharma, Gagan Bansal 
  Abstract: Researchers have developed several authentication techniques for route predictions based on user requirements. These techniques estimate the shortest path and available resources in vehicular communication networks. In the current research, the existing authentication techniques for vehicular communication are compared and their inadequacies are identified. Then, new authentication technique based on route forecasting are presented for vehicular communication networks, with the service provider anticipating alternate routes for customers if the current routes have more network traffic congestion. By presenting the most efficient route, the suggested model allows users to maximize their time efficiency. Using A* algorithm, VCN agent seeks path with less network traffic congestion. This algorithm determines the shortest path between a source and a destination. Users are provided with several options by the service provider. User accepts the finest option that meets their needs. This method allows the service provider to deliver at least 15 routes within three seconds. This strategy is beneficial when a significant number of vehicles are stuck in traffic and consumers require network resources to utilize their time effectively
  Keywords: vehicular communication network; route prediction-based authentication scheme; network traffic congestion; network traffic index.
  DOI: 10.1504/IJVICS.2023.10055779
   
  
• Vehicle parallel integrated control strategy based on coordinated SAS and ABS  
  by Yayu Qiu 
  Abstract: To improve the braking performance and ride comfort of vehicle, the parallel integrated control strategy based on coordinated SAS and ABS is studied. The relationship between the chassis structure and the external flow field is analysed by the vehicle geometric model. The parametric model of the chassis is constructed to study the relationship between energy recovery and stability control. Taking the vehicle battery, voltage and fuel quantity as control parameters, the integrated control strategy of parallel vehicle energy recovery and vehicle stability is designed, and the vehicle parallel integrated control based on coordinated SAS and ABS is completed. In order to verify the effectiveness of the proposed control strategy, simulation experiments are designed. The results show that the braking performance of the designed control strategy is better, the overall performance of the vehicle is effectively improved, and the energy loss area is significantly smaller than that of the traditional method.
  Keywords: SAS; ABS; automobile control; parallel integrated control; fuzzy control.
  DOI: 10.1504/IJVICS.2023.10055883
   
  
• Research on the application of multiple target cluster intelligent algorithm in the design of door-to-door carriage of cargoes in railway carriage enterprises  
  by Xiumiao Liu, Wei Shi 
  Abstract: In recent years, RFT has gradually transformed into a service-oriented business, and door-to-door transport has become an important advancement direction of RFT transport To improve carriage quality and efficiency of railway carriage enterprises, the majorization pattern of RFTD2DT route design is studied by using IMOQPSO algorithm The astringency performance is improved by improving the operation of parameter setting and location updating The research results indicate that the average GD value of IMOQPSO is 0 04 and 0 01, and average IGD value is 0 81 and 0 01, which is obviously superior to IMOMPPSO, MOQPSO and other algorithms, and has good Pareto front astringency The majorization pattern of RFTD2DT route design based on IMOQPSO can provide technical support for railway carriage enterprises to make route design decisions, effectively reduce the carriage time and cost of RFT, and has theoretical value and practical relevance to accelerate the advancement of railway carriage.
  Keywords: railway; carriage; IMOMPPSO; multi-objective majorization; by route.
  DOI: 10.1504/IJVICS.2023.10055998
   
  
• Research on automatic early warning of UAV attitude abnormal state based on MEMS sensor  
  by Xiaoqiang Wang 
  Abstract: The unmanned aerial vehicle’s (UAV) attitude control is crucial to the success of the mission. On the basis of this, the paper suggests a paradigm for autonomous early warning of improper UAV attitude based on MEMS sensors. To obtain early warning of anomalous UAV attitude, the model solves UAV attitude using the quaternion approach and employs a fading Kalman filter to correct for MEMS gyroscope inaccuracy. The simulation test demonstrates that in the static state, the errors of the drone’s pitch angle and roll angle are within 0.2
  Keywords: MEMS sensor; unmanned aerial vehicle; abnormal attitude; fading Kalman filter; gyro error; quaternion; Allan variance; early warning.
  DOI: 10.1504/IJVICS.2023.10056035
   
  
• Application of model predictive control on metro train scheduling problems  
  by Isna Silvia, Salmah Salmah, Ari Suparwanto 
  Abstract: The arrangement of the metro train scheduling problem aims to maintain headway regularity and the number of passengers; therefore, it does not exceed capacity. In this study, the metro train traffic model was developed without referring to the nominal schedule, then the running time model and the dwell time model were added to the metro train traffic model, and considering changes in the number of passengers. A model predictive control is applied to control the metro train scheduling problems. The control adjusts the dwell time and the running time of the train. The optimization problem in this study is a quadratic programming problem consisting of a quadratic cost function and linear constraints related to the train scheduling problems. Based on the simulation results, the headway deviation and the deviation of the number of train passengers in the metro train scheduling problem can be minimized using the model predictive control.
  Keywords: train scheduling; metro line; model predictive control; quadratic programming.
  DOI: 10.1504/IJVICS.2023.10056455
   
