Industry 4.0 and beyond will rely heavily on sustainable Business Decision Modelling (BDM) that can be accelerated by blockchain and Digital Twin (DT) solutions. BDM is built on models and frameworks refined by key identification factors, data analysis, and mathematical or computational aspects applicable to complex business scenarios. Gaining actionable intelligence from collected data for BDM requires a carefully considered infrastructure to ensure data transparency, security, accessibility and sustainability. Such an infrastructure must also be aligned with the social, economic and environmental factors. A notable direct impact on BDM concerning resource optimisation, stakeholder engagement, regulatory compliance and environmental impacts has been identified in this space. Taxonomies are defined in this paper to further understand these segments and evaluate blockchain and DT sustainability features based on an in-depth review of the current state of the art. Based on a rigorous selection procedure, 250 papers were used to evaluate the context of this study using a range of text available from scientific databases, including ACM Digital Library, IEEE Xplore, Web of Science, ScienceDirect and Scopus. Detailed comparative evaluations provide insight into the reachability of the sustainable solution in terms of ideologies, accessibility and performance overheads. In the context of blockchain, it is understandable that further research is required to attain practically sustainable solutions. Several questions are put forward to motivate further research that significantly impacts BDM. Finally, a case study based on an exemplary supply chain management system is presented to show blockchain and DT's interoperability with BDM.

This study realizes secure near-field location division multiple access through employing both a variational quantumcircuit and a nature-inspired algorithm. With the escalating demands of the sixth generation (6G) and beyond, there is growing interest in using large numbers of antenna elements,making near-field communications more practical Seizing this opportunity, we leverage near-field communications for a distinct multiple access technique, termed location division multipleaccess (LDMA), which capitalizes on spatial orthogonality to distinguish users by both angle and distance. Nevertheless, relying on beamforming to direct signals to distinct users poses a clear physical-layer security risk: adversaries might eavesdrop on messages intended for legitimate users, prompting the need tooptimize beamforming to enhance security while adhering towireless systems’ constraints. To make matters worse, conventional approaches typically entail multiple matrix inversions, and the optimization problem is far from trivial to solve due toits non-convexity and NP-hardness. To this end, our solutionleverages variational quantum circuits (VQC), motivated bythe potential benefits offered by quantum computing. On topof that, we improve upon the existing VQC workflows by integrating a classical algorithm, particularly, the differential evolution algorithm, thereby reducing quantum computational resource demands while maintaining high exploration efficiency. We further investigate how different quantum circuit depths influence the balance between expressibility and convergence. Simulation results reveal that our proposed scheme consistently outperforms conventional benchmarks in terms of secrecyrates, while simultaneously satisfying quality-of-service (QoS)constraints and power allocation requirements.

This paper investigates a drone (aka UAV)-assisted physical layer security framework for space–air–ground integrated networks (SAGINs) in the presence of multiple eavesdroppers. A single full-duplex UAV is deployed to support satellite-to-ground communications by simultaneously relaying desired signals to legitimate users and transmitting artificial noise to degrade the reception quality of eavesdroppers. To enhance secure connectivity, we formulate a max–min secrecy rate optimization problem that jointly considers sub-channel allocation and power distribution. The sub-channel allocation is optimized using a constrained genetic algorithm, which efficiently handles the combinatorial nature of the problem. Additionally, power allocation is optimized through a nested-loop approach, in which the outer loop employs Bayesian optimization to address complex objective functions, while the inner loop makes the allocation tractable using variable substitutions and approximation methods to overcome non-convexity. The simulation results demonstrate that the proposed method outperforms the benchmark schemes in terms of secrecy performance, particularly under stringent resource and security constraints in SAGINs.

Energy-efficient space–air–ground integrated networks (SAGINs) are vital for sustainable communications. This study presents an energy-aware SAGIN framework that utilizes a uncrewed aerial vehicle (UAV)-mounted mobile edge computing (MEC) platform enhanced by digital-twin technology, UAV energy harvesting via wireless power transfer, and a nano-satellite constellation with MEC facilities. We formulate a joint optimization problem for UAV trajectory planning, task offloading, computational resource allocation, and satellite load balancing as a mixed-integer nonlinear programming (MINLP) problem that minimizes the weighted system cost while satisfying energy and latency constraints. To address this complex problem, two quantum-driven deep reinforcement learning (QD-DRL) algorithms namely quantum-driven cost-effective advantage actor–critic (QD-CE-A2C) and quantum-driven cost-effective proximal policy optimization (QD-CE-PPO) are proposed. These algorithms employ angle encoding with learnable parameters and variational quantum neural networks to enhance policy exploration and accelerate convergence. Simulation results demonstrate that the proposed QD-DRL approaches achieve superior cost efficiency and ensure effective service to all access points within the defined mission duration. Moreover, QD-DRL approaches achieved higher cumulative rewards and faster convergence compared to classical DRL baselines. Consequently, the proposed frameworks provide a scalable and intelligent paradigm for cost-efficient resource management in future 6G-enabled SAGINs.

