Driven by the explosive data traffic and new quality of service requirement of mobile users, the communication industry has been experiencing a new evolution by means of network infrastructure densification. With the increase of the density as well as the variety of access points (APs), the network benefits from proximal transmissions and increased spatial reuse of system resources, thus introducing a new paradigm named ultra-dense networks (UDNs). Since the limited available resources are shared by ubiquitous APs in UDNs, the demand for efficient resource allocation schemes becomes even more compelling. However, the large scale of UDNs impedes the exploration of effective resource allocation approaches particularly on the computational complexity and significance overhead or feedback. In this paper, we provide a survey-style introduction to resource allocation approaches in UDNs. Specifically, we first present some common scenarios of UDNs with the relevant special issues. Second, we provide a taxonomy to classify the resource allocation methods in the existing literatures. Then, to alleviate the main difficulties of UDNs, some prevailing and feasible solutions are elaborated. Next, we present some emerging technologies thriving UDNs with special RA features discussed. Additionally, the challenges and open research directions are outlined in this field.

Resource Allocation for Ultra-Dense Networks: A Survey, Some Research Issues and Challenges

Intelligent Traffic Systems (ITS) are currently evolving in the form of a cooperative ITS or connected vehicles. Both forms use the data communications between Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I/I2V) and other on-road entities, and are accelerating the adoption of self-driving cars. The development of cyber-physical systems containing advanced sensors, sub-systems, and smart driving assistance applications over the past decade is equipping unmanned aerial and road vehicles with autonomous decision-making capabilities. The level of autonomy depends upon the make-up and degree of sensor sophistication and the vehicle’s operational applications. As a result, self-driving cars are being compromised perceived as a serious threat. Therefore, analyzing the threats and attacks on self-driving cars and ITSs, and their corresponding countermeasures to reduce those threats and attacks are needed. For this reason, some survey papers compiling potential attacks on VANETs, ITSs and self-driving cars, and their detection mechanisms are available in the current literature. However, up to our knowledge, they have not covered the real attacks already happened in self-driving cars. To bridge this research gap, in this paper, we analyze the attacks that already targeted self-driving cars and extensively present potential cyber-attacks and their impacts on those cars along with their vulnerabilities. For recently reported attacks, we describe the possible mitigation strategies taken by the manufacturers and governments. This survey includes recent works on how a self-driving car can ensure resilient operation even under ongoing cyber-attack. We also provide further research directions to improve the security issues associated with self-driving cars.

Attacks on Self-Driving Cars and Their Countermeasures: A Survey

Vehicular-ad hoc networks (VANETs) hold great importance because of their potentials in road safety improvement, traffic monitoring, and in-vehicle infotainment services. Due to high mobility, sparse connectivity, road-side obstacles, and shortage of roadside units, the links between the vehicles are subject to frequent disconnections; consequently, routing is crucial. Recently, to achieve more efficient routing, reinforcement learning (RL)-based routing algorithms have been investigated. RL represents a class of artificial intelligence that implements a learning procedure based on previous experiences and provides a better solution for future operations. RL algorithms are more favorable than other optimization techniques owing to their modest usage of memory and computational resources. Because a VANET deals with passenger safety, any kind of flaw is intolerable in VANET routing. Fortunately, RL-based algorithms have the potentials to optimize the different quality-of-service parameters of VANET routing such as bandwidth, end-to-end delay, throughput, control overhead, and packet delivery ratio. However, to the best of the authors’ knowledge, surveys on RL-based routing protocols for VANETs have not been conducted. To fulfill this gap in the literature and to provide future research directions, it is necessary to aggregate the scattered works on this topic. This study presents a comparative investigation of RL-based routing protocols, by considering their working procedure, advantages, disadvantages, and applications. They are qualitatively compared in terms of key features, characteristics, optimization criteria, performance evaluation techniques, and implemented RL techniques. Lastly, open issues and research challenges are discussed to make RL-based VANET routing protocols more efficient in the future.

/pdf/reinforcement-learning-based-routing-protocols-for-vehicular-y9edp6nhtl.pdf

Reinforcement Learning-Based Routing Protocols for Vehicular Ad Hoc Networks: A Comparative Survey

By enabling autonomous vehicles (AVs) to share data while driving, 5G vehicular communications allow AVs to collaborate on solving common autonomous driving tasks. AVs often rely on machine learning models to perform such tasks; as such, collaboration requires leveraging vehicular communications to improve the performance of machine learning algorithms. This paper provides a comprehensive literature survey of the intersection between machine learning for autonomous driving and vehicular communications. Throughout the paper, we explain how vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communications are used to improve machine learning in AVs, answering five major questions regarding such systems. These questions include: 1) How can AVs effectively transmit data wirelessly on the road? 2) How do AVs manage the shared data? 3) How do AVs use shared data to improve their perception of the environment? 4) How do AVs use shared data to drive more safely and efficiently? and 5) How can AVs protect the privacy of shared data and prevent cyberattacks? We also summarize data sources that may support research in this area and discuss the future research potential surrounding these five questions.