  
• Research on pattern recognition of automobile anti-lock braking system  
  by Jiangang Li 
  Abstract: As one of the active safety devices of automobiles, the anti-lock braking system (ABS) plays a pivotal role in ensuring the stability of driving operation, and the optimisation of its controller has been very necessary. The study proposes an ABS based on pavement identification based on the establishment of a vehicle dynamics model, then realises braking control through adaptive fuzzy PID. The results show that on dry concrete roads, the wheel speed under the control of this method quickly tracks up to the body speed after 0.1 s and maintains a stable body speed. In the actual docking road test, the method can quickly and accurately identify the optimal slip rate, and a braking time of only 3.842 s, indicating that the method can achieve effective identification and efficient control of the car ABS, which provides a reference technical means to further improve the car driving safety.
  Keywords: anti-lock braking; road surface recognition; fuzzy control; PID.
  
  
• Research on the interactive design of electric vehicle interior based on voice sensing and visual imagery  
  by Tao Ba, Shan Li, Ying Gao, Diyuan Tan 
  Abstract: With the complete function of modern automobiles, in-vehicle intelligent devices are becoming more and more complex, and the requirements for human-computer interaction are also increasing. The research proposes a speech recognition method that combines multi-window estimation spectral subtraction and dynamic time warping to enhance the denoising ability and speech recognition ability of in-vehicle devices. It also proposes actions based on a Gaussian hybrid segmentation algorithm and a visual image functional space segmentation algorithm. The automatic identification method and the validity of the algorithm are verified. The results show that under different input signal-to-noise ratios, the denoising capability of the method is improved by 2.45%-31.47% over the baseline method. And the accuracy of speech recognition in the vehicle environment is 92.3%-98.7%. It is hoped that this research can make some contributions to the upgrading of voice and visual interaction within electric vehicles.
  Keywords: speech recognition; multi-window spectral subtraction; dynamic time warping; visual imagery; space segmentation.
  
  

Special Issue on: Big Data Innovation For Sustainable VANET Management

• An automatic moving vehicle detection system based on hypothesis generation and verification in a traffic surveillance system  
  by Smitha Jolakula Asoka, N. Rajkumar 
  Abstract: An intelligent transportation system has a major topic called traffic surveillance. In a complex urban traffic surveillance system, booming of vehicle detection and tracking is an problematic dilemma. To overcome this, a two-stage approach for a moving vehicle detection system is proposed in this paper. The proposed system mainly consists of two stages namely, hypothesis generation and hypothesis verification. At the first step, hypotheses are generated using the concept that shadows beneath the vehicles are darker than the road region. The second step verifies whether a generated hypothesis is correct or not using an optimal artificial neural network (ANN). The weights corresponding to the ANN are optimally selected using the grasshopper optimisation algorithm. Through experimental results, it is shown that the proposed moving vehicle detection system performs with better accuracy than other methods.
  Keywords: traffic surveillance system; moving vehicle detection; tracking; hypothesis generation; hypothesis verification; feedforward neural network; grasshopper optimisation algorithm.
  
  

Special Issue on: AIST2019 Empowering Intelligent Transportation Using Artificial Intelligence Technologies

• Network traffic analysis using machine learning techniques in IoT network  
  by Shailendra Mishra 
  Abstract: End-node internet-of-things devices are not very intelligent and resource-constrained; thus, they are vulnerable to cyber threats. They have their IP address, and once the hacker traces the IP, it becomes easy to get into the network and exploit the other devices. Cyber threats can become potentially harmful and lead to infection of machines, disruption of network topologies, and denial of services to their legitimate users. Artificial intelligence-driven methods and advanced machine learning-based network investigation protect the network from malicious traffic. The support vector machine learning technique is used to classify normal and abnormal traffic. Network traffic analysis has been done to detect and protect the network from malicious traffic. Static and dynamic analysis of malware has been done. Mininet emulator is selected for network design, VMware fusion is used for creating a virtual environment, the hosting OS is Ubuntu Linux, and the network topology is a tree topology. Wireshark was used to open an existing packet capture file that contains network traffic. Signature-based and heuristic detection techniques were used to analyse the signature of the record, which is found using a hex editor, and proposed rules are applied for searching for and detecting these files that have this signature. The support vector machine classifier demonstrated the best performance with 99% accuracy
  Keywords: network traffic analysis; IoT; cyber threats; cyber attacks; machine learning.
  DOI: 10.1504/IJVICS.2022.10047575
   
