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Long Range (LoRa) technology, characterized by extended transmission range and low power consumption, is considered a crucial technology for the Industrial Internet of Things (IIoT). However, with increasing network scale, existing LoRa networks have begun to exhibit low energy efficiency (EE) due to severe collisions. Existing methodologies predominantly assume perfect SF orthogonality, neglecting the detrimental effects of interference between signals using different spreading factors (SFs) in the same channel (CH). This study investigates a joint SF, CH, and transmit power (TP) allocation problem with the objective of optimizing the system's uplink EE while fully considering quasiorthogonal SFs, which is an NP-hard optimization problem. To enhance tractability, we decompose the original problem into two subproblems: CH/SF assignment and TP allocation. First, an attention-based deep neural network is designed to generate a near-optimal CH/SF assignment, leveraging both channel state information and channel activity detection-based historical channel occupancy information. Second, given the CH/SF assignment, the optimal TP allocation is determined through the resolution of a convex optimization problem. Experimental results on a real-world LoRa network demonstrate that our proposed method significantly improves the system's uplink EE.

Unmanned aerial vehicles (UAVs) empowered with artificial intelligence (AI) have become a new paradigm for on-demand and intelligent marine monitoring. To enable diverse AI applications, numerous computation-intensive tasks (e.g., image recognition, video processing, path planning, etc.) that cannot be locally executed by UAVs need to be timely and effectively offloaded. Multiple unmanned surface vehicles (USVs) integrated into a USV fleet is appealingly advocated to provide abundant computation resources for computation tasks. In this paper, we propose an energy-efficient USV fleets-assisted collaborative computation offloading scheme for smart maritime services. Specifically, we first propose a collaborative computation offloading framework, where UAVs act as the requesters of computation offloading services, and USV fleets are the helpers. Then, the first-price sealed reverse auction with reserve price is utilized to incentivize USV fleets to assist in executing computation tasks of UAVs, where the reserve price guarantees the satisfied benefits of UAVs. Afterwards, to minimize the energy consumption of executing tasks within the USV fleet under the delay constraint, the joint allocation optimization scheme for computation subtasks and computation capacities is proposed based on the Block Coordinate Descent (BCD) and Alternating Direction Method of Multipliers (ADMM). Simulation results demonstrate that the proposed scheme improves the expected revenue and participation degree of the USV fleet and reduces the overall energy consumption of computation offloading compared to conventional schemes.

A key feature of federated learning (FL) is to preserve the data privacy of end users. However, there still exist potential privacy leakage in exchanging gradients under FL. As a result, recent research often explores the differential privacy (DP) approaches to add noises to the computing results to address privacy concerns with low overheads, which however degrade the model performance. In this paper, we strike the balance of data privacy and efficiency by utilizing the pervasive social connections between users. Specifically, we propose SCFL, a novel Social-aware Clustered Federated Learning scheme, where mutually trusted individuals can freely form a social cluster and aggregate their raw model updates (e.g., gradients) inside each cluster before uploading to the cloud for global aggregation. By mixing model updates in a social group, adversaries can only eavesdrop the social-layer combined results, but not the privacy of individuals. As such, SCFL considerably enhances model utility without sacrificing privacy in a low-cost and highly feasible manner. We unfold the design of SCFL in three steps. i) Stable social cluster formation. Considering users’ heterogeneous training samples and data distributions, we formulate the optimal social cluster formation problem as a federation game and devise a fair revenue allocation mechanism to resist free-riders. ii) Differentiated trust-privacy mapping. For the clusters with low mutual trust, we design a customizable privacy preservation mechanism to adaptively sanitize participants’ model updates depending on social trust degrees. iii) Distributed convergence. A distributed two-sided matching algorithm is devised to attain an optimized disjoint partition with Nash-stable convergence. Experiments on Facebook network and MNIST/CIFAR-10 datasets validate that our SCFL can effectively enhance learning utility, improve user payoff, and enforce customizable privacy protection.

Efficient route planning and traffic scheduling are the key enabling technologies to achieve intelligent transportation systems (ITS). However, the existing schemes that use driving time or driving distance as indicators cannot meet the personalized requirements of vehicles. In addition, the existing research usually optimizes the route planning and traffic scheduling separately, which is difficult to effectively improve the traffic efficiency in practical applications. In this paper, by considering the personalized requirements of vehicles and the target of the traffic manager (TM), a digital-twin (DT) enabled integrated framework is proposed to jointly optimize the personalized route planning and global traffic scheduling. Specifically, based on the collection of traffic data, we first develop a DT architecture for route planning and traffic scheduling. With this DT architecture, we then consider the traffic condition of each road section and design a global traffic scheduling algorithm to help the DT of the TM (DT-TM) set the optimal reward for each road section to motivate and guide the DTs of vehicles (DT-Vs) to select their routes. After that, by considering the preference of each DT-V and the reward of each road section, a personalized route planning algorithm is designed to plan the optimal route for the DT-V. Since the reward of each road section set by the DT-TM and the route selected by each DT-V affect each other, we thus design a joint optimization algorithm based on dynamic iteration to obtain an equilibrium solution for the DT-Vs and the DT-TM. The simulation results show that the proposed framework can schedule the traffic more effectively and bring higher utility to both the vehicles and the TM than the benchmark schemes.

This paper investigates the utilization of simultaneously transmitting and reflecting RIS (STAR-RIS) in supporting joint physical layer security (PLS) and covert communications (CCs) in a multi-antenna millimeter wave (mmWave) system, where the base station (BS) communicates with both covert and security users while defeating eavesdropping by wardens with the help of a STAR-RIS. Specifically, analytical derivations are performed to obtain the closed-form expression of warden’s minimum detection error probability (DEP). Furthermore, the asymptotic result of the minimum DEP and the lower bound of the secure rates are derived, considering the practical assumption that BS only knows the statistical channel state information (CSI) between STAR-RIS and the wardens. Subsequently, an optimization problem is formulated with the aim of maximizing the average sum of the covert rate and the minimum secure rate while ensuring the covert requirement and quality of service (QoS) for legal users by jointly optimizing the active and passive beamformers. Due to the strong coupling among variables, an iterative algorithm based on the alternating strategy and the semi-definite relaxation (SDR) method is proposed to solve the non-convex optimization problem. Simulation results indicate that the performance of the proposed STAR-RIS-assisted scheme greatly surpasses that of the conventional RIS scheme, which validates the superiority of STAR-RIS in simultaneously implementing PLS and CCs.

