Federated Domain Generalization (FDG) aims to collaboratively train a global model across distributed clients that can generalize well on unseen domains. However, existing FDG methods typically struggle with cross-client data heterogeneity and incur significant communication and computation overhead. To address these challenges, this paper presents a new FDG framework, dubbed FaST-PT, which facilitates local feature augmentation and efficient unseen domain adaptation in a distributed manner. First, we propose a lightweight Multi-Modal Style Transfer (MST) method to transform image embedding under text supervision, which could expand the training data distribution and mitigate domain shift. We then design a dual-prompt module that decomposes the prompt into global and domain prompts. Specifically, global prompts capture general knowledge from augmented embedding across clients, while domain prompts capture domain-specific knowledge from local data. Besides, Domain-aware Prompt Generation (DPG) is introduced to adaptively generate suitable prompts for each sample, which facilitates unseen domain adaptation through knowledge fusion. Extensive experiments on four cross-domain benchmark datasets, e.g., PACS and DomainNet, demonstrate the superior performance of FaST-PT over SOTA FDG methods such as FedDG-GA and DiPrompt. Ablation studies further validate the effectiveness and efficiency of FaST-PT.

The rapid advancement of large language models (LLMs) has resulted in increasingly sophisticated AI-generated content, posing significant challenges in distinguishing LLM-generated text from human-written language. Existing detection methods, primarily based on lexical heuristics or fine-tuned classifiers, often suffer from limited generalizability and are vulnerable to paraphrasing, adversarial perturbations, and cross-domain shifts. In this work, we propose SentiDetect, a model-agnostic framework for detecting LLM-generated text by analyzing the divergence in sentiment distribution stability. Our method is motivated by the empirical observation that LLM outputs tend to exhibit emotionally consistent patterns, whereas human-written texts display greater emotional variability. To capture this phenomenon, we define two complementary metrics: sentiment distribution consistency and sentiment distribution preservation, which quantify stability under sentiment-altering and semantic-preserving transformations. We evaluate SentiDetect on five diverse domains and a range of advanced LLMs, including Gemini-1.5-Pro, Claude-3, GPT-4-0613, and LLaMa-3.3. Experimental results demonstrate its superiority over state-of-the-art baselines, with over 16% and 11% F1 score improvements on Gemini-1.5-Pro and GPT-4-0613, respectively. Moreover, SentiDetect also shows greater robustness to paraphrasing, adversarial attacks, and text length variations, outperforming existing detectors in challenging scenarios.

As 6G moves toward Satellite–Aerial–Ground Integrated Networks, unmanned swarms must sense, communicate, and decide collaboratively at the edge under tight spectrum, power, and latency budgets. Missions are short-range, highly dynamic, and deadline-aware, which makes what is transmitted as critical as how it is transmitted. First, exchanging raw or task-agnostic compressed sensor streams quickly saturates short-range links and inflates end-to-end decision latency during agile maneuvers. Second, although THz bands offer very wide spectrum, free-space loss and molecular absorption grow rapidly with distance, so any benefit depends on payloads that are truly task-efficient.We present SemTHz-FL, a semantic THz-enabled federated framework for collaborative mission planning in unmanned swarms. Each agent extracts compact, task-aligned descriptors from point clouds and images—obstacle/layout cues with lightweight kinematic tokens—prioritized by safety relevance and transported over a calibrated THz link model; a lightweight federated loop keeps local planners consistent under a fixed communication budget.We evaluate two core aspects that map directly to the challenges above. (i) Semantic vs. task-agnostic compression at matched task utility: we compare the bits per frame required by semantic tokens versus voxel downsampling with entropy coding to reach the same planning-level fidelity, measured by BEV boundary distance and BEV occupancy IoU. (ii) THz vs. mmWave link capability at short range: using the measured payloads from (i), we contrast path loss and achievable throughput across formation distances typical of UAV swarms. Results show that semantic descriptors achieve substantially fewer bits per frame than task-agnostic compression at the same boundary accuracy and occupancy overlap, and that within short-range operations THz provides a clear throughput advantage despite higher path loss, while mmWave becomes preferable as separation grows.

Federated Learning (FL) enables collaborative model training without data sharing, yet participants face a fundamental challenge, e.g., simultaneously ensuring fairness across demographic groups while protecting sensitive client data. We introduce a differentially private fair FL algorithm (FedPF) that transforms this multi-objective optimization into a zero-sum game where fairness and privacy constraints compete against model utility. Our theoretical analysis reveals an inverse relationship: privacy mechanisms that protect sensitive attributes can reduce the statistical power available for detecting and correcting demographic biases under finite samples in federated settings. We further show that our theoretical bounds are consistent with a non-monotonic fairness-utility relationship, which is empirically validated by experiments where moderate fairness constraints improve generalization before excessive enforcement degrades performance. Compared with mainstream algorithms, even under strict privacy constraints, FedPF still maintains the lowest discrimination level among all tested algorithms while retaining high utility. Experimental validation demonstrates up to 42.9 % discrimination reduction across three datasets while maintaining competitive accuracy, but more importantly, reveals that achieving strong privacy and fairness simultaneously requires carefully balanced tradeoffs rather than optimizing either objective in isolation. Furthermore, hardware-level simulations demonstrate that FedPF maintains a low computational footprint, making it suitable for resource-constrained edge devices. The source code for our proposed algorithm is publicly accessible at https://github.com/szpsunkk/FedPF.

Quantum computing threatens blockchain security by compromising widely used cryptographic algorithms such as ECDSA, which could be efficiently broken by Shor’s algorithm, undermining transaction verification. Meanwhile, blockchain-based architectures are increasingly popular in edge computing and Internet of Things (IoT) environments, where resource-constrained devices dominate. To counteract the threat of quantum attack, post-quantum cryptographic schemes, including lattice-based and hash-based signatures standardized by NIST, have been proposed. However, deploying computationally intensive post-quantum schemes in edge environments poses significant challenges due to limited processing power, strict hardware constraints, and efficiency bottlenecks, presenting a critical barrier for practical implementation. To address these issues, this paper proposes an efficient post-quantum transaction scheme for blockchain in edge environments via physically unclonable function (PUF)-enabled persistent verification. More specifically, a novel blockchain data structure designed with hybrid signatures and a secure hash-chain mechanism uniquely binds PUF responses to transaction-specific nonce values, ensuring persistent verification and robustness against PUF modeling attacks. In addition, to achieve efficient quantum resistance, we innovatively distribute cryptographic complexity by offloading computationally demanding post-quantum signature verifications to resource-rich edge servers, while end devices only execute lightweight tasks such as traditional ECDSA key generation, efficient transaction signing, and PUF response computations. We rigorously define our security goals through formal models and provide security proofs. Experimental results on an emulated edge platform demonstrate significant speed gains over conventional schemes.

With powerful cloud computing capacities, machine learning as a service (MLaaS) framework provides intelligent services and well-trained artificial intelligence (AI) models by clouds for resource-constrained end devices. However, there are AI model leakage and illegal abuse issues during model transmission and deployment. Existing mainstream protection methods face the following problems: (i) The watermark-based methods only provide passive verification afterward rather than active protection. (ii) Encryption-based methods are low efficiency in computation and low security in key storage. (iii) Existing methods are not device-bind without the ability to avoid illegal abuse issues. To deal with these problems, we propose a device-bind and key-storageless cloud-AI model intellectual property (IP) protection mechanism. First, a physical unclonable function (PUF) and permute-diffusion encryption empowered cloud-AI model protection framework is proposed, including the PUF-based secret key generation and the geometric-value transformation based weights encryption. Second, we design an Anderson PUF based key generation protocol to generate device-bind robust secret keys. Third, a permutation and diffusion-based intelligent model weights encryption/decryption method is proposed for effective cloud-AI model protection, where chaos theory is utilized to convert PUF-based secret keys to encryption/decryption keys. Finally, experimental evaluation demonstrates the effectiveness and reliability of the proposed intelligent model IP protection mechanism.

Multi-agent architectures leveraging Large Language Models (LLMs) have significantly advanced the precision of Question Answering (QA) systems across diverse domains. However, existing frameworks remain vulnerable to adversarial manipulations, including poisoning, backdoor, and jailbreak at tacks, primarily due to their reliance on centralized orchestration. To mitigate these risks, we propose AgentChain, a framework that substitutes centralized control with a distributed semantic consensus process. By modeling the blockchain as an ideal functionality, AgentChain establishes a secure distributed layer to coordinate role allocation, answer proposal, evaluation and voting through a decentralized council. Specifically, we design Proof-of-Content-Quality (PoCQ) mechanism to ensure that the f inal answers reflect a robust semantic agreement among the majority of honest agents. Furthermore, we propose an incentive mechanism based on stake reassignment that penalizes malicious agents by reducing their rewards, ultimately phasing them out of the network. Comprehensive evaluations across eight datasets demonstrate that AgentChain achieves superior performance and resilience. AgentChain minimizes the impact of poisoning attacks on precision to less than 3% and reduces the success rate of backdoor and jailbreak attacks to less than 4%. These findings highlight the effectiveness and trustworthiness of AgentChain in mitigating security threats while maintaining high QA accuracy.

The intellectual property (IP) rights of artificial intelligence (AI) models are the prerequisite for the flourishing machine learning as a service (MLaaS) market. Recently, studies on AI model copyright protection have been burgeoning, but still face challenges. On the one hand, current AI model copyright sharing is unfair. Collaborative MLaaS nodes that contribute more training resources usually cannot gain proportional copyright benefits, severely dampening their enthusiasm for participating in AI model trading. On the other hand, untrustworthy third-party verifiers may act erroneously or unreliably when handling AI model ownership disputes, which greatly diminishes the credibility of the verification results. To address the above challenges, we propose AIIP-Chain, a fair AI model copyright sharing with credible ownership verification framework. First, we design a contribution-aware training effort evaluation scheme to ensure the fairness of copyright sharing, in which MLaaS nodes obtain copyright shares according to their consumed training resources. Moreover, to enhance the trustworthiness of the verification results, we propose a crowdsourcing credible AI model ownership verification scheme that balances node motivation, verification cost, and node reputation. Finally, we evaluate the fairness and credibility of AIIP-Chain and the results demonstrate the effectiveness of our schemes.

Graph Neural Networks (GNNs) with differential privacy have been proposed to preserve graph privacy when nodes represent personal and sensitive information. However, existing methods ignore that nodes with different importance may yield diverse privacy demands, which may lead to over-protecting some nodes and decrease model utility. In this paper, we study the problem of importance-grained privacy, where nodes contain personal data that need to be kept private but are critical for training a GNN. We propose NAP-GNN, a node-importance-grained privacy-preserving GNN algorithm with privacy guarantees based on adaptive differential privacy to safeguard node information. First, we propose a Topology-based Node Importance Estimation (TNIE) method to infer unknown node importance with neighborhood and centrality awareness. Second, an adaptive private aggregation method is proposed to perturb neighborhood aggregation from node-importance-grain. Third, we propose to privately train a graph learning algorithm on perturbed aggregations in an adaptive residual connection mode over multi-layer convolution for node- wise tasks. The theoretical analysis shows that NAP-GNN can guarantee privacy. Empirical experiments over five real-world graph datasets show that NAP-GNN achieves a better trade-off between privacy and accuracy.

Coded distributed machine learning mitigates straggler effects and provides privacy protection by introducing redundancy through coded computing. However, the system remains vulnerable to privacy breaches when the number of honest-but-curious nodes surpasses the designed threshold, or when outsider adversaries eavesdrop on sensitive data. To address these limitations, we propose a privacy-preserving and straggler-resilient distributed learning framework, namely, differential privacy-based Lagrange coded computing (DP-LCC). First, we design a three-layer protection strategy against privacy threats and stragglers by retaining labels at the master, obfuscating features via Lagrange interpolation, and injecting calibrated noise into local computations. Second, we theoretically prove that the aggregated gradient is an unbiased estimator with bounded variance, and derive convergence bounds under both Gaussian and Laplace mechanisms, revealing the trade-off between privacy budgets and model utility. Third, we provide a comprehensive analysis of the system's computational complexity, privacy composition, and heterogeneity to verify the framework's efficiency and adaptability in realistic distributed environments. Extensive experiments on four benchmark datasets validate the theoretical results, demonstrating the robustness of DP-LCC against varying system parameters and heterogeneous environments.

Retrieval-Augmented Generation (RAG) grounds large language models (LLMs) in external knowledge, yet it is faces a fundamental trade-off between knowledge confidentiality and retrieval efficiency. Existing approaches fail to reconcile this trade-off: i) Cryptography-based solutions (e.g., homomorphic encryption and secure multi-party computation) provide strong privacy guarantees but incur prohibitive computation and communication overheads. ii) Lightweight perturbation-based methods (e.g., differential privacy) offer higher efficiency at the cost of degraded retrieval accuracy or weakened security guarantees. In this paper, we propose CipheRAG, an efficient vector-multiplicative privacy-preserving RAG framework that achieves a principled balance between robust privacy and high-performance retrieval. Technically, we first propose an efficient searchable inner product functional encryption (IPFE) mechanism enhanced with asymmetric locality-sensitive hashing (ALSH), enabling the retrieval of sensitive knowledge while effectively preserving data confidentiality. Secondly, we propose a decryption-enabled attention mechanism that uses linear weights of the attention layer to decrypt knowledge. This mechanism seamlessly integrates decrypted knowledge into the LLM's generation process, achieving efficiency and accuracy. Extensive experiments demonstrate that CipheRAG achieves up to 35x faster generation and 15x faster QKV computation compared to FHE- and OT-based baselines. By avoiding linear retrieval and full-parameter encryption, CipheRAG enables privacy-preserving RAG with bounded computational and communication overheads, making it well-suited for deployment in privacy-sensitive environments.

Reliable encrypted traffic classification is crucial for fine-grained and efficient network security management, enabling accurate user behavior recognition and cybercrime forensics. While AI-based methods can automatically extract subtle features from traffic data, existing approaches often fail to effectively capture and integrate features across different levels of traffic granularity, namely the byte, packet and flow levels. Current graph-based methods heavily rely on manual feature engineering to construct global IP-based graphs, overlooking critical packet-level temporal features and byte-level raw information. Focusing on only one or two levels of traffic granularity is unreliable and insufficient, ultimately compromising model accuracy and robustness. To address these limitations, we propose BPF-DAG, a byte-packet-flow feature fusion framework based on dynamic attributed graphs, for reliable encrypted traffic classification. To the best of our knowledge, this is the first method that integrates temporal packet relations into flow interaction patterns while directly leveraging raw byte-level data. Specifically, we introduce a multi-granularity feature fusion strategy that dynamically updates an IP-based graph by iteratively assigning edge attributes derived from evolving flow representations. During the joint training of the Transformer and the graph neural network, temporal representations are learned from raw packet sequences and reflected in edge attributes dynamically for further message aggregation. Experiments on the ISCX VPN-nonVPN, Tor-nonTor, MIRAGE-2019 and MIRAGE-2024 datasets show that BPF-DAG outperforms recent state-of-the-art methods in terms of classification performance.

Federated learning (FL) enables collaborative training across distributed clients but remains vulnerable to Byzantine attacks, especially stealthy ones. The threat is even amplified in non-IID settings, where client heterogeneity causes greater divergence in feature distributions and inter-client distances. Existing defenses often rely on strong assumptions or raw update distances, limiting their effectiveness under such heterogeneity. To address this gap, we proposeFedRefiner, a decoupled dual-refined aggregation algorithm designed to mitigate stealthy attacks on heterogeneous data. Our intuition is that the significance distribution of client updates reveals subtle malicious evasion, altering critical features for attack while perturbing unimportant ones, thereby exposing true inter-client distances.FedRefinergoes beyond norm-based filtering by refining both weighted scores and aggregated updates, enabling more accurate distinction between malicious behavior and benign non-IID variation. It first derives significance distribution vectors as refined updates by sparsity, then clusters them to compute weighted similarity scores for group reliability. These clusters then align raw updates into groups for group-wise refinement, yielding robust aggregated updates. We theoretically prove the convergence ofFedRefinerunder Byzantine attacks in non-IID settings. Extensive evaluation on 8 datasets against 10 attacks (including 2 adaptive ones) and 13 defenses shows thatFedRefineroutperforms state-of-the-art defenses, achieving up to a 10% gain in overall accuracy and a 14.8% improvement in worst-case performance under both IID and non-IID settings. Ablation studies further demonstrate its robustness across different hyperparameters, attacker ratios, data heterogeneity, and model/client scales, while incurring low computation and no storage overhead.

Due to its capability to ensure communication security and improve spectral efficiency, Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS)-assisted covert communications in Rate-Splitting Multiple Access (RSMA) networks have drawn widespread attention. However, existing studies often overlook the impact of the mobility of wardens and users on long-term covert performance, and lack joint optimization of covert rate and energy consumption. Meanwhile, introducing STAR-RIS significantly increases system parameter dimensionality, rendering traditional methods inefficient. Although reinforcement learning offers advantages, it still faces challenges in training efficiency and resource overhead within high-dimensional state-action spaces. Therefore, this paper proposes a quantum reinforcement learning-based algorithm, named QUERC, for STAR-RIS-assisted covert communications in RSMA networks. Specifically, we first formulate a long-term covert energy efficiency maximization problem under a dynamic environment with mobile users and wardens. Then, we propose a novel hybrid quantum neural network architecture to solve this problem. This architecture integrates a fully connected layer, a variational quantum circuit, an action post-processing mechanism, and a regularized objective, enhancing policy stability and generalization in high-dimensional action spaces. Finally, extensive experiments demonstrate that, compared with TPG, QPG, RIS-NOMA, RPS, and greedy approaches, QUERC algorithm achieves superior average covert energy efficiency and offers significant advantages in computational and inference performance.

Semantic Communication (SC) achieves meaning transmission instead of bitstreams by deep semantic encoding decoding. Since the encoder-decoder contains sensitive and proprietary knowledge, its illicit leakage infringes commercial benefits and copyright, which warrants corresponding protection. However, current SC security paradigms narrowly emphasize transmission data protection while neglecting encoding knowl edge safeguarding. To bridge this gap, we present SemanAegis, the first SC knowledge protection framework. SemanAegisinte grates a built-in-system access control mechanism that remains effective even if the system is stolen, ensuring that unauthorized access attempts yield unacceptable low-fidelity outputs, while credential-embedded inputs from authorized entities are met with accurate responses. Specifically, we establish access control through backdoor implantation, whereby only inputs embedded with credentials activate the backdoor and access system, while source inputs are constrained to generate erroneous results. Moreover, we adopt a synthesizer to generate imperceptible credentials, thus guaranteeing their confidentiality. Additionally, a dedicated contrastive learning strategy is implemented to accelerate the convergence of backdoor implanting. Empirical evaluations across SC systems and benchmark datasets demonstrate SemanAegis precisely rejects unauthorized inputs, effectively mitigates knowledge extractions, and consistently preserves SC regular functionality.