  
• A novel framework for efficient information dissemination for V2X  
  by Ravi Tomar, Hanumat G. Sastry, Manish Prateek 
  Abstract: This paper is focused on presenting a robust framework for information dissemination in vehicular networks using both Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication modes. The framework is designed to first prioritise the generated information and then, based on the priority, the message is disseminated over the network using one of the techniques for V2V or V2I. The paper first discusses the need for information dissemination and further proposes the novel framework for efficient information dissemination. The framework comprises two techniques for disseminating the information through V2V or V2I. The two techniques are presented and supported by the experimental, simulation and statistical analysis results. The results obtained are compared with existing mechanisms for information dissemination and are found to be performing better than standard information dissemination mechanisms.
  Keywords: information dissemination; V2V; V2I; priority based.
  
  
• Automated storyboard generation with parameters dependencies for regression test cases  
  by Nishant Gupta, Vibhash Yadav, Mayank Singh 
  Abstract: In recent trends and advancement of agile technology, the industry demand is for an effective and useful specification from the customer to reduce the effort, time and cost of software development. The storyboard is an effective tool to cater for the customer's requirements in an efficient manner. Our proposed framework and tool STORB will provide the platform where customer and business analyst may use the tool to generate a storyboard based on provided functionalities and parameters. The tool will provide detailed information about the customers requirements and generate the storyboard. Further, test data can also be generated for testing test cases. The tool has been used for three functionalities and their parameters on login functionalities of web application. The tool also defines the dependencies among parameters so that regression test cases can be generated. The result shows a useful significance of the tool in the software industry for the current trend of agile development.
  Keywords: agile testing; regression testing; storyboard;test cases; functionalities.
  
  
• Machine learning techniques applied to call admission control in 5G mobile networks  
  by Charu Awasthi, Prashant Kumar Mishra 
  Abstract: Highly reliable applications with low latency are key feature in 5G networks. In the prevailing scenario of efficient mobile network systems, the Quality of Service (QoS) depends on the regulation of traffic volume in wireless communications, known as the Call Admission Control (CAC). 5G networks are also very important for Intelligent Transportation Systems (ITS) as they can be used for quick detection and controlling of traffic, hence can be informative, sustainable, and more effective. Machine learning is the concept of providing the power to learn and develop mechanically, by practising. It also provides the power to attain learning and development in the absence of classical methods such as programming. It also permits wireless networks such as 5G to be increasingly dynamic and predictive. With this feature, the formulation of the 5G vision seems possible. With the use of machine learning and neural networks, this paper proposes various CAC methods deployed for 5G multimedia mobile networks. This can be achieved by delivering the best from all the attributes of soft computing that are deployed in the current mobile networks for ensuring recovery of efficiency of the prevailing CAC methods.
  Keywords: artificial intelligence; machine learning; neural networks; 5G mobile networks; wireless networks; intelligent transportation system.
  
  
• PALCT: vehicle-to-vehicle communication based on pseudonym assignment and encryption scheme using delay minimisation cover tree algorithm  
  by Righa Tandon, P.K. Gupta 
  Abstract: Vehicle-to-Vehicle communication is one of the new paradigms of networking which should be secure, fast and efficient. In this paper, we propose a framework that implements the pseudonym-based authentication scheme in which communication among vehicles is encrypted by using matrix array symmetric key (MASK), digital signature algorithm (DSA) and intelligent water drop (IWD). The proposed security scheme also ensures handling of many security attacks such as key-guessing, non-repudiation, replay and modification. In the proposed scheme, to preserve the vehicles identity, we have provided different pseudonyms to each vehicle in the network, which ensures secure communication among vehicles. Furthermore, the proposed delay minimisation cover tree algorithm ensures the issue of time-delay during vehicle to vehicle communication. In this algorithm, we have used Dijkstras algorithm for finding the optimal shortest path during vehicular communication. Obtained results show that the proposed scheme is effective and efficient as it reduces the time-delay by 4% for 140 vehicle nodes and 28.4% for 1000 vehicle nodes.
  Keywords: pseudonyms; vehicle-to-vehicle communication; security; time-delay.
  
  

Special Issue on: Artificial Intelligence Driven Vehicular Communication Networks in Beyond 5G Era

• Real-time separation optimisation algorithm for hybrid blind speech signal in vehicular communication networks in beyond the 5G era  
  by Guoan Hu 
  Abstract: In this paper, we conduct an in-depth analysis on the real-time separation optimisation algorithm for the hybrid blind signal in wireless communication networks. In the process of the real-time separation of blind signals in network communication, firstly, the hybrid signal in the network communication is whitened to remove the core correlation between the blind signals. In this foundation determined in the network correspondence the mixed signal matrix of linear transformation, it uses transformation matrix to separate in the network correspondence in the blind signal independent signal component, from this the network correspondence area south of core signal is restored. The data analytic framework is suggested to optimise the framework of the proposed tool. Then, the designed model is applied to the vehicular communication networks in beyond the 5G era, and the simulation gives the overall performance estimation to test the robustness.
  Keywords: real-time separation; optimisation algorithm; blind signal; wireless communication; vehicular communication; 5G technology.
  