All-solid-state batteries have attracted increasing attention due to their excellent stability and safety. In this study, solid main-chain fluorinated alternating polymer electrolyte (MCFPE) is prepared through visible light-induced step-transfer-addition-radical-termination (START) polymerization using a,?-diiodo perfluoroalkanes (monomer A) and a,?-nonconjugated dienes (monomer B) as monomers at room temperature. The prepared MCFPEs exhibit high structural tunability, allowing for the systematic investigation of various factors affecting their performance by adjusting the structures of monomers A and B. Through structural optimization, MCFPE demonstrates a remarkable ionic conductivity of 4.08 × 10–5S cm–1at 30 °C and 2.55 × 10–4S cm–1at 60 °C, along with a high tLi+value of 0.27 and an electrochemical stability window of 5.0 V at room temperature. Notably, Li//MCFPE//Li cells incorporating (A1B4)nexhibit excellent stability lasting over 500 h at 0.1 mA cm–2. Furthermore, in Li//MCFPE//LiFePO4batteries, this polymer electrolyte demonstrates impressive performance, achieving a high Coulombic efficiency of 92.3%. In conclusion, this work highlights the advantage of high tunability in the structure of a novel host material, providing expanded possibilities for the synthesis and performance improvement of various fluorine-containing polymers.

With the wide adoption of unmanned aerial vehicles (UAVs) in our daily life, using UAVs to collect sensing data from the surrounding environment has become a practice. Notably, people use UAVs to capture videos and upload them to Youtube. Accordingly, using distributed UAVs in the city to collect real-time data represents a key enabling approach for smart city, e.g., air pollution and traffic monitoring. To achieve this goal, in this work, we develop a UAV-based crowdsensing system that allows UAVs to dynamically join and contribute to data collection. We make three contributions. First, due to UAVs' highspeed spatial mobilities, diverse sensing capabilities, and limited energy reserves, it is key to stimulate UAVs to cooperatively perform sensing tasks. To address this issue, we develop a game-theory-based distributed UAV incentive scheme, named BASIC. Second, to make BASIC work fully distributed and scalable, we first develop a mathematical model to characterize the interdependent operations of task requesters and collaboratively data sensing. We then devise a centralized auction mechanism with truthful bidding for UAVs. A heuristic algorithm is then devised to select the winning UAVs for sensing tasks and determine the proper payments to winners. Last, we perform extensive simulations to validate the effectiveness of proposed scheme and show that BASIC can provide effective incentive as compared to the existing schemes.

In order to save computing power yet enhance safety, there is a strong intention for autonomous vehicles (AVs) in future to drive collaboratively by sharing sensory data and computing results among neighbors. However, the intense collaborative computing and data transmissions among unknown others will inevitably introduce severe security concerns. Aiming at addressing security concerns in future AVs, in this paper, we develop SPAD, a secured framework to forbid free-riders and promote trustworthy data dissemination in collaborative autonomous driving. Specifically, we first introduce a publish/subscribe framework for inter-vehicle data transmissions. To defend against free-riding attacks, we formulate the interactions between publisher AVs and subscriber AVs as a vehicular publish/subscribe game, and incentivize AVs to deliver high-quality data by analyzing the Stackelberg equilibrium of the game. We also design a reputation evaluation mechanism in the game to identify malicious AVs in disseminating fake information. Furthermore, for lack of sufficient knowledge on parameters of the network model and the user cost model in dynamic game scenarios, a reinforcement learning based algorithm with hotbooting is developed to obtain the optimal strategies of subscriber AVs and publisher AVs with free-rider prevention. Extensive simulations are conducted, and the results validate that our SPAD can effectively prevent free-riders and enhance the dependability of disseminated contents, compared with conventional schemes.

Smart city aims to enhance the efficacy of urban management and improve quality of citizen life. This however faces a fundamental difficulty due to the inadequate data sharing among social entities, which has become one of the key barriers to the in-depth development of smart city, making wasted information resources and application silos ubiquitous. As an emerging communication paradigm, the digital twin network (DTN) has potential to eliminate the data sharing barrier in smart city. By mapping the social physical entities (PEs) in smart city to digital virtual agents, namely data twins (DTs), valuable data can be directly shared among the DTs over DTN without physical constraints, and form decisions that feed back and guide the operation of PEs. In this article, we explore the role of DTN in supporting the smart city and promoting data sharing construction. We first introduce the basic concepts related to DTN and propose the architecture of DTN-enabled smart city. The key features of DTN-enabled smart city are then discussed with an emphasis on the security and privacy issues with fundamental solutions around data sharing in DTN-enabled smart city. As a case study, we investigate the data sharing in DTN-enabled smart city and design the secure and fair data sharing mechanism. Finally, we propose open challenges for achieving more reliable DTN-enabled smart city.

As a key paradigm of future 6G networks, Space-Air-Ground Integrated Networks (SAGIN) has been envisioned to provide numerous intelligent applications that necessitate the cooperation of a multitude of terrestrial devices for machine learning (ML) model training. Utilizing the satellite as the central server, federated learning (FL) offers a promising strategy for distributed training with enhanced data security and privacy. However, employing FL in SAGIN faces challenges in managing vast datasets, training complicated ML models, and ensuring security in long-distance transmission of ML models. In this article, we propose a quantum-empowered FL framework integrating variational quantum algorithms (VQA) and quantum relays in SAGIN. Our approach employs VQA-based ML for local training of FL, addressing the complexity emerging from vast datasets and ML models. Furthermore, supported by unmanned aerial vehicles (UAVs) and high-altitude platform stations (HAPS), the proposed quantum relay scheme via quantum teleportation guarantees security in long-distance model transmission. We present a case study to validate the proposed VQA-based local training and quantum relaying model transmission. The numerical results demonstrate the feasibility and efficiency of the proposed VQA-based FL framework and the quantum relay-based model transmission. Our approach highlights the potential of integrating quantum techniques with FL in SAGIN, which can enable secure, efficient, and advanced edge intelligence applications.

