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Open Radio Access Network (RAN) introduces a groundbreaking industry standard for Radio Access Networks, fostering vendor interoperability and network flexibility through open interfaces while leveraging network softwarization, Artificial, and Machine Learning Intelligence; however, it also poses significant security challenges due to its unique configuration, prompting stakeholders to cautiously approach its deployment and necessitating thorough analysis and implementation of security measures and standards. This paper systematically examines existing literature and case studies to underscore the indispensable role of Intrusion Detection Systems (IDS) in identifying and mitigating security breaches within Open RAN environments. We elucidate the distinct challenges that Open RAN’s disaggregated architecture introduced and classify them into technical and non-technical threats. Finally, we discussed a series of new advancements gaining momentum in the Open RAN security domain and provided insights for future research directions.

Resource trading between vehicles, which involves the buying and selling of computing and bandwidth resources, is a promising approach for cost-effectively provisioning services in safety-critical applications such as autonomous driving. These applications require a guarantee and the timely receipt of resources through efficient advance reservations. However, due to uncertainties in future reservation duration and resource costs, vehicles exhibit two distinct patterns: some may have reserved insufficient resources and need to purchase more (acting as vehicle requesters), while others may have overbooked resources and need to sell (acting as vehicle providers). In this paper, we formulate the resource trading problem from both the requester and provider perspectives and propose a resource trading architecture to optimize bandwidth reservation. It utilizes blockchain smart contracts for secure and efficient resource exchange within a mobile network operator (MNO) environment. Two algorithms are introduced: a provider selection algorithm to enhance system efficiency by selecting cost-effective providers, and a decision-making algorithm to assist providers in choosing between selling overbooked bandwidth or canceling it. Through simulations, the results show that these algorithms lead to significant cost reductions for requesters and profit gains for providers, up to 59% and 19%, respectively, compared to reservation schemes without resource trading in such a dynamic environment.

Edge computing resources are crucial for time-sensitive and safety-critical vehicular (TSSCV) applications, such as autonomous and remote driving. These applications require guaranteed resource availability for real-time communication and computation to ensure safety and efficiency. With the emergence of integrated sensing and communication (ISAC) in 6G communication systems, extensive sensing data will be added to the communication data. To meet these challenges, resource reservation becomes a necessity. Resource reservation approaches can be either network-side or vehicle-side. Most existing approaches are network-side. However, these approaches are insufficient for these applications because they only guarantee resources to individual vehicles with a certain probability. Vehicle-side reservation is challenging because vehicles usually do not have sufficient information to reason about future available resources and their costs. This comprehensive survey aims to define the characteristics of TSSCV applications and to analyze existing reservation approaches and cost-effective schemes, considering different reservation scenarios in single and multiple mobile network operator (MNO) environments. In addition, it explores strategies to revise or update reservations when faced with uncertain reservation times and costs. To this end, we propose to enable resource trading and reservation exchange facilitated by blockchain smart contracts. This approach promises a secure and efficient method for resource management in future smart vehicles.

