Over the past decade, Unmanned Aerial Vehicles (UAVs) have provided pervasive, efficient, and cost-effective solutions for data collection and communications. Their excellent mobility, flexibility, and fast deployment enable UAVs to be extensively utilized in agriculture, medical, rescue missions, smart cities, and intelligent transportation systems. Machine learning (ML) has been increasingly demonstrating its capability of improving the automation and operation precision of UAVs and many UAV-assisted applications, such as communications, sensing, and data collection. The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasize the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before the full automation of UAVs and potential cooperation between UAVs and humans come to fruition.

The advancements in information and communication technology in the past decades have been converging into a new communication paradigm in which everything is expected to be interconnected. The Internet of Things (IoT), more than a buzzword, is becoming a reality, and is finding its way into the industrial domain, enabling what is now dubbed as the Industry 4.0. Among several standards that help in enabling Industry 4.0, the IEEE 802.15.4e standard addresses requirements such as increased robustness and reliability. Although the standard seems promising, the technology is still immature and rather unproven. Also, there has been no thorough survey of the standard with emphasis on the understanding of the performance improvement in regards to the legacy protocol IEEE 802.15.4. In this survey, we aim at filling this gap by carrying out a performance analysis and thorough discussions of the main features and enhancements of IEEE 802.15.4e. We also provide a literature survey concerning the already proposed add-ons and available tools. We believe this work will help to identify the merits of IEEE 802.15.4e and to contribute towards a faster adoption of this technology as a supporting communication infrastructure for future industrial scenarios.

The Internet of Things (IoT) can be defined as the network of physical objects, or "things," embedded with sensors and software for processing and exchanging data with other devices and ecosystems using the Internet as a medium. With its rapid growth over the past decade, it has permeated several application domains, including intelligent vehicular systems. The Internet of Autonomous Vehicles (IoAV) is a subset of IoT that envisions dynamic autonomous driving without human intervention. The dynamic nature of the environment in which autonomous vehicles operate introduces significant challenges, such as real-time communication and security vulnerabilities. These challenges cannot be directly addressed by standard cybersecurity solutions designed primarily for static IoT environments. In this work, we outline the various vulnerabilities of the IoAV systems, and we delve into the critical importance of adopting lightweight security protocols. These protocols are crucial to ensure robust protection while at the same time not jeopardizing the performance of the IoAV system. We also highlight the fast lightweight security protocols implemented on heterogeneous embedded, low-power, high-performance computing platforms as a viable solution to address these challenges.

WBAN (wireless body area networks) is an upcoming technology which stands to be a base for wearable and implantable sensors. The IEEE 802.15.6 formulates the physical and medium access for body area networks. The Body area networks can be implemented in several applications like health monitoring, ambient living environments and consumer electronics. This paper gives a clear overview about the functions of WBAN. The medium access layers and the physical layers of IEEE 802.15.6 are deeply examined and studied in this work. The access mechanisms of the protocol are explained in this paper. A clear literature review has also been stated to know the current state of art of this technology. The future possibilities and area to be explored also has been defined in this work.

With an advancement towards the paradigm of Internet of Things (IoT), in which every device will be interconnected and communicating with each other, the field of wireless sensor networks has helped to resolve an ever-growing demand in meeting deadlines and reducing power consumption. Among several standards that provide support for IoT, the recently published IEEE 802.15.4e protocol is specifically designed to meet the QoS requirements of industrial applications. IEEE 802.15.4e provides five Medium-Access Control (MAC) behaviors, including three that target time-critical applications: Deterministic and Synchronous Multichannel Extension (DSME); Time Slotted Channel Hopping (TSCH) and Low Latency Deterministic Network (LLDN). However, the standard and the literature do not provide any worst-case bound analysis of these behaviors, thus it is not possible to effectively predict their timing performance in an application and accurately devise a network in accordance to such constraints. This paper fills this gap by contributing network models for the three time-critical MAC behaviors using Network Calculus. These models allow deriving the worst-case performance of the MAC behaviors in terms of delay and buffering requirements. We then complement these results by carrying out a thorough performance analysis of these MAC behaviors by observing the impact of different parameters.

The exploitation of radio channels’ inherent randomness for generating secret keys within a vehicular platoon offers a promising approach to securing communications in dynamic and unpredictable environments. The channel-based key generation leverages the fact that the physical characteristics of the radio channel, such as fading, shadowing, and multipath propagation, vary in a complex manner that makes it difficult for external adversaries to predict or replicate. A challenge lies in accurately assessing the channel’s randomness to ensure the generated keys are both secure and consistent across the platooning vehicles, especially in vehicular environments with high mobility and the ever-changing urban landscape. This paper proposes a novel channel-based key generation (DRL-KeyAgree) technique to enhance communication security within vehicular platoons through combinatorial deep reinforcement learning (DRL). DRL-KeyAgree addresses key disagreement among platooning vehicles by training advantage Actor-Critic (A2C), which integrates policy-and value-based strategies to dynamically select optimal quantization intervals adapting to the random wireless channels. Further incorporation of Long Short-Term Memory (LSTM) allows DRL-KeyAgree to capture the characteristics of partially observable radio channels, significantly enhancing the key agreement rate among vehicles. DRL-KeyAgree is rigorously evaluated using the standard National Institute of Standards and Technology (NIST) test suite.