Considering the overarching trend in mobile communication development, the extensive coverage and seamless connectivity offered by non-terrestrial networks (NTN) are becoming increasingly essential. NTN communications, characterized by extensive data volumes, hyper-connectivity, and large-scale fading, present notable challenges to traditional communication methods. This paper proposes a semantic communication-based NTN (SC-based NTN) architecture, highlighting its potential applications. However, the integration of semantic communication within NTN faces three key challenges, including multimodality, security, and resource allocation. This paper explores the opportunities presented by artificial intelligence, physical layer security, and intelligent resource management technologies, which promote multimodal, secure, and efficient communications for NTN. Finally, the paper outlines a visionary outlook for SC-based NTN, highlighting its potential impact on the future of mobile networks.

The rapid development of immersive technologies is propelling the emergence of extended reality (XR), a promising use case that submerges users into a virtual universe. To enable XR, devices equipped with a large number of sensors are necessary, and the diverse types and functions of sensors lead to different sensing and communication paradigms in the same network. Therefore, an appropriate integration of sensing and communication from all connected devices in the network is essential to XR. In this article, we propose a framework for the networked integration of sensing and communication (NISC), targeting the XR use case. In the proposed NISC framework, the sensing and communication resources from the devices are pooled and managed in a unified manner to satisfy the diverse quality of service (QoS) requirements of XR users. In addition, we illustrate the challenges for the NISC and present the potential solutions. A representative case study of the NISC framework is provided to demonstrate its performance advantages. Open research directions are finally provided.

With the development of wireless communication technologies, the space-air-ocean integrated unmanned surface vehicle (USV) networks are significantly promising to provide various intelligent services, e.g., marine resource exploration, disaster rescue, maritime monitoring, etc. However, owing to the node diversity, data vulnerability and resource limitation, the USV networks face a series of security threats, e.g., eavesdropping attack and sybil attack, etc. In this article, to enhance the security of maritime communication networks, we investigate the security and privacy in space-air-ocean integrated USV networks. Specifically, we first introduce the architecture of space-air-ocean integrated USV networks and present its typical applications. Second, we discuss the security threats faced by space-air-ocean integrated USV networks in different application fields including sybil attack, collusion attack, and eavesdropping attack, etc. Third, a case study of space-air-ocean integrated USV networks regarding data backup perspective is presented to enhance the security and integrity of the stored data. Finally, we discuss the future research directions in this area.

With the rapid advancement of air interface technology and the exponential growth of mobile services, mobile networks have become significantly complex. Traditional network security models, relying on regional defense strategies, are no longer sufficient to meet the current security demands. This article proposes the zero-trust architecture as a potential security mode for 6G. However, the distributed network architecture, the proliferation of connected devices, and the diverse service requirements of 6G pose sub-stantial challenges to this security model implementation. To address these issues, the article explores the opportunities presented by artificial intelligence (Al) and novel air interface technologies, which promote robust and efficient identity authentication, access control, and confidential data transmission for 6G. Finally, the article outlines a visionary outlook for the zero trust-based 6G security architecture, highlighting its potential impact on the future of mobile networks.

The increasing demand for online high-quality video streaming has brought huge challenges to the traditional client-server video streaming systems due to the high feedback delay, rigorous bandwidth requirement, and the lack of a mechanism of centralized resource management between users. In this work, we propose AMIS-MU, an edge computing-based mobile video streaming system that optimizes the watching experience of users via playback adaptation and channel resource allocation. AMIS-MU fully explores the power of edge servers from three perspectives. First, by pre-caching videos from the cloud, AMIS-MU analyzes video contents at the edge, and achieves a nearly imperceptible content-based playback speed adaptation. Second, as the edge server controls the channel resources of users in a centralized fashion, AMIS-MU adaptively updates the channel configuration to optimize the overall watching experience. Last, the plenty of computational power available at the edge enables a more intelligent playback control by using deep reinforcement learning (DRL). We propose a novel usage of DRL which significantly reduces the complexity of the cross-layer joint optimization problem and solve the non-convex channel resource allocation problem by Lyapunov optimization. Experiments show that AMIS-MU outperforms other existing algorithms in terms of average QoE and fairness.

Mobile crowdsensing (MCS) has emerged as a new sensing paradigm that relies on the sensing capabilities of the crowd to aggregate data. Unlike traditional MCS systems, where sensing data are traded via a third-party sensing platform, we propose a distributed data trading framework and investigate the potential of consortium blockchain to ensure the privacy and security of data transactions in MCS systems. The interactions between selling mobile users (SMUs) and buying mobile users (BMUs) are modeled as a Stackelberg game. Then, the amount of sensing time to purchase from each SMU and the price per unit sensing time are determined according to two auto-executing smart contracts. Notably, SMUs are compensated according to not only the amount of sensing time but also their reputation so that SMUs are encouraged to contribute high-quality data. Furthermore, the distributed ledger technology guarantees that the reputations of SMUs are updated and recorded in an immutable and traceable manner. Experimental results confirm that the proposed mechanism achieves near-optimal social welfare without requiring SMUs to know the price and data quality of each other.

Precise positioning is a key factor and enabler technology for many use cases on intelligent transportation systems (ITS) and connected and automated vehicles (CAVs). Recently, the quantum positioning system (QPS) based on quantum ranging has emerged as a novel way to improve security and precision. As a key process of QPS, the entangled photons based ranging technology has picosecond-level clock synchronization, and the ranging accuracy can reach the Heisenberg limit. If promising QPS is deployed in the ITS and CAVs, it will cause a profound change. However, the existing QPS still lacks accuracy and robustness in different scenarios. To solve this problem, we proposed an efficient and robust fusion positioning system based on entangled photons. In this system, we derive the ranging accuracy limit with many factors and propose a fast data grouping and selection algorithm to improve real-time performance. Furthermore, we propose a fusion extended fingerprint localization method for robust positioning in the dynamic environment. The effectiveness and robustness of the system are verified by extensive experiments. When the range is 15m, the ranging accuracy can be limited to 0.0018m. The proposed system achieves the probability of positioning errors 90% within 0.13m with only two APs.