The evolution of 6G envisions a wide range of applications and services characterized by highly differentiated and stringent Quality of Service (QoS) requirements. Open Radio Access Network (O-RAN) technology has emerged as a transformative approach that enables intelligent software-defined management of the RAN. A cornerstone of O-RAN is the RAN Intelligent Controller (RIC), which facilitates the deployment of intelligent applications (xApps and rApps) near the radio unit. In this context, QoS management through O-RAN has been explored using network slice and machine learning (ML) techniques. Although prior studies have demonstrated the ability to optimize RAN resource allocation and prioritize slices effectively, they have not considered the critical integration of Service Level Agreements (SLAs) into the ML learning process. This omission can lead to suboptimal resource utilization and, in many cases, service outages when the target Key Performance Indicators (KPIs) are not met. This work introduces RSLAQ, an innovative xApp designed to ensure robust QoS management for RAN slicing while incorporating SLAs directly into its operational framework. RSLAQ translates operator policies into actionable configurations, guiding resource distribution and scheduling for RAN slices. Using deep reinforcement learning (DRL), RSLAQ dynamically monitors RAN performance metrics and computes optimal actions, embedding SLA constraints to mitigate conflicts and prevent outages. Extensive system-level simulations validate the efficacy of the proposed solution, demonstrating its ability to optimize resource allocation, improve SLA adherence, and maintain operational reliability (> 95%) in challenging scenarios.

As 6G scenarios grow in complexity, network operators need an automated and optimized approach to managing Quality-of-Service (QoS) in the radio access network (RAN). Transitioning from resource-focused management to one that considers evolving operator intents is essential. This can be achieved by translating service-level agreement (SLA) intents into operational rules to ensure compliance and save costs. O-RAN introduced the RAN intelligent controller (RIC) and intelligent applications (xApps) to enhance the RAN operation. Furthermore, RAN slicing has been shown as the most promising method to provide O-RAN-based QoS in 6G. However, while existing methods optimize resources allocated per slice, they often overlook SLAs. This leads to suboptimal resource usage and outages when the system fails to meet target key performance indicators (KPIs). This work introduces SLAQ, an xApp designed to translate service operator requirements to optimize spectrum resource sharing. SLAQ monitors key performance metrics (KPMs) and employs deep reinforcement learning (DRL) to make decisions within the RAN. SLA policies are modeled and integrated into the xApp to optimize resource distribution while preventing SLA conflicts and outages. Our highly-detailed system-level simulations show that SLAQ effectively learns optimal actions, achieving high communication reliability, i.e., close to $99 \%$ for ultra-reliable low-latency communications.

We investigate the secrecy performance of integrated satellite and terrestrial networks (ISTNs) with the support of a drone (aka UAV). An optimisation problem is formulated to maximise the secrecy rate while guaranteeing the quality of service, including the minimum secrecy rate of the legitimate user, the minimum data rate of normal users, and power consumption. A nested-loop algorithm including outer and inner loops is proposed to convert the initial non-convex problem into multiple convex problems, which are solved by the Dinkelbach algorithm. Simulation results prove the efficiency of our methods in terms of secrecy rate compared to traditional benchmarks.

The rapid growth of edge computing has enabled low-latency and high-efficiency processing for a wide range of applications; however, it also leads to significant energy consumption and carbon emissions. In this context, this study investigates a CO2 emission minimisation problem in a digital twin-aided edge computing system, aiming to optimise task offloading decisions, transmit power, and processing rates of Internet of Things (IoT) devices. To address the formulated mixed-integer non-linear programming problem, we propose two solutions: an alternating optimisation method based on the successive convex approximation framework and a deep reinforcement learning (DRL) approach. Extensive simulations validate the effectiveness of the proposed solutions, demonstrating significant reductions in CO2 emissions, robust optimisation performance, and superior results compared to benchmark schemes. The findings highlight the feasibility of integrating advanced optimisation and artificial intelligence-driven techniques to achieve environmentally sustainable and high-performance edge computing systems, paving the way for greener technological innovation.

