Harrison Kurunathan

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.

Visible Light Communication (VLC) is an emerging technology that uses light sources such as light-emitting diodes (LEDs) and lasers for data transmission. This is enabled by the IEEE 802.15.7 protocol, which supports deterministic communication through its guaranteed timeslot mechanism. VLC networks provide several advantages in terms of high bandwidth, security, and immunity to electromagnetic interference. Most of the works in VLC are centred around point-to-point communication and do not necessarily provide emphasis on the challenges due to blockage, deafness, or hidden node problems. In this work, we present a machine learning (ML)-optimized routing protocol (Q Light) that improves network throughput and reliability through the selection of efficient routes for the IEEE 802.15.7 protocol.

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.

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.

Visible Light Communication (VLC) has been emerging as a promising technology to address the increasingly high data-rate and time-critical demands that the Internet of Things (IoT) and 5G paradigms impose on the underlying Wireless Sensor Actuator Networking (WSAN) technologies. In this line, the IEEE 802.15.7 standard proposes several physical layers and Medium Access Control (MAC) sub-layer mechanisms that support a variety of VLC applications. Particularly, at the MAC sub-layer, it can support contention-free communications using Guaranteed Timeslots (GTS), introducing support for time-critical applications. However, to effectively guarantee accurate usage of such functionalities, it is vital to derive the worst-case bounds of the network. In this paper, we use network calculus to carry out the worst-case bounds analysis for GTS utilization of IEEE 802.15.7 and complement our model with an in-depth performance analysis. We also propose the inclusion of an additional mechanism to improve the overall scalability and effective bandwidth utilization of the network.

The unprecedented pervasiveness of IoT systems is pushing this technology into increasingly stringent domains. Such application scenarios become even more challenging due to the demand for encompassing the interplay between safety and security. The IEEE 802.15.4 DSME MAC behavior aims at addressing such systems by providing additional deterministic, synchronous multi-channel access support. However, despite the several improvements over the previous versions of the protocol, the standard lacks a complete solution to secure communications. In this front, we propose the integration of TAKS, an hybrid cryptography scheme, over a standard DSME network. In this paper, we describe the system architecture for integrating TAKS into DSME with minimum impact to the standard, and we venture into analysing the overhead of having such security solution over application delay and throughput. After a performance analysis, we learn that it is possible to achieve a minor impact of 1% to 14% on top of the expected network delay, depending on the platform used, while still guaranteeing strong security support over the DSME network.

Deterministic Synchronous Multichannel Extension (DSME) is a prominent MAC behavior first introduced in IEEE 802.15.4e. It can avail deterministic and best effort Service using its multisuperframe structure. RPL is a routing protocol for wireless networks with low power consumption and generally susceptible to packet loss. These two standards were designed independently but with the common objective to satisfy the requirements of IoT devices in terms of limited energy, reliability and determinism. A combination of these two protocols can integrate real-time QoS demanding and large-scale IoT networks. In this paper, we propose a new multi-channel, multi-timeslot scheduling algorithm called Symphony that provides QoS efficient schedules in DSME networks. In this paper we provide analytical and simulation based delay analysis for our approach against some state of the art algorithms. In this work, we show that integrating routing with DSME can improve reliability by 40% and by using Symphony, we can reduce the network delay by 10--20% against the state of the art algorithms.

Deterministic Synchronous Multichannel Extension (DSME) is a prominent MAC behavior first introduced in IEEE 802.15.4e supporting deterministic guarantees using its multisuperframe structure. DSME also facilitates techniques like multi-channel and Contention Access Period (CAP) reduction to increase the number of available guaranteed timeslots in a network. However, any tuning of these functionalities in dynamic scenarios is not explored in the standard. In this paper, we present a multisuperframe tuning technique called DynaMO which tunes the CAP reduction and Multisuperframe Order in an effective manner to improve flexibility and scalability, while guaranteeing bounded delay. We also provide simulations to prove that DynaMO with its dynamic tuning feature can offer up to 15--30% reduction in terms of latency in a large DSME network.