With the accelerated popularization of 5G applications, accurate cellular traffic prediction is becoming increasingly important for efficient network management. Currently, the latest algorithms for cellular traffic prediction generally neglect extraction of the shallow features of cellular traffic and the prediction accuracy is hence limited. Therefore, we propose a global-local spatial-temporal transformer network (GLSTTN) that can fully excavate diverse spatial-temporal characteristics of cellular traffic for accurate cellular traffic prediction. Specifically, GLSTTN achieves this goal by constructing two modules: the global spatial-temporal module and the local spatial-temporal module. In the global spatial-temporal module, GLSTTN captures global correlations using stacked spatial-temporal blocks, where each block is composed of one spatial transformer and one temporal transformer. A skip connection is then used in each block to strengthen feature propagation. In the local spatial-temporal module, GLSTTN fully extracts the local spatial-temporal dependencies hidden in globally encoded features using densely connected convolutional neural networks. Extensive experiments demonstrate that GLSTTN achieves more accurate cellular traffic prediction than existing approaches on a real-world cellular traffic dataset.

With the increasing exploration of marine resources, various marine wireless devices have been rapidly deployed for different marine applications such as marine navigation, ocean environment monitoring, and seabed resource exploitation. However, due to long transmission delay and low data rate between marine wireless devices and the cloud, it is challenging to satisfy the service requirements of computing-intensive and delay-sensitive tasks. By migrating computing resources from cloud to the near side of ocean, the paradigm of marine edge computing networks, which integrates communication and computation capacities in marine wireless devices, is expected to support a variety of marine tasks (e.g., data collection, monitoring and processing) with low delay and high data rate. However, considering the rationality and selfishness of marine wireless devices and their limited computing-capacity, how to motivate marine wireless devices to conduct task processing becomes an important problem for improving computing efficiency. To address this issue, in this paper, we propose an incentive oriented two-tier task offloading scheme for marine edge computing networks via hybrid Stackelberg-auction game approach, with the objective of improving the offloading efficiency and maximizing marine wireless devices’ utilities. Specifically, for underwater acoustic transmission tier, we exploit multi-access task offloading scheme, in which underwater wireless sensor (UWS) uploads its workloads to an unmanned underwater vehicle (UUV) and a sea surface sink node (SN) via non-orthogonal multiple access (NOMA) transmission. We formulate the utility of each party and model the task offloading process among UWS, UUV and SN as a Stackelberg game to optimize the UWS’s offloading strategy, UUV’s and SN’s price strategies. For radio frequency transmission tier, SN can offload its partial workloads to an unmanned aerial vehicle (UAV) via frequency division multiple access (FDMA) transmission. We provide their utilities and model the offloading process between a SN and a UAV as a double auction game to optimize their bidding strategies. Extensive simulation results are provided to validate the performance of the proposed scheme. Numerical results demonstrate that the proposed algorithms can obtain the optimal solutions and increase the utilities for marine wireless devices.

Unmanned aerial vehicles (UAVs) have the potential to establish flexible and reliable emergency networks in disaster sites when terrestrial communication infrastructures go down. Nevertheless, potential security threats may occur on UAVs during data transmissions due to the untrusted environment and open-access UAV networks. Moreover, UAVs typically have limited battery and computation capacity, making them unaffordable for heavy security provisioning operations when performing complicated rescue tasks. In this paper, we develop RescueChain, a secure and efficient information sharing scheme for UAV-assisted disaster rescue. Specifically, we first implement a lightweight blockchain-based framework to safeguard data sharing under disasters and immutably trace misbehaving entities. A reputation-based consensus protocol is devised to adapt the weakly connected environment with improved consensus efficiency and promoted UAVs’ honest behaviors. Furthermore, we introduce a novel vehicular fog computing (VFC)-based off-chain mechanism by leveraging ground vehicles as moving fog nodes to offload UAVs’ heavy data processing and storage tasks. To offload computational tasks from the UAVs to ground vehicles with idle computing resources, an optimal allocation strategy is developed by choosing payoffs that achieve equilibrium in a Stackelberg game formulation of the allocation problem. For lack of sufficient knowledge on network model parameters and users’ private cost parameters in practical environment, we also design a two-tier deep reinforcement learning-based algorithm to seek the optimal payment and resource strategies of UAVs and vehicles with improved learning efficiency. Simulation results show that RescueChain can effectively accelerate consensus process, improve offloading efficiency, reduce energy consumption, and enhance user payoffs.

With rapid developments of artificial intelligence (AI) and wireless communication technologies, autonomous vehicular networks (AVNs) have emerged to provide convenient, comfortable, and safe driving services. Especially, due to the provable and unconditional security, quantum communication technology is increasingly being applied to promote the security of data delivery in AVNs, which forms the promising quantum AVNs (QAVNs). Federated learning (FL) is essential in QAVNs to implement intelligent and autonomous driving, where extensive distributed data is utilized to cooperatively train AI models, preserve user privacy, and reduce resource consumption. However, the FL unavoidably faces several possible privacy and security problems as a result of the intricate interactions among several autonomous vehicles or infrastructures. In this article, we investigate the security and privacy of FL in QAVNs. Specifically, we first establish the system architecture, including quantum key distribution protocol, space-air-ground integrated network (SAGIN)-enabled QAVNs model, and FL framework in QAVNs. Furthermore, we describe the privacy and security threats to FL in QAVNs, i.e., inference attack, eavesdropping attack, and sybil attack. Following that, we give a case study for designing a secure FL scheme using the local differential privacy mechanism and the homomorphic encryption mechanism. Finally, we suggest several potential future research directions in QAVNs.

Wireless federated learning (FL), which allows edge devices to perform local deep/machine learning (DL/ML) training and further aggregates the locally trained models from them via radio channels, establishes a promising framework for enabling various DL/ML-based services in future B5G/6G networks. Despite respecting the data privacy, periodically performing the local model training is not friendly to energy-constrained edge devices and degrades the sustainability and performance of FL services. In this article, motivated by the advanced simultaneous wireless information and power transfer (SWIPT), we propose a framework of SWIPT-empowered wireless FL that can provide over-the-air wireless power transfer in parallel with the transmission of global/local models. We present the key approaches of leveraging SWIPT for FL with their advantages illustrated. The practical challenging issues in reaping the benefits of integrating SWIPT are then discussed and we also provide the potential solutions to address these issues. A representative case study of FL via SWIPT is presented to validate the advantages of exploiting SWIPT. To this end, we present a joint design of SWIPT policy and the client-scheduling for FL, which is firstly formulated as a finite horizon dynamic optimization problem and then is solved by an actor-critic-based deep reinforcement learning algorithm. We finally articulate some potential open future directions regarding the SWIPT-empowered wireless FL.