Leveraging the collective intelligence of large language models (LLMs)-based multiagent collaboration has led to significant advancements in intelligent applications across multiple domains. However, due to the untruthful content generated by LLMs, these collaborations face the challenge of continuously amplifying hallucinations, causing hallucination snowballing effect. Currently, existing research only discussed this concern in the context of a single model without analyzing or addressing it in agent collaborations. To tackle these challenges, this article proposes a context-aware hallucination analysis framework that captures token-level dependencies, leveraging semantic reasoning to validate and mitigate the snowballing effect in sequential multiagent collaboration. Specifically, we first propose a contextually embedded probabilistic modeling enabled hallucination analysis framework that systematically identifies and analyzes how collaborative processes propagate hallucinations. In addition, we construct a token-level disruption sequence detection approach for different task sequences to recognize and validate this effect across different domains. Finally, to mitigate hallucination snowballing without modifying the model architecture, we design a semantic reasoning empowered mitigation strategy based on a more effective bidirectional entailment clustering which mitigates hallucination propagation caused by the model itself, alleviate it caused by external knowledge deficiencies. Our extensive experiments with real datasets validate the existence of this effect in multiagent collaborations across various domains and demonstrate that our proposed mitigation strategy effectively reduces the propagation of hallucinations.

The emergence of the large language model (LLM) accelerates network intelligence and supports numerous applications across various areas. Pre-trained LLM is trained based on massive amounts of public data, and domain-specific data is required to fine-tune LLM when it is adopted to specific fields. However, fine-tuning LLM with domain-specific data faces isolated data silos and data security issues. Although the federated learning framework mitigates the data silos issue and avoids direct exposure of local data, there are still the following challenges: i) The contradiction between the high computing/storage resources requirements of LLM fine-tuning and resource-constrained local devices, ii) privacy leakage through fine-tuning parameters. Efficient privacy-preserving LLM finetuning is becoming increasingly important and is still an open issue. To solve the above problems and challenges, we propose a selective privacy-preserving federated LLM fine-tuning mechanism. First, a selective privacy-preserving federated learning framework is designed to fine-tune LLM to specific domains efficiently while protecting sensitive information. Second, we propose a selective privacy-preserving exponential mechanism, which adds customized noise to private tokens of local data to protect sensitive information. Third, an adapter-enabled privacy-preserving LLM federated fine-tuning mechanism is proposed for high efficiency and security. Finally, experimental evaluations verify the effectiveness of our proposed mechanism.

Multimodal 6G-Vehicular radio access, integrating diverse data modalities such as video, sensor, and positional information with heterogeneous wireless communication technologies, has emerged as a pivotal technology enabling ultrahigh reliability, ultra-low latency, and pervasive connectivity in future vehicular communication scenarios. However, this advanced multimodal integration significantly amplifies privacy risks due to the increased complexity and heterogeneity in mobile vehicular environments. Traditional vehicular communication frameworks often struggle to effectively handle these multidimensional datasets while simultaneously safeguarding sensitive user information against inference attacks. Furthermore, conventional privacy-preserving methods typically introduce considerable computational overhead, which are critical for safetysensitive vehicular applications. To address these challenges, we propose a privacy-preserving multimodal vehicular radio access framework for 6G, integrating Diffusion Probabilistic Models (DPMs) and Cross-Attention Mechanisms (CAMs). Specifically, DPMs safeguard data privacy through generative modeling and differential privacy techniques, while CAMs efficiently fuse multimodal sensor data, reducing latency and enhancing communication reliability. The synergistic integration of DPMs and CAMs provides a dual advantage-robust privacy preservation and efficient multimodal vehicular radio access. The proposed synergy ensures robust privacy, adaptive multimodal integration, and lays a solid foundation for secure and scalable 6G vehicular networks.

With the advent of 6G and the evolution of vehicular communication networks, the Internet of Vehicles (IoV) is facing unprecedented demands for ultra-high data rates and ultra-low latency to support advanced autonomous driving applications. However, deploying terahertz (THz) communication in such networks presents significant challenges. First, the massive exchange of raw sensor data with semantic information among vehicles creates severe communication bottlenecks, thereby undermining the real-time decision-making required by autonomous systems. Second, inherent characteristics of the THz band—such as high attenuation and pronounced molecular absorption—substantially restrict the effective transmission range, particularly in shortrange real-time scenarios, thus imposing stringent requirements on the timeliness of driving decisions. To address these challenges, we propose a Semantic THz Communication Empowered Wireless Federated Learning framework for unmanned vehicle decision-making. Our approach leverages semantic compression to extract and transmit only task-relevant information, thereby dramatically reducing the data volume and alleviating network congestion. Moreover, by integrating wireless federated learning into the IoV architecture, vehicles collaboratively train robust autonomous driving models while preserving data privacy. This distributed learning mechanism—by treating each vehicle as a node that contributes to the overall model—effectively compensates for the short-range limitations of THz links by harnessing their ultra-wideband capability. Numerical results demonstrate that our proposed framework significantly improves throughput and reduces latency compared to conventional mmWave-based approaches, ultimately enhancing the accuracy and efficiency of driving decisions.

Connected and Autonomous Vehicles (CAVs) rely on vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communication for navigation and collision avoidance, yet ensuring the authenticity and integrity of shared trajectory data is a significant challenge. Traditional trust models, which depend on centralised or pre-established trust, are susceptible to cyberattacks, such as trajectory spoofing and misinformation propagation. This paper introduces a Zero Trust-based Lightweight Trajectory Endorsement Model (ZT-LTE) that combines Zero Trust principles with a lightweight verification mechanism, employing cryptographic identity verification, Bloom Filter-based trajectory lookup, and a federated trust consensus mechanism to ensure reliable trajectory endorsement in real time. Simulation results indicate that ZT-LTE reduces unauthorised trajectory endorsements by 85% and improves real-time threat detection by 72% compared to traditional models, based on a comparison of the False Acceptance Rate and detection accuracy using a dataset of 10,000 simulated trajectory transactions. These findings advance the development of secure, efficient, and scalable communication frameworks for CAVs.

The development of Artificial Intelligence-based Internet of Things (AIoT) enables wireless vehicular networks to provide energy-efficient and sustainable solutions for green supply chains through optimized data processing, adaptive resource allocation, and automated control. However, there are still many challenges in resource allocation and network congestion, especially in large-scale AIoT-enabled vehicular networks. Traditional approaches often struggle with scalability due to static resource allocation, lack of incentive mechanisms, and high computational complexity. Therefore, we propose a contract theory and harmony search optimization algorithm(HSO-Con) for AIoT-enabled green supply chain management. This method uses the combination of contract theory and VEC to optimize resource allocation in the AIoT environment. Harmony Search dynamically optimizes task scheduling and data processing and uses a cache mechanism to reduce redundant data transmission. In addition, the graph neural network (GNN) further enhances the adaptability of the AIoT network, enabling adaptive parameter adjustments and efficient processing of data streams. Simulation results show that the HSO-Con algorithm has significant potential in reducing network congestion, and enhancing supply chain efficiency in VEC by AIoT. Specifically, our algorithm improves cache hit rate by 4.27% and 5.05% respectively, and bandwidth utilization by 7.01%.

Generative artificial intelligence (GAI) models are pretrained using extensive public data. However, in the Internet of Things (IoT) domain, distributed and heterogeneous data from end devices lacks contextual relations, which impairs IoT domain GAI training efficiency and leads to inaccurate applications. Nowadays, knowledge graph (KG) offers an effective solution for enhanced performance and interpretability of GAI. Nevertheless, specific data collection and KG creation in IoT scenarios still pose quality and cost challenges for GAI developers. Therefore, designing an effective IoT KG collection and trading strategy is needed to provide reliable knowledge support for IoT GAI from professional data providers. Currently, establishing fair KG pricing, the effective construction of knowledge relations in edge IoT scenarios and preventing data providers from accessing private information remain significant open issues for IoT KG trading. To address these issues, we propose an incentive distributed IoT knowledge market framework to facilitate effective and secure KG trading for IoT GAI. Specifically, first, we design a utility incentive and GAI demand-driven KG pricing strategy, which establishes a three-layer game with trading participants and KG embedding utility function to obtain fair pricing. Second, we devise a smart contract based distributed knowledge aggregation method, which provides collaborative IoT KG relation creation. Third, we propose a privacy-preserving KG construction scheme via homomorphic encryption that achieves consensus of encrypted KG to prevent knowledge providers from accessing IoT privacy. Finally, experimental results of real KG verify the KG-enhanced GAI and proposed market availability.

Due to the ability to improve data transmission efficiency and extend coverage, simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and autonomous aerial vehicle (AAV)-assisted Internet of unmanned agents (IUAs) has become an attractive paradigm. However, in multi-AAV and multi-STAR-RIS coexistence scenarios, device data transmission and energy harvesting (EH) processes are highly coupled with phase and amplitude optimization of STAR-RISs, which involve a large number of coupled variables that need to be carefully decoupled and processed. Therefore, to address the above challenges, we propose a distributed scheduling algorithm, MINT, for multi-STAR-RIS and multi-AAV assisted IUA, by jointly scheduling AAV trajectories, AAV association variable, charging time allocation, and STAR-RIS coefficient matrices to maximize system energy-efficiency. First, by considering device energy constraint, AAV energy consumption, and time constraints, we formulate the energy-efficiency maximization problem and model it as a Markov decision process. Second, we design a multiagent deep reinforcement learning-based MINT algorithm to solve the formulated optimization problem. Finally, experimental results demonstrate that MINT algorithm outperforms the existing algorithms regarding energy-efficiency, the number of uploaded bits, and convergence performance.

Static analysis presents significant challenges in alarm handling, where probabilistic models and alarm prioritization are essential methods for addressing these issues. These models prioritize alarms based on user feedback, thereby alleviating the burden on users to manually inspect alarms. However, they often encounter limitations related to efficiency and issues such as false generalization. While learning-based approaches have demonstrated promise, they typically incur high training costs and are constrained by the predefined structures of existing models. Moreover, the integration of large language models (LLMs) in static analysis has yet to reach its full potential, often resulting in lower accuracy rates in vulnerability identification. To tackle these challenges, we introduce BinLLM, a novel framework that harnesses the generalization capabilities of LLMs to enhance alarm probability models through rule learning. Our approach integrates LLM-derived abstract rules into the probabilistic model, using alarm paths and critical statements from static analysis. This integration enhances the model’s reasoning capabilities, improving its effectiveness in prioritizing genuine bugs while mitigating false generalizations. We evaluated BinLLM on a suite of C programs and observed 40.1% and 9.4% reduction in the number of checks required for alarm verification compared to two state-of-the-art baselines, Bingo and BayeSmith, respectively, underscoring the potential of combining LLMs with static analysis to improve alarm management.

Distributed learning empowers social media platforms to handle massive data for image sentiment classification and deliver intelligent services. However, with the increase of privacy threats and malicious activities, three major challenges are emerging: securing privacy, alleviating straggler problems, and mitigating Byzantine attacks. Although recent studies explore coded computing for privacy and straggler problems, as well as Byzantine-robust aggregation for poisoning attacks, they are not well-designed against both threats simultaneously. To tackle these obstacles and achieve an efficient Byzantine-robust and straggler-resilient distributed learning framework, in this article, we present Byzantine-robust and cost-effective distributed machine learning (BCML), a codesign of coded computing and Byzantine-robust aggregation. To balance the Byzantine resilience and efficiency, we design a cosine-similarity-based Byzantine-robust aggregation method tailored for coded computing to filter out malicious gradients efficiently in real time. Furthermore, trust scores derived from similarity are published to the blockchain for the reliability and traceability of social users. Experimental results show that our BCML can tolerate Byzantine attacks without compromising convergence accuracy with lower time consumption, compared with the state-of-the-art approaches. Specifically, it is 6x faster than the uncoded approach and 2x faster than the Lagrange coded computing (LCC) approach. Besides, the cosine-similarity-based aggregation method can effectively detect and filter out malicious social users in real time.

Large language models (LLMs) have cultivated impressive semantics capabilities and expert knowledge from their vast pre-training corpora, especially showing prospects in automated software testing. However, LLMs are designed for human interaction, which poses the following challenges when interacting with programs for testing: 1) LLMs cannot communicate directly with programs, and there is no existing paradigm to establish interaction between them. 2) Existing evaluation methods are unable to assess the quality of LLMgenerated tests during software testing. 3) Current LLM-guided testing generation cannot be optimized in real time, resulting in low testing efficiency. To address these challenges, we present PILLM, a program interoperable LLM scheme. First, we designed a prompt mechanism for interactive program testing based on the source code semantics and expert knowledge from the LLM. Second, we proposed an evaluation mechanism for the PILLM's test code generation, thus obtaining test seeds that are semantically related to the corresponding source code. Third, PILLM optimizes the next generated tests based on the coverage and source code execution information obtained in the program execution. In the 24-hour running experiment, PILLM improved the coverage by 45.7% and 14.9% compared to Fuzz4all and Fuzzilli respectively. PILLM proves its effectiveness by finding four new real-world bugs in the JavaScript engine. We have released the source code of PILLM as open source on Github.

Currently, the security of the control logic of Programmable Logic Controllers (PLCs) is facing a serious threat, significantly impacting industrial production. Consequently, ensuring the security of PLC control logic becomes imperative. Formal verification emerges as a promising methodology for verifing PLC security through behavioral modeling and security testing. However, existing formal verification approaches primarily focus on modeling the PLC source code, overlooking the identification of compile-time errors and real-time runtime logic checks. Therefore, it is essential to apply formal verification to PLC control logic at the binary level. In this study, we introduce VoICS, a system designed to facilitate binary-level formal verification. Using reverse engineering, VoICS automatically parses PLC programs written by various programming languages at the binary level and constructs control flow graphs (CFGs). Furthermore, we use an algorithm combining two model optimization methods (i.e., trim invalid states and unnecessary states compression) to convert the reversed PLC assembly program into nuXmv format model. Lastly, VoICS establishes the corresponding constraints and performs formal verification on the model using nuXmv. The evaluation results demonstrate the capability of VoICS in identifying instances of unreliable control logic within PLC control programs, thus reinforcing the dependability of the industrial automation system.

Differential private learning is widely used in machine learning (ML) to protect continuous and scalar-valued data. The demand for discrete and matrix-valued computations is increasing, particularly in quantized neural networks and graph learning, which require discrete-valued parameters and large-scale matrix operations for efficient data processing. However, privacy protection for discrete and matrix-valued data is less explored. Traditional differential private mechanisms fail to maintain the discrete nature of data after perturbation and often overlook data correlations, struggling to balance privacy and utility. In this paper, we propose a Discrete Matrixing Differentially Private Learning (DM-DPL) framework, which protects the privacy of discrete and matrix-valued data during ML training by adding discrete matrix-variate Gaussian noise. First, we propose a novel Discrete Matrix-Variate Gaussian (DMVG) mechanism with rigorous conditions necessary to guarantee (ϵ, δ)-differential privacy. Additionally, we present an eigenvalue-weighted analysis-based precision budget allocation strategy, designed to maintain the utility of significant dimensions while providing consistent privacy guarantees. Finally, the results illustrate that our approach significantly surpasses existing state-of-the-art methods when applied to quantized federated learning. To the best of our knowledge, this is the first work to specifically protect discrete and matrix-valued data during ML training.

Federated graph attention networks (FGATs) are gaining prominence for enabling collaborative and privacy-preserving graph model training. The attention mechanisms in FGATs enhance the focus on crucial graph features for improved graph representation learning while maintaining data decentralization. However, these mechanisms inherently process sensitive information, which is vulnerable to privacy threats like graph reconstruction and attribute inference. Additionally, their role in assigning varying and changing importance to nodes challenges traditional privacy methods to balance privacy and utility across varied node sensitivities effectively. Our study fills this gap by proposing an efficient privacy-preserving FGAT (PFGAT). We present an attention-based dynamic differential privacy (DP) approach via an improved multiplication triplet (IMT). Specifically, we first propose an IMT mechanism that leverages a reusable triplet generation method to efficiently and securely compute the attention mechanism. Second, we employ an attention-based privacy budget that dynamically adjusts privacy levels according to node data significance, optimizing the privacy-utility trade-off. Third, the proposed hybrid neighbor aggregation algorithm tailors DP mechanisms according to the unique characteristics of neighbor nodes, thereby mitigating the adverse impact of DP on graph attention network (GAT) utility. Extensive experiments on benchmarking datasets confirm that PFGAT maintains high efficiency and ensures robust privacy protection against potential threats.

The success of graph neural networks stimulates the prosperity of graph mining and the corresponding downstream tasks including graph anomaly detection (GAD). However, it has been explored that those graph mining methods are vulnerable to structural manipulations on relational data. That is, the attacker can maliciously perturb the graph structures to assist the target nodes in evading anomaly detection. In this article, we explore the structural vulnerability of two typical GAD systems: unsupervised FeXtra-based GAD and supervised graph convolutional network (GCN)-based GAD. Specifically, structural poisoning attacks against GAD are formulated as complex bi-level optimization problems. Our first major contribution is then to transform the bi-level problem into one-level leveraging different regression methods. Furthermore, we propose a new way of utilizing gradient information to optimize the one-level optimization problem in the discrete domain. Comprehensive experiments demonstrate the effectiveness of our proposed attack algorithm $\textsf {BinarizedAttack}$ .

The integration of covert communication in vehicle-to-everything (V2X) network has recently shown great potential to improve efficiency and reliability of data transmission under adversarial eavesdropping scenarios. In this paper, we propose a covert and reliable communication (CRC) framework for V2X networks, where the legitimate transmitter (Alice) attempts to communicate with a mobile receiver (Bob) in the presence of the location uncertainties of the eavesdropper (Willie). Specifically, the Bob adjusts the artificial noise power and position dynamically to communicate with Alice aided by full duplex antenna. In this context, we derive two key performance indicators of covert communication, namely the detection error probability and the effect covert throughput (ECT). Subsequently, we consider the worst case of CRC in the presence of single uncertain Willie, and derive the approximate maximum ECT expression by two-stage robust optimization. Building on this foundation, for more complex CRC scenario with multi uncertain Willies exist, we propose a deep reinforcement learning-empowered adaptation (DRLA) algorithm to maximize accumulated ECT. Extensive experiments compared to benchmarks (including stochastic selection, TD3 and DDPG) demonstrate the superiority of CRC. Specially, the designated DRLA algorithm not only can achieve a higher accumulated ECT but also can converge quickly compared with the benchmark schemes.

Age of Information (AoI) has received much attention from researchers as the latest metric to quantify the freshness of data. It is necessary to jointly schedule Unmanned Aerial Vehicles (UAVs) and sensors to reduce the system AoI in wireless powered Internet of things. However, constraints on UAV flight time, charging time, and data collection time, as well as constraints of half-duplex hardware for sensors make it difficult to efficiently jointly schedule UAVs and sensors by traditional methodes. Thus, we design a multi-agent Deep Reinforcement Learning (DRL)-based UAV cooperative scheduling algorithm that jointly optimizes sensor charging time, UAV trajectories and sensor update scheduling with AoI as the optimization objective. Initially, we define the AoI minimization problem, portraying it as a Markov decision process. Then, we design a multi-agent DRL algorithm founded on factorizing value functions to address this issue. Finally, experiments demonstrate that the MAPLE algorithm can effectively coordinate the scheduling of UAVs and sensors.