The advancement of sophisticated communication technologies and robust computing systems has unlocked opportunities for new applications across various domains. While these applications promise enhanced convenience and improved living standards, they also raise a critical concern regarding the trade-off between convenience and environmental sustainability. This paper addresses this concern by investigating sustainable resource management, employing a digital twin approach to minimise CO2emissions in edge computing systems. Specifically, our aim is to reduce the amount of CO2emissions by optimising the allocation of computing and communication resources. This includes optimising transmit power, adjusting the clock speed for task processing, and making optimal decisions regarding task offloading. To tackle this complex optimisation problem, we employ an iteratively alternating optimisation algorithm. Through extensive simulations, we illustrate the efficacy of our proposed solution in not only mitigating CO2emissions but also optimising resource allocation, thereby contributing to both environmental sustainability and technological efficiency.

Supply chain management is one of the leading applications of chain-based distributed ledger technology, such as blockchain, due to its features of traceability, automation through smart contracts, tamper-resistance and immutability. One of the most common types of blockchain implementation for a supply chain ecosystem is Hyperledger Fabric, which follows a modular architecture. Within the supply chain, innovators have been emphasising scenario generation for trade optimisation via predictive algorithms using content from the blockchain. However, to support such utilities, a pipeline with data accessibility is required, which is the motivation behind this work. Chaincode, InterPlanetary File System (IPFS) and off-chain execution enable the pipeline to provide knowledge graphs that can further facilitate insights into the type of blockchain data with better control and privacy-aware filtration.

This study explores implementing a digital twin network (DTN) for efficient 6 G wireless network management, aligning with the fault, configuration, accounting, performance, and security (FCAPS) model. The DTN architecture comprises the Physical Twin Layer, implemented using NS-3, and the Service Layer, featuring machine learning and reinforcement learning for optimizing carrier sensitivity threshold and transmit power control in wireless networks. We introduce a robust “What-if Analysis” module, utilizing conditional tabular generative adversarial network for synthetic data generation to mimic various network scenarios. These scenarios assess four network performance metrics: throughput, latency, packet loss, and coverage. Our findings demonstrate the efficiency of the proposed what-if analysis framework in managing complex network conditions, highlighting the importance of the scenario-maker and the impact of twinning intervals on network performance.

User demand on network communication infrastructure has never been greater with applications such as extended reality, holographic telepresence, and wireless brain-computer interfaces challenging current networking capabilities. Open RAN (O-RAN) is critical to supporting new and anticipated uses of 6G and beyond. It promotes openness and standardisation, increased flexibility through the disaggregation of Radio Access Network (RAN) components, supports programmability, flexibility, and scalability with technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), and cloud, and brings automation through the RAN Intelligent Controller (RIC). Furthermore, the use of xApps, rApps, and Artificial Intelligence/Machine Learning (AI/ML) within the RIC enables efficient management of complex RAN operations. However, due to the open nature of O-RAN and its support for heterogeneous systems, the possibility of misconfiguration problems becomes critical. In this paper, we present a thorough analysis of the potential misconfiguration issues in O-RAN with respect to integration and operation, the use of SDN and NFV, and, specifically, the use of AI/ML. The opportunity for AI/ML to be used to identify these misconfigurations is investigated. A case study is presented to illustrate the direct impact on the end user of conflicting policies amongst xApps along with a potential AI/ML-based solution to this problem. This research presents a first analysis of the impact of AI/ML on misconfiguration challenges in O-RAN.

Web applications have become central in the digital landscape, providing users instant access to information and allowing businesses to expand their reach. Injection attacks, such as SQL injection (SQLi), are prominent attacks on web applications, given that most web applications integrate a database system. While there have been solutions proposed in the literature for SQLi attack detection using learning-based frameworks, the problem is often formulated as a binary, single-attack vector problem without considering the prioritization and prevention component of the attack. In this work, we propose a holistic solution, SQLR34P3R, that formulates the SQLi attack as a multi-class, multi-attack vector, prioritization, and prevention problem. For attack detection and classification, we gathered 457,233 samples of benign and malicious network traffic, as well as 70,023 samples that had SQLi and benign payloads. After evaluating several machine-learning-based algorithms, the hybrid CNN-LSTM models achieve an average F1-Score of 97% in web and network traffic filtering. Furthermore, by using CVEs of SQLi vulnerabilities, SQLR34P3R incorporates a novel risk analysis approach which reduces additional effort while maintaining reasonable coverage to assist businesses in allocating resources effectively by focusing on patching vulnerabilities with high exploitability. We also present an in-the-wild evaluation of the proposed solution by integrating SQLR34P3R into the pipeline of known vulnerable web applications such as Damn Vulnerable Web Application (DVWA) and Vulnado and via network traffic captured using Wireshark from SQLi DNS exfiltration conducted with SQLMap for real-time detection. Finally, we provide a comparative analysis with state-of-the-art SQLi attack detection and risk ratings solutions.

