The 3rd Generation Partnership Project (3GPP) 5G Non-Terrestrial Networks (NTN) adopt Orthogonal Frequency Division Multiplexing (OFDM) to enable integrated space-ground networks via Low Earth Orbit (LEO) satellite global connectivity. However, the rapid movement of LEO satellites induces a significant non-uniform Doppler shift across subcarriers, resulting in the signal bandwidth changes that leads to sampling point offsets and severely impacting demodulation performance. Traditional Doppler compensation algorithms focus mainly on addressing the uniform Carrier Frequency Offset (CFO) caused by crystal oscillation. In LEO satellite broadband communication systems, the Sampling Frequency Offset (SFO), caused by the non-uniform Doppler frequency shift, can be tens of times greater than the CFO, leading to non-negligible phase rotation, inter-carrier interference (ICI), and inter-symbol interference (ISI). Consequently, a low-complexity algorithm is required to address the fast time-varying SFO — one of the core challenges in these systems. In this paper, we derive a closed-form expression for the signal distortion and propose a model-driven compensation method that leverages the predictability of satellite trajectories. The proposed method effectively removes phase rotation and mitigates ICI and ISI through fast Fourier transform (FFT) window adjustment and phase rotation compensation. The accuracy of the model is validated through extensive simulations and a real satellite communication trial. Results demonstrate that the proposed method supports higher-order modulations, leading to an average spectral efficiency improvement of approximately 50%. This pioneering research promises to ensure robust performance in dynamic LEO satellite environments.

Computation offloading and resource allocation are critical in mobile edge computing (MEC) systems to handle the massive and complex requirements of applications restricted by limited resources. In a multiuser multiserver MEC network, the mobility of terminals causes computing requests to be dynamically distributed in space. At the same time, the non-negligible dependencies among tasks in some specific applications impose temporal correlation constraints on the solution as well, leading the time-adjacent tasks to experience varying resource availability and competition from parallel counterparts. To address such dynamic spatial-temporal characteristics as a challenge in the allocation of communication and computation resources, we formulate a long-term delay-energy tradeoff cost minimization problem in the view of jointly optimizing task offloading and resource allocation. We begin by designing a priority evaluation scheme to decouple task dependencies and then develop a grouped Knapsack problem for channel allocation considering the current data load and channel status. Afterward, in order to meet the rapid response needs of MEC systems, we exploit the double duel deep Q network (D3QN) to make offloading decisions and integrate channel allocation results into the reward as part of the dynamic environment feedback in D3QN, constituting the joint optimization of task offloading and channel allocation. Finally, comprehensive simulations demonstrate the performance of the proposed algorithm in the delay-energy tradeoff cost and its adaptability for various applications.

This paper investigates the uplink resource allocation issue for radio access network (RAN) slicing with a focus on three primary fifth generation (5G) use cases, namely ultra-reliable low-latency communications (uRLLC), enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC). Most of the existing resource allocation schemes for RAN slicing were designed to address the use cases in downlink communications. However, these schemes are not applicable to the uplink RAN slicing, even though the primary use cases also entail stringent requirements in the uplink. To this end, we aim to develop a new, fairness-aware uplink resource allocation scheme for RAN slicing, catering to the three 5G use cases. Firstly, we formulate a new uplink resource allocation optimization problem that maximizes a sum logarithmic utility for ensuring proportional-fair effective capacity distribution and resource allocation among RAN slices, subject to the distinct quality of service (QoS) requirements of the uRLLC, eMBB and mMTC slices. Then, an efficient, hierarchical resource allocation framework is then proposed to solve the problem deterministically, whereby resource block assignment is first performed at the base station via a demand-oriented greedy algorithm, followed by power allocation using a bisection method at individual user equipment. Results show that the proposed scheme outperforms the baseline schemes with fairness and QoS provisioning performance gains of up to 44.3% and 19.17% respectively.