Recently, the promotion of green cryptocurrencies has attracted attention due to the huge resource consumption brought by cryptocurrency transactions. The main driver of green cryptocurrencies is to reduce resource consumption and reduce the number of transactions. Accurate prediction of cryptocurrency prices is difficult because they are influenced by diverse kinds of factors besides supplydemand relationship. First, the price of cryptocurrency changes rapidly and fluctuates violently, so the traditional econometric methods cannot respond well to the drastic price changes in a short period of time. Second, artificial intelligence (AI) models are relatively separated from econometric cryptocurrency price prediction models, which leads to deviations when forecasting in the financial field. Third, existing AI models have not been well optimized specially for cryptocurrency prediction. To address the above challenges, in this article, we propose the optimized AI and econometric model to empower virtual-fiat settled price prediction in green cryptocurrency networks. In our proposed econometric model renew autoregressive integrated moving average (REARIMA), the problem of poor econometric model response to drastic changes in a short time is solved by the joint design with AI. Moreover, the AI model innovation optimization for the prediction of cryptocurrency is carried out in dense long-short term memory (DENSE_LSTM). Finally, DENSE_LSTM are used to optimize the econometric model. The feasibility of the proposed model is verified by experiments.

PLC control programs are vulnerable to real-time threats, where attackers can disrupt the backhaul/front-end network of industrial production by creating numerous loops or I/O operations, leading to severe consequences. Therefore, formal verification of PLC control logic at the binary level is essential. In this study, we introduce a framework designed for formal verification of PLC control logic at the binary level. Our verification framework is based on simulation execution, which extracts the core control logic from PLC binary code. Initially, we develop an efficient framework for automating the parsing of PLC programs at the binary level and constructing their control flow graphs (CFGs). Next, we devise an algorithm to transform the reversed PLC assembly program into an smv model, a widely accepted formal verification tool. Subsequently, we generate real-time requirements relevant to industrial production and perform formal verification on the constructed models. To assess the real-time performance of our framework in safeguarding PLC systems, we implement a prototype and evaluated it across various representative ICS scenarios. The evaluation results demonstrate the capability of our proposed approach to effectively detect synchronization threats in PLC logic control programs.

Industrial communication system (ICS) is widely used in critical infrastructure. As the scale of ICS increases, more and more devices are equipped with wireless capabilities which widen the scope of attack vectors. Therefore, its security has draw significant attention in recent years. As a key component of ICS, the security of Programmable Logic Controller (PLC) is directly related to ICS security. However, due to PLC's special mechanism, as long as the attack successfully break into the PLC, conventional control flow integrity (CFI) mechanism can't effectively protect its control flow. To address this problem, we propose a novel CFI mechanism to enhance the security of PLC. First, we design an instrumentation framework, with which we can add custom features such as CFI to enhance the PLC. Second, to effectively protect the control flow, we design a CFI mechanism based on sensitive memory protection using hash check. Last, to ensure the instrumented binary can be correctly loaded and match the constraint of runtime, we design a control binary reconstruction method. To evaluate the correctnessof our CFI mechanism, we perform experiments in two different PLCs using 15 different control binaries. The result shows that our CFI mechanism can successfully protect PLC's control flow. Besides, according to our evaluation, the size of the generated control binary instrumented with CFI mechanism code merely grow a little compared with its original size. As for the dynamically cost, our instrumented code does influence the count of the instructions non-ignorable but is within the acceptable range.

Recently, Peer-to-Peer Semantic Network (P2PSN) has significantly boosted transmission efficiency among humans, machine agents, and smart devices. Despite these enhancements, the intelligent components within P2PSN pose vulnerabilities to backdoor attacks, where adversaries introduce specific pattern triggers to poison the training set, which prompts the well-trained P2PSN system to generate targeted malicious predictions when inputted with trigger-embedded data. Current backdoor methodologies exhibit several deficiencies: 1) pattern-based trigger lacking physical meaning and explainability; 2) visible trigger design that can be easily detected by defenders; 3) unstable attack performance resulting from communication interference. To overcome these shortcomings, we propose a novel invisible instruction backdoor attack scheme on Peer-to-Peer Semantic Networks: InviINS. The proposed method embeds text instructions on partial training samples as invisible triggers instead of pattern triggers, thereby poisoning the training set of P2PSN model before learning without visually discernible changes in data, and subsequently backdooring the model via training. In InviINS, adversaries can directly set instructions based on practical scenarios to launch attacks. Meanwhile, to accelerate backdoor convergence, a contrastive backdoor training methodology is presented to enhance the model’s sensitivity to instruction triggers and bolster its prediction performance on normal samples. Experiments with different poisoning-rates, signal-to-noise ratios, channel usages, and trigger types demonstrate that the InviINS can achieve a high attack success rate (~ 100%) while preserving the model performance on main tasks (accuracy drop < 3%).

In blockchain based decentralized finance, cross-chain payment channel is a crucial component for achieving the interoperability among different software-defined blockchain systems. However, there are still some challenges in existing payment channels scenarios. First, payment channels need to be established through intermediaries with service fees, but no one has developed a suitable strategy for pricing the service fees currently. Second, these scenarios disclose sensitive information when intermediaries are engaged with cross-chain requests. Last but not least, the payment channel established currently tend to lack robust mechanisms for protecting the details of transactions during trading phase. To address the above challenges, this paper proposes a zero knowledge empowered payment channel to enhance privacy and transaction details protection with intermediary pricing strategy. Specifically, we develop the pricing strategy based on GARCH model with market dynamics. Moreover, we design a mechanism based on Pedersen commitments for verifiable proofs to preserve privacy in cross-chain requests. Finally, we construct the payment channel with zk-SNARKs to guard transaction details during trading phase. The experiment demonstrates our approach develops optimal pricing strategy and offer enhanced privacy and anonymity assurances.

Spatial crowdsourcing (SC) systems have emerged as an advanced crowdsourcing paradigm to revolutionise the efficient development of smart city services. SC engages participants and their sensitive data to accomplish spatiotemporal tasks on platforms. However, revealing sensitive data in SC for smart cities exacerbates cybersecurity and privacy concerns, especially Membership Inference Attacks (MIA). To address the problems, this research proposes a Federated Learning (FL) and Incremental Learning (IL) based framework in SC that integrates advanced privacy-preserving techniques. By leveraging FL and adaptive differential privacy, sensitive data remains in decentralised devices while local models are trained without exchanging raw data to a server. We integrate additive secret sharing, a secure multi-party computation technique to protect data during transmission and aggregation. IL enhances the framework using a generative replay approach to ensure continuous adaptation to new data without forgetting existing knowledge to overcome catastrophic forgetting. We broadly evaluate our work against MIA and catastrophic forgetting using Yelp datasets. Compared with other baseline approaches, our experimental results demonstrate that the proposed framework significantly mitigates the risk of MIAs by around 50% and improves forgetting accuracy by up to 13%, thereby providing robust privacy-preserving mechanisms.

Natural disasters such as earthquakes can cause damage to critical infrastructures and limit access to vital information, making it difficult for disaster response teams to respond effectively. Unmanned aerial vehicles (UAVs) have the potential to aid and provide real-time information for disaster response teams, however, the need to process distributed learning for huge amounts of interconnected nodes in a graph network poses several challenges. First, distributed learning in graph networks for UAVs is still an open issue, making it difficult to train and share models on such networks. Second, such a network can leak privacy-sensitive information, making it harder to ensure data security. To address these challenges, we propose, in this paper, a novel privacy and blockchain-empowered UAVs-enabled decentralized graph federated learning (DPBE-DGFL) framework for disaster response. The framework includes three phases: (i) local model training utilizing stochastic gradient descent with differential privacy, (ii) model weights integrity authentication using blockchain to ensure secure and efficient sharing of model weights, and (iii) final validator selection and model weights aggregation using a dedicated proof-of-stake, (DPoS), consensus mechanism to ensure efficient and decentralized consensus while maintaining security and integrity. Our DPBE-DGFL framework was evaluated using extensive simulations on EMNIST and real-world disaster datasets from Tonga. The results show that it offers a promising solution for privacy-preserving federated learning in graph networks, balancing privacy protection and model accuracy while maintaining latency, communication, and computational efficiency.

Federated learning (FL) protects data privacy by sharing gradients across clients rather than local training data. However, malicious clients (e.g., attackers and stragglers) hiding in large-scale FL will severely reduce the learning performance. Thus, how to efficiently detect and defend Byzantine attacks in large-scale FL remains an urgent issue. This article proposes a reputation-aware mean-field-game-based FL (FedMFG) framework, which aims to defend against Byzantine attacks and improve learning performance. Precisely, we first model the process of large-scale FL as a mean-field game problem across clients and prove the existence and uniqueness of mean-field FL gradients (i.e., Nash equilibrium). We then design a mean-field FL gradient calculation algorithm based on stochastic differential equations, i.e., Hamilton-Jacobi–Bellman and Fokker-Planck–Kolmogorov equations. Based on comparing the cosine similarity of obtained mean-field and individual FL gradients, we build reputation-aware malicious client detection and defense mechanism, which improves the Byzantine robustness of FL with the global learning performance guarantee. Finally, experimental results show that our proposed framework outperforms the baseline algorithms in realizing Byzantine robustness and improving learning performance. Specifically, our algorithm improves the model accuracy by 10% and 72.7% for the no-attacker and attacker scenarios, respectively.

The technology of the Internet of Vehicles (IoV) and digital twins (DTs) is driving deeper connectivity between vehicles and road infrastructure. Through the data exchange of IoV and the simulation of DT technology, vehicle driving decisions, traffic management, and road planning are optimized. However, DT models contain a large amount of private vehicle data, causing the risk of privacy leakage. Distributed artificial intelligence (AI) methods, particularly federated learning (FL) algorithms, ensure data security and privacy by sharing data models rather than sharing private data. Current mainstream algorithms use FL and local differential privacy (LDP) or blockchain approaches to protect data security at the cost of lower model accuracy and larger computation time. In the vehicle road cooperation, we designed a three-layer DT-driven personalized privacy-preserving framework, which includes a physical layer, a DT layer, and an application layer. In our proposed framework, to improve the security and performance of DT models, a time-sensitive PLDP-based FL (TimeSenFLDP) mechanism is proposed to achieve different privacy levels of the DT model of vehicles over sharing time steps. Compared with the mainstream algorithm (e.g., DP-SGD), the experiments prove that our proposed algorithm has 18.07%, 16.32%, and 7.5% accuracy improvement in FedAvg, FedProx, and FedDyn, respectively.

The convergence of blockchain and deep learning (DL) drives the intelligence of the Internet of Things (IoT) with security guarantees. However, the soaring resource consumption resulting from blockchain mining and DL model training has overwhelmed the extremely resource-constrained IoT. In this article, we first build a blockchain and DL-empowered cloud–edge orchestrated framework for an extremely resource-constrained IoT environment. To solve the resource bottleneck of this framework, we then propose a Zero-knowledge Proof of Learning (ZPoL) consensus approach to channel the meaningless Proof of Work (PoW) mining energy waste to valuable DL model training, while protecting the DL model privacy. Besides, to encourage resource-constrained IoT devices to perform meaningful DL model mining in our ZPoL consensus, we design a model quality-aware incentive mechanism based on a two-stage Stackelberg game. Moreover, we conduct extensive simulations and experiments to evaluate our proposed ZPoL-based framework. The numerical simulation illustrates that our proposed incentive mechanism could motivate IoT devices to actively join in DL model mining. Compared with the existing blockchain and DL-enabled IoT system, experimental results demonstrate that our proposed ZPoL-based framework could significantly reduce the communication, computation, and storage cost, which is more applicable to a resource-constrained IoT environment.

Blockchain data mining has the potential to reveal the operational status and behavioral patterns of anonymous participants in blockchain systems, thus providing valuable insights into system operation and participant behavior. However, traditional blockchain analysis methods suffer from the problems of being unable to handle the data due to its large volume and complex structure. With powerful computing and analysis capabilities, graph learning can solve the current problems through handling each node's features and linkage relationships separately and exploring the implicit properties of data from a graph perspective. This paper systematically reviews the blockchain data mining tasks based on graph learning approaches. First, we investigate the blockchain data acquisition method, integrate the currently available data analysis tools, and divide the sampling method into rule-based and cluster-based techniques. Second, we classify the graph construction into transaction-based blockchain and account-based methods, and comprehensively analyze the existing blockchain feature extraction methods. Third, we compare the existing graph learning algorithms on blockchain and classify them into traditional machine learning-based, graph representation-based, and graph deep learning-based methods. Finally, we propose future research directions and open issues which are promising to address.

Artificial intelligence (AI)-based cybersecurity services offer significant promise in many scenarios, including malware detection, content supervision, and so on. Meanwhile, many commercial and government applications have raised the need for intellectual property protection of using deep neural network (DNN). Existing studies (e.g., watermarking techniques) on intellectual property protection only aim at inserting secret information into DNNs, allowing producers to detect whether the given DNN infringes on their own copyrights. However, since the availability protection of learning models is rarely considered, the piracy model can still work with high accuracy. In this paper, a novel model locking (M-LOCK) scheme for the DNN is proposed to enhance its availability protection, where the DNN produces poor accuracy if a specific token is absent, while it maps only the tokenized inputs into correct predictions. The proposed scheme performs the verification process during the DNN inference operation, actively protecting models' intellectual property copyright at each query. Specifically, to train the token-sensitive decision-making boundaries of DNNs, a data poisoning-based model manipulation (DPMM) method is also proposed, which minimizes the correlation between the dummy outputs and correct predictions. Extensive experiments demonstrate the proposed scheme could achieve high reliability and effectiveness across various benchmark datasets as well as typical model protection methods.

Industrial Control System (ICS) depends on the underlying Programmable Logical Controllers (PLCs) to run. As such, the security of the internal control logic of the PLCs is the top concern of ICS. Reversing analysis and forensic work against PLC require extracting control logic from the control application running inside PLC, which is still an unresolved problem. To address the challenge, we propose a PLC decompile framework named CLEVER, which can analyze the control application and extract the control logic. First, we propose a simulation execution based code extraction method, which is utilized to filter the control logic related data. Then, to normalize the control application decompile process, an intermediate representation (IR) is designed, which can simplify the analysis process and enhance the extensibility of CLEVER. Finally, a heuristic data flow analysis algorithm is proposed to find variable dependency, and a sequential parsing method is utilized to reconstruct the source code from the control application. To evaluate our work, real world PLC hardware and programming software are used for the experiment. We use 22 real-world, 58 hand-written, and 150 auto-generated control applications to demonstrate the usability, correctness, and operational efficiency of CLEVER.

As 5-th Generation (5G) mobile communication and edge computing technologies mature, Intelligent Transportation System (ITS) are gradually becoming a reality. In the 5G heterogeneous network, resources such as computing, storage, and communication are allocated to each Road Side Unit (RSU) to provide intelligent services for vehicles. However, the existing average allocation method based on historical experience can easily lead to over-concentration or insufficient resources, which causes waste and reduces the Quality of Service (QoS). To solve this problem, this paper proposes a Multi-Objective Neural Time-series Prediction (M-ONTP) scheme for resource pre-allocation scenario in ITS. The scheme takes into account the complexity and diversity of service resource, innovatively treats the number of vehicles and communication power as joint optimization metrics, and proposes a multi-objective learning model. Benefiting from the vehicle data collected by RSUs in real time, we utilize historical traffic information to predict future road load and rely on Software Defined Network (SDN) to design a flexible resource pre-allocated architecture for ITS. To enhance the effectiveness of feature capture, M-ONTP also organically integrates various neural networks, which can appropriately handle large-scale time-series traffic flow. And we choose two layers of road data for fitting, which ensures that the model has a wide horizon to receive sufficient information. SUMO-based simulation experiments show that our scheme accurately realizes the prediction of joint objective and has significant performance advantage over other models. Meanwhile, our pre-allocation strategy reduces the total resource consumption by about 7%, increases the sufficiency rate by about 7%, and decreases the redundancy by about 12% while ensuring enough service resource to maintain normal QoS, which validates the effectiveness of M-ONTP.

The combination of mobile communication and the Internet of Things (IoT) has made physical devices more intelligent, bringing great convenience to our lives. However, the deep integration of personal information and the Internet increases the risk of data leakage and is easily exploited maliciously. In addition, due to limited system resources, smart devices with lightweight design are required. Therefore, it is necessary to realize low-energy and effective abnormal behavior detection of IoT devices, but the existing detection methods have disadvantages such as leakage of user privacy, low accuracy, and difficulty in dynamically improving the effect. To address these issues, this paper proposes a dynamic interactive minor anomaly detection scheme called ADONIS based on Swarm Learning (SL). The scheme combines the concept of swarm defense and utilizes SL to achieve local data fusion, which improves the detection effect and protects user privacy. Moreover, the decentralized structure of SL can cope with the impact of single node damage to enhance the robustness of IoT services. Furthermore, we propose training and detection decoupling framework to achieve high accuracy, low energy consumption, and low latency. It improves the performance by fitting the training model with full data, and simplifies the complexity of the detection model using knowledge distillation. We also design a self-enhancing dynamic strategy based on the decoupling framework to maintain powerful detection capability through human-computer interaction (HCI) and continuous learning. The framework relies on traffic data to keep the model sensitive to new behavior through iterative training without disturbing the user. Finally, simulation experiments show that our proposed scheme can achieve 82.2% accuracy, reduce the average detection time to 8.22$ ms$, and simplify the model complexity by 15.9%. Compared with existing methods, ADONIS can provide lighter, safer and more accurate anomaly detection.

Blockchain has revolutionized a variety of fields by providing decentralization, immutability, transparency, and auditability. This paper designs Blockchained Edge Resource Auction (BERA) for edge computing systems to allocate computing resources to application service providers (ASP) in a secure manner. BERA comprises two key components: Blockchain-based Sealed-Bid Auction (BSBA) and Graph Neural Network (GNN)-based Fraud Detection (GFD). BSBA designs smart contracts to realize sealed-bid auctions overhead blockchain. It incorporates the homomorphic commitment technique to guarantee the transactional privacy of ASPs’ bidding information and performs interval membership zero-knowledge proof to verify the legitimacy of auction results. While the privacy-preserving property of BSBA is desirable, the veiled bidding information tends to breed fraudulent behaviors. Therefore, GFD is further proposed to identify abnormal auction behaviors in BSBA without revealing bidding information of ASPs. GFD converts the blockchain data of BSBA to an auction behavioral graph of ASPs, and uses GNN to discover stealth frauds based on interactive patterns. In addition, we design a subgraph extraction scheme for GFD to improve its scalability. We implement BERA on a private Ethereum blockchain and successfully realize edge resource auctions. We simulate several types of auction frauds and identify them with GFD. The experimental results show that our method outperforms other benchmarks.