This paper exploits a quantum-empowered machine learning algorithm to enhance computation learning speed. We leverage quantum phenomena such as superposition and entanglement to work on large-scale multi-dimensional data represented by quantum states. Under stochastic behaviors and quantum uncertainty, we examine the offloading problem to maximize the computational task processing efficiency, considering the computation latency, energy consumption, and quantum network adaptability. From the Markov decision process, the paper proposes a novel quantum-empowered deep reinforcement learning (Qe-DRL) approach, combining quantum computing theory and machine learning to achieve exploration and exploitation trade-off via quantum parallelism significantly. Furthermore, we develop a modified Grover’s algorithm with exponential convergence speed to provide a searching strategy for transition quantum states probabilities. Simulation results establish the effectiveness of the proposed Qe-DRL algorithm and its superior computational learning speed. Our proposed Qe-DRL algorithm outperforms other benchmarks in terms of energy efficiency performance.

This paper exploits a quantum-empowered machine learning algorithm to enhance computation learning speed. Under stochastic behaviours and quantum uncertainty, we examine the offloading problem to maximize the computational task processing efficiency, considering the computation latency, energy consumption, and quantum network adaptability. From the Markov decision process, the paper proposes a novel quantumempowered deep reinforcement learning (Qe-DRL) approach, combining quantum computing theory and machine learning to achieve exploration and exploitation trade-off via quantum parallelism significantly. Furthermore, we develop a modified Grover’s algorithm with exponential convergence speed to provide a searching strategy for transition quantum states probabilities. Simulation results establish the effectiveness of the proposed QeDRL algorithm and its superior computational learning speed.

This article studies the integration of laser-beamed wireless power transfer (WPT) into high-altitude platform (HAP)-aided multiaccess edge computing (MEC) systems for the HAP-connected aerial user equipments (AUEs). By discretizing the 3-D coverage space of the HAP, we present a multitier tile grid-based spatial structure to provide the aerial locations in the form of tile grids to AUEs for laser charging. We identify a new privacy vulnerability caused by the openness during the WPT signaling transfer in the presence of a terrestrial adversary, which is able to launch the attacks by distributing the false tile grids to the AUEs. To address this vulnerability and enhance the location privacy of AUEs, we then propose a Colonel Blotto (CB) game framework to formulate the competitive tile grid allocation problem for the HAP and the adversary. The attack-defense interaction between the adversary and the HAP as a defender in their tile grid allocations to the AUEs is formulated as a CB game, which models the competition of two players for limited resources over multiple battlefields. Moreover, we derive the mixed-strategy Nash equilibria of the game for both symmetric and asymmetric tile grids between two players. Simulation results show that the proposed framework significantly outperforms the design baselines with a given privacy protection level in terms of system-wide expected total utilities.

The Internet of Medical Things (IoMT) has risen to prominence as a possible backbone in the health sector, with the ability to improve quality of life by broadening user experience while enabling crucial solutions such as near real-time remote diagnostics. However, privacy and security problems remain largely unresolved in the safety area. Various rule-based methods have been considered to recognize aberrant behaviors in IoMT and have demonstrated high accuracy of misbehavior detection appropriate for lightweight IoT devices. However, most of these solutions have privacy concerns, especially when giving context during misbehavior analysis. Moreover, falsified or modified context generates a high percentage of false positives and sometimes causes a by-pass in misbehavior detection. Relying on the recent powerful consolidation of blockchain and federated learning (FL), we propose an efficient privacy-preserving framework for secure misbehavior detection in lightweight IoMT devices, particularly in the artificial pancreas system (APS). The proposed approach employs privacy-preserving bidirectional long-short term memory (BiLSTM) and augments the security through integrating blockchain technology based on Ethereum smart contract environment. The effectiveness of the proposed model is bench-marked empirically in terms of sustainable privacy preservation, commensurate incentive scheme with an untraceability feature, exhaustiveness, and the compact results of a variant neural network approach. As a result, the proposed model has a 99.93% recall rate, showing that it can detect virtually all possible malicious events in the targeted use case. Furthermore, given an initial ether value of 100, the solution's average gas consumption and Ether spent are 84,456.5 and 0.03157625, respectively.

