5G is ongoing, and it is an emerging platform that not only aims to augment existing but also introduce a plethora of novel applications that require ultra-reliable low-latency communication. It is a new radio access technology that provides building blocks to retrofit existing platforms (e.g., 2G, 3G, 4G, and WiFi) for greater coverage, accessibility, and higher network density with respect to cells and devices. It implies that 5G aims to satisfy a diverse set of communication requirements of the various stakeholders. Among the stakeholders, vehicles, in particular, will benefit from 5G at both the system and application levels. The authors present a tutorial perspective on vehicular communications using the building blocks provided by 5G. First, we identify and describe key requirements of emerging vehicular communications and assess existing standards to determine their limitations. Then we provide a glimpse of the adopted 5G architecture and identify some of its promising salient features for vehicular communications. Finally, key 5G building blocks (i.e., proximity services, mobile edge computing and network slicing) are explored in the context of vehicular communications, and associated design challenges are highlighted.

In recent years, the Internet has enabled access to widespread remote services in the distributed computing environment; however, integrity of data transmission in the distributed computing platform is hindered by a number of security issues. For instance, the botnet phenomenon is a prominent threat to Internet security, including the threat of malicious codes. The botnet phenomenon supports a wide range of criminal activities, including distributed denial of service (DDoS) attacks, click fraud, phishing, malware distribution, spam emails, and building machines for illegitimate exchange of information/materials. Therefore, it is imperative to design and develop a robust mechanism for improving the botnet detection, analysis, and removal process. Currently, botnet detection techniques have been reviewed in different ways; however, such studies are limited in scope and lack discussions on the latest botnet detection techniques. This paper presents a comprehensive review of the latest state-of-the-art techniques for botnet detection and figures out the trends of previous and current research. It provides a thematic taxonomy for the classification of botnet detection techniques and highlights the implications and critical aspects by qualitatively analyzing such techniques. Related to our comprehensive review, we highlight future directions for improving the schemes that broadly span the entire botnet detection research field and identify the persistent and prominent research challenges that remain open.

Vehicular communication requires vehicles to self-organize through the exchange of periodic beacons. Recent analysis on beaconing indicates that the standards for beaconing restrict the desired performance of vehicular applications. This situation can be attributed to the quality of the available transmission medium, persistent change in the traffic situation and the inability of standards to cope with application requirements. To this end, this paper is motivated by the classifications and capability evaluations of existing adaptive beaconing approaches. To begin with, we explore the anatomy and the performance requirements of beaconing. Then, the beaconing design is analyzed to introduce a design-based beaconing taxonomy. A survey of the state of the art is conducted with an emphasis on the salient features of the beaconing approaches. We also evaluate the capabilities of beaconing approaches using several key parameters. A comparison among beaconing approaches is presented, which is based on the architectural and implementation characteristics. The paper concludes by discussing open challenges in the field.

The cloud-fog computing paradigm is a novel hybrid computing model that delivers computational services to Fog nodes situated near data sources. This paradigm features a volatile and dynamic network topology, comprising heterogeneous IoT devices with varying computational capabilities, alongside a large number of diverse end-user requests. These complexities present significant challenges for researchers in establishing a robust, energy-efficient, and reliable communication environment. Efficient and optimal task scheduling is among these challenges, as it involves finding appropriate computing resources for processing tasks. Assigning tasks to fog nodes reduces delay but increases energy consumption, while routing tasks to cloud servers conserves energy but prolongs transmission delay. Therefore, it is essential to develop an optimal task scheduling algorithm for a reliable, delay-efficient, and energy-efficient communication environment. To address this, we propose a multiobjective Harris hawks optimization (HHO)-based task scheduling algorithm (MoHHOTS) for cloud-fog computing networks, aiming to optimize task scheduling with the objectives of minimizing delay and energy consumption. MoHHOTS is implemented in MATLAB and evaluated against state-of-the-art benchmark algorithms, including MOGWO and the cloud-fog cooperation algorithm. Leveraging the high convergence and stochastic operators of the HHO algorithm, alongside a balanced approach to iteration between diversification and intensification, the proposed algorithm provides a set of tradeoff solutions via the Pareto-optimal Front. Simulation results demonstrate the efficacy of the proposed solution, achieving improvements of up to 25% over a similar scheduling algorithm in terms of optimizing transmission delay and energy consumption.

Periodic beacons in vehicular ad hoc networks are transmitted with high message frequency to achieve the higher level of awareness required for the vehicular safety applications. Currently, the existing 10-MHz control channel in the dedicated short range communication (DSRC) standard is not compliant with the communication requirements in vehicular safety applications, i.e., the stipulated amendments in the DSRC offer little relief to congestion caused by the periodic beacon transmissions on the control channel, which adversely affects the message reception. In this paper, we seek to address the problem of congestion with a transmit power adaptation approach, based on the principles of cooperative game theory. The proposed approach, Adaptive transmit power Cooperative Congestion Control (AC3), is designed to allow vehicles to select their transmit power autonomously with respect to their local channel congestion. Since the number of neighbours of each vehicle and their corresponding transmit power levels vary, AC3 requires that each vehicle reduces its transmit power fairly during congestion. Explicitly, the proposed approach introduces the notion of marginal contributions of vehicles towards congestion and determines a fair power decrease for vehicles using a shapely value system model. This model requires the vehicles with the highest marginal contributions to reduce the most transmit power and vice versa. The simulation results demonstrate the utility of the proposed approach (i.e., capability to determine fair power decrease for an effective congestion control).