Quantum Key Distribution (QKD) is currently being explored as a solution to the threats posed to current cryptographic protocols by the evolution of quantum computers and algorithms. However, single-photon quantum signals used for QKD permit to achieve key rates strongly limited by link performance (e.g., loss and noise) and propagation distance, especially in multi-node QKD networks, making it necessary to design a scheme to efficiently and timely distribute keys to the various nodes. In this work, we introduce the new problem of joint Routing, Channel, Key-rate and Time-slot Assignment (RCKTA), which is addressed with four different network settings, i.e., allowing or not the use of optical bypass (OB) and trusted relay (TR). We first prove the NP-hardness of the RCKTA problem for all network settings and formulate it using a Mixed Integer Linear Programming (MILP) model that combines both quantum channels and quantum key pool (QKP) to provide an optimized solution in terms of number of accepted key rate requests and key storing rate. To deal with problem complexity, we also propose a heuristic algorithm based on an auxiliary graph, and show that it is able to obtain near-optimal solutions in polynomial time. Results show that allowing OB and TR achieves an acceptance ratio of 39% and 14% higher than that of OB and TR, respectively. Remarkably, these acceptance ratios are obtained with up to 46% less QKD modules (transceivers) compared to TR and only few (less than 1 per path) additional QKD modules than OB.

As a promising technology, intelligent reflecting surface (IRS) enables future communications and networks to realize programmable data transmissions. Due to the untrustworthiness of the communication environment and the selfishness of wireless devices, secure and intelligent IRS resource management is still an open issue. In this paper, we aim to implement IRS resource scheduling with properties of security, intelligence, efficiency, and fairness. To realize the above goals, we propose the blockchain and multi-agent learning empowered incentive scheduling system for tamper-proof and undeniable IRS resource management. To overcome the low throughout and intensive computation issues of blockchain, we devise a hybrid framework combining traditional Satoshi-style and directed acyclic graph blockchain for IRS resource scheduling. Due to the storage limitation of wireless devices, an intelligent blockchain storage reduction mechanism is proposed, where a multi-dimensional multi-hierarchy feature-based scheme is designed to determine block storage priority. Based on this storage priority and device states, the selection of storage-reduction devices is formulated as a cooperative multi-agent decision problem. Then, a multi-agent deep reinforcement learning-driven scheme is proposed to determine reduction strategies. To facilitate IRS providers/subscribers participating in the proposed system and maintain the efficiency of resource scheduling, an auction-based incentive mechanism is devised. In this mechanism, we propose the IRS resource allocation scheme and the payment scheme to achieve economic robustness and high efficiency. Finally, security analysis and experiment analysis indicate the feasibility and effectiveness of the proposed IRS resource scheduling in intelligent reconfigurable networks.

To detect software vulnerabilities with better performance, deep neural networks (DNNs) have received extensive attention recently. However, these vulnerability detection DNN models trained with code representations are vulnerable to specific perturbations on code representations. This motivates us to rethink the bane of software vulnerability detection and find function-agnostic features during code representation which we name as semantic redundant features. This paper first identifies a tight correlation between function-agnostic triggers and semantic redundant feature space (where the redundant features reside) in these DNN models. For correlation identification, we propose a novel Backdoor-based Semantic Redundancy Exploration (BSemRE) framework. In BSemRE, the sensitivity of the trained models to function-agnostic triggers is observed to verify the existence of semantic redundancy in various code representations. Specifically, acting as the typical manifestations of semantic redundancy, naming conventions, ternary operators and identically-true conditions are exploited to generate function-agnostic triggers. Extensive comparative experiments on 1,613,823 samples of eight representative vulnerability datasets and state-of-the-art code representation techniques and vulnerability detection models demonstrate that the existence of semantic redundancy determines the upper trustworthiness limit of DNN-based software vulnerability detection. To the best of our knowledge, this is the first work exploring the bane of software vulnerability detection using backdoor triggers.

The flexibility and low cost of unmanned aerial vehicles (UAVs) offer great potential for them in areas such as disaster relief, energy line inspection, and traffic monitoring. Multiple UAVs form an airborne UAV network to share geo-tagged observation data for better collaborative missions. Blockchain can solve the security threats caused by the environment’s untrustworthiness and the UAV networks’ openness. However, the key to sharing data in blockchain-assisted UAV networks is identifying and understanding observational data and providing authentication query services in free boundary spatial. This paper proposes a blockchain-assisted UAV network data-sharing framework based on Non-Fungible Token (NFT). First, we design a marking and describing data method based on NFT to help geo-tagged data be effectively understood. Moreover, we propose a free-boundary spatial index tree to manage data and provide efficient queries. Furthermore, combined with the consensus mechanism and the blockchain transaction tree, the proposed sharing framework can provide query results authentication. Compared with the existing schemes, analysis and experiments demonstrate that our scheme could support the arbitrary expansion of UAV flight range and the random distribution of observation data in space, save at least 22% of storage overhead and reduce more than 36% of query time overhead.

Artificial intelligence has been utilized to analyze patients' electroencephalograms (EEG) to diagnose depression. However, attackers can deduce patients' privacy after analyzing patients' EEG time series. Therefore, researchers propose to operate ciphertext calculation in depression diagnosis models based on homomorphic encryption. Nevertheless, homomorphic encryption requires consistent private keys during training, which could result in other participants decrypting the ci-phertexts. Additionally, existing EEG-based models neglect the relationship among electrode positions during EEG acquisition. To address these issues, we propose a novel training strategy for the depression diagnosis model based on fully homomorphic en-cryption (FHE) and electrode topology. Specifically, we establish a training strategy that prioritizes the privacy of patients' EEG data without compromising the cost-effectiveness of the diagnosis model. Furthermore, we incorporate the attention mechanism of electrode topology into our model to improve its performance and verify the relationship among topology locations. Our proposed model outperforms the original convolution neural network model, achieving higher accuracy in depression diagnosis and identifying virtual electrode channels for the first time.

The Internet of Vehicles (IoV) based on the 6-th Generation (6G) communication brings convenience, but also raises anxiety about information security. Researchers have developed static security schemes based on the blockchain, but it results in excessive resource occupation. And it is difficult to resist some attacks such as desynchronization, Denial of Service (DoS), and covert intrusion. In response to the above problems, this paper proposes the Floating Blockchain Consensus Security (FBCS) scheme. It models the attack risk of Road Side Unit (RSU) through the traffic flow prediction to present the credibility evaluation and constructs the floating blockchain based on the results. And the security capability is adjusted through the dynamic joining and exit mode of trusted nodes and untrusted nodes. FBCS also establishes a relationship between blockchain size and energy consumption. Once the system resources are found to be insufficient, it applies the cloud center-based supplementary certification mechanism to provide authentication to untrusted nodes to enhance the stability of the IoV.Theoretical analysis and simulation experiments prove that the FBCS can afford data privacy, maintain moderate security capabilities, and adapt defense capabilities according to the attack environment, reduce resource occupation to save cost.

The emerging Metaverse has challenging requirements for the reliability of extended reality (XR) data transmission. Configurable communication is a promising technology to improve the XR communication performance, where the intelligent reflecting surface (IRS) is representative of the ability to control transmission channels. However, because of the absence of incentives and untrust among IRS and Metaverse users, there is no easy way to establish the configuration resource scheduling for XR communication. Existing trusted third party-based methods face single-point/collusion attacks, inefficiency in arbitration, and low intelligence problems. To solve these problems, we propose a swarm learning (SL)-based secure configurable resource trading mechanism for reliable 6G-Metaverse XR communication. First, an SL-based configurable resource trading framework is established, which includes two designed subchains for decentralized IRS resource management and intelligent allocation. Second, a smart contract-enabled configurable resource trading scheme is designed, where decentralized trust is built among IRS devices, Metaverse users, and base stations. Third, we propose a decentralized federated learning (FL)-driven IRS allocation scheme, which consists of XR communication-related data collection, model training, and resource configuration. Finally, experimental results demonstrate the effectiveness of the proposed SL-based configurable resource trading for reliable XR communication.

Digital twins have been extensively explored in vehicular social networks, while wireless communication quality is limited to support digital twins and their applications due to the high mobility of the vehicular environment. To address this issue, we propose a semantic communication empowered two-level digital twin architecture, called Semantic Twin, which supports reliable communication of efficient cloud-edge collaborative vehicular digital twin, specifically comprising low-level semantic twin (L-SemTwin) and high-level semantic twin (H-SemTwin). First, we design a social behavior semantic extraction scheme based on semantic encoder on the vehicle side to capture essential content features. Second, a semantic transmission scheme in vehicle-to-everything communication is performed to reduce the overall transmission burden and error rate, and build L-SemTwin. Moreover, we propose a deep reinforcement learning-based semantic caching strategy with the assistance of city-wise semantic information in cloud-side H-SemTwin. The experiment results demonstrate the promotion under the proposed architecture compared to conventional methods in terms of the quality of communication and user experience in vehicular social networks.

Swarm learning (SL) exploits the blockchain to realize a federated and decentralized learning, which is very suitable for internet of things (IoT). Different from FL using central server to update global parameter, SL using edge node (header) to do that. However, poisoning attack is also an unresolved problem to SL. Because if header is malicious, it can pollute global parameter more easily than edge nodes. Moreover, there are following important limitations in existing defense schemes for FL, which cannot be used in SL directly. First, existing defense schemes focus on building a whitelist, which obstructs the decentralization because it can just provide decentralization in honest nodes instead of all of nodes. Second, existing schemes just consider poisoning attacks from edge nodes, they cannot defend attacks from header. Third, most existing schemes will let server execute the defense algorithm, but in SL, malicious header can return wrong defense results to deceive managers. To address above challenges, in this paper, we propose a protection system that leverages the concept of zero-trust architecture for SL, which achieves continuous risk calculation, analysis of learning behavior and abnormal parameter detection based on Manhattan distance and accuracy difference of parameters. We also evaluate the performance in the presence of random and customized malicious edge nodes. Experimental results demonstrate that our scheme can achieve higher accuracy than the other existing schemes.

Data dissemination is critical for 6G autonomous driving (AD) service because of the extensive demand for real-time traffic information. However, the heavier data transmission burden and more stringent requirements of AD service bring challenges for current data dissemination methods. In this paper, we first propose a novel digital twin (DT)-based semantic dissemination architecture to better support 6G AD service. Under this architecture, an energy-efficient semantic communication mechanism is developed to reduce the data dissemination burden while keeping low semantic model update costs. Meanwhile, the DT network is leveraged to disseminate semantic data in parallel with the physical vehicular networks, which alleviates the physical transmission contention and improves the dissemination efficiency. Second, we design a deep-reinforcement-learning (DRL)-driven semantic data dissemination scheme for the proposed architecture, named Proximal-policy-optimization for Digital-twin-aided Data Dissemination (PD3), which seeks the optimal DT transfer and semantic transmission scheduling strategy. Finally, experimental results show that our approach surpasses the state-of-the-art methods by 18.36% lower dissemination delay and 4.51% higher dissemination ratio on average.

Nowadays, vehicle edge computation supports a novel computing resource provisioning roadmap for green smart cities, which benefits the distributed intelligent applications, such as unmanned vehicle. Despite the fact that vehicle edge computation can better offload computing resource, there are still certain problems in implementing vehicle edge computation in green smart cities. To begin, the geographical imbalance in computing resource results in a time latency when computation offloading. Second, the security of computing resource is an issue that cannot be disregarded. This is because the loss of some sensitive data in computing resource may result in repercussions that cannot be undone. To address aforementioned challenges, we present a collaborative-filtering privacy-preserving vehicular edge computation offloading approach (CVECO). By utilizing collaborative filtering, the CVECO algorithm is able to reduce the latency of the computation offloading. Meanwhile, the CVECO algorithm is able to efficiently provide high security and protect computing resource privacy by applying multiple privacy mechanisms. Finally, the results of the simulation indicate that the CVECO algorithm is capable of lowering the latency associated with the computation offloading while simultaneously preserving a high degree of safety regarding the computing resource. To the best of our knowledge, our proposed approach is capable of performing vehicle edge computation offloading well, which permits a rational use of electricity in green smart cities, further lowering greenhouse gas emissions.

Nowadays, empowered by mobile networks, electronic health (E-health) can bring more advanced medical services. Electronic health records (EHRs) with rich medical knowledge have gained increasing popularity for supporting E-health. Blockchain-based secure storage and access are novel trends for EHRs, but there are still following unresolved problems in this area. On one hand, specific medical tasks (e.g., COVID-19 diagnosis) require corresponding EHRs, but it lacks the medicine knowledge graph to help doctors or researchers find the EHRs on-demand in the massive distributed and encrypted medical data. On the other hand, as the documents include knowledge, EHRs with digital copyrights shared by medical institutions or patients need to be protected during sharing. To address these challenges, this paper proposes a blockchain and Non-Fungible Token (NFT) empowered on-demand medicine knowledge sharing architecture for EHRs. First, we propose a medicine knowledge graph construction scheme based on smart contracts and medical task knowledge relationships. Second, to provide on-demand EHRs sharing, we design the EHRs matching and clustering algorithms, regarding the dynamic importance and similarity of graph nodes. Third, we establish the interplanetary file address driven NFT minting mechanism for EHRs to protect digital copyrights. Finally, we conduct experiments in Ethereum using real medical datasets, which demonstrates the feasibility and efficiency of the proposed architecture. To our best knowledge, this work is the first to realize the medicine knowledge graph with copyright protection for EHRs.

Federated learning (FL) naturally faces the problem of data heterogeneity in real-world scenarios, but this is often overlooked by studies on FL security and privacy. On the one hand, the effectiveness of backdoor attacks on FL may drop significantly under non-IID scenarios. On the other hand, malicious clients may steal private data through privacy inference attacks. Therefore, it is necessary to have a comprehensive perspective of data heterogeneity, backdoor, and privacy inference. In this paper, we propose a novel privacy inference-empowered stealthy backdoor attack (PI-SBA) scheme for FL under non-IID scenarios. Firstly, a diverse data reconstruction mechanism based on generative adversarial networks (GANs) is proposed to produce a supplementary dataset, which can improve the attacker's local data distribution and support more sophisticated strategies for backdoor attacks. Based on this, we design a source-specified backdoor learning (SSBL) strategy as a demonstration, allowing the adversary to arbitrarily specify which classes are susceptible to the backdoor trigger. Since the PI-SBA has an independent poisoned data synthesis process, it can be integrated into existing backdoor attacks to improve their effectiveness and stealthiness in non-IID scenarios. Extensive experiments based on MNIST, CIFAR10 and Youtube Aligned Face datasets demonstrate that the proposed PI-SBA scheme is effective in non-IID FL and stealthy against state-of-the-art defense methods.

Differentially private distributed machine learning protects privacy by injecting artificial noise to the computing results. To further improve energy efficiency, the natural noise in the wireless environment can be used to protect privacy. In this paper, we study the problem of coded distributed machine learning over Gaussian multiple-access wireless channels to achieve differential privacy by exploiting the natural noise. Firstly, we propose an aggregation scheme using differentially private Lagrange encoding in a wireless environment, where the local computing results are uploaded to the master through orthogonal channels. Then, we develop an achievable privacy protection level to illustrate the impact of transmit power and power allocation on privacy. Additionally, we establish a theoretical convergence upper bound of the proposed scheme, providing a clear understanding of the potential limitations and capabilities of the system. Finally, we demonstrate a trade-off between system resource settings, convergence, and privacy protection levels through experiments. Specifically, increasing the signal-to-noise ratio (SNR) and power allocated for gradient computation leads to a decrease in the privacy protection level of the system and an increase in training accuracy. Moreover, reducing the dataset partitions results in better training accuracy.

The great achievement of IoT and the wide use of edge devices have brought explosive growth in data. The quality and scale of data determine the performances of machine learning models. Federated learning has attracted widespread attention for its ability to use isolated data and protect data privacy. Models can represent excellent generalization capabilities through federated training. However, the large number of devices and complex models involved in federated training exacerbate the communication costs and degrade the performance of the global model. Although existing approaches can reduce communication costs, they ignore the degradation of global model accuracy in a heterogeneous environment. To alleviate the huge communication costs in federated learning, this paper focuses on reducing upstream and downstream communication frequency while ensuring global model accuracy. We propose a Two-way Delayed Updates method with Model Similarity in Communication-Efficient Federated Learning (FedTDMS). FedTDMS employs personalized local computation to improve global model accuracy on heterogeneous data. Combining 10-cal update relevance check and global model compensation, FedTDMS reduces the communication frequency in Federated Learning. We conduct experiments on the MNIST-FL and CFAR-10-FL datasets. Results show that FedTDMS can greatly optimize communication efficiency while maintaining good global model accuracy.

Today, with more and more devices in the industrial control system (ICS), the risk becomes higher and brings more attack surfaces. The need for reliable anomaly detection systems is increasing. Traditional SCADA-based detection systems deployed are difficult to assess large-scale control systems accurately, and novel AI-based technologies struggle to ensure timely response. In this paper, we propose an edge computing assisted delay-aware anomaly detection (ECADA) scheme for ICS, which considers both the accuracy and timeliness, and ensures that abnormal conditions can be accurately detected and handled in a short time. First, we model the components in ICS as three layers, taking network resources, delay, and reliability into consideration. Second, we convert the anomaly detection procedure into a decision making problem. By dividing the warning capabilities into various levels, the flexibility of the anomaly detection system is enhanced. Third, we cast a mixed-integer linear programming (MILP) problem to find the efficient anomaly detection mechanism, so that it can be dynamically scheduled to achieve the tradeoff between reliability and timeliness. We use an real-world industrial system dataset for experimental evaluation. By comparing with various traditional anomaly detection methods, it is proved that ECADA can always ensure reliable response of anomaly detection system in various network environments.

The emerging federated learning (FL) enables distributed data mining for Internet of Things (IoT) big data while avoiding data outsourcing privacy risks via local data training and knowledge (i.e., model) sharing. However, only simplified local knowledge sharing will also cause user privacy leaks due to advanced attacks (e.g., model inversion or gradient leakage). Further, how to realize fine-grained and personalized privacy protection for IoT users is still a challenge. In this paper, we first propose a hierarchical cloud-edge orchestrated federated learning architecture for IoT, named HCE-FL, which aims to provide intelligent and distributed data analysis for IoT users. To address the FL privacy issues, we then design a multi-level access control encryption-based IoT knowledge sharing approach for HCE-FL. In our approach, IoT users could be classified into different levels according to their individual privacy requirements. In addition, the proposed multi-level access control encryption algorithm could ensure the confidentiality of the IoT knowledge flow, which runs through local clients, edge sanitizers, and cloud servers in HCE-FL. Moreover, security theoretical analysis shows that our HCE-FL could satisfy“no read” and no write” security rules for the mandatory IoT knowledge access control. Finally, we conduct experiments based on classic MNIST and CIFARIO datasets to evaluate our HCE-FL. The experimental results demonstrate that our solution can achieve personalized privacy-preserving FL without losing IoT data availability and users can obtain better model accuracy and convergence rate through secure IoT knowledge access and sharing.