Vehicle authentication is an essential component validating the vehicle’s identity and ensuring the integrity of transformed data for intelligent transport vehicles (ITS) in the vehicular ad hoc network (VANET). Easy to deploy and operate privacy-enhancing vehicle authentication mechanisms are the mainstay for the widespread ITS in the VANET. Very recently, VANET security architectures are constituting by IEEE 1609.2 group, NoW project, the SeVeCom project. However, these approaches heavily depend on the consuming public key infrastructure (PKI) and certification authorities (CA). In this work, walking along the research line, we attempt to design authentication protocols with two diverse factors for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) networks, respectively, without depending on the stumbling block PKI/CA. In addition, a smooth projective hash function (SPHF) (a.k.a., a special case of the designated-verifier zero-knowledge proof system) guarantees any recipient can confirm the authenticity and integrity of the received messages without knowing the authentication factors. Thus, to optimize the communication round, SPHF is used to design a (group) two-factor authenticated key exchange (AKE) with low-interactive communication rounds. The proof-of-concept implementation indicates that the computation and communication overheads introduced by our solution are acceptable in real-world deployments. The security of the proposed approach is validated using Bellare-Pointcheval-Rogaway (BPR) model along with the experimental evaluation and the theoretical analysis.

Accelerator virtualization offers several advantages in the context of cloud-edge computing. Relatively weak user devices can enhance performance when running workloads by accessing virtualized accelerators available on other resources in the cloud-edge continuum. However, cloud-edge systems are heterogeneous, often leading to compatibility issues arising from various hardware and software stacks present in the system. One mechanism to alleviate this issue is using containers for deploying workloads. Containers isolate applications and their dependencies and store them as images that can run on any device. In addition, user devices may move during the course of application execution, and thus mechanisms such as container migration are required to move running workloads from one resource to another in the network. Furthermore, an optimal destination will need to be determined when migrating between virtual accelerators. Scheduling and placement strategies are incorporated to choose the best possible location depending on the workload requirements. This paper presentsAVEC, a framework for accelerator virtualization in cloud-edge computing. The AVEC framework enables the offloading of deep learning workloads for inference from weak user devices to computationally more powerful devices in a cloud-edge network. AVEC incorporates a mechanism that efficiently manages and schedules the virtualization of accelerators. It also supports migration between accelerators to enable stateless container migration. The experimental analysis highlights that AVEC can achieve up to 7x speedup by offloading applications to remote resources. Furthermore, AVEC features a low migration downtime that is less than 5 seconds.

Strategic evaluations of blockchain systems allow a better understanding of the security of the mining process. In recent years, many researchers have focused on developing optimal strategies to evaluate the impact of an adversary on the mining process using different attack situations such as selfish mining, double-spending, feather-forking, Denial of Service. These strategies rely on the use of the Markov Decision Process (MDP) to find optimal settings that an adversary can exploit to earn maximum profit in every round. However, these strategies do not consider a case where adversaries turn abusive, and their only aim is to harm the mining process without profit. Motivated by this, a self-defying adversary model is proposed that uses ZEBRA (Zero Expectation-Based Reward Abuse) strategy to cause a maximum impact on the rewards of the honest players at lower settings. With the proposed method, the adversary itself may not be profitable, but has better control over the chain growth and causes maximum damage to reward by delaying the blocks and inducing forks subject to its compliance degree. The evaluations are demonstrated to show the reward control by the adversary along with the impact on delays and forks, followed by the possibilities of attacks using the hashing powers of different mining pools.

We address the problem of minimising latency with computation offloading in digital twin wireless edge networks in industrial Internet-of-Things environment via ultra-reliable and low latency communications links. The minimised latency is obtained by jointly optimising both communication and computation variables, namely transmit power, user association of IoT devices, offloading portions, the processing rate of users and edge servers. To deal with this challenging problem, we propose an iterative algorithm based on alternating optimisation approach combined with inner convex approximation framework. Simulation results demonstrate the proposed algorithm’s effectiveness in reducing the latency compared with other benchmark schemes.

5G-assisted device-to-device (D2D) communication plays an instrumental role in minimizing latency, maximizing resource utilization, improving speed, and boosting system capacity. However, the technology confronts several challenges to realize its enormous potential fully. Security and privacy concerns are at the top of the list that can jeopardize the regular operation of D2D communication by executing various assaults such as free-riding and impersonation. Although several researchers suggested different solutions to these concerns, most are too heavy for resource-constrained devices or are vulnerable to security risks. Consequently, we proposed a lightweight and provably secure D2D communication protocol comprising initialization, device discovery, and link setup phases. The protocol is light in terms of computational overhead and communication latency while verifiably secure through formal security analysis. The protocol relies on a new network function, called D2D Security Management Function (DSMF), located near the devices to facilitate secure communication and improve performance. Moreover, we used deep learning-based UE trust score forecasting to better handle and prioritize communication requests when the network is overloaded. The comparative analysis against state-of-the-art security schemes concerning computational and communication overheads shows that our protocol is a superior alternative for resource-constrained IoT devices wishing to perform D2D communication in a 5G network.