In order to provide universal ability to access information and communication among Internet-connected devices, the Sustainable Internet of Things (IoT) is on a mission to bring all objects or devices under one roof. Future Internet architecture, especially Information-Centric Networking (ICN), can easily handle the connectivity offered and information created by the massive amount of devices to make it as sustainable IoT applications. Named Data Networking (NDN), one of the several future Internet designs that employ ICN as its foundation, shows promise. NDN integration with IoT-based applications gives solutions to numerous problems. However, this fusion makes accessing the IoT content easier, provided that an effective naming scheme is created to execute this operation. In this work, we build an innovative NDN-based naming scheme (NDN–NS) and put it into practise for consumer, producer, and content routers using our own secure forwarding schemes (NDN–NFS). Due to its scalability, heterogeneity, and security needs, IoT-based Smart Campus (IoT-SC) scenarios are taken into consideration for design and evaluation. We give a complete activity list based on NDN–NS that is split into two communication models (PusH Type Communication (PHTC) and PulL Type Communication (PLTC)) that can be applied to any IoT application. In terms of interest satisfaction rate (ISR), delay, and number of transmissions, we compare the NDN–NFS to legacy NDN. The outcomes demonstrate that NDN–NFS outperforms classic NDN in terms of performance and efficiency.

This article studies mobile edge computing technologies enabled by unmanned aerial vehicles (UAVs) in disasters. First, considering that the ground servers may be damaged in emergency scenarios, we proposed an air-ground cooperation architecture based on ad-hoc UAV networks. We defined the system cost as the weighted sum of task delay and energy consumption because of different delay sensitivity and energy sensitivity tasks in emergency communication networks. Then, we formulated the system cost-minimization problem of task scheduling and multi-UAV deployments. To solve the proposed mixed integer nonlinear programming problem, we decomposed it to two sub-problems that were solved by proposing a swap matching-based task scheduling sub-algorithm and a successive convex approximation-based multi-UAV deployment sub-algorithm. Accordingly, we propose a joint optimization algorithm by iterating the two sub-algorithms to obtain a low complexity sub-optimal solution. Finally, the simulation results show that (i) the proposed algorithm converges in several iterations, and (ii) compared with the benchmark algorithms, the proposed algorithm has better performance of reducing task delay and energy consumption and achieves a good trade-off between them for diverse tasks.

An investigation of the recent advancements in modelling and optimisation techniques to develop maintenance strategies for offshore floating systems have been carried out in this paper and identified that the impact of time required to carry out activities have not been considered as an influencing factor in any of the existing formulations reviewed. The influence of time required to complete the activity, on the prioritisation of activities have been demonstrated in this work by means of a novel optimisation problem formulation for Floating Production Storage and Offloading Facility (FPSO) that maximises maintenance personnel resource utilisation and enables FPSO condition enhancement. To find the Pareto-optimal solution, an overall objective function has been developed considering the priorities with respect to design features, operating conditions, deteriorations, and the consequences of not doing the maintenance, taking into consideration the personnel resource time required for activity completion. This formulation provides flexibility to direct the focus of the overall objective function towards any one or more of the objective functions by adjusting their respective weight according to the maintenance strategy followed, which would supplement Regulatory oversight requirements of the FPSO.

The development of cryptographic protocols goes through two stages, namely, security verification and performance analysis. The verification of the protocol’s security properties could be analytically achieved using threat modelling, or formally using formal methods and model checkers. The performance analysis could be mathematical or simulation-based. However, mathematical modelling is complicated and does not reflect the actual deployment environment of the protocol in the current state of the art. Simulation software provides scalability and can simulate complicated scenarios, however, there are times when it is not possible to use simulations due to a lack of support for new technologies or simulation scenarios. Therefore, this paper proposes a formal method and analytical model for evaluating the performance of security protocols using applied pi-calculus and Markov Chain processes. It interprets algebraic processes and associates cryptographic operatives with quantitative measures to estimate and evaluate cryptographic costs. With this approach, the protocols are presented as processes using applied pi-calculus, and their security properties are an approximate abstraction of protocol equivalence based on the verification from ProVerif and evaluated using analytical and simulation models for quantitative measures. The interpretation of the quantities is associated with process transitions, rates, and measures as a cost of using cryptographic primitives. This method supports users’ input in analysing the protocol’s activities and performance. As a proof of concept, we deploy this approach to assess the performance of security protocols designed to protect large-scale, 5G-based Device-to-Device communications. We also conducted a performance evaluation of the protocols based on analytical and network simulator results to compare the effectiveness of the proposed approach.