With increasingly strict data privacy regulations, federated learning (FL) has become one of the most often heard machine learning techniques due to its privacy-preserving trait. To efficiently implement the FL intelligence, researchers recently resort to a newly emerged computing paradigm, mobile-edge computing (MEC), and bring about a burst of works. However, most existing works neglect practical issues in MEC systems, e.g., device heterogeneity, unstable channel conditions, and unknown user mobility. Any of them, if not handled properly, can cause fatal failures to FL. This article proposed a novel FL framework, called multicore FL (MC-FL), to help FL intelligence land successfully on realistic MEC systems. A distinct feature of MC-FL is maintaining and training multiple global models (GMs) that exhibit different tradeoffs between learning performances and computational complexity. While this modification seems simple, it can effectively handle the device heterogeneity and device status variations, and improve the compatibility and robustness of FL. Furthermore, MC-FL employs a partial client participation scheme that allows participating clients to vary across time. This enables MC-FL to function under uncertain mobile environments. We rigorously prove the convergence of the designed MC-FL framework. In particular, we propose an online client scheduling scheme for MC-FL to judiciously schedule clients for training multiple GMs in a manner that minimizes the completion time of MC-FL. We also provide a service provisioning scenario with MC-FL to show how service subscribers could benefit from multiple GMs and improve their Quality of Experience (QoE). We evaluate our method on real-world data sets, and the results show that MC-FL outperforms state-of-the-art benchmarks.

With the rapid development of the Internet of Things (IoT), millions of IoT devices are constantly generating massive amounts of data. The development of artificial intelligence (AI) and edge computing makes it possible to conduct data analysis and knowledge mining efficiently among IoT edge devices. Knowledge, including intermediate results and training models obtained by large amounts of redundant data, is the core and foundation of edge intelligence. However, the sharing and utilization of knowledge still face a series of security and privacy issues, such as illegal knowledge access, knowledge tampering, and privacy leakage. To address these issues, in this article, we propose a blockchain-based knowledge storage and sharing architecture that enables secure knowledge management in intelligent IoT. We first design a permissioned blockchain-based decentralized and trusted knowledge storage scheme, which includes the on-chain encrypted knowledge storage and an improved Delegated Proof of Stake (DPoS) consensus protocol. Besides, we propose a lightweight attribute-based searchable encrypted knowledge-sharing mechanism, in which fine-grained and privacy-preserving knowledge collaboration is achieved through smart contracts and keyword search. Moreover, we reduce the computing overhead of edge devices through the design of partial outsourcing decryption. Finally, we analyze the security performance of our system as well as verify its practicality and ability to reject dishonest servers by simulation.

The industrial Internet of Things (IoT) system is integrated with the emerging artificial intelligence (AI) paradigms to empower industrial automation and self-evolving capabilities. AI-driven resource allocation across cyber-physical domains for mobile industrial IoT must consider its fundamental requirements and key characteristics such as high reliability, low latency, and environmental dynamics. The challenge is twofold. Industrial systems are fault-sensitive, which makes them intolerable of trial-and-error-based learning and optimization approaches. In addition, learning models cannot adapt to changing industrial IoT environment with dynamic communication noise and machinery disturbances. In this paper, we propose joint optimization for the nonorthogonal multiple access (NOMA) and multi-tier hybrid cloud-edge computing empowered industrial IoT that results in improved utilization of communication and computing resources. Second, we establish the fine-grained digital twin for industrial IoT (DT-IIoT) to simulate the changing industrial environment to support trial-and-error-based safe learning. Third, we leverage meta reinforcement learning (meta RL) to improve the generalization and fast adaptation of the learning models for DT-IIoT. Finally, the feasibility and efficiency of these schemes are evaluated through extensive experiments.

Cloud and edge computing are gradually used to achieve complex energy operation control and massive information processing in conventional power grid. Meanwhile, with the tremendous number of distributed energy resources and power equipment integrated into the smart grid enabled by cloud and edge computing, the centralized distribution network cannot realize the flexible and realtime energy supply due to the unpredictable and wide distribution of distributed energy resources, which will further deteriorate the stability of smart grid. To solve those problems, this article proposes energy centric smart grid to achieve power dispatching with the help of cloud-edge computing. Our solution uses energy caching and energy multiple addressing of the edge router to eliminate the intermittency of renewables and speed up energy response. For the stability of energy market and to encourage users to participate in power dispatching, a cloud-edge computing-driven energy cache orchestration mechanism is designed. The empirical results show that the response time is greatly reduced to meet the stringent quality of service requirement in smart grid integrated with distributed energy resources.

Differential-private federated learning (DP-FL) has emerged to prevent privacy leakage when disclosing encoded sensitive information in model parameters. However, the existing DP-FL frameworks usually preserve privacy homogeneously across clients, while ignoring the different privacy attitudes and expectations. Meanwhile, DP-FL is hard to guarantee that uncontrollable clients (i.e., stragglers) have truthfully added the expected DP noise. To tackle these challenges, we propose a heterogeneous differential-private federated learning framework, named HDP-FL, which captures the variation of privacy attitudes with truthful incentives. First, we investigate the impact of the HDP noise on the theoretical convergence of FL, showing a tradeoff between privacy loss and learning performance. Then, based on the privacy-utility tradeoff, we design a contract-based incentive mechanism, which encourages clients to truthfully reveal private attitudes and contribute to learning as desired. In particular, clients are classified into different privacy preference types and the optimal privacy-price contracts in the discrete-privacy-type model and continuous-privacy-type model are derived. Our extensive experiments with real datasets demonstrate that HDP-FL can maintain satisfactory learning performance while considering different privacy attitudes, which also validate the truthfulness, individual rationality, and effectiveness of our incentives.

The emergence of new communication applications adds high heterogeneity to 5G-networks. With the increase of heterogeneity, handover of user equipment between different service HetNets is frequent. It must smoothly realize user-free switching to provide services continuously. Although the 3 rd Generation Partnership Project (3GPP) has proposed a standard protocol for this scenario, it is found that these protocols cannot satisfy key forward/backward secrecy, lacks mutual authentication, etc. Further, it can be subjected to replay, DoS and other attacks. To alleviate these problems, we propose a mask random array protocol, MRSA. For efficient, secure handover authentication in 5G HetNets, we first design a verification mechanism called mask array, which depends on a random number self-circulating encryption structure. The mechanism can not only check the identity of the communication entity but also evaluate the freshness of the message. Second, we devise the mask array-based key derivation method to ensure the whole mechanism's key security. Third, formal proof and automated analysis are established to verify the efficiency and safety of the proposed MRSA protocol. Finally, function and robustness analysis illustrate the ability to resist attacks, while the simulation base station communication analysis shows the efficiency of the protocol from three aspects of data, time and energy. MRSA has significant performance advantages compared to existing schemes in 5G HetNets.

Software-defined industrial Internet of things (SD-IIoT) exploits federated learning to process the sensitive data at edges, while adaptive poisoning attacks threat the security of SD-IIoT. To address this problem, this article proposes a multi-tentacle federated learning (MTFL) framework, which is essential to guarantee the trustness of training data in SD-IIoT. In MTFL, participants with similar learning tasks are assigned to the same tentacle group. To identify adaptive poisoning attacks, a tentacle distribution-based efficient poisoning attack detection (TD-EPAD) algorithm is presented. And also, to minimize the impact of adaptive poisoning data, a stochastic tentacle data exchanging (STDE) protocol is also proposed. Simultaneously, to protect the tentacle’s privacy in STDE, all exchanged data will be processed by differential privacy technology. A MTFL prototype system is implemented, which provides extensive ablation experiments and comparison experiments, demonstrating that the accuracy of the global model under attack scenario can be improved with 40%.

The industrial visual sensing-actuation system is an implementation approach to construct the loop between the digital twin and physical systems, which is facing the following challenges. First, the cross-digital-physical information exchanges bring a high end-to-end delay that threatens the functional safety of industrial systems. Second, industrial scenarios are diverse, such as manufacturing, chemical engineering, etc., which makes the intelligent sensing strategies for one scenario inapplicable to others, especially for few-shot cases. Third, intelligent actuation strategies cannot allocate resources across digital and physical domains. We propose the delay-minimization-based intelligent digital twin approach to address the above challenges. The digital twin framework incorporates samples from the physical domain to train the learning models in the digital domain. The proposed scheme tailors and adapts transferable and reinforced learning models with end-to-end delay analysis to optimize the training process. The feasibility and efficiency of the scheme are validated by simulations.

The Internet of Things (IoT) is delivering solutions with improved efficiency and security, and providing better productivity in manufacturing, retail, and other sectors. Maritime Transportation Systems (MTSs) is currently adopting the IoT to move toward a digitalized, data-driven world with increased efficiency and lower costs, and creating new revenue opportunities. Integration of the IoT also enables real-time tracking of shipments, improved efficiency in cargo handling, pre-emptive maintenance, route optimization, reduced fuel consumption, and improved safety in maritime transportation systems. With IoT technology expanding and evolving rapidly, more applications are predicted to assist and improve all aspects of MTSs, making them hassle-free and safe.

In order to fulfill the requirements of Intelligent Transportation Systems (ITS) on ultra-delay service response, task scheduling for trajectory analysis is being shifted from the data center into the network edge of ITS. Such a decentralized paradigm motivates the computing power of the edge device and makes traditional analysis tasks open to the users around ITS. However, since these ITS users have differentiated identities and roles with differentiated security demands and privacy protection, assigning tasks for different users requires identifying and assessing the vulnerability of edge intelligence entities (EIEs). Otherwise, sensitive tasks assigned to the vulnerable EIEs will extremely increase the security risks of industrial control networks. To solve these problems, this paper proposes a vulnerability-aware task scheduling (VATS) mechanism, which integrates vulnerability assessment and access control. With VATS, secure EIEs can obtain more permissions and join in the privacy-sensitive trajectory analysis task, which is essential to enhance privacy protection at edges and ultimately improve the efficiency of task scheduling. The simulation results demonstrate the validity of the proposed scheme to defend insecure task scheduling like trajectory analysis.

As city boundaries expand and the vehicles continues to proliferate, the transportation system is increasingly overloaded, greatly increasing people’s commuting burden and extending the resulting negative effects to all areas of work and life. It is a big issue that needs to be solved urgently. However, due to the development of infrastructure and technologies in 6G-driven Intelligent Transportation Systems (ITS), it becomes possible to alleviate urban congestion. Existing solutions either optimize the path planning of each vehicle, or only focus on solving the problem of resource allocation of a single road, neither can take advantage of self-organizing networks and easily fall into local optimum. Combining the above reasons, we propose the Direction Decide as a Service (DDaaS) scheme. First, it contains a novel three-layer service architecture based on Swarm Learning (SL), which enables orderly transmission of traffic data and control instructions and protects user privacy. Second, an improved local model and aggregation method is incorporated into DDaaS, which enables to make accurate predictions when the road resources at a single intersection are insufficient. Third, we propose a dynamic traffic control algorithm to provide signal light switching decisions for rapidly changing ITS. Finally, constructing an urban road simulation experiment combined with SUMO, we prove that DDaaS can reduce traffic congestion effectively and has significant advantages compared to other schemes.

Traffic forecasting is essential in improving and maintaining safety and orderliness in intelligent transportation systems (ITS). As a deep learning approach, graph neural networks (GNN) based spatial-temporal association mining methods are promising in traffic forecasting. However, current GNN-based methods usually require a high number of training data, and when the sample volume is small, the performance of the model drops dramatically. The existing transfer methods can solve this problem by leveraging knowledge from other data-rich areas, but the domain adaption method with access to source data still faces the non-neglectable problem of private information leakage in the source area. A solution that can solve cross-area transfer without access to source data is still missing. In this paper, to fill the gap, we propose a Transferable Federated Inductive Spatial-Temporal Graph Neural Network (T-ISTGNN) framework to transfer spatial-temporal dependency information in cross-area data to accomplish traffic state forecasting. First, we introduce a multi-source model aggregation scheme based on federated learning to retain the traffic information of the source areas. Second, we propose a transfer method between source and target areas based on hypothesis transfer learning to achieve domain adaption under source domain data protection. Third, we propose a GNN-based method called Inductive Spatial-Temporal Graph Neural Network (ISTGNN) for traffic forecasting. Experiments on real-world datasets demonstrate that T-ISTGNN is capable of cross-area traffic state forecasting under the restriction of preserving the privacy of source areas.

The emergence of Digital Twin Edge Networks (DTENs) achieves the mapping of real physical entities to digital models of cyberspace. By offloading real-time mobile data to Mobile Edge Computing (MEC) servers for processing and modeling, communication-efficient Digital Twin (DT) services could be achieved. However, the spatio-temporal dynamic DT service demand stochastically generated by mobile users easily causes service congestion, which challenges the long-term DT service stability. Meanwhile, current DT services still lack long-term effective incentive designs for participants. To solve these issues, we design an incentive-based congestion control scheme for stochastic demand response in DTENs. First, we adopt the Lyapunov optimization theory to decompose the long-term congestion control decision into a sequence of online edge association decisions, with no need for future system information. We then present a contract-based incentive design to optimize the long-term profit of the DT service provider, comprehensively considering the delay sensitivity, incentive compatibility, and individual rationality. Finally, experimental simulations are carried out to verify the superiority of the proposed scheme with the base station dataset of Shanghai Telecom. Theoretical and simulation analysis demonstrates that compared with benchmarks, our scheme could effectively avoid long-term service congestion with an arbitrarily near-optimal profit.

Recently, wireless edge networks have realized intelligent operation and management with edge artificial intelligence (AI) techniques (i.e., federated edge learning). However, the trustworthiness and effective incentive mechanisms of federated edge learning (FEL) have not been fully studied. Thus, the current FEL framework will still suffer untrustworthy or low-quality learning parameters from malicious or inactive learners, which undermines the viability and stability of FEL. To address these challenges, the potential social attributes among edge devices and their users can be exploited, while not included in previous works. In this paper, we propose a novelSocialFederatedEdgeLearning framework (SFEL) over wireless networks, which recruits trustworthy social friends as learning partners. First, we build a social graph model to find like-minded friends, comprehensively considering the mutual trust and learning task similarity. Besides, we propose a social effect based incentive mechanism for better personal federated learning behaviors with both complete and incomplete information. Finally, we conduct extensive simulations with the Erdos-Renyi random network, the Facebook network, and the classic MNIST/CIFAR-10 datasets. Simulation results demonstrate our framework could realize trustworthy and efficient federated learning over wireless edge networks, and it is superior to the existing FEL incentive mechanisms that ignore social effects.

The integration of satellite communication technology and terrestrial infrastructure has resulted in an un-precedented increase in network services covering the world. The main effect of the rapid growth of satellite networks is a broader range of data exchange and business interaction between the internal and external systems, making the network boundaries blur or even disappear. As a result, traditional passive security mechanisms based on dividing network boundaries cannot provide sufficient protection. To address this issue, in this paper, we propose a zero-trust and edge intelligence (ZTEI) empowered continuous authentication for satellite networks. We build an improved zero-trust architecture (ZTA) for satellite networks, which expands the traditional zero-trust concept to the multi-dimensional zero-trust that focuses on subject, object, environment, behavior, and physical entity. Then we propose a continuous authentication scheme in the proposed zero-trust architecture, enabling proactive and continuous authentication by periodically monitoring and re-evaluating variable attributes throughout the request lifecycle. Besides, in this scheme, we also design a Neural-Backed Decision Trees (NBDTs) based edge intelligence algorithm to improve the authentication accuracy. Finally, we build a testbed to evaluate the performance of the proposed architecture. Compared with the attribute-based access control (ABAC) under the traditional zero-trust architecture, our proposed architecture can improve the authentication accuracy of dynamic illegal requests by about 27%. In addition, according to standard network performance evaluation criteria, the loss of processing performance caused by our solution is also within an acceptable range.

The traffic anomaly analysis in IoT-based intelligent transportation system (ITS) is crucial to improving public transportation safety and efficiency. The issue is also challenging due to the unbalanced distribution of anomaly data in IoT-based ITS, which may cause overfitting or underfitting in the training phase. However, some research on traffic anomaly analysis injected limited data to address the shortage of anomaly samples or even neglects this issue, which overlooks the potential representation of nodes in graph neural networks. In this paper, we propose an improved contrastive GNN-based learning framework for traffic anomaly analysis that alleviates the problem of imbalanced datasets in the training phase. In this framework, we provide a graph augmentation approach with coupled features to learn different views of graph data. Besides, we design an effective training method based on the contrastive loss for our framework, which can learn the better performance of latent representations utilized in the downstream tasks. Finally, we conduct extensive experiments to evaluate the performance of our proposed frame-works based on real-world datasets. We demonstrate that our framework achieves as high as 6.45% precision improvement compared to the state-of-the-art.

Blockchain-based federated learning, also being named as swarm learning, is perceived to have great potential to support decentralized and privacy-enhancing big data processing. However, numerous serious vulnerabilities found on blockchain and federated learning enforce us to concern about the security of swarm learning. Some seemingly-unrelated combinations of known vulnerabilities may derive highly-converted and unknown threats to swarm learning. In this paper, we first investigate the security threats of the swarm learning framework. And then, leveraging backdoor attacks and eclipse attacks, a novel hybrid vulnerability that can furtively propagate backdoors among swarm learning nodes is identified. To speed up the backdoor propagation and reduce attack costs, a sample-specific eclipse (SSE) strategy that can select the swarm network node with a high data contribution rate as the attack object is also proposed. Finally, by adjusting the trigger size, the data distribution rate, and the poisoning ratio, we conduct various comparison experiments to validate the feasibility of the proposed methods. To the best of our knowledge, this is the first article to study the epidemicity of backdoors in swarm learning.

Quantum Key Distribution (QKD) is a recent technology for secure distribution of symmetric keys, which is currently being deployed to increase communications security against quantum attacks. However, the key rate achievable over a weak quantum signal is limited by the link performance (e.g., loss and noise) and propagation distance, especially in multi-node QKD networks, making it necessary to design a scheme to efficiently and timely distribute keys to the various nodes. In this work, we formulate, using a Mixed Integer Linear Programming (MILP) model, a novel Routing, Channel, and Key-rate Assignment (RCKA) problem for QKD with Quantum Key Pool (QKP), which exploits the opportunity of using trusted relays and optical bypass. Our formulation accounts for the possibility to build a quantum key distribution path that combines both quantum channels and trusted relays to increase the acceptance ratio of key rate requests. Leveraging different versions of the proposed MILP model, we evaluate several strategies exploiting different combinations of trusted relays and optical bypass for the RCKA problem. Results show how different trade-offs between security and resource-efficiency (expressed in terms of acceptance ratio of key rate requests vs. key storing rate in QKP) can be achieved when adopting trusted-relay and/or optical-bypass technologies. Trusted relays can provide a higher acceptance ratio when the number of QKD modules (transmitters or receivers) is sufficiently large, while optical bypass, which does not require the implementation of expensive trusted relays, is preferable when the number of QKD modules is a limiting factor.