Decentralized control, low-complexity, flexible and efficient communications are the requirements of an architecture that aims to scale blockchains beyond the current state. Such properties are attainable by reducing ledger size and providing parallel operations in the blockchain. Sharding is one of the approaches that lower the burden of the nodes and enhance performance. However, the current solutions lack the features for resolving concurrency during cross-shard communications. With multiple participants belonging to different shards, handling concurrent operations is essential for optimal sharding. This issue becomes prominent due to the lack of architectural support and requires additional consensus for cross-shard communications. Relying on the advantages of hybrid Proof-of-Work/Proof-of-Stake (PoW/PoS), like Ethereum , hybrid consensus and 2-hop blockchain , we propose Reinshard , a new blockchain that inherits the properties of hybrid consensus for optimal sharding. Reinshard uses PoW and PoS chain-pairs with PoS sub-chains for all the valid chain-pairs where the hybrid consensus is attained through Verifiable Delay Function (VDF). Our architecture provides a secure method of arranging nodes in shards and resolves concurrency conflicts using the delay factor of VDF. The applicability of Reinshard is demonstrated through security and experimental evaluations. A practical concurrency problem is considered to show the efficacy of Reinshard in providing optimal sharding.

With the evolution of wireless technologies, 5G and Beyond (5GB) communication is paving a way for efficient, ultrareliable, low-latent, and high converging services for the Internet of Things (IoT). Along with efficient communication, the security of messages is one of the concerns that must be maintained throughout the operations. Backhaul forms an essential part of 5GB with an ability to enhance the coverage and quality of service for IoT. However, conventional wired backhaul connection would cost operators thousands of dollars in the construction of 5GB infrastructure considering the ultradense nature of IoT. As a result, wireless backhaul is quickly becoming a feasible alternative to address 5GB’s direction toward network densification without affecting its other provisions. Wireless backhaul is expected to increase the landscape, covering from islands to mountains, which were difficult to access in the existing network generation. Moreover, it can effectively respond to the situation where the data traffic tremendously increased. Despite such provisioning, the wireless backhaul poses relatively various security threats and vulnerabilities due to the characteristics of wireless technologies. Several studies have been conducted to address the security problems; however, existing protocols do not support dynamic security policy and key management in a decentralized structure as well as secure handover in a specific scenario where Terminals (TMs) are moving. Motivated by this, we proposed the Mobile Terminal Handover (MoTH) security protocol to provide secure handover of mobile terminals between hubs. To solve the problem of existing protocols, a new entity called BMF is introduced to support distributed and dynamic security policy and key management in each serving network of the 5GB backhaul environment. The proposed protocol satisfies security requirements, including authentication and key management, confidentiality, integrity, and perfect forward secrecy. Additionally, it supports policy and key update services, and optimized handover. The security and correctness of the proposed protocol are thoroughly verified using the two formal security analysis tools: 1) BAN logic and 2) Scyther. Additionally, the performance evaluation shows that the proposed protocol is efficient.

This paper addresses the problem of minimising latency in computation offloading with digital twin (DT) wireless edge networks for industrial Internet-of-Things (IoT) environment via ultra-reliable and low latency communications (URLLC) links. The considered DT-aided edge networks provide a powerful computing framework to enable computation-intensive services, where the DT is used to model the computing capacity of edge servers and optimise the resource allocation of the entire system. The objective function is comprised of local processing latency, URLLC-based transmission latency and edge processing latency, subject to both communication and computation resources budgets. In this regard, the minimum latency is obtained by jointly optimising the transmit power, user association, offloading portions, the processing rate of users and edge servers. The formulated problem is highly complicated due to complex non-convex constraints and strong coupling variables. To deal with this computationally intractable problem, we propose an iterative algorithm which decomposes the original problem into three sub-problems and resolve this problem in the fashion of alternating optimisation approach combined with an inner convex approximation framework. Simulation results demonstrate the effectiveness of the proposed method in reducing the latency compared with other benchmark schemes.

Critical infrastructure comprising on-demand devices, including secondary servers, comes into play when a situation like an overload is involved. The on-demand servers and devices require smart management solutions that form an integral part of Artificial Intelligence of Things (AIoT). This work considers AIoT as a combination of Mobile-Internet of Things (M-IoT) and AI requiring immediate response, secondary support system, and computational resources. Privacy in AIoT is always a concern when sharing information as intruders can eavesdrop on the settings of the system. This article uses an osmotic computing paradigm, which enables the derivation of strategies to decide on the methods of sharing services via optimal and privacy-aware resource management in AIoT. A safety competition is built on top of configuration rewards that help to attain privacy-by-design. The contributions of this article are expressed using theoretical analysis and numerical simulations.