The explosively increasing development of marine communication networks will improve the quality of service (QoS) of marine applications (e.g., ocean farm and marine tourism), which has attracted much attention from both academia and industrial in recent years. However, real-time data processing for diverse marine tasks (especially those computing-intensive and latency-sensitive tasks) is still challenging due to the limited marine communication and computing resources. Mobile edge computing (MEC) driven by powerful computing capability is envisioned as a promising solution to address the issue for resource-constrained marine services. In this paper, we propose a hybrid offshore and aerial-based multi-access edge computing scheme in marine communication networks to improve the QoS of marine applications. Specifically, we consider a scenario that both offshore base-station and unmanned aerial vehicles (UAVs) are equipped with edge-servers, and the computation workloads of unmanned surface vehicle (USV) can be simultaneously offloaded to offshore base-station and UAVs via multi-access manner. To minimize the latency of completing USV’s workloads and reduce USV’s energy consumption, we formulate a joint optimization problem to optimize the offloading decision, transmission time, and computing-rate allocation, with the objective of Minimizing the Maximum Workloads Latency (MMWL). Exploiting the features of the formulated problem, we present a layered structure approach and decompose it into three subproblems. We propose efficient algorithms to obtain the optimal solutions and validate the optimality of the proposed algorithms. Finally, we provide simulation results and analysis to demonstrate the effectiveness and efficiency of the proposed scheme and algorithms in comparison with benchmark algorithms.

Fused Deposition Modelling (FDM) is affected by various factors such as process parameters during the layer-by-layer printing process, and the parts will have serious defects and unstable mechanical properties. Therefore, the real-time monitoring of the processing process can better study the relationship between the parameters and defects. In particular, the direct measurement of thin-wall component dimensions and surface defects can better realize the adjustment of process parameters to improve the mechanical properties. However, existing monitoring methods either indirectly predict part quality by checking equipment health situation or directly inspect the component with inflexible field-of-view. Hence, this paper proposes a novel monitoring proof-of-concept prototype based on the phenomenon of laser self-mixing to directly inspect for part quality. The system primarily uses a laser diode with a self-encapsulated photodiode which is capable of collecting optical feedback signal once the laser hits a target. And data processing and calculation are performed on the feedback signal to obtain the geometric size and defect size of the target. The measurement object is FDM single-wall plates. The results show that the laser scanning monitoring system can successfully measure the target, and the measurement accuracy of the average size of waviness defects can reach up to 99.36 %. To the best of our knowledge, this study is the first to apply the self-mixing phenomenon as a metrology technique for the monitoring of an additive manufacturing process.

In commercial unmanned aerial vehicle (UAV) applications, one of the main restrictions is UAVs' limited battery endurance when executing persistent tasks. With the mature of wireless power transfer (WPT) technologies, by leveraging ground vehicles mounted with WPT facilities on their proofs, we propose a mobile and collaborative recharging scheme for UAVs in an on-demand manner. Specifically, we first present a novel air-ground cooperative UAV recharging framework, where ground vehicles cooperatively share their idle wireless chargers to UAVs and a swarm of UAVs in the task area compete to get recharging services. Considering the mobility dynamics and energy competitions, we formulate an energy scheduling problem for UAVs and vehicles under practical constraints. A fair online auction-based solution with low complexity is also devised to allocate and price idle wireless chargers on vehicular proofs in real time. We rigorously prove that the proposed scheme is strategy-proof, envy-free, and produces stable allocation outcomes. The first property enforces that truthful bidding is the dominant strategy for participants, the second ensures that no user is better off by exchanging his allocation with another user when the auction ends, while the third guarantees the matching stability between UAVs and UGVs. Extensive simulations validate that the proposed scheme outperforms benchmarks in terms of energy allocation efficiency and UAV's utility.

Epstein–Barr virus (EBV) is an oncogenic herpesvirus associated with several cancers of lymphocytic and epithelial origin1–3. EBV encodes EBNA1, which binds to a cluster of 20 copies of an 18-base-pair palindromic sequence in the EBV genome4–6. EBNA1 also associates with host chromosomes at non-sequence-specific sites7, thereby enabling viral persistence. Here we show that the sequence-specific DNA-binding domain of EBNA1 binds to a cluster of tandemly repeated copies of an EBV-like, 18-base-pair imperfect palindromic sequence encompassing a region of about 21 kilobases at human chromosome 11q23. In situ visualization of the repetitive EBNA1-binding site reveals aberrant structures on mitotic chromosomes characteristic of inherently fragile DNA. We demonstrate that increasing levels of EBNA1 binding trigger dose-dependent breakage at 11q23, producing a fusogenic centromere-containing fragment and an acentric distal fragment, with both mis-segregated into micronuclei in the next cell cycles. In cells latently infected with EBV, elevating EBNA1 abundance by as little as twofold was sufficient to trigger breakage at 11q23. Examination of whole-genome sequencing of EBV-associated nasopharyngeal carcinomas revealed that structural variants are highly enriched on chromosome 11. Presence of EBV is also shown to be associated with an enrichment of chromosome 11 rearrangements across 2,439 tumours from 38 cancer types. Our results identify a previously unappreciated link between EBV and genomic instability, wherein EBNA1-induced breakage at 11q23 triggers acquisition of structural variations in chromosome 11.

The data aggregation technique has been widely adopted in the Internet of Things (IoT) to protect data privacy while ensuring data availability. Homomorphic encryption is a typical technique that guarantees accurate computing results. However, it brings heavy computation overhead for edge nodes and exposes the aggregated results to the central server, which significantly threatens the confidentiality of results. This article gets rid of the server-centric style existing in most data aggregation schemes and proposes a scalable and decentralized data aggregation scheme for edge-enabled IoT. In the proposed scheme, edge nodes can freely form, join, and exit from the data aggregation group to aggregate data correctly, securely, and efficiently. Besides, two structure-based data aggregation methods are proposed to reduce the aggregation overhead to $O(n\sqrt{n})$ with constant rounds, as opposed to $O(n\log n)$ with $O(n)$ round. Symmetric encryption and online/offline signature computation are adopted to mitigate the online computation burden. Moreover, the proposed scheme can rigorously defend against forgery attack, eavesdropping attack, and collusion attack. The performance evaluation and experiment results show that the proposed scheme improves the efficiency of communication with affordable computation costs for edge nodes.