The development of backhaul/fronthaul networks can enable low latency and high reliability, but nodes in future networks like Internet of Things (IoT) can conduct malicious activities like flooding attack and DDoS attack, which can decrease QoS of smart backhaul/fronthaul network. Timely detection of malicious nodes in future networks is significant for low-latency backhaul/fronthaul networks. However, conventional supervised learning-based detection models require abundant malicious training samples, while capturing adequate malicious samples can not meet the requirement of timely detection. In this paper, we propose a novel few-shot malicious node detection system for improving QoS of IoT backhaul/fronthaul network, which can detect malicious nodes with unknown malicious activities through a limited number of network traffic samples. In our proposed system, we first design a fresh IoT traffic sample processing approach, which integrates normal activity samples and known malicious activity samples to generate training pairs. Then, we design a metric learning-based malicious node detection model training method, which employs a contrastive loss over distance metric to distinguish between similar and dissimilar pairs of samples. Besides, the trained model can detect nodes with unknown malicious activities by comparing real-time samples with few-shot samples of malicious nodes. Finally, the proposed system is evaluated on a real-world IoT network dataset named N-BaIoT. The exhaustive experiment results show that our model can achieve an average accuracy around 97.67 % when detecting malicious nodes with unknown malicious activities, which is comparable to state-of-the-art supervised learning models while our model only needs 5-shot samples of malicious node.

In the 6G era, the Intelligent Internet of Vehicles (IIoV) usually faces multiple security threats, which causes a trade-off of computation resources between security defense and vehicular artificial intelligence (AI). On-demand resource allocation in accordance with attack strength is a must for 6G vehicular edge learning. The existing works just focus on realizing low latency for AI resource allocation in vehicular edge learning, which ignores vehicles’ high security-defense demands for computation resources in the face of attacks. To address this, this paper proposes an on-demand incentive mechanism to achieve coordinated optimization of security defense resource allocation over 6G vehicular edge learning. First, we propose budget-feasible incentive contracts for computation resource allocation based on vehicles’ security-defense demands, which maximizes the learning utility of each vehicle type with a particular demand level. The contracts are tailored with the optimal resource allocation and incentive rewards with respect to different demand sensitivities. Next, apart from minimizing the single iteration time with the designed contracts, we design an optimization model of learning parameters for local accuracy to minimize the overall iteration time. Finally, simulation results show the feasibility and efficiency of the security-defense recourse allocation. This work is significant to improve the defense capability of 6G vehicle-edge learning against dynamic threats.

With the advent of the Internet of Things (IoT) era and 5G, ubiquitous sensing devices (e.g., smartphones, surveillance sites, and security cameras) have been widely used in various fields, resulting in the generation of a huge amount of monitoring data. The rise of federated learning makes it possible to leverage monitoring data to train deep neural networks through cloud-edge collaboration without compromising privacy. However, the non identically and independently distributed (called Non-IID) data collected by IoT devieces creates a client drift phenomenon, resulting in a slow convergence of the global model. To this end, we propose a new Federated learning framework based on Gradient Variance Reduction with a correction weight control mechanism and Global gradient descent with Momentum, named FedGVRGM to conduct gradient correction and reduce the negative impacts of prediction parameters. Specifically, in the local training phase, FedGVRGM combines gradient variance reduction with a correction weight control mechanism to further correct the local model parameters, thus reducing the dispersion of model parameters among clients. In the global aggregation phase, FedGVRGM integrates the historical change states of the global model through the gradient descent with momentum to reduce the oscillations and improve the convergence speed of the global model. We refer to the above methods of gradient adjustment in the local and global training phases as FedGVR and FedGM, respectively. Numerous evaluations are conducted on CIFAR-100, CIFAR-10, and MNIST datasets to prove that FedGVRGM has a faster convergence rate than other stateof-the-art approaches such as Federated Averaging (FedAvg), FedProx, FedReg, FedGVR, and FedGM.

Abstract In edge computing, though distributed training of Deep Neural Networks (DNNs) is expected to exchange massive gradients between parameter servers and working nodes, the high communication cost constrains the training speed. To break this limitation, gradient compression algorithms expect the ultimate compression ratio at the expense of the accuracy of the trained model. Therefore, new gradient compression techniques are necessary to ensure both communication efficiency and model accuracy. This paper introduces a novel technique—an Adaptive Sparse Ternary Gradient Compression (ASTC) scheme, which relies on the number of gradients in model layers to compress gradients. ASTC establishes the model compression selection criterion by gradients’ amount, compresses the network layer that meets the model’s standard, evaluates the gradients’ importance based on entropy to adaptively perform sparse compression, and finally conducts ternary quantization compression and a lossless code scheme on sparse gradients. Using public datasets (MNIST, CIFAR-10, Tiny ImageNet) and deep learning models (CNN, LeNet5, ResNet18) for experimental evaluation, we exhibit excellent results that the training efficiency of ASTC is about 1.6 times, 1.37 times, and 1.1 times higher than that of Top-1, AdaComp, and SBC, respectively. Furthermore, ASTC can be improved by an average of about $$1.9\%$$ 1.9% in training accuracy compared with the above approaches.

The existing Underwater Internet of Things (UIoT) is based on the IP architecture, which is not conducive to the efficient storage and distribution of huge amounts of content generated in underwater. Actively pushing all content to users causes much unnecessary resource consumption in the UIoT. The information-centric networking (ICN) architecture opens new horizons up for these challenges. However, the slowness of underwater propagation speed makes traditional ICN not suitable for UIoT, especially considering about delay time. In this article, we propose an information-centric wireless sensor networking scheme with water-depth-aware content caching (ICWSN-WDA) to solve the above challenges. First, we design a naming scheme and a hybrid communication mode suitable for ICWSN-WDA. The communication mode in underwater we design is divided into push and pull traffic, which balances energy consumption and delay time. Second, we define a push level to decide which water depth the content actively pushes to, finding a suitable junction point of two modes. Third, as the water depth is deeper, it becomes more difficult to replace the sensor battery. To save energy consumption of deep-water sensors, the water-depth-aware caching mechanism is proposed based on water depth, popularity, and senor energy. Our extensive evaluation confirms the effectiveness of our proposed scheme, and it balances energy consumption constraints and latency.

Recently, edge artificial intelligence techniques (e.g., federated edge learning) are emerged to unleash the potential of big data from Internet of Things (IoT). By learning knowledge on local devices, data privacy preserving and Quality of Service (QoS) are guaranteed. Nevertheless, the dilemma between the limited on-device battery capacities and the high energy demands in learning is not resolved. When the on-device battery is exhausted, the edge learning process will have to be interrupted. In this article, we propose a novel wirelessly powered edge intelligence (WPEG) framework, which aims to achieve a stable, robust, and sustainable edge intelligence by energy harvesting (EH) methods. First, we build a permissioned edge blockchain to secure the peer-to-peer (P2P) energy and knowledge sharing in our framework. To maximize edge intelligence efficiency, we then investigate the wirelessly powered multiagent edge learning model and design the optimal edge learning strategy. Moreover, by constructing a two-stage Stackelberg game, the underlying energy-knowledge trading incentive mechanisms are also proposed with the optimal economic incentives and power transmission strategies. Finally, simulation results show that our incentive strategies could optimize the utilities of both parties compared with classic schemes, and our optimal learning design could realize the optimal learning efficiency.

In edge networks, distributed computing resources have been widely utilized to collaboratively perform a machine learning task by multiple nodes. However, the model training time in heterogeneous edge networks is becoming longer because of excessive computation and delay caused by slow nodes, namely, stragglers. The parameter server even abandons stragglers which fail to return the outcome within a reasonable deadline, called straggler dropout, decreasing the model accuracy. To optimize the computation cost and maintain the model accuracy, we focus on mitigating the heavy computation of stragglers and preventing straggler dropout. Therefore, we propose a novel scheme named dynamic grouping and heterogeneity-aware gradient coding (DGH-GC) to tolerate stragglers by employing dynamic grouping and gradient coding. DGH-GC evenly distributes stragglers in each group and encodes gradients based on their computation capacity to prevent them drop out. However, DGH-GC exacerbates the communication burden by making data duplication to tolerate stragglers. Relying on the scheme, we further propose an algorithm called DGH-(GC)2 to compress transferred gradients in both upstream communication and downstream communication. Experimental evaluations prove that DGH-(GC) outperforms all state-of-the-art methods and DGH-(GC)2 further speeds up the convergence time of the trained model and saves about 26% average iteration time compared to the DGH-(GC).

In the era of the sixth generation (6G), the deployment of massive Internet of Things (IoT) generates and processes large amounts of data, resulting in high energy demand and huge challenges to the energy-limited IoT devices. To achieve green and sustainable communication, energy harvesting is a feasible technology to prolong the lifetime of IoT. However, the existing energy harvesting architecture cannot guarantee the privacy of energy users while improving the intelligence and effectiveness of energy transmission. To solve these issues, we propose a differential privacy and intelligent reflecting surface empowered privacy-preserving energy harvesting framework for 6G-enabled IoT. First, a secure and intelligent energy harvesting framework is designed, which includes an intelligent reflecting surface-aided radio frequency power transmission mechanism and a differential privacy-based energy harvesting mechanism. Second, an exponential mechanism-based privacy-preserving energy harvesting scheme is established, where we analyze the adversary mode, propose the differential privacy-enabled location-preserving algorithm, and provide security analysis and proof. Third, we quantify the user satisfaction for energy harvesting and propose a deep reinforcement learning empowered resource allocation scheme to maximize the weighted satisfaction of all system users. Finally, simulation results show the effectiveness of the proposed secure and intelligent energy harvesting architecture for 6G IoT.

Advanced persistent threats (APT) have novel features such as long-term latency, precision strikes and uncertain strategies. APT poses severe threats to the resource-limited edge devices in advanced networks. Cyber threat intelligence (CTI) conducts data analysis on attack strategies by artificial intelligence (AI) and generates threat intelligence to optimize the detection model and guide defense strategies. However, AI lacks explanations for the decisions and thus reduces the transparency and performance of the detection model. Besides, the tradeoff between the detection accuracy and the computational resource limitation of edge devices needs an optimal and rapid dynamic resource allocation method, which edge game and AI can help. In this paper, we propose an explainable intelligence-driven APT edge defense mechanism. The proposed mechanism provides guidelines and explanations for designing the defense strategy and resource allocation scheme of the edge defender to detect APT. The edge defense strategy model is based on edge Bayesian Stackelberg game and CTI. Meanwhile, we implement a DRL-based resource allocation scheme to meet rapid response requirements at the edges. We demonstrate that the proposed mechanism can improve the protection level of edges and defense capability against APT through extensive experiments.

Accessibility to smart devices provides opportunities for side-channel attacks (SCAs) on artificial intelligent (AI) models in the intelligent Internet of Things (IoT). However, the existing literature exposes some shortcomings: 1) incapability of quantifying and analyzing the leaked information through side channels of the intelligent IoT and 2) inability to devise efficient and accurate SCA algorithms. To address these challenges, we propose a side-channel fuzzy analysis-empowered AI model extraction attack in the intelligent IoT. First, the integrated AI model extraction framework is proposed, including power trace-based structure, execution time-based metaparameters, and hierarchical weight extractions. Then, we develop the information theory-based analysis for the AI model extraction via SCA. We derive a mutual information-enabled quantification method, theoretical lower/upper bounds of information leakage, and the minimum number of attack queries to obtain accurate weights. Furthermore, a fuzzy gray correlation-based multiple-microspace parallel SCA algorithm is proposed to extract model weights in the intelligent IoT. Based on the established information-theoretic analysis model, the proposed fuzzy gray correlation-based SCA algorithm obtains high-precision AI weights. Experimental results, consisting of simulation and real-world experiments, verify that the developed analysis method with the information-theoretic perspective is feasible and demonstrate that the designed fuzzy gray correlation-based SCA algorithm is effective for AI model extraction.

Recently, the Internet of Medical Things (IoMT) could offload healthcare services to 5G edge computing for low latency. However, some existing works assumed altruistic patients will sacrifice quality of service for the global optimum. For priority-aware and deadline-sensitive healthcare, this sufficient and simplified assumption will undermine the engagement enthusiasm, i.e., unfairness. To address this issue, we propose a long-term proportional fairness-driven 5G edge healthcare, i.e., FairHealth. First, we establish a long-term Nash bargaining game to model the service offloading, considering the stochastic demand and dynamic environment. We then design a Lyapunov-based proportional-fairness resource scheduling algorithm, which decouples the long-term fairness problem into single-slot subproblems, realizing a tradeoff between service stability and fairness. Moreover, we propose a block-coordinate descent method to iteratively solve nonconvex fair subproblems. Simulation results show that our scheme can improve 74.44% of the fairness index (i.e., Nash product), compared with the classic global time-optimal scheme.

Energy harvesting (EH) is a promising and critical technology to mitigate the dilemma between the limited battery capacity and the increasing energy consumption in the Internet of everything. However, the current EH system suffers from energy-information cross threats, facing the overlapping vulnerability of energy deprivation and private information leakage. Although some existing works touch on the security of energy and information in EH, they treat these two issues independently, without collaborative and intelligent protection cross the energy side and information side. To address the aforementioned challenge, this article proposes a joint protection framework of energy security and information privacy for EH with an incentive federated learning approach. First, we design a federated-learning-based malicious energy user detection method according to energy status and behaviors to provide energy security protection. Second, a differential-privacy-empowered information preservation scheme is devised, where sensitive information is perturbed and protected by the customized demand-based noise. Third, a noncooperative-game-enabled incentive mechanism is established to encourage EH nodes to participate in the joint energy-information protection system. The proposed incentive mechanism derives the optimal energy-information security strategy for EH nodes and achieve a tradeoff between the protection of energy security and information privacy. Evaluation results have verified the effectiveness of our proposed joint protection mechanism.

Digital Twin (DT) has become the key technology in the Intelligent Transportation Systems (ITS) in smart cities to keep the health and reliability of various DT requesters, such as private vehicles, public transportation, energy systems, etc. The combination of DT and ITS can further release the potential of participants in smart cities and guarantee their efficiency and reliability. Despite the advantages of DT-enabled ITS, not all requesters need the same level of DT service due to the highly dynamic nature of ITS. Safe and reliable matching between DT and ITS still needs to be resolved. To address these issues, we propose the blockchain-enabled Digital Twin as a Service (DTaaS) for ITS. First, we propose an on-demand DTaaS architecture to fully utilize the sensing capabilities of ITS and the macro perspective of DT. Second, a double-auction model and a price adjustment algorithm are proposed to realize the optimal DT matching for ITS requesters and ensure the benefits of participants. Third, a permissioned blockchain and a novel DT-DPoS consensus mechanism are established to enhance the security and efficiency of DTaaS. Simulation shows that the proposed DTaaS and double-auction can efficiently stimulate and facilitate DT transactions. The proposed DT-DPoS also has obvious advantages.

The ever-increasing traffic, various delay-sensitive services, and energy consumption-constrained requirements have brought huge challenges to the current communication networks in the vehicular ad-hoc networks (VANETs). These challenges motivate academia and industry to investigate novel architectures with powerful data transmission and processing capabilities for low-latency and high energy-efficiency vehicular communication. In this paper, we propose an artificial intelligence (AI) and intelligent reflecting surface (IRS) empowered energy-efficiency communication system for VANETs. First, we design a smart and efficient hybrid vehicular communication framework, where IRS-aided dedicated short-range communication and long term evolution-based cellular communication are combined for data transmission in VANETs. Secondly, an IRS-aided data transmission is proposed to improve vehicular communication, in which the head vehicles selection method is designed. Based on the direct and IRS-reflecting signal propagation, fine-grained beamforming is achieved for directional vehicular transmission. Thirdly, a deep reinforcement learning (DRL) empowered network resource control and allocation scheme is proposed. In this scheme, we formulate an energy efficiency-maximizing model under the given transmission latency for VANETs and jointly optimize the settings of all participants to achieve efficient and low-latency communication. Finally, experimental results verify the effectiveness of our proposed communication system for VANETs.

Currently, the highly dynamic fog computing resource requirements introduced by the diverse services of the Internet of Things (IoT) result in an imbalance between computing resource providers and consumers. However, current computing resource scheduling schemes cannot cognize the dynamic resources available and do not possess decision-making or management capabilities, which leads to inefficient use of computing resources and a decreased quality of service (QoS). Balancing computing resources cognitively at the IoT edge remains unresolved. In this paper, a cognition-centric fog computing resource balancing (CFCRB) scheme is proposed for edge intelligence-enabled IoT. First, we propose a cognitive balance architecture with a cognition plane, which includes service demand monitoring, policy processing and knowledge storage of cognitive fog resources. Second, we propose the fog functions structure with sensing, interaction and learning functionalities, realizing the knowledge-based proactive discovery and dynamic orchestration of resource sharing nodes. Finally, a distributed edge learning algorithm is proposed to construct knowledge of the balance between computing resource helpers and requesters in cognitive fogs, which is further proved with mathematics. The simulation results indicate the efficiency of the proposed scheme.

In presence of multiple failures affecting their network infrastructure, operators are faced with the Progressive Network Recovery (PNR) problem, i.e., deciding the best sequence of repairs during recovery. With incoming deployments of 5G networks, PNR must evolve to incorporate new recovery opportunities offered by network slicing. In this study, we introduce the new problem of Progressive Slice Recovery (PSR), which is addressed with eight different strategies, i.e., allowing or not to change slice embedding during the recovery, and/or by enforcing different versions of slice connectivity (i.e., network vs. content connectivity). We propose a comprehensive PSR scheme, which can be applied to all recovery strategies and achieves fast recovery of slices. We first prove the PSR’s NP-hardness and design an integer linear programming (ILP) model, which can obtain the best recovery sequence and is extensible for all the recovery strategies. Then, to address scalability issues of the ILP model, we devise an efficient two-phases progressive slice recovery (2-phase PSR) meta-heuristic algorithm, small optimality gap, consisting of two main steps: i) determination of recovery sequence, achieved through a linear-programming relaxation that works in polynomial time; and ii) slice-embedding recovery, for which we design an auxiliary-graph-based column generation to re-embed failed slice nodes/links to working substrate elements within a given number of actions. Numerical results compare the different strategies and validate that amount of recovered slices can be improved up to 50% if operators decide to reconfigure only few slice nodes and guarantee content connectivity.

The threat of data-poisoning backdoor attacks on learning algorithms typically comes from the labeled data. However, in deep semi-supervised learning (SSL), unknown threats mainly stem from the unlabeled data. In this paper, we propose a novel deep hidden backdoor (DeHiB) attack scheme for SSL-based systems. In contrast to the conventional attacking methods, the DeHiB can inject malicious unlabeled training data to the semi-supervised learner so as to enable the SSL model to output premeditated results. In particular, a robust adversarial perturbation generator regularized by a unified objective function is proposed to generate poisoned data. To alleviate the negative impact of the trigger patterns on model accuracy and improve the attack success rate, a novel contrastive data poisoning strategy is designed. Using the proposed data poisoning scheme, one can implant the backdoor into the SSL model using the raw data without hand-crafted labels. Extensive experiments based on CIFAR10 and CIFAR100 datasets demonstrated the effectiveness and crypticity of the proposed scheme.