The resource allocation of the federated learning (FL) for unmanned aerial vehicle (UAV) swarm systems are investigated. The UAV swarms based on FL realize the artificial intelligence (AI) applications by means of distributed training on the basis of ensuring the security of private data. However, the direct application of the FL in UAV swarms will incur high overhead. Therefore, in this article, we consider the resource allocation problem in FL for UAV swarms. To avoid the high communication overhead between UAVs and the central server, we proposed an FL framework for UAV swarms based on mobile edge computing (MEC) in which model aggregation is migrated to edge servers. In the proposed framework, the total cost of the FL is defined as the weighted sum of the total delay of UAV swarms to complete the FL and system energy consumption. In order to minimize the total cost of FL, we propose a resource allocation algorithm for joint optimization of computing resources and multi-UAV association based on deep reinforcement learning (DRL). The simulation result shows that: 1) compared with the benchmark algorithm, the proposed algorithm can effectively reduce the total cost of FL; 2) the proposed algorithm can realize the trade-off between task completion delay and system energy consumption through weight changes.

To effectively prevent crimes, it is vital to anticipate their patterns and likely occurrences. Our efforts focused on analyzing diverse open-source datasets related to London, such as the Met police records, public social media posts, data from transportation hubs like bus and rail stations etc. These datasets provided rich insights into human behaviors, activities, and demographics across different parts of London, paving the way for a machine learning-driven prediction system. We developed this system using unique crime-related features extracted from these datasets. Furthermore, our study outlined methods to gather detailed street-level information from local communities using various applications. This innovative approach significantly enhances our ability to deeply understand and predict crime patterns. The proposed predictive system has the potential to forecast potential crimes in advance, enabling government bodies to proactively deploy targeted interventions, ultimately aiming to prevent and address criminal incidents more effectively.

The correlation between events within the same document plays a crucial role in event detection. Most existing detection models often ignore event correlations, which is not applicable to multievent detection at the document level. In the real world, it is a common phenomenon that the probability of correlated events occurring simultaneously is much greater than the probability of uncorrelated events occurring simultaneously. Based on this observation, we propose an event correlation-based document-level event detection model (EventCo-ED) to capture the documentlevel association between events. Specifically, EventCo-ED first constructs a novel event relation graph (ERG) to capture the correlation between events and uses this correlation to extract the topic features of a document. Secondly, DMBERT is employed to get sentence-level contextual representation as the local features. Finally, a gated feature fusion module is used to aggregate topic features and local features, and a correlation suppression module is used to increase the probability that related events are detected simultaneously and suppress the probability that unrelated events are detected simultaneously. Experimental results show that the proposed model can simultaneously improve the precision and recall of multi-event detection and achieve 1.56% and 3.63% F1 improvements on the LEVEN and MAVEN corpuses, respectively.

The rapid expansion of Internet of Things (IoT) devices has resulted in significant progress and developments in various sectors, such as smart healthcare, self-driving vehicles, smart banking, smart home, Industry 4.0, etc. Traditional centralised access control methods are inadequate to deploy in decentralised IoT networks. Although the existing Blockchain-based access control approaches provide a better way of managing access permission for IoT systems, they are ineffective in addressing critical security gaps and preventing unauthorised access while detecting malicious anomalies. To address these two challenges in a single platform, in this paper, we propose a novel approach towards a deep-learning-based authorisation solution that is deployed within the Hyperledger-Fabric (HLF) private Blockchain. Our approach allows smart-contract to define attribute-based access control policies augmented with the Artificial Neural Network (ANN) model, which can effectively identify and isolate malicious anomalies and prevent unauthorised access from malicious devices. We run experiments to evaluate our platform in terms of security and performance, and our results show positive indicators essential for addressing security issues in decentralised IoT networks.

This article investigates the semantic extraction task-oriented dynamic multi-time scale user admission and resource allocation in mobile edge computing (MEC) systems. Amid prevalence artificial intelligence applications in various industries, the offloading of semantic extraction tasks which are mainly composed of convolutional neural networks of computer vision is a great challenge for communication bandwidth and computing capacity allocation in MEC systems. Considering the stochastic nature of the semantic extraction tasks, we formulate a stochastic optimization problem by modeling it as the dynamic arrival of tasks in the temporal domain. We jointly optimize the system revenue and cost which are represented as user admission in the long term and resource allocation in the short term respectively. To handle the proposed stochastic optimization problem, we decompose it into short-time-scale subproblems and a long-time-scale subproblem by using the Lyapunov optimization technique. After that, the short-time-scale optimization variables of resource allocation, including user association, bandwidth allocation, and computing capacity allocation are obtained in closed form. The user admission optimization on long-time scales is solved by a heuristic iteration method. Then, the multi-time scale user admission and resource allocation algorithm is proposed for dynamic semantic extraction task computing in MEC systems. Simulation results demonstrate that, compared with the benchmarks, the proposed algorithm improves the performance of user admission and resource allocation efficiently and achieves a flexible trade-off between system revenue and cost at multi-time scales and considering semantic extraction tasks.