This article investigates physical layer security (PLS) in reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output multiple-antenna-eavesdropper (MIMOME) channels. Existing researches ignore the problem that secrecy rate can not be calculated if the eavesdropper’s instantaneous channel state information (CSI) is unknown. Furthermore, without the secrecy rate expression, beamforming and phase shifter optimization with the purpose of PLS enhancement is not available. To address these problems, we first give the expression of secrecy outage probability for any beamforming vector and phase shifter matrix as the RIS-assisted PLS metric, which is measured based on the eavesdropper’s statistical CSI. Then, with the aid of the expression, we formulate the minimization problem of secrecy outage probability that is solved via alternately optimizing beamforming vectors and phase shift matrices. In the case of single-antenna transmitter or single-antenna legitimate receiver, the proposed alternating optimization (AO) scheme can be simplified to reduce computational complexity. Finally, it is demonstrated that the secrecy outage probability is significantly reduced with the proposed methods compared to current RIS-assisted PLS systems.

With the rapid development of 6G, the intelligent space-air-ground integrated vehicular network (ISAGIVN) has been proposed to efficiently provide vehicles with ubiquitous services. Faced with the new features such as massive nodes, heterogeneous networks, and diverse data presented by the ISAGIVN, a new network architecture is urgently needed to enhance the flexibility of the network, the efficiency of decision-making and the orderliness of data management. In this article, we design a digital twin (DT)-enabled architecture to facilitate the practical deployment and application of ISAGIVN. We first review the conventional network architecture of ISAGIVN. Then, we discuss the challenges of combining DTs and ISAGIVN. On this basis, we design the framework of DT-enabled ISAGIVN, where the creation, deployment, update and migration of DTs in ISAGIVN are analyzed in detail. Finally, we conclude the article and discuss future research directions.

Social metaverse is a shared digital space combining a series of interconnected virtual worlds for users to play, shop, work, and socialize. In parallel with the advances of artificial intelligence (AI) and growing awareness of data privacy concerns, federated learning (FL) is promoted as a paradigm shift towards privacy-preserving AI-empowered social metaverse. However, challenges including privacy-utility tradeoff, learning reliability, and AI model thefts hinder the deployment of FL in real metaverse applications. In this article, we exploit the pervasive social ties among users/avatars to advance a social-aware hierarchical FL framework, i.e., SocialFL for a better privacy-utility tradeoff in the social metaverse. Then, an aggregator-free robust FL mechanism based on blockchain is devised with a new block structure and an improved consensus protocol featured with on/off-chain collaboration. Furthermore, based on digital watermarks, an automatic federated AI (FedAI) model ownership provenance mechanism is designed to prevent AI model thefts and collusive avatars in social metaverse. Experimental findings validate the feasibility and effectiveness of proposed framework. Finally, we envision promising future research directions in this emerging area.

In this paper, we consider task offloading in edge computing systems, where tasks are offloaded by the base station to resourceful mobile users. With the consideration of unique characteristics in practical edge computing systems, such as dynamic arrival of computation tasks, and energy constraints at battery-powered mobile users, we formulate an incentive mechanism design problem by jointly optimizing task offloading decisions, and allocation of both communications (i.e., power and bandwidth), and computation resources. In order to tackle the nonlinear issue in the designed mechanism, a novel online incentive mechanism is proposed. We first convert the original mechanism design problem into several one-shot design problems by temporally removing the energy constraint. Then, we propose a new mechanism design framework, called the Integrate Rounding Scheme based Maxima-in-distributional Range (IRSM), and based on that, design a new incentive mechanism for each one-shot problem. Finally, we reconsider energy constraints to design a new nonlinear online incentive mechanism by rationally combining the previously derived one-shot ones. Theoretical analyses show that our proposed nonlinear online incentive mechanism can guarantee individual rationality, truthfulness, a sound competitive ratio, and computational efficiency. We further conduct comprehensive simulations to validate the effectiveness and superiority of our proposed mechanism.

Timely and accurate cellular traffic prediction is difficult to achieve due to the complex spatial-temporal characteristics of cellular traffic. The latest approaches mainly aim to model local spatial-temporal dependencies of cellular traffic based on deep learning techniques but lack the consideration of diverse global spatial-temporal correlations hidden in cellular traffic. To tackle this issue, we propose a novel multi-view spatial-temporal graph network (MVSTGN), which combines attention and convolution mechanisms into traffic pattern analysis, enabling the comprehensive excavation of spatial-temporal characteristics. Specifically, the MVSTGN realizes the above statement from three spatial-temporal views: 1) From a global spatial view, two spatial attention modules are proposed to capture the global spatial correlations between different regions at node and trend levels; 2) From a global temporal view, a temporal attention module is employed to capture and encode global temporal correlations between traffic at different times; 3) From a local spatial-temporal view, a dense convolution module is developed to further excavate the local spatial-temporal dependencies in cellular traffic. Consequently, a successful cellular traffic prediction strategy is constructed to fully explore the spatial-temporal characteristics from multiple views. The experimental results on a popular real-world cellular traffic dataset demonstrate that the MVSTGN achieves obvious improvements over baselines.

With the rapid advance of wireless communication technologies, the promising space-air-ground integrated networks (SAGINs) are advocated to provide ubiquitous multicast transmission services for the social community constituted by a group of mobile users that have strong social ties and similar content interests. However, due to the limited yet valuable spectrum resources, the network heterogeneity, and diverse service demands of mobile users, it is challenging to efficiently allocate bandwidth for social communities with the objective of achieving satisfactory quality of experience (QoE) in SAGINs. To address this problem, in this paper, we propose a hierarchical bandwidth allocation scheme to enable high-quality multicast services for social communities in SAGINs. Specifically, we first develop a hierarchical bandwidth allocation framework. Wherein, the low earth orbit (LEO) satellite is utilized to provide space-to-air (S2A) unicast bandwidth for unmanned aerial vehicles (UAVs) at a certain price. Each UAV is employed to provide air-to-ground (A2G) multicast bandwidth for ground social communities with a certain A2G multicast bandwidth charge. We then formulate the hierarchical bandwidth allocation problem as a four-stage Stackelberg game, where the target of each participant is to maximize its own utility. Afterward, through the game analysis by the backward induction method, the existence of the Stackelberg equilibrium is proved, where the closed-form solutions on the optimal policies of both the social communities and UAVs are derived by the convex optimization method, and the optimal pricing policies of the LEO satellite is achieved by a proposed gradient descent iteration algorithm. Finally, extensive experiments are conducted to demonstrate that the proposed scheme can greatly increase the utilities of social communities while consuming a less bandwidth compared to conventional schemes.