As the security requirements increases, 5G slicing-enabled Internet of things needs to adopt end-to-end standalone networking, which limits the consensus convergence of mobile blockchain. Although the rise of FIBRE (Fast Internet Bitcoin Relay Engine) gives a huge promotion to block propagation, existing approaches cannot consider the influence of link outages among 5G slices on mobile blockchain. In this paper, we focus on decreasing the block propagation time among blockchain peers under the scenario where link outages among 5G slices exist. A predictive fast consensus convergence (PFCC) scheme is proposed for mobile blockchain over 5G slicing-enabled internet of things. In PFCC, federated semi-supervised learning is used to learn the features of withdraw packets, reroutes the packets of blockchain peers, and ultimately reduces the scale of link outages quickly. With PFCC, different blockchain peers located in standalone 5G slices of IoT can transact local sensing data more efficiently. To the best of our knowledge, this is the first work to improve the consensus convergence speed of mobile blockchain by optimizing communications between 5G slices. Experiments shows the feasibility of proposed scheme.

The evolution of deep learning has promoted the popularization of smart devices. However, due to the insufficient development of computing hardware, the ability to conduct local training on smart devices is greatly restricted, and it is usually necessary to deploy ready-made models. This opacity makes smart devices vulnerable to deep learning backdoor attacks. Some existing countermeasures against backdoor attacks are based on the attacker’s ignorance of defense. Once the attacker knows the defense mechanism, he can easily overturn it. In this paper, we propose a Trojaning attack defense framework based on moving target defense(MTD) strategy. According to the analysis of attack-defense game types and confrontation process, the moving target defense model based on signaling game was constructed. The simulation results show that in most cases, our technology can greatly increase the attack cost of the attacker, thereby ensuring the availability of Deep Neural Networks(DNN) and protecting it from Trojaning attacks.

Being the revolutionary future networking technology, 6G networks are expected to solve the issue of the drastic data demand and satisfy emerging applications and services. As one of the most feasible nanonetwork paradigms in 6G networks, molecular communication provides opportunities for targeted delivery, which solves nanodevice communication problems. The delivery communication is implemented by the biochemical reaction between the targeted molecule and the released specific Information Molecule (IM). The unprotected characteristic of this communication makes it vulnerable to be attacked on the biological process, adversely affecting the targeted delivery. In this paper, we propose the communication model for targeted delivery under the Bio-Denial of Service (Bio-DoS) attack. We first put forward the concept of the Bio-DoS attack and prove its feasibility. Moreover, we establish the diffusion-based multi-target model for the delivery process considering the combined characteristic of traditional communication and biology features. Simulation results show that the model accurately evaluate the signal receiving and processing in targeted delivery process under attack. This work is of a great significance to analysis the communication performance of the targeted delivery under attack.

The challenges of the Internet of Things (IoT) sensor networks include connectivity, interoperability, security, and privacy. The Institute of Electrical and Electronics Engineers (IEEE) P1451.0 standard is being revised based on these challenges and requirements to achieve sensor data interoperability and security for IoT applications. This paper analyzes the security requirements of sensor networks for IoT applications and proposed security solutions for IEEE P1451.0-based IoT sensor networks. It specifies security policies and levels of IEEE P1451.0-based sensor networks to meet the security requirements and defines security transducer electronic data sheets (TEDS) to describe security protocol information for IEEE P1451.0-based sensor networks. An implementation of the security TEDS for IEEE P1451.0 and P1451.5-802.11 wireless sensor networks is provided to illustrate how to access security TEDS that contain detailed security parameters and information to achieve sensor data security and interoperability.

Influence diffusion estimation is a crucial problem in social network analysis. Most prior works mainly focus on predicting the total influence spread, i.e., the expected number of influenced nodes given an initial set of active nodes (aka. seeds). However, accurate estimation of susceptibility, i.e., the probability of being influenced for each individual, is more appealing and valuable in real-world applications. Previous methods generally adopt Monte Carlo simulation or heuristic rules to estimate the influence, resulting in high computational cost or unsatisfactory estimation error when these methods are used to estimate susceptibility. In this work, we propose to leverage graph neural networks (GNNs) for predicting susceptibility. As GNNs aggregate multi-hop neighbor information and could generate over-smoothed representations, the prediction quality for susceptibility is undesirable. To address the shortcomings of GNNs for susceptibility estimation, we propose a novel DeepIS model with a two-step approach: (1) a coarse-grained step where we estimate each node's susceptibility coarsely; (2) a fine-grained step where we aggregate neighbors' coarse-grained susceptibility estimations to compute the fine-grained estimate for each node. The two modules are trained in an end-to-end manner. We conduct extensive experiments and show that on average DeepIS achieves five times smaller estimation error than state-of-the-art GNN approaches and two magnitudes faster than Monte Carlo simulation.

The development of massive IoT has not only brought about a wealth of hardware resources but also brought about the problems of difficult data management, resource running and low efficiency. The emergence of sixth-generation (6G) network will not only provide faster data rates, more device connections but also bring ubiquitous virtual reality/augmented reality (VR/AR) services. In the 6G era, large-scale IoT devices will generate VR/AR service and resource requirements, and the network will also face unprecedented pressure to respond to the ubiquitous VR/AR requirements. To address the above issues, this article proposes the information-centric massive Internet of Things (IC-mIoT) suitable for 6G large-scale VR/AR content distribution to improve the efficiency of IC-mIoT and fully guarantee the Quality of Service (QoS) of users. First, this article introduces the blockchain for IC-mIoT nodes and proposes a new consensus mechanism Proof-of-Cache-Offloading (PoCO). Second, an architecture using blockchain-enabled IC-mIoT for VR/AR is proposed in this article. The massive IoT resources are fully integrated and scheduled to support large-scale VR/AR applications and IC-mIoT. Third, a Stackelberg game model and a cache index selection and calculation algorithm are formulated for blockchain-enabled cache offloading. The analysis and performance simulation results indicate the superiority and effectiveness of the proposed scheme.

The cybertwin and 6G-enabled Industrial Internet of Things (6G-IIoT) are the critical technologies that create the digital counterparts for physical systems and enable the near-instant interconnectivity in the industrial domain. It is in demand but challenging to conduct the integrated design for 6G-IIoT, which intertwines the cyber subsystems, such as control, communication, computing (3C), and the physical industrial factories and plants. Therefore, the cybertwin, which synchronizes between the digital counterparts and its physical entities during the system runtime, is the ideal proving ground for conducting the integrated design on the highly intertwined 3C of 6G-IIoT. However, the cybertwin lacks artificial intelligence to capacitate the automated integrated design for the 6G-IIoT. In this article, we first demonstrate the architecture of the machine-learning-based cybertwin for 6G-IIoT. Then, we leverage deep reinforcement learning (DRL) to conduct the integrated design via systematic trial and error in the cybertwin model, which is otherwise costly and dangerous in real industrial systems. Moreover, we invent the adaptive observation window for deep$Q$-network (AOW-DQN), which generates system states adaptive to the control system’s physical dynamics. Finally, the experimental results demonstrate the effectiveness and efficiency of our approach. To the best of our knowledge, we are the first to present the machine-learning-based cybertwin for carrying out the integrated design on the 3C for 6G-IIoT.

Semi-Supervised Learning (SSL) is a powerful derivative for humans to discover the hidden knowledge, and will be a great substitute for data taggers. Although the availability of unlabeled data rises up a huge passion to SSL, the untrustness of unlabeled data leads to many unknown security risks. In this paper, we first identify an insidious backdoor threat of SSL where unlabeled training data are poisoned by backdoor methods migrated from supervised settings. Then, to further exploit this threat, a Deep Neural Backdoor (DeNeB) scheme is proposed, which requires less data poisoning budgets and produces stronger backdoor effectiveness. By poisoning a fraction of unlabeled training data, the DeNeB achieves the illegal manipulation on the trained model without modifying the training process. Finally, an efficient detection-and-purification defense (DePuD) framework is proposed to thwart the proposed scheme. In DePuD, we construct a deep detector to locate trigger patterns in the unlabeled training data, and perform secured SSL training with purified unlabeled data where the detected trigger patterns are obfuscated. Extensive experiments based on benchmark datasets are performed to demonstrate the huge threatening of DeNeB and the effectiveness of DePuD. To our best knowledge, this is the first work to achieve the backdoor and its defense in semi-supervised learning.

The industrial control and automation systems have played an increasingly important role in critical manufacturing processes. In such systems, many Internet of Things devices continuously collect large number of streaming data for real-time processing. Verifiable data streaming (VDS) addresses such authenticity issue for streaming data, but most VDS schemes are not efficient and lightweight, do not support range querying, and cannot be used in practice. To improve the efficiency and achieve a verifiable range query in data streaming, we present here a new primitive, namely, a chameleon authentication tree with prefixes (PCAT), which is extended from the PBTree and chameleon authentication tree. Our scheme is not only lightweight but also supports dynamic expansion and verifiable range query in data streaming, making it more suitable for resource-constrained devices. We separate the PCAT's algorithms into the following phases: initialization, data appending, query, and verification. Our analyses prove that the PCAT satisfies all the security requirements of VDS. Moreover, an efficiency analysis and performance evaluation demonstrate that our scheme not only supports lightweight data streaming authentication but also has high efficiency, which means that the PCAT is easier to apply in the industrial control and automation systems.

The papers in this special section focus on the topic of green industrial Internet of Things (IIoT). These papers aim to consolidate the current state of the art in terms of fundamental research ideas and network engineering, geared toward exploiting greenness of IIoT. The IIoT is a new ecosystem that combines intelligent and autonomous machines, advanced predictive analytics, and machine–human collaboration to improve productivity, efficiency, and reliability. IIoT connects billions of mobile digital devices, manufacturing machines, industrial equipment, etc., and generates an unprecedented volume of industrial data. The gap between the rapidly growing demands of data rate and existing bandwidth-limited network infrastructures has become ever prominent. Moreover, the interaction and connection of things in IIoT will consume substantial energy in contrast with limited energy storage of the things. Therefore, the greenness of IIoT is crucial for the success of IIoT. In particular, with the prevalence of mobile devices, electronic devices, cameras, social networks, social media, etc., ourworld is generating big data and multimedia big data, which further aggregate the energy demand in terms of the data transmission and processing of IIoT.

As major infrastructures are increasingly depending on electricity, the smart grid has become an important base for industrial manufacturing and residential living. Despite the benefits of smart grids, the reliability and continuity of power services are often threatened by severe nature disasters and human errors. In smart grids, the centralized and often large-sized grid equipment hinder the rapid recovery and flexible reconfiguration in an emergency. Meanwhile, the large amount of personal equipment and their invisibility make it difficult for the grid operators to utilize assets optimally and easily. In addition, since the power service is provided by multiple energy functions, which consists voltage transformation, transmission, and storage, only considering the restoration of power generation function will restrict the service capacity and lengthen the response time. To address these problems, this article proposes an energy function virtualization for smart grid to decouple the implementation of energy functions from the underlying physical infrastructure to speed up the deployment and test of energy functions. With the help of distributed infrastructure resources, manager can redeploy energy functions and accelerate the service response in smart grid. To motivate prosumers to contribute private function resources, an optimized network calculus performance assessment scheme and a game theory-based resource orchestration scheme are proposed. Simulation results show that proposed scheme can dynamically adjust the delay factor to shorten the emergency response time.

A large scale fast-growing data generated in intelligent transportation systems (ITS) has become a ponderous burden on the coordination of heterogeneous transportation networks, which makes the traditional cloud-centric storage architecture no longer satisfy new data analytics requirements. Meanwhile, the lack of storage trust between ITS devices and edge servers could lead to security risks in the data storage process. However, a unified data distributed storage architecture for ITS with intelligent management and trustworthiness is absent in the previous works. To address these challenges, this paper proposes a distributed trustworthy storage architecture with reinforcement learning in ITS, which also promotes edge services. We adopt an intelligent storage scheme to store data dynamically with reinforcement learning based on trustworthiness and popularity, which improves resource scheduling and storage space allocation. Besides, trapdoor hashing based identity authentication protocol is proposed to secure transportation network access. Due to the interaction between cooperative devices, our proposed trust evaluation mechanism is provided with extensibility in the various ITS. Simulation results demonstrate that our proposed distributed trustworthy storage architecture outperforms the compared ones in terms of trustworthiness and efficiency.

Fatigue driving is one of main causes of traffic accidents. To avoid such traffic accidents, divers' fatigue detection has been used in Intelligent Internet of Vehicles (IIoV). IIoV usually dynamically allocate computing resources according to drivers' fatigue degree to improve the real-time of fatigue detection model. However, the traditional fatigue detection model may have bias on certain groups, which would further cause unfair resource allocation. To solve the problem, this paper proposes an improved IIoV framework, named Fair-Intelligent Internet of Vehicles (FIIoV). Compared with IIoV, we improve two layers in FIIoV, i.e., the detection layer and the normalization layer. The detection layer uses Convolutional Neural Network (CNN) to detect drivers' fatigue degree, and then uses adversarial network to achieve fairness of detection models. The normalization layer achieves the distribution of different sensitive feature values from historical detection results generated in the detection layer, and then uses the distribution to normalize the output of the detection layer to improve the fairness and accuracy of fatigue detection models. Simulation results show that both accuracy and fairness of FIIoV is improved compared with the original IIoV.

As increasingly advanced data collection and computing abilities are equipped by devices at the network edge, accompanying the vigorous development of machine learning, edge devices become both the consumer and provider of data. Due to the timeliness of some learning demands and the necessity of learning results, learning resources such as data collection, transmission, and learning should be unified and converged to meet timely learning needs. In this paper, we propose a framework to implement a distributed Learning-as-a-Service function by edge-cloud collaboratively integrating resources required by a learning task. First, the architecture of RALaaS and underlying information interaction are proposed. We then formulate the learning-resource allocation problem and propose a deep reinforcement learning based solution to minimize the required learning resource and achieve better accuracy. More precisely, an A3C algorithm is presented to schedule tasks among smart connected communities and aggregate models. Finally, evaluation results show that our proposed framework can improve the accuracy by 10% compared with conventional algorithms and save about 50% edge resources when 30 nodes participate in the learning task.

As a new network architecture, Information-Centric Networks (ICN) has great advantages in content distribution and can better meet our needs. But it faced with many threats unavoidably. There are four types of attack in ICN: naming related attacks, routing related attacks, caching related attacks and miscellaneous attacks. These attacks will undermine the availability of ICN, the confidentiality and privacy of data. In addition, routers store a large amount of content for the users' request, and it is necessary to protect these intermediate nodes. Since the styles of content stored in nodes are not the same, using a unified set of intrusion detection rules simply will cause a large number of false positives and false negatives. Therefore, every node should perform intrusion detection according to its own characteristics. In this paper, we propose an intrusion detection mechanism to alert for abnormal packets. We introduce a extensive solution using machine learning for attacks in ICN. Moreover, the nodes in this scheme can adapt to the external environment and intelligently detect packets. Simulation on the machine learning algorithm involved prove that the algorithm is effective and suitable for network packets.

Standardization facilitates the management of Internet of Things (IoT) and expedites the generation of IoT big data. However, there is not yet a big data management architecture matching such IoT. Current methodologies, which mainly adopts Simple Network Management Protocol (SNMP), is defective in the following two aspects. First, facing ubiquitous sensor and actuator nodes, timeliness and scalability can hardly be assured by the centralized paradigm. Second, existing management infrastructure cannot perform data analysis and is thus not smart enough, which wastes the value of big data. To address these issues, we propose a big data management architecture for standardized IoT. First, we design a scalable and smart SNMP, which has a hierarchical and decentralized paradigm, and is embedded with edge MapReduce to perform distributed big data analysis. Second, we put forward an Edge MapReduce-based Random Matrix Model (RMM) algorithm for anomaly detection in IoT, which is parallelized and particularly suitable for high-dimensional big data. Third, we conduct a case study of smart grids, where the architecture is implemented using virtual machines and deployed to detect malfunctions in electrical grids. Experiment results demonstrate that the architecture has good performance in terms of timeliness and scalability.

Advanced driver-assistance systems (ADASs) have been proposed as an alternative to driverless vehicles to provide support for automotive vehicle decisions. As a significant driving force for ADASs, the augmented reality (AR) provides comprehensive location-based content services for in-vehicle consumers. With the increase in request for information sharing, the current standalone mode of ADASs needs a shift to the multiuser sharing mode. In this article, to address the high mobility and real time requirements of ADASs in 5G environments, and also to address the resource orchestration and service management of big data in intelligent transportation systems, we integrate the information-centric network (ICN) and the network function virtualization (NFV) with ADASs to support an efficient AR-assisted content sharing and distribution. This integration eliminates the imbalance between the content requests and the resource limitation by splitting the virtual resources and providing an on-demand network and resource slicing in ADASs. We propose an incentive trading model for assistance content caching services and also propose a novel mechanism for optimal content cache allocation. Our extensive evaluation confirms that our proposed mechanism outperforms the past literature in terms of the cache hit ratio and latency.

With development of online social networks, many criminal suspects use social network to communicate with each other. In order to obtain valuable criminal clues, considerable research works have been done to analyze criminal suspects' social data. However, most of them did not pay much attention on privacy-preserving problems, which may leak some sensitive data in the analysis process. To solve this problem, we propose a novel analysis approach of criminal suspects by exploiting social data and crime data that are collected by social network and police information systems. We enable the social cloud server and public security cloud server to exchange social information of criminal suspects and user's public information in a privacy-preserving way. Specifically, we propose a privacy-preserving data retrieving method based on oblivious transfer to guarantee that only the authorized entities can perform queries on suspects' social data, while the social cloud server cannot infer anything during the query. Moreover, several building blocks, such as encrypted data comparing, secure classification and regression tree (CART) model are also proposed. Based on these building blocks, we designed a privacy-preserving criminal suspects sensing scheme. Finally, we demonstrate a performance evaluation which shows that our scheme can enhance analysis of criminal suspects without privacy leakage, while with low overhead.

Individually reinforcing the robustness of a single deep learning model only gives limited security guarantees especially when facing adversarial examples. In this article, we propose DeSVig, a decentralized swift vigilance framework to identify adversarial attacks in an industrial artificial intelligence systems (IAISs), which enables IAISs to correct the mistake in a few seconds. The DeSVig is highly decentralized, which improves the effectiveness of recognizing abnormal inputs. We try to overcome the challenges on ultralow latency caused by dynamics in industries using peculiarly designated mobile edge computing and generative adversarial networks. The most important advantage of our work is that it can significantly reduce the failure risks of being deceived by adversarial examples, which is critical for safety-prioritized and delay-sensitive environments. In our experiments, adversarial examples of industrial electronic components are generated by several classical attacking models. Experimental results demonstrate that the DeSVig is more robust, efficient, and scalable than some state-of-art defenses.

The Internet of Multimedia Things (IoMT) has become the backbone of innumerable multimedia applications in various fields. The wide application of IoMT not only makes our life convenient but also brings challenges to service discovery. Service discovery aims to leverage location information and trust evidence scattered in a variety of multimedia applications to find trusted IoMT devices that can provide specific service in target areas. However, the eavesdropping and tampering to these sensitive IoMT data during the trust propagation process invalidate the service discovery process. To address these challenges, we propose Secure Service Discovery (SSD) for IoMT using cross-blockchain-enabled fog computing. To resist the tampering and eavesdropping during the trust propagation process, a scalable cross-blockchain structure consisting of multiple parallel blockchains is first proposed based on fog, in which different parallel blockchains can be orchestrated to propagate encrypted location information and trust evidence of different applications. Moreover, to enable a cross-blockchain structure to leverage encrypted location information and trust evidence to find trusted IoMT devices in preset areas, a novel privacy-preserving range query is proposed to query and aggregate trust evidence. Security analysis and simulations are carried out to demonstrate the effectiveness and security of the proposed SSD.