In response to the ever-increasing demands of users for delay-sensitive applications, issues on shortening the task completion time in the mobile edge computing (MEC) system has aroused widespread concern. From the perspective of task execution order, this work provide a task scheduling scheme for multiple edge nodes (EN) while federated learning (FL) is utilized for the collaboration of the ENs in the MEC system. First, to acquire an efficient execution order for the pending computational tasks that dynamically generated on one edge node, a task scheduling algorithm based on deep Q network (DQN) is proposed, which reduces the average task completion time. Then, based on the federated learning, of which characteristic matches the edge system well, we integrate an aggregation mechanism to take advantage of every participating edge node to obtain a set of global parameter that optimizes task completion delay for all nodes in the MEC system. Simulations verify the effectiveness and the superiority of the proposed algorithm in processing delaysensitive tasks and analyze the key factors that contributes to the system performance.

Maintenance planning program of offshore assets is a complex activity due to its impact on the operational and safety risks and consequences, dependence on personnel resource availabilities, site constraints due to operational requirements and environmental factors, and uncertainties related to various vulnerabilities on asset. This paper elaborates the challenges on offshore maintenance frameworks and have carried out a review of recent state-of-the-art literature from which have observed that the current state-of-the-art does not incorporate site constraints of the asset related to offshore personnel resource availability and impact of time required to carry out activities, into the maintenance plan and its impact on other activities due to the maintenance. Also, it has been identified that dynamic and autonomous resource allocations for maintenance activities are not employed in the offshore maintenance planning program that allows each maintenance item to independently adjust its resource allocation based on the time required to complete the activity, to improve the resource utilisation.

Fifth Generation Mobile Network (5G) is a heterogeneous network in nature, made up of multiple systems and supported by different technologies. It will be supported by network services such as device-to-device (D2D) communications. This will enable the new use cases to provide access to other services within the network and from third-party service providers (SPs). End-users with their user equipment (UE) will be able to access services ubiquitously from multiple SPs that might share infrastructure and security management, whereby implementing security from one domain to another will be a challenge. This highlights a need for a new and effective security approach to address the security of such a complex system. This article proposes a network service security (NSS) modular framework for 5G and beyond that consists of different security levels of the network. It reviews the security issues of D2D communications in 5G, and it is used to address security issues that affect the users and SPs in an integrated and heterogeneous network such as the 5G enabled D2D communications network. The conceptual framework consists of a physical layer, network access, service and D2D security levels. Finally, it recommends security mechanisms to address the security issues at each level of the 5G-enabled D2D communications network.

With the rapid new advancements in technology, there is an enormous increase in devices and their versatile need for services. Fifth-generation (5G) cellular networks (5G-CNs) with network slicing (NS) have emerged as a necessity for future mobile communication. The available network is partitioned logically into multiple virtual networks to provide an enormous range of users’ specific services. Efficient resource allocation methods are critical to delivering the customers with their required Quality of Service (QoS) priorities. In this work, we have investigated a QoS based resource allocation (RA) scheme considering two types of 5G slices with different service requirements; (1) enhanced Mobile Broadband (eMBB) slice that requires a very high data rate and (2) massive Machine Type Communication (mMTC) slice that requires extremely low latency. We investigated the device-to-device (D2D) enabled 5G-CN model with NS to assign resources to users based on their QoS needs while considering the cellular and D2D user’s data rate requirements. We have proposed a Distributed Algorithm (DA) with edge computation to solve the optimization problem, which is novel as edge routers will solve the problem locally using the augmented Lagrange method. They then send this information to the central server to find the global optimum solution utilizing a consensus algorithm. Simulation analysis proves that this scheme is efficient as it assigns resources based on their QoS requirements. This scheme is excellent in reducing the central load and computational time.