In this letter, we propose a novel decentralized spectrum access algorithm based on the multi-agent reinforcement learning (MARL) for cellular vehicle-to-everything (C-V2X) networks. The agents make decisions independently with the global objective of maximizing vehicle-to-infrastructure (V2I) users’ sum throughput while meeting vehicle-to-vehicle (V2V) users’ latency and reliability requirements. Specifically, to achieve better collaboration among agents we introduce the inter-agent communication mechanism into MARL. So, in the proposed algorithm each agent consists of an action selector module and a message generator module, i.e., the agents learn emergent communication via an additional dedicated channel to enable explicit collaboration, which helps with learning effective policies for channel access. Simulation results show the effectiveness of the proposed algorithm, in improving both V2I users’ throughput and V2V users’ packet delivery ratio.

With the flourishing and advancement of autonomous vehicles, the vehicular digital twin (VDT) has become an emerging paradigm, consisting of inter-twin communication and intra-twin communication. VDT can effectively address the current shortcomings of the autonomous vehicle and provide enhanced and scalable services to the users. Nevertheless, strict security and privacy concerns arise, as VDT collects a wide range of privacy-sensitive information of the users in real-time and is exposed to an open network environment in both physical and virtual horizons. In this article, we investigate the security and privacy of VDT. We first describe the vehicular digital twin network architecture in detail and then investigate some VDT-related applications. After that, we discuss the security and privacy issues in VDT. Finally, we put forward several potential countermeasures and open research issues for VDT from the perspective of security and privacy. We expect this work will bring more attention to further investigation on security and privacy solutions for vehicular digital twin networks.

The metaverse, as an instance of cyber–physical–social systems (CPSS) that originates in cyber–physical systems (CPS), features growing complexity, and diversity in terms of functionalities, as well as the exponentially increasing demand in network bandwidth and computational resources, thereby leading to exaggerated security threats. However, compared with the extensive attention received by the metaverse, solutions defending against the threats have not kept pace. A major obstacle to such solutions is virtuality–reality-synthesized threats. Therefore, it is imperative to design new paradigms to defend the metaverse effectively. In this article, we advance a parallel system, dubbed ParaDefender, to defend the metaverse against emerging new threats effectively. Inspired by parallel intelligence, ParaDefender comprises artificial cyberspace, computational experiments, and parallel execution. The basic idea is to make artificial and real cyberspaces executed in parallel to mutually guide each other for enhanced security, wherein the parallel execution is scenario driven in the sense that the scenarios originate from all possible spatial–temporal combinations of security threats in the metaverse. We also demonstrate how to land ParaDefender onto real-world applications, including the Industrial Internet of Things (IIoT) security operation application in the industrial metaverse, and the social governance application.

The indoor navigation system supported by spatial crowdsourcing emerges as a promising application to provide customized location service for requesters. An important stage of crowdsourcing is to select trustworthy workers. Workers’ reputation, as an essential criterion of this selection, is usually evaluated by feedback ratings from requesters. However, the reputation in the crowdsourcing-based indoor navigation system is vulnerable to the collusion attack, that is malicious workers (i.e., attackers) collude with requesters to illegally increase reputation. In this paper, we propose a collusion detection scheme to distinguish attackers and provide a secure reputation mechanism. Specifically, we first identify collusive requesters categorized into three different levels according to their feedback rating behaviors. Then, the weighted logistic regression (WLR) is developed to distinguish the collusive requesters who provide exorbitant feedback ratings. Furthermore, we employ an outlying sequence detection based on the maximum mean discrepancy (MMD), to resist the multiple location queries initiated by the same collusive requester through analyzing the distribution distance. In addition, we propose a community detection algorithm, named Fastgreedy, to identify the collusion from many requesters. Finally, the extensive simulation results demonstrate that the proposed scheme can effectively detect collusive requesters and significantly outperform other methods.

Several studies are conducted to analyze the validity of the Purchasing Power Parity (PPP) exhibited in different countries. PPP is shown to be validated in the case of stationary real exchange rates.

Nickel-plated graphite particles and unmodified graphite particles with different contents were added to the Fe-based diamond composites. The basic properties of those specimens were measured, including relative density, hardness, bending strength, abrasion ratio and holding force coefficient. And also, SEM, XRD and EDS were used to carry out microstructure characterization, phase analysis and element distribution of these specimens. The results show that nickel plating effectively improves the surface wettability of graphite particles. And it is determined that an element diffusion zone is formed on the transition interface between the nickel-plated graphite and the matrix materials, effectively enhancing the interfacial bonding strength. Also, the pores and cracks in the matrix generated by adding the graphite particles are reduced after nickel plating. Thus, the loss of basic properties of the specimens is restrained. But it is found the higher the graphite content is, the weaker the positive effect of nickel plating is. In addition, it is revealed that nickel plating plays a conducive part in the formation of graphite lubricants on the working surface, and nickel-plated graphites can slow down the thermal corrosion of the diamond particles inside the high-temperature sintered specimens.

The wide use of unmanned aerial vehicles provides a promising paradigm for improving air-ground services and applications (e.g., urban sensing, disaster relief) in air-ground integrated networks (AGINs). Digital twin (DT), which is an emerging technology that utilizes data, models, and intelligent algorithms to integrate cyber physical networks and digital virtual models, provides a real-time and dynamic simulation platform for strategy optimization and decision making in AGINs. Due to the openness and massive connectivity of AGINs, the security and reliability services in this system become an important issue. In this article, we investigate the DT envisioned secure federated aerial learning for AGINs via an aerial blockchain approach. Specifically, we propose a layered framework of DT envisioned AGINs, which comprises the construction segment, communication segment, aggregation segment, analysis segment, and operation segment. Based on this framework, we offer the applications of the proposed DT envisioned AGINs. To guarantee the security of data transmission in AGINs, we investigate the aerial blockchain-based approach for ensuring data security. Furthermore, we provide a case study of DT envisioned secure federated aerial computing in AGINs to validate the effectiveness of the proposed approach through designing the aerial blockchain and training model.