  
• Hybrid deep learning-based intrusion detection system for wireless sensor networks  
  by V. Gowdhaman, R. Dhanapal 
  Abstract: Wireless sensor networks (WSNs) play an important role in the modern era and security has become an important research area. Intrusion detection system (IDS) improve network security by monitoring the network state so that threats and attacks can be detected and rectified. With decades of development, IDS still lags in performance in terms of detection accuracy, false alarm rate, and unknown attack detection. To overcome this performance issue researchers implemented numerous machine learning techniques to detect the attacks. Conventional machine learning models identify the essential features through specific feature extraction techniques which increases the computation complexity of the system. For detailed attack details and their sub-categories, deep learning technique is used in the proposed work. The detection model incorporates ResNet based on Inception with a support vector machine to detect WSN intrusions. Proposed algorithm is applied to Standard NSL-KDD dataset and performance metrics like recall, precision, accuracy, and f1-score are considered for analysis. The comparative analysis demonstrates the proposed model performance of 99.46% accuracy is better than traditional approaches like random forest, decision tree, deep neural network and convolutional neural network.
  Keywords: deep learning; intrusion detection system; wireless sensor network; deep neural network; convolutional neural network.
  
  

Special Issue on: Intelligent Transportation and Connected and Automated Vehicles Empowered by Artificial Intelligence

• Automatic control method of automobile steering-by-wire based on fuzzy PID  
  by Linghan Meng, Caifeng Sun 
  Abstract: Automatic driving is gradually becoming popularised, and automatic control of automobile steering by wire has also become a topic worthy of research. This paper combines the fuzzy control algorithm and PID to construct a fuzzy PID control strategy, and integrates the genetic algorithm into the basic fuzzy rules to form an improved new algorithm. The algorithm mainly focuses on multiple changes in the steering-by-wire of the car, including angular momentum, angular velocity, linear velocity and linear acceleration, to build more complex fuzzy rules, and then use the three most basic parameters of PID. The results show that the average precision rate, average recall rate and average average F1 score of the improved algorithm are 91.27%, 73.67% and 80.94%, which are significantly higher than the other two algorithms for comparison, indicating that the improved algorithm has better performance.
  Keywords: fuzzy control strategy; PID controller; genetic algorithm; vehicle control-by-wire; autonomous driving.
  
  
• Real-time load balancing and dynamic profile management in mobile data networks  
  by Ajay Dureja, Aman Dureja, Suman , Payal Pahwa 
  Abstract: As mobile data grows rapidly, carriers must intelligently manage network traffic. Owing to economic realities and the physical limitations of the available spectrum, operators cannot add more network capacity. To satisfy investors, operators must maximize their service returns. The more services a subscriber receives, which costs more, the higher their expectations of acceptable network availability and quality. So it's become very challenging for operators to provide QoS to their high-value subscribers and manage network traffic dynamically without impacting the user experience of their potential and key users. In this paper, all literature surveys are carried out on different schemes and solutions to handle mobile data networks' profile management. This paper aims to identify the effective way of managing real-time data traffic by adjusting QoS/profile parameter dynamically so that high-value subscribers get better QoS during congestion than regular users.
  Keywords: quality of service; quality of experience; dynamic profile management; HVC.
  
  
• Research on primary traffic congestion point identification method based on fuzzy logic  
  by Haitao Gao, Lunhui Xu 
  Abstract: This study proposes a method to discriminate primary traffic congestion points based on interval type two fuzzy logic combined with improved generative adversarial network, and proposes quantitative congestion point change criteria by using the difference in spatio-temporal order between primary and secondary congestion, and classifies congestion points into four types: primary congestion, primary dissipation, and secondary congestion and secondary dissipation. The methods are also subjected to comparative analysis and ablation experiments to determine the improvement of the optimisation on performance and efficiency. The experimental results show that the method proposed in the study improves the accuracy by 20% to 89% after training, and the average error rate is only 5.5%, which is better than the mainstream congestion point discrimination methods in terms of convergence and efficiency. Finally, the discriminative law of primary traffic congestion points is summarised with the congestion discriminative results of a city for a week.
  Keywords: type two fuzzy logic; generative adversarial networks; dual attention mechanism; primary congestion; secondary congestion.