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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.

The fifth generation (5G) of wireless networks promises to meet the stringent requirements of vehicular use cases that cannot be supported by previous technologies. However, the stakeholders of the automotive industry (e.g., car manufacturers and road operators) are still skeptical about the capability of the telecom industry to take the lead in a market that has been dominated by dedicated intelligent transport systems (ITS) deployments. In this context, this paper constructs a framework where the potential of 5G to support different vehicular use cases is thoroughly examined under a common format from both the technical and business perspectives. From the technical standpoint, a storyboard description is developed to explain when and how different use case scenarios may come into play (i.e., pre-conditions, service flows and post-conditions). Then, a methodology to trial each scenario is developed including a functional architecture, an analysis of the technical requirements and a set of target test cases. From the business viewpoint, an initial analysis of the qualitative value perspectives is conducted considering the stakeholders, identifying the pain points of the existing solutions, and highlighting the added value of 5G in overcoming them. The future evolution of the considered use cases is finally discussed.

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 [...]

The ongoing transition towards 5G technology expedites the emergence of a variety of mobile applications that pertain to different vertical industries. Delivering on the key commitment of 5G, these diverse service streams, along with their distinct requirements, should be facilitated under the same unified network infrastructure. Consequently, in order to unleash the benefits brought by 5G technology, a holistic approach towards the requirement analysis and the design, development, and evaluation of multiple concurrent vertical services should be followed. In this paper, we focus on the Transport vertical industry, and we study four novel vehicular service categories, each one consisting of one or more related specific scenarios, within the framework of the “5G Health, Aquaculture and Transport (5G-HEART)” 5G PPP ICT-19 (Phase 3) project. In contrast to the majority of the literature, we provide a holistic overview of the overall life-cycle management required for the realization of the examined vehicular use cases. This comprises the definition and analysis of the network Key Performance Indicators (KPIs) resulting from high-level user requirements and their interpretation in terms of the underlying network infrastructure tasked with meeting their conflicting or converging needs. Our approach is complemented by the experimental investigation of the real unified 5G pilot’s characteristics that enable the delivery of the considered vehicular services and the initial trialling results that verify the effectiveness and feasibility of the presented theoretical analysis.

The economic and social impact of poor air quality in towns and cities is increasingly being recognized, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and the resultant datasets also featuring missing labels, computational data-driven mechanisms are needed to address the data sparsity challenge. In this paper, we propose a machine learning-based method to accurately predict the air quality index, using environmental monitoring data together with meteorological measurements. To do so, we develop an air quality estimation framework that implements a neural network that is enhanced with a novel non-linear autoregressive neural network with exogenous input model, especially designed for time series prediction. The framework is applied to a case study featuring different monitoring sites in London, with comparisons against other standard machine-learning-based predictive algorithms showing the feasibility and robust performance of the proposed method for different kinds of areas within an urban region.

Internet of Things (IoT) data analytics is underpinning numerous applications, however, the task is still challenging predominantly due to heterogeneous IoT data streams, unreliable networks, and ever increasing size of the data. In this context, we propose a two-layer architecture for analyzing IoT data. The first layer provides a generic interface using a service oriented gateway to ingest data from multiple interfaces and IoT systems, store it in a scalable manner and analyze it in real-time to extract high-level events; whereas second layer is responsible for probabilistic fusion of these high-level events. In the second layer, we extend state-of-the-art event processing using Bayesian networks in order to take uncertainty into account while detecting complex events. We implement our proposed solution using open source components optimized for large-scale applications. We demonstrate our solution on real-world use-case in the domain of intelligent transportation system where we analyzed traffic, weather, and social media data streams from Madrid city in order to predict probability of congestion in real-time. The performance of the system is evaluated qualitatively using a web-interface where traffic administrators can provide the feedback about the quality of predictions and quantitatively using F-measure with an accuracy of over 80%.

Mobile sensing techniques have been increasingly deployed in many Internet of Things-based applications because of their cost efficiency, wide coverage, and flexibility. However, these techniques are unreliable in many situations due to noise of different kinds, loss of communication, or insufficient energy. As such, datasets created from mobile sensing scenarios are likely to contain large amount of missing data, which makes further data analysis difficult, inaccurate, or even impossible. We find that the existing estimation models and techniques developed for static sensing do not work well in the mobile sensing scenarios. To address the problem, we propose a spatio-temporal method, which is specifically designed for answering queries in such applications. Experiments on a real-world, incomplete mobile sensing dataset show that the proposed method outperforms the state-of-the-art noticeably in terms of estimation errors. More importantly, the proposed model is tolerant to datasets with extremely high-missing data rates. Training with the proposed model is also efficient, which makes it suitable for deployment on computationally constrained devices and platforms that need to process massive amounts of data in real time.

This paper presents a task allocation-oriented framework to enable efficient in-network processing and cost-effective multi-hop resource sharing for dynamic multi-hop multimedia wireless sensor networks with low node mobility, e.g., pedestrian speeds. The proposed system incorporates a fast task reallocation algorithm to quickly recover from possible network service disruptions, such as node or link failures. An evolutional self-learning mechanism based on a genetic algorithm continuously adapts the system parameters in order to meet the desired application delay requirements, while also achieving a sufficiently long network lifetime. Since the algorithm runtime incurs considerable time delay while updating task assignments, we introduce an adaptive window size to limit the delay periods and ensure an up-to-date solution based on node mobility patterns and device processing capabilities. To the best of our knowledge, this is the first study that yields multi-objective task allocation in a mobile multi-hop wireless environment under dynamic conditions. Simulations are performed in various settings, and the results show considerable performance improvement in extending network lifetime compared to heuristic mechanisms. Furthermore, the proposed framework provides noticeable reduction in the frequency of missing application deadlines.