A Survey of Collaborative Machine Learning Using 5G Vehicular Communications

Vehicular ad hoc networks (VANETs) have recently attracted considerable attention owing to their wide range of applications. However, there are several challenges, such as mobility, routing, scalability, quality of services, and security. Clustering is an important control mechanism in high‐mobility networks and has been verified to be a promising approach in VANETs as well, as it ensures a basic level of network performance. Accordingly, several clustering algorithms have been proposed for these networks, and different protocols typically focus on various performance metrics. In this study, we provide a thorough review of clustering algorithms in VANETs. First, we present background material regarding the clustering process. Secondly, we propose a new taxonomy that categorizes clustering algorithms in VANETs based on different design aspects and provides a description of the algorithms in each category. Thirdly, an analysis of the algorithms in each category is provided according to various comparison metrics. Fourthly, we highlight the main challenges for each category and discuss some open research issues. Finally, we provide a general comparison of different clustering algorithms according to selected key parameters. Thus, this study provides a more thorough understanding of VANET clustering algorithms and the research trends in this area.

Survey on vehicular ad hoc networks clustering algorithms: Overview, taxonomy, challenges, and open research issues

Clustering is considered as the potential approach for network management in vehicular ad hoc network (VANET). The performance of clustering is often assessed based on the stability of the clusters. Hence, most of the clustering methods aim to establish stable clusters. However, besides the stability of cluster, good link quality must be provided, especially when reliable and high-capacity transmission is demanded. Therefore, this paper proposes a clustering method based on coalitional game theory with the purpose to improve the average of vehicle-to-vehicle (V2V) signal-to-noise ratio (SNR) and channel capacity while maintaining the stability of the cluster. In the proposed method, each vehicle attempts to form a cluster with other vehicles according to coalition value. To attain the purpose of clustering, the value of coalition is formulated based on the V2V SNR, connection lifetime, and speed difference between vehicles. In fast-changing network topology, the higher average of SNR can be achieved but the stability of cluster becomes hard to be maintained. Based on the simulation results, SNR improvement can be adjusted in order to balance with the cluster stability by setting the parameters in the proposed method accordingly. Further simulation results show that the proposed method can obtain a higher average of V2V SNR and channel capacity than other relevant methods.

/pdf/coalitional-game-theoretical-approach-for-vanet-clustering-5xjzt0zfn2.pdf

Coalitional Game Theoretical Approach for VANET Clustering to Improve SNR

Vehicular ad hoc network (VANET) enables the vehicles to communicate each other. This technology could lead the future of transportation system by enabling the safety application, traffic management, and passenger entertainment. However, due to the fast moving nodes and very dynamic environment, VANET has some challenges waiting to be solved. One of the problems is related with vehicle to vehicle (V2V) handover decision. In this paper, the problem related to V2V handover decision is investigated. Furthermore, several potential methods to solve the problem are presented. The implementations of those methods in VANET are surveyed to assess the potential. Finally, the advantages and disadvantages of each method related to the implementation for V2V handover decision are presented in discussion.

Review of Potential Methods for Handover Decision in V2V VANET

/pdf/utility-based-horizontal-handover-decision-method-for-3bkxyzrsv5.pdf

Utility-Based Horizontal Handover Decision Method for Vehicle-to-Vehicle Communication in VANET

This paper proposes transmission power control for vehicular ad hoc network (VANET) using fuzzy system. Despite the potential advantages of VANET especially for safety and intelligent transportation system, some challenges are discovered during the implementation of VANET. Main challenges emerge because of the dynamic environment and high mobility of vehicle. Furthermore, the interference due to the shared‐spectrum usage can significantly decrease the quality of signal. Fuzzy system is implemented to control the transmission power based on the signal to interference and noise ratio (SINR) difference with the targeted value of the receiver vehicle and the interference inflicted by the transmitter vehicle. The algorithm of fuzzy power control for VANET is proposed, and the performance is evaluated through the simulations. The results of simulations show that the proposed algorithm can increase SINR of vehicles especially the vehicles with SINR value below the target. Thus, the average of SINR and the throughput of the system can be increased as well.

SINR and throughput improvement for VANET using fuzzy power control

Marine radar commonly used for ship navigation. Nowadays, utilization of this radar to another application has expanded. For further applications, several marine radars have some limitations such as monochrome display and require new signal acquisition and processing. This research developed marine radar signal acquisition and processing system based on Furuno x-band marine radar 1932 Mark-2. The developed tool consist of marine radar signal conditioner, ADC and marine radar signal processing using Matlab. This signal processing system display provide signal echo strength visualization, gain control, sea clutter control and rain clutter control. The simulation test was conducted to test the signal processing system. The result was the GUI display at full and half gain are quite similar with marine radar display unit view. The sea and rain clutter control test results were relevant with its function in reducing more close echoes rather than far echoes. While the time required for creating an image is still need for improvement.

Development of marine radar signal acquisition and processing system

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