Aqueous zinc ion batteries (AZIBs) have been attracting significant attention as promising next-generation electrical energy storage (EES) devices due to their low cost and intrinsically high thermal safety. Identifying suitable electrode materials is regarded as the key to constructing high-performance AZIB full cells. NASICON-type Na3V2(PO4)3(NVP) material possesses an open three-dimensional structure and can reversibly accommodate zinc ion intercalation. Here, we develop a hybrid carbon coated NVP interconnected with carbon nanofibers (NVP/C/CNF) as an AZIB cathode. Cyclic voltammogram analysis shows that, after the addition of CNF, the electrode material possesses a higher Zn2+diffusion coefficient with reduced electrochemical polarization. The NVP/C/CNF composite exhibits a reversible capacity of 95.1 mAh g–1at 100 mA g–1, higher than NVP/C composite (77.3 mAh g–1). The uniform amorphous carbon alleviates vanadium dissolution during cycling, while the CNF network further increases the electrical conductivity and structural stability of the hybrid, which can effectively suppress the material’s irreversible deformation caused by deep and rapid intercalation of Zn2+. Therefore, the NVP/C/CNF composite cathode features a higher capacity retention (75.6% after 100 cycles) than NVP/C (63.1%). As verified, NVP/C/CNF is expected to be a great potential electrode in next-generation AZIBs.

I. INTRODUCTION Political turmoil is a chronic problem in many developing countries or any other country undergoing significant political reforms. Economists have often ignored this important issue when modelling empirical [...]

Aqueous rechargeable lithium-ion batteries (ARLBs) are considered to be promising alternatives to traditional Li+-ion batteries. Since the advent of the development of ARLB technology, vanadium oxide has been widely used as an anode material because of its great potential. However, several key flaws require urgent attention, for example, low-rate performance and meager cyclic stability. In this paper, a strategy is proposed to augment the interlayer spacing of the vanadium-oxide structure to boost its electrochemical performance. This work aims at exploiting vanadium oxide by modifying its interlayer spacing by introducing a NH4+molecule to achieve the pillar effect and the as-prepared NH4V4O10, which exhibits a higher interlayer spacing of 9.8 Å. The increased interlayer spacing allows more Li+to accommodate in the host material and to facilitate their migration. The NH4V4O10coupled with a commercial LiMn2O4cathode is able to deliver a capacity of 61.5 mAhg–1at a current density of 3 Ag–1in saturated aqueous LiNO3electrolyte. The decent cyclic capacity retention of 69.5% is attained for NHVO//LiNO3//LiMn2O4following 300 cycles of deep charge–discharge at 3 Ag–1. On the basis of the communal findings, the incorporation of NH4+molecules into the interlayer spacing offers great promise as a high-performance anode for ARLBs.

Reliable and flexible emergency networks are of paramount essence for disaster relief during or after the event of disasters due to the destruction or lack of terrestrial communication infrastructures. Thanks to fast deployment and flexible mobilities, unmanned aerial vehicles (UAVs) emerge as a promising paradigm to efficiently establish emergency networks and perform immediate disaster relief tasks in affected areas. However, in such UAV-assisted disaster relief networks (UDRNs), the limited onboard batteries and computational capacities of UAVs hinder them from performing computation-intensive missions. Moreover, critical security vulnerabilities arise in data transmission among UAVs owing to the untrusted environment, open communication channels, and unreliable misbehavior tracing. To this end, this article investigates UDRNs based on blockchain and machine learning to achieve secure and efficient data transmission. Specifically, we first present a lightweight blockchain-enabled collaborative aerial-ground networking framework to safeguard data delivery under disasters, where a credit-based delegated proof-of-stake consensus protocol is further devised to enhance consensus efficiency while promoting UAVs' honest behaviors. In addition, by harnessing the idle computing power of ground vehicles (referred to as vehicular fog computing), a novel reinforcement learning-based algorithm is developed to intelligently offload UAVs' computation missions to the moving vehicles in the dynamic environment. Experimental results demonstrate that the proposed framework outperforms the existing approaches, in terms of consensus security, user utility, task latency, and energy consumption. Finally, future research directions in this emerging area are discussed.

Amid the COVID-19 pandemic, the Internet of Things (IoT) has rapidly taken on a powerful role to combat it with the emerging smart medicine, smart community, smart city, smart home, and so on. IoT devices are widely deployed in various places to provide feasible solutions for personnel temperature detection, medical treatment, and quarantine control during the pandemic. However, IoT-enabled applications inevitably face serious security challenges due to the massive amount of sensitive data. In this article, we first investigate several strategies of pandemic control based on IoT and the critical supporting technologies, 5G, artificial intelligence, and blockchain. Then we present the security issues in the process of data collection to usage in the applications. Through the analysis of specific paradigms, the Internet of Medical Things (IoMT) and a location check-in system, we exploit the potential security challenges, including data leakage and false sensing. Countermeasures are proposed to address these issues and are effectively validated by simulation results. Finally, some open issues instead of data security threats of IoT are outlined.

With the ever-expanding applications of vehicles and the development of wireless communication technology, the burgeoning unmanned aerial vehicle (UAV) assisted vehicular internet of things (UVIoTs) has emerged, where the ground vehicles can experience more efficient wireless services by employing UAVs as a temporary mobile base station. However, due to the diversity of UAVs, there exist UAVs such as jammers to degenerate the performance of wireless communication between the normal UAVs and vehicles. To solve above the problem, in this paper, we propose a game based secure data transmission scheme in UVIoTs. Specifically, we exploit the offensive and defensive game to model the interactions between the normal UAVs and jammers. Here, the strategy of the normal UAV is to determine whether to transmit data, while that of the jammer is whether to interfere. We then formulate two optimization problems, i.e., maximizing the both utilities of UAVs and jammers. Afterwards, we exploit the backward induction method to analyze the proposed countermeasures and finally solve the optimal solution. Lastly, the simulation results show that the proposed scheme can improve the wireless communication performance under the attacks of jammers compared with conventional schemes.