In this paper, we present a method that facilitates Internet of Things (IoT) for building a product passport and data exchange enabling the next stage of the circular economy. SmartTags based on printed sensors (i.e., using functional ink) and a modified GS1 barcode standard enable unique identification of objects on a per item-level (including Fast-Moving Consumer Goods—FMCG), collecting, sensing, and reading of parameters from environment as well as tracking a products’ lifecycle. The developed ontology is the first effort to define a semantic model for dynamic sensors, including datamatrix and QR codes. The evaluation of decoding and readability of identifiers (QR codes) showed good performance for detection of sensor state printed over and outside the QR code data matrix, i.e., the recognition ability with image vision algorithm was possible. The evaluation of the decoding performance of the QR code data matrix printed with sensors was also efficient, i.e., the QR code ability to be decoded with the reader after reversible and irreversible process of ink (dis)appearing was preserved, with slight drop in performance if ink density is low.

Understanding home activities is important in social research to study aspects of home life, e.g., energy-related practices and assisted living arrangements. Common approaches to identifying which activities are being carried out in the home rely on self-reporting, either retrospectively (e.g., interviews, questionnaires, and surveys) or at the time of the activity (e.g., time use diaries). The use of digital sensors may provide an alternative means of observing activities in the home. For example, temperature, humidity and light sensors can report on the physical environment where activities occur, while energy monitors can report information on the electrical devices that are used to assist the activities. One may then be able to infer from the sensor data which activities are taking place. However, it is first necessary to calibrate the sensor data by matching it to activities identified from self-reports. The calibration involves identifying the features in the sensor data that correlate best with the self-reported activities. This in turn requires a good measure of the agreement between the activities detected from sensor-generated data and those recorded in self-reported data. To illustrate how this can be done, we conducted a trial in three single-occupancy households from which we collected data from a suite of sensors and from time use diaries completed by the occupants. For sensor-based activity recognition, we demonstrate the application of Hidden Markov Models with features extracted from mean-shift clustering and change points analysis. A correlation-based feature selection is also applied to reduce the computational cost. A method based on Levenshtein distance for measuring the agreement between the activities detected in the sensor data and that reported by the participants is demonstrated. We then discuss how the features derived from sensor data can be used in activity recognition and how they relate to activities recorded in time use diaries.

The integration of things’ data on the Web and Web linking for things’ description and discovery is leading the way towards smart Cyber–Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber–Physical–Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human–machine intelligence.

Data searching and retrieval is one of the fundamental functionalities in many Web of Things applications, which need to collect, process and analyze huge amounts of sensor stream data. The problem in fact has been well studied for data generated by sensors that are installed at fixed locations; however, challenges emerge along with the popularity of opportunistic sensing applications in which mobile sensors keep reporting observation and measurement data at variable intervals and changing geographical locations. To address these challenges, we develop the Geohash-Grid Tree, a spatial indexing technique specially designed for searching data integrated from heterogeneous sources in a mobile sensing environment. Results of the experiments on a real-world dataset collected from the SmartSantander smart city testbed show that the index structure allows efficient search based on spatial distance, range and time windows in a large time series database.

In this article, an Arrival and Departure Time Predictor (ADTP) for scheduling communication in opportunistic Internet of Things (IoT) is presented. The proposed algorithm learns about temporal patterns of encounters between IoT devices and predicts future arrival and departure times, therefore future contact durations. By relying on such predictions, a neighbour discovery scheduler is proposed, capable of jointly optimizing discovery latency and power consumption in order to maximize communication time when contacts are expected with high probability and, at the same time, saving power when contacts are expected with low probability. A comprehensive performance evaluation with different sets of synthetic and real world traces shows that ADTP performs favourably with respect to previous state of the art. This prediction framework opens opportunities for transmission planners and schedulers optimizing not only neighbour discovery, but the entire communication process.

The Web of Things aims to make physical world objects and their data accessible through standard Web technologies to enable intelligent applications and sophisticated data analytics. Due to the amount and heterogeneity of the data, it is challenging to perform data analysis directly; especially when the data is captured from a large number of distributed sources. However, the size and scope of the data can be reduced and narrowed down with search techniques, so that only the most relevant and useful data items are selected according to the application requirements. Search is fundamental to the Web of Things while challenging by nature in this context, e.g., mobility of the objects, opportunistic presence and sensing, continuous data streams with changing spatial and temporal properties, efficient indexing for historical and real time data. The research community has developed numerous techniques and methods to tackle these problems as reported by a large body of literature in the last few years. A comprehensive investigation of the current and past studies is necessary to gain a clear view of the research landscape and to identify promising future directions. This survey reviews the state-of-the-art search methods for the Web of Things, which are classified according to three different viewpoints: basic principles, data/knowledge representation, and contents being searched. Experiences and lessons learned from the existing work and some EU research projects related to Web of Things are discussed, and an outlook to the future research is presented.

The main challenge for a cognitive radio is to detect the existence of primary users reliably in order to minimise the interference to licensed communications. Hence, spectrum sensing is a most important requirement of a cognitive radio. However, due to the channel uncertainties, local observations are not reliable and collaboration among users is required. Selection of fusion rule at a common receiver has a direct impact on the overall spectrum sensing performance. In this paper, optimisation of collaborative spectrum sensing in terms of optimum decision fusion is studied for hard and soft decision combining. It is concluded that for optimum fusion, the fusion centre must incorporate signal-to-noise ratio values of cognitive users and the channel conditions. A genetic algorithm-based weighted optimisation strategy is presented for the case of soft decision combining. Numerical results show that the proposed optimised collaborative spectrum sensing schemes give better spectrum sensing performance.