Internet of Things (IoT) is the utmost assuring framework to facilitate human life with quality and comfort. IoT has contributed significantly to numerous application areas. The stormy expansion of smart devices and their credence for data transfer using wireless mechanics boost their susceptibility to cyberattacks. Consequently, the cybercrime rate is increasing day by day. Hence, the study of IoT security threats and possible corrective measures can benefit researchers in identifying appropriate solutions to deal with various challenges in cybercrime investigation. IoT forensics plays a vital role in cybercrime investigations. This review paper presents an overview of the IoT framework consisting of IoT architecture, protocols, and technologies. Various security issues at each layer and corrective measures are also discussed in detail. This paper also presents the role of IoT forensics in cybercrime investigation in various domains like smart homes, smart cities, automated vehicles, and healthcare. The role of advanced technologies like artificial intelligence, machine learning, cloud computing, edge computing, fog computing, and blockchain technology in cybercrime investigation is also discussed. Lastly, various open research challenges in IoT to assist cybercrime investigation are explained to provide a new direction for further research.

The coordination between aerial and ground nodes has enhanced the versatility and quality of the traditional networks. The application of aerial systems in mission‐critical operations, as well as civilian applications, brings in the context of safeguarding unmanned aerial systems (UAS) from malicious attackers. This study discusses the threats and attacks mounted on UAS, alongside the challenges introduced by the unmanned aerial vehicle (UAV) network structure itself. A framework for safeguarding UAS against malicious attackers and recovering the rogue UAVs is proposed in the study. The proposed framework enforces a dynamic conceptual grid‐based layout over the actual geographical deployment. The dynamically shuffling grid ascertains the security of transmission channels, as every time the grid is shuffled periodically or based on abnormal behaviour, the safety paradigm is reinitiated. Public key cryptographic algorithms are deployed for securing the communication links. Neural networks‐based predictions are used for detecting abnormality in behavioural, statistical, and mobility patterns. Principal component analysis based on multivariate statistical analysis is used for detecting outliers in the aerial network environment. The behaviour prediction and outlier detection algorithms significantly improve the overall performance of the network and provide immunity against the intruders with reduced false positives, high accuracy, and better detection rate.

Mobile edge computing (MEC) reduces the computational distance between the source and the servers by fortifying near-user site evaluations of data for expedited communications, using caching. Caching provides ephemeral storage of data on designated servers for low-latency transmissions. However, with the network following a hierarchical layout, even the near-user site evaluations can be impacted by the overheads associated with maintaining a perpetual connection and other factors (e.g., those relating to the reliability of the underpinning network). Prior solutions study reliability as a factor of throughput, delays, jitters, or delivery ratio. However, with modern networks supporting high data rates, a current research trend is in ultrareliability. The latter is defined in terms of availability, connectivity, and survivability. Thus, in this paper, we focus on the ultrareliable communication in MEC. Specifically, in our setting, we use drones as on-demand nodes for efficient caching. While some existing solutions use cache-enabled drones, they generally focus only on the positioning problem rather than factors relating to ultrareliable communications. We present a novel neural-blockchain-based drone-caching approach, designed to ensure ultrareliability and provide a flat architecture (via blockchain). This neural-model fortifies an efficient transport mechanism, since blockchain maintains high reliability amongst the peers involved in the communications. The findings from the evaluation demonstrate that the proposed approach scores well in the following metrics: the probability of connectivity reaches 0.99; energy consumption is decreased by 60.34%; the maximum failure rate is affected by 13.0%; survivability is greater than 0.90; reliability reaches 1.0 even for a large set of users.

The rapid development of Internet-of-ings (IoT) techniques in G/ G deployments is witnessing the generation of massive amounts of data which are collected, stored, processed, and presented in an ea ...

Unmanned Aerial Vehicles (UAV)-based civilian or military applications become more critical to serving civilian and/or military missions. The significantly increased attention on UAV applications also has led to security concerns particularly in the context of networked UAVs. Networked UAVs are vulnerable to malicious attacks over open-air radio space and accordingly intrusion detection systems (IDSs) have been naturally derived to deal with the vulnerabilities and/or attacks. In this paper, we briefly survey the state-of-the-art IDS mechanisms that deal with vulnerabilities and attacks under networked UAV environments. In particular, we classify the existing IDS mechanisms according to information gathering sources, deployment strategies, detection methods, detection states, IDS acknowledgment, and intrusion types. We conclude this paper with research challenges, insights, and future research directions to propose a networked UAVIDS system which meets required standards of effectiveness and efficiency in terms of the goals of both security and performance.