The proliferation of the Internet of Things (IoT) is occurring swiftly and is all-encompassing. The cyber attack on Dyn in 2016 brought to light the notable susceptibilities of intelligent networks. The issue of security in the realm of the Internet of Things (IoT) has emerged as a significant concern. The security of the Internet of Things (IoT) is compromised by the potential danger posed by exploiting devices connected to the Internet. The susceptibility of Things to botnets poses a significant threat to the entire Internet ecosystem (smart devices). In recent years, there has been a simultaneous evolution in the complexity and variety of security attack vectors. Therefore, it is imperative to analyse IoT methodologies to detect and alleviate emerging security breaches. The present study analyses network datasets, distinguishing between those of the Internet of Things (IoT) and those that do not, and provides a thorough overview of the findings. Our primary focus is on IoT Network Intrusion Detection (NID) studies, wherein we examine the available datasets, tools, and machine learning (ML) techniques employed in the implementation of network intrusion detection (NID). Subsequently, an evaluation, assessment, and summary of the current state-of-the-art research on IoT-related Network Intrusion Detection (NID) conducted between 2018 and 2024 is presented. This includes an analysis of the publication year, dataset, attack types, experiment results, and the advantages, disadvantages, and classifiers employed in the studies. This review emphasises research related to IoT NID that employs Supervised Machine Learning classifiers, owing to the high success rate of such classifiers in security and privacy domains. Furthermore, this survey incorporates a comprehensive analysis of research endeavours on IoT NID. Furthermore, we have identified publicly available IoT datasets that can be utilised for NID experiments, which would benefit academic and industrial research purposes. Moreover, we analyse potential prospects and future advancements. The review’s findings indicate that the Internet of Things (IoT) has been substantiated by its swift proliferation in recent times, leading to even broader network coverage. This study presented conventional datasets gathered over a decade ago and current datasets published within the past decade and utilised in recent research. The survey provides a succinct overview of prevailing research trends in IoT NID for security professionals.

The interest in performing scientific computations using commercially available cloud computing resources has grown rapidly in the last decade. However, scheduling multiple workflows in cloud computing is challenging due to its non-functional constraints and multi-dimensional resource requirements. Scheduling algorithms proposed in literature use search-based approaches which often result in very high computational overhead and long execution time. In this paper, a Deadline-Constrained Cost Minimisation (DCCM) algorithm is proposed for resource scheduling in cloud computing. In the proposed scheme, tasks were grouped based on their scheduling deadline constraints and data dependencies. Compared to other approaches, DCCM focuses on meeting the user-defined deadline by sub-dividing tasks into different levels based on their priorities. Simulation results showed that DCCM achieved higher success rates when compared to the state-of-the-art approaches.

Ultra-reliable low-latency communication (URLLC) has been introduced in the 5th Generation (5G) radios for mission-critical applications that demand strict reliability and latency traffic to guarantee the rapid delivery of short packets (up to 1 ms) with a success probability rate of 99.999%. The challenging reliability and latency requirements of URLLC have significant impact on the air-interface design, especially on the Hybrid Automatic Repeat reQuest (HARQ) mechanism. This study focuses on satisfying link latency requirements by reducing the delay that arises in the presence of the HARQ operation. To this end, we propose a Swift HARQ protocol empowered by machine learning techniques to estimate the decodability of a packet early enough within its maximum number of allowable retransmission attempts. This can allow the transmitter to react faster by dropping the non-decodable packets, or activating the repetition mode where parts of the HARQ feedback can be omitted. As shown through system-level simulations, the proposed model achieves a delay reduction of more than 50% compared to the traditional HARQ, and increases the system throughput by up to 40% when multiple HARQ retransmissions are required.

LoRa technology, renowned for its low-power, long-range capabilities in IoT applications, faces challenges in real-world scenarios, including fading channels, interference, and environmental obstacles. This paper aims to study the reliability of LoRa in Non-Line-of-Sight (NLoS) conditions and in noisy and mobile environments for Industrial IoT (IIoT) applications. Experimental measurements consider factors like vegetation and infrastructure, introducing mobility to replicate NLoS conditions. Utilizing an open-source LoRa Physical Layer (PHY) Software-Defined Radio (SDR) prototype developed with GNU Radio, we assess communication reliability through metrics such as Block Error Rate (BLER), Signal-to-Noise-Interference-plus-Noise Ratio (SINR), and data rate. The study reveals the estimated overall reliability of the LoRa signal at 90.23%, emphasizing specific configuration details. This work contributes to the broader field of LoRa communication, encompassing hardware, software, protocols, and management, enhancing our understanding of LoRa’s dependability in challenging IIoT environments.

Advances made in the Internet of Things (IoT) and other disruptive technological trends, including big data analytics and edge computing methods, have contributed enabling solutions to the numerous challenges affecting modern communities [...]