Intelligent embedded-enable smart manufacturing is an important infrastructure for future industries. Increasing security threats are disturbing the normal operations of smart manufacturing. As a novel type of threat, an advanced persistent threat (APT) has the novel features of strong concealment, latency, and long-term entanglement, which can penetrate the core systems of smart manufacturing, especially for intelligent embedded systems, and cause great destruction from the cyber side to physical side. However, the existing security schemes cannot provide sustainable resource management, which causes the core system in smart manufacturing not to perform sustainable secure detection and defense against APTs. To address this challenge, this paper proposes a sustainable secure management mechanism for smart manufacturing against APTs. The proposed mechanism includes two parts: sustainable threat intelligence analysis and sustainable secure resource management. Sustainable threat intelligence analysis provides sustainable discovery of the indications of potential APTs, which has features of a weak signal, low correlation, and slow time variation. The sustainable secure resource management provides deep and continuous protection for intelligent embedded systems in smart manufacturing. The evaluations show the defense capabilities and the feasibility of the proposed mechanism.

Network edge equipment has generated a large amount of fast- growing data, which has placed a heavy burden on the collaboration of heterogeneous networks. Due to the diversity of edge computing application scenarios, many new requirements are advocated for unified data storage management, such as latency and processing efficiency. Traditional centralized cloud storage can no longer meet the on- demand of edge computing in the case of a surge in data volume. Therefore, a unified storage architecture is required for the current improvements in computational offloading schemes and storage optimization algorithms. To solve these challenges and make data intelligent collaborative storable, this paper proposes a novel unified storage architecture for big data in the edge-cloud, which supports edge services in order to extend Hadoop at the edge. The functions of the edge nodes are proposed to synchronize the edge nodes of the same neighborhood and store data dynamically via Q- learning based on popularity, in order to mitigate network load pressure and improve the efficiency of edge services. An intelligent scheme that impacts the quality of service (QoS) through data marginal storage is proposed to improve the resource scheduling and to the distribution of storage space. Simulation results demonstrate the merits and efficiency of the proposed intelligent architecture is superior to the comparison schemes.

Due to the shortage and unbalance of medical resources, it is difficult for patients in the countryside to get high-quality and timely medical services from the central medical facility. Existing researches of fog e-health has the potential of providing real-time medical services for the countryside with body sensor networks (BSN), but there are two limitations. On one hand, because of the medical services requiring not only low-latency but also high-quality, constructing an AI e-health service on resource-constrained fog with edge AI is necessary but unsolved. On the other hand, because of the regional differences in disease risk, there is a lack of an effective mechanism to provide a customized fog AI e-health service for patients in different regions. To address these issues, a smart customized e-health (SCEH) framework is proposed in this paper to provide edge-intelligent and customized medical services for the countryside. Firstly, semantics-based lightweight and meticulous load management mechanism is designed to reduce data load and involve medical semantic. Secondly, model-ensemble based fog AI collaborative analysis mechanism is proposed for load balance and knowledge integration. Thirdly, an attention-weight based customized fog AI e-health generation mechanism is devised for regional medical model reconstruction. The simulation results demonstrate the effectiveness of SCEH which ensures both the accuracy and low latency of fog e-health with limited resource.

With the development of smart cities, the demand for artificial intelligence (AI) based services grows exponentially. The existing works just focus on cloud- edge or edge-device cooperative AI which suffers low learning efficiency of AI, while edge-to-edge cooperative AI is still an unresolved issue. Moreover, the existing researches concentrate on the computation offloading of the AI-based task, ignoring that it is a brain-like task performing sophisticated processing to raw data, which leads to the high latency and low quality of the learning services. To address these challenges, this paper proposes an on-demand learning offloading mechanism for edge-to-edge cooperative AI. Firstly, the principle of the learning capability and its offloading are proposed for the formal description of the learning resources migration. Secondly, the proposed mechanism realizes the bilateral learning offloading utilizing edge-to-edge and cloud-edge collaborations to handle AI-based tasks with high learning efficiency and resource utilization rate. Moreover, we model the edge-to-edge learning offloading allocation based on the concatenation of deep neural network (DNN) subtasks and their heterogeneous requirement of learning resources. Simulation results indicate the rationality and efficiency of the proposed mechanism.

Internet of Things(IoT) is an important part of the new generation of information technology and an important stage of development in the era of informatization. As a next generation network, Information Centric Network (ICN) has been introduced into the IoT, leading to the content independence of IC-IoT. To manage the changing network conditions and diagnose the cause of anomalies within it, network operators must obtain and analyze network status information from monitoring tools. However, traditional network supervision method will not be applicable to IC-IoT centered on content rather than IP. Moreover, the surge in information volume will also bring about insufficient information distribution, and the data location in the traditional management information base is fixed and cannot be added or deleted. To overcome these problems, we propose a name-based smart tracking system to store network state information in the IC-IoT. Firstly, we design a new structure of management information base that records various network state information and changes its naming format. Secondly, we use a tracking method to obtain the required network status information. When the manager issues a status request, each data block has a defined data tracking table to record past requests, the location of the status data required can be located according to it. Thirdly, we put forward an adaptive network data location replacement strategy based on the importance of stored data blocks, so that the information with higher importance will be closer to the management center for more efficient acquisition. Simulation results indicate the feasibility of the proposed scheme.

Software-defined networking (SDN) allows the smart grid to be centrally controlled and managed by decoupling the control plane from the data plane, but it also expands attack surface for attackers. Existing studies about the security of SDN-enabled smart grid (SDSG) mainly focused on static methods such as access control and identity authentication, which is vulnerable to attackers that carefully probe the system. As the attacks become more variable and complex, there is an urgent need for dynamic defense methods. In this paper, we propose a security function virtualization (SFV) based moving target defense of SDSG which makes the attack surface constantly changing. First, we design a dynamic defense mechanism by migrating virtual security function (VSF) instances as the traffic state changes. The centralized SDN controller is re-designed for global status monitoring and migration management. Moreover, we formalize the VSF instances migration problem as an integer nonlinear programming problem with multiple constraints and design a pre-migration algorithm to prevent VSF instances' resources from being exhausted. Simulation results indicate the feasibility of the proposed scheme.

Information-centric networking (ICN) is a novel networking architecture with subscription-based naming mechanism and efficient caching, which has abundant semantic features. However, existing defense studies in ICN fails to isolate or block efficiently novel content threats including malicious penetration and semantic obfuscation for the lack of researches considering ICN semantic features. More importantly, to detect potential threats, existing security works in ICN fail to use semantic reasoning to construct security knowledge-based defense mechanism. Thus ICN needs a smart and content-based defense mechanism. Current works are not able to block content threats implicated in semantics. Additionally, based on traditional computing resources, they are incompatible with ICN protocols. In this paper, we propose smart reasoning based content threat defense for semantics knowledge enhanced ICN. A fog computing based defense mechanism with content semantic awareness is designed to build ICN edge defense system. In addition, smart reasoning algorithms is proposed to detect implicit knowledge and semantic relations in packet names and contents with context communication content and knowledge graph. On top of inference knowledge, the mechanism can perceive threats from ICN interests. Simulations demonstrate the validity and efficiency of the proposed mechanism.

With the advent of 5G mobile communication systems, the evolution of the Internet of Vehicles (IoV) will be greatly promoted, and it will be able to meet people's demands extremely in terms of performance and functional requirements. Moreover, fog computing has been proposed at the edge of the 5G-enabled IoV architecture, to provide data storage, calculation, and interaction with vehicles. Furthermore, the Global Navigation Satellite Systems (GNSS) play a significant role in future 5G-enabled IoV environment. However, navigation systems usually lose effectiveness because of various factors and disturbances in the 5G-enabled IoV, such as severe weather, GNSS device failure, and skyscrapers that block GNSS signals. Meanwhile, since some measurements come from the NLOS path, this can result in very large positioning errors. To address these challenges, this paper proposes a GNSS-free emergency location-based service (LBS) using fog computing in 5G-enabled IoV. Firstly, we propose the topology structure of the 5G-enabled IoV to address the problem of GNSS failure. Then, the operation procedures of emergency location service and the hierarchical logical architecture of the fog based service networks are designed. Finally, a vehicle positioning algorithm is proposed based on semi-definite programming (SDP) for positioning errors in the 5G-enabled IoV environment. Simulation results verify that the method can achieve high-precision location estimation for the 5G-enabled IoV.

Mobile Edge Computing (MEC) is a novel platform to bring computation resources close to local users in vicinity constrained, obtaining the nickname of Cloudlet on the edge of the network. However, due to users' behaviors, computation resources demands show spatial and temporal dynamics among different Cloudlets, which is hardly to achieve on-demand computation workload balance management. While vehicles, unique for their mobility and powerful on- board equipments, could act as computation resources transporters breaking geographically restriction, which have potential to balance computation demands in the city. To address the issue above, in this paper, we design a novel computation demand response management (DRM) mechanism called Vehicle-to-Cloudlet (V2C), considering the mobility of vehicles, computation states of vehicles, and computation demands of Cloudlets. There exists two phases in V2C mechanism: cognitive phase and game phase, respectively. In cognitive phase, which Cloudlets are computation-scarce and which vehicles are potential computation resources can be cognized. Then, in game phase, to simulate computation resources trading process among Cloudlet service provider and individual vehicles, we formulate a price-based two-stage Stackelberg game, jointly maximizing the utility of the Cloudlet and the individual utility of each vehicles. We prove that unique Nash Equilibrium (NE) and Stackelberg Equilibrium (SE) exist in this game and propose a gradient iterative algorithm to obtain the optimal solution. Finally, numerical simulations show that our solution has good scalability and also encourages vehicles to trade their own computation resources to the Cloudlet.

The Information-Centric Network possessing the content-centric features, is the innovative architecture of the next generation of network. Collaborating with fog computing characterized by its strong edge power, ICN will become the development trend of the future network. The emergence of Information-Centric Multimedia Network (ICMN) can meet the increasing demand for transmission of multimedia streams in the current Internet environment. The data transmission has become more delay-constrained and convenient because of the distributed storage, the separation between the location of information and terminals, and the strong cacheability of each node in ICN. However, at the same time, the security of the multimedia streams in the delivery process still requires further protection against wiretapping, interception or attacking. In this paper, we propose the delay-constrained green security communications for ICMN based on chaotic encryption and fog computing so as to transmit multimedia streams in a more secure and time-saving way. We adapt a chaotic cryptographic method to ICMN, implementing the encryption and decryption of multimedia streams. Meanwhile, the network edge capability to process the encryption and decryption is enhanced. Thanks to the fog computing, the strengthened transmission speed of the multimedia streams can fulfill the need for short latency. The work in the paper is of great significance to improve the green security communications of multimedia streams in ICMN.

The cyber-physical system (CPS) is a promising technique that enables a safety-critical industry ecosystem. In general, wireless sensor networks (WSNs) and the Internet of Things are the sensing and communication infrastructures for CPS. Currently, software-defined networking (SDN) have been used as the new networking architecture for typical WSNs and CPS. However, there are two unresolved problems for a software-defined CPS. First, a feasible systemic architecture is a must for software-defined CPS, which should provide a global virtualization management and closed-loop control between the cyber side and the physical side in a CPS. Second, the lifetime of a software-defined CPS scenario needs to be extended for critical applications. To address the above challenges, this paper proposes a systematic virtual networking architecture to perform the global virtualization control and monitoring of a CPS, in which network functions virtualization (NFV) configuration and orchestration can be realized. Moreover, based on the proposed architecture, a novel lifetime extension scheme (NLES), is proposed for a software-defined CPS. To orchestrate the resource dynamically and efficiently, the instant programmability of an SDN and instant deployment capability of NFV are utilized to control the topology of node modes of a CPS. Then, a game theoretic topology decision approach is proposed to control the topology of the clustering and virtual network function deployment of sensors at run-time in a CPS. The experimental results show that NLES has longer lifetime compared to those of the traditional schemes.

With the ubiquity of wireless network and the intelligentization of machines, Internet of Things (IoT) has come to people's horizon. Device-to-device (D2D), as one advanced technique to achieve the vision of IoT, supports a high speed peer-to-peer transmission without fixed infrastructure forwarding which can enable fast content distribution in local area. In this paper, we address the content distribution problem by multihop D2D communication with decentralized content providers locating in the networks. We consider a cross-layer multidimension optimization involving frequency, space, and time, to minimize the network average delay. Considering the multicast feature, we first formulate the problem as a coalitional game based on the payoffs of content requesters, and then, propose a time-varying coalition formation-based algorithm to spread the popular content within the shortest possible time. Simulation results show that the proposed approach can achieve a fast content distribution across the whole area, and the performance on network average delay is much better than other heuristic approaches.

Currently, blockchain technology has been widely used due to its support of transaction trust and security in next generation society. Using Internet of Things (IoT) to mine makes blockchain more ubiquitous and decentralized, which has become a main development trend of blockchain. However, the limited resources of existing IoT cannot satisfy the high requirements of on-demand energy consumption in the mining process through a decentralized way. To address this, we propose a decentralized on-demand energy supply approach based on microgrids to provide decentralized on-demand energy for mining in IoT devices. First, energy supply architecture is proposed to satisfy different energy demands of miners in response to different consensus protocols. Then, we formulate the energy allocation as a Stackelberg game and adapt backward induction to achieve an optimal profit strategy for both microgrids and miners in IoT. The simulation results show the fairness and incentive of the proposed approach.

Nowadays, benefit from more powerful edge computing devices and edge artificial intelligence (edge-AI) could be introduced into Internet of Things (IoT) to find the knowledge derived from massive sensory data, such as cyber results or models of classification, and detection and prediction from physical environments. Heterogeneous edge-AI devices in IoT will generate isolated and distributed knowledge slices, thus knowledge collaboration and exchange are required to complete complex tasks in IoT intelligent applications with numerous selfish nodes. Therefore, knowledge trading is needed for paid sharing in edge-AI enabled IoT. Most existing works only focus on knowledge generation rather than trading in IoT. To address this issue, in this paper, we propose a peer-to-peer (P2P) knowledge market to make knowledge tradable in edge-AI enabled IoT. We first propose an implementation architecture of the knowledge market. Moreover, we develop a knowledge consortium blockchain for secure and efficient knowledge management and trading for the market, which includes a new cryptographic currency knowledge coin, smart contracts, and a new consensus mechanism proof of trading. Besides, a noncooperative game based knowledge pricing strategy with incentives for the market is also proposed. The security analysis and performance simulation show the security and efficiency of our knowledge market and incentive effects of knowledge pricing strategy. To the best of our knowledge, it is the first time to propose an efficient and incentive P2P knowledge market in edge-AI enabled IoT.

Information-centric networking (ICN) is a significant next-generation network architecture with caching ability that greatly affects the network service performance. Because of limited caching space, on-demand cache configuration is an important issue to optimize redundant cache, shorten the time delay and reduce the traffic overhead. Existing works on caching mainly realize cache configuration in a single dimension such as content popularity or node weight, which cannot realize multilevel comprehensive decision-making for efficient on-demand caching services in complex ICN. Moreover, existing works just focus on centralized cache configuration, which causes low efficiency and high latency. To address aforementioned challenges, we propose an on-demand fog caching service for ICN, named FogCache, which is realized by perceiving synthetical content popularity, traffic and time cost, and node importance. Regional fog nodes are adapted to configure caching service at the edge of ICN, in which the additional complexity is low because just minimal byte information is added at the end of the data packets. FogCache achieves multiple dimensional synthesis of awareness unified by building a hierarchical factor structure into four layers: target layer, criteria layer, indicator layer, and observation layer. Simulation results show that the proposed FogCache gets better performances of caching than related schemes.

The diverse applications and high-quality services in satellite-enabled smart cities have led to geographical unbalance of computation requirements. Traditional centralized cloud services and massive migration of computing tasks result in the increase of network delay and the aggravation of network congestion. Deploying fog nodes at the network edge has become a way to improve the quality of service (QoS). However, the dynamic requirements and application in various scenarios still challenge the network, resulting in geographical unbalance of computing resource demands. Nowadays, computing resources of on-board computers and devices in the Internet of Vehicles (IoV) are abundant enough to mitigate the geographical unbalances in computing power demand. Efficient usage of the natural mobility of constantly moving vehicles to solve the problems above remains an urgent need. In this paper, vehicle mobility-based geographical migration model of vehicular computing resource is established for satellite-enabled smart cities. By using the road- status-awareness of fog nodes, the status of roads is precisely quantified as the basis for vehicle mobility- based resource migration. An incentive scheme that affects the vehicle path selection through resource pricing is proposed to balance the resource requirements and to geographically allocate computing resources. Simulation results indicate that the advantages and efficiency of the proposed scheme are significant.

As a next-generation networking architecture, information-centric networks (ICN) has strengthened the focus on physical location-independent content sharing, which introduces abundant semantic features and novel access approach. However, the semantic modeling of ICN is an unresolved problem, thus current ICN lacks the capabilities of smart content analysis and understanding to support the knowledge decision for optimized user experience. To address this issue, we propose a semantic ICN model, Sema-ICN, that can provide logically related information depending on content-relevance-based relationships extraction and name-based weight setting. Moreover, besides the great benefits brought into ICN by semantic features, Sema- ICN will also contribute to security protection against anomalous access, which usually the basis of further threats. In this paper, we additionally design a smart anomalous access detection scheme supported by Sema- ICN, in which semantic communities are partitioned utilizing spectral clustering according to content name with semantic attributes. And a forecast model is introduced to predict access situation based on triple exponential smoothing algorithm using historical request data, access traffic that is beyond the forecast results will be considered as anomalous. The simulation results demonstrate the efficiency of the proposed scheme. To the best of our knowledge, this work is the first to propose a novel semantic model for ICN.

Recently, an accelerating number of studies are dedicated to deploying various IoT applications in the information centric network paradigm which has lower system complexity than traditional network architectures. However, such a paradigm poses a number of security challenges especially when it is applied in real-time e-health applications. Firstly, it is difficult to ensure security of sensitive data in such a distributed data caching environment because after the data is published in the form of a packet to the information centric network (ICN), it is no longer controlled by the data publisher. Secondly, in some real-time e-health applications, terminal medical sensors are usually resource-constrained, limiting the direct adoption of expensive cryptographic primitives. In order to address these challenges, a resource-efficient secure data sharing scheme in information centric e-health system is proposed, one that utilizes ciphertext-policy attribute based encryption (CP-ABE) and adapts it to the above-mentioned system with respect to necessary security requirements. It also exploits computation resources of fog nodes and employs outsourcing cryptography to improve system efficiency.The evaluation demonstrates that the scheme can significantly reduce the computation overheads of the resource-constrained terminal medical devices, and can better support the real-time e-health applications.

Information Centric Network (ICN) is one of the promising architectures in the next generation networks. The content-based routing in ICN can satisfy the content distribution of large-scale data. For prompt content obtainment, it is