This article discusses a lightweight behavior rule specification-based monitoring solution for identifying misbehavior of an embedded IoT device. These unusual activities are exhibited because of attacks exploiting the vulnerability exposed through automatic model checking and formal verification. It is conclusive in the presented research that rule specification-based misbehavior detection technique outperforms contemporary anomaly-based misbehavior detection techniques for an unmanned aerial vehicle (UAV) cyber-physical system.

Use of social network is the basic functionality of today's life. With the advent of more and more online social media, the information available and its utilization have come under the threat of several anomalies. Anomalies are the major cause of online frauds which allow information access by unauthorized users as well as information forging. One of the anomalies that act as a silent attacker is the horizontal anomaly. These are the anomalies caused by a user because of his/her variable behavior towards different sources. Horizontal anomalies are difficult to detect and hazardous for any network. In this paper, a self-healing neuro-fuzzy approach (NHAD) is used for the detection, recovery, and removal of horizontal anomalies efficiently and accurately. The proposed approach operates over the five paradigms, namely, missing links, reputation gain, significant difference, trust properties, and trust score. The proposed approach is evaluated with three datasets: DARPA'98 benchmark dataset, synthetic dataset, and real-time traffic. Results show that the accuracy of the proposed NHAD model for 10 to 30 percent anomalies in synthetic dataset ranges between 98.08 and 99.88 percent. The evaluation over DARPA'98 dataset demonstrates that the proposed approach is better than the existing solutions as it provides 99.97 percent detection rate for anomalous class. For real-time traffic, the proposed NHAD model operates with an average accuracy of 99.42 at 99.90 percent detection rate.

Simultaneous Information and Power Transfer (SWIPT) for wireless communication systems presents a new paradigm, allowing wireless nodes to recharge their rechargeable batteries from RF signals (instead of fixed line or traditional energy sources) while decoding information. In this approach, the energy is harvested from ambient electromagnetic sources available within the communication system or from sources that directionally transmit RF energy. This work presents an overview, and advancement to date, as well as identifies research issues and challenges in SWIPT and RF Wireless Power Transfer (WPT) assisted technologies. The paper, in principal, addresses innovative 5G communications and Internet of Things (IoT) technologies associated with SWIPT/WPT. The paper finally presents recommendations and future trends associated with this emerging future electricity technique. This provides a valuable reference and new avenues for the future research in this direction.

Summary Integrated frameworks have extended the applications of networks beyond a simple data sharing unit. Simultaneously, operating networks can form a layered structure that can operate as homogeneous as well as dissociated units. Networks using unmanned aerial vehicles follow similar criteria in their operability. Unmanned aerial vehicles can act as single searching unit controlled by human or can form an aerial swarm that can fly autonomously with the capability of forming an aerial network. Such aerial swarms are categorized as aerial ad hoc networks. Cooperation amongst different networks can be realized using various frameworks, models, architectures and middlewares. Several solutions have been developed that can provide easy network deployment of aerial nodes. However, a combined literature is not present that provides a comparison between these approaches. Keeping this in view, various cooperative approaches for similar formation using aerial vehicles have been discussed in this paper. The detailed study and comparative analysis of these approaches have been included. Further, the paper also includes various software solutions and their comparisons based on common parameters. Finally, various open issues have been discussed that can provide insight of ongoing research and problems that are yet to be resolved in these networks. Copyright © 2016 John Wiley & Sons, Ltd.

“Internet of Things” (IoT) bridges the communication barrier between the computing entities by forming a network between them. With a common solution for control and management of IoT devices, these networks are prone to all types of computing threats. Such networks may experience threats which are launched by exploitation of vulnerabilities that are left unhandled during the testing phases. These are often termed as “zero-day” vulnerabilities, and their conversion into a network attack is named as “zero-day” attack. These attacks can affect the IoT devices by exploiting the defense perimeter of the network. The existing solutions are capable of detecting such attacks but do not facilitate communication, which affects the performance of the network. In this paper, a consensus framework is proposed for mitigation of zero-day attacks in IoT networks. The proposed approach uses context behavior of IoT devices as a detection mechanism followed by alert message protocol and critical data sharing protocol for reliable communication during attack mitigation. The numerical analysis suggests that the proposed approach can serve the purpose of detection and elimination of zero-day attacks in IoT network without compromising its performance.