The end user’s Quality of Experience (QoE) will be improved while accessing services in Fifth Generation Mobile Network (5G), supported by enhanced security and privacy. The security guarantees offered by the Authentication and Key Agreement (AKA) protocols will be depended upon by end users and network operators. The AKA protocols have been standardized for 5G networks, and the Extensible Authentication Protocol (EAP)-AKA’ protocol is one of the main authentication mechanisms that has been specified for User Equipment (UE) and network mutual authentication. This article models the EAP-AKA’ protocol and conducts an extensive formal verification of the EAP-AKA’ protocol as defined in the 5G security standard to determine whether the protocol is verifiably secure for 5G. It provides a security evaluation of the EAP-AKA’ protocol based on the current 5G specifications using ProVerif, a security protocol proof verifier. It also presents security properties that support the security verification, as well as quantitative properties that are used to assess the protocol’s performance. Finally, it compares the EAP-AKA’ and 5G-AKA protocols’ security and performance results.

The explosive computation demands in the Internet of Things (IoT) have triggered the research interests on unmanned aerial vehicle (UAV) assisted mobile-edge computing (MEC) systems even though there are still many challenges, such as computing delay requirement, multi-UAV cooperation, and resource management. This letter focuses on the computing delay issue in MEC systems assisted by multiple UAVs with the goal of task completion time minimization. In particular, both the partial offloading and binary offloading modes are considered by jointly optimizing time slot size, terminal devices scheduling, computation resource allocation, and UAVs’ trajectories. Particularly, an non-LoS channel model is adopted for UAV-ground communication. To handle the formulated problems, we develop alternating optimization algorithms by invoking the successive convex approximation method, Karush-Kuhn-Tucker conditions and penalized method. Numerical results show that the completion time is significantly decreased by the proposed algorithms.

Echocardiography image quality assessment is not a trivial issue in transthoracic examination. As the in vivo examination of heart structures gained prominence in cardiac diagnosis, it has been affirmed that accurate diagnosis of the left ventricle functions is hugely dependent on the quality of echo images. Up till now, visual assessment of echo images is highly subjective and requires specific definition under clinical pathologies. While poor-quality images impair quantifications and diagnosis, the inherent variations in echocardiographic image quality standards indicates the complexity faced among different observers and provides apparent evidence for incoherent assessment under clinical trials, especially with less experienced cardiologists. In this research, our aim was to analyse and define specific quality attributes mostly discussed by experts and present a fully trained convolutional neural network model for assessing such quality features objectively. A total of 1,650 anonymized B-Mode images with dissimilar frame lengths were stratified from most popular ultrasound vendors equipment and clinical quality scores were provided for each echo cine by Cardiologists at England's Hammersmith Hospital which fed our multi-stream architecture model. The regression model assesses the quality features for depth-gain, chamber clarity, interventricular (on-Axis) orientation and foreshortening of the left ventricle. Four independent scores are thus displayed on each frame which compares against cardiologists' manually assigned scores to validate the degree of objective accuracy or its absolute errors. Absolute errors were found to be ±0.02 and ±0.12 for model and inter observer variability, respectively. We achieved a computation speed of 0.0095 ms per frame on GeForce 970, with feasibility for 2D/3D real-time deployment. The research outcome establishes the modality for the objective standardization of 2D echocardiographic image quality and provides a consistent objective scoring mechanism for echo image reliability and diagnosis.

Device-to-Device (D2D) communications will be used as an underlay technology in the Fifth Generation mobile network (5G), which will make network services of multiple Service Providers (SP) available anywhere. The end users will be allowed to access and share services using their User Equipments (UEs), and thus they will require seamless and secured connectivity. At the same time, Mobile Network Operators (MNOs) will use the UE to offload traffic and push contents closer to users relying on D2D communications network. This raises security concerns at different levels of the system architecture and highlights the need for robust authentication and authorization mechanisms to provide secure services access and sharing between D2D users. Therefore, this paper proposes a D2D level security solution that comprises two security protocols, namely, the D2D Service security (DDSec) and the D2D Attributes and Capability security (DDACap) protocols, to provide security for access, caching and sharing data in network-assisted and non-network-assisted D2D communications scenarios. The proposed solution applies Identity-based Encryption (IBE), Elliptic Curve Integrated Encryption Scheme (ECIES) and access control mechanisms for authentication and authorization procedures. We formally verified the proposed protocols using ProVerif and applied pi calculus. We also conducted a security analysis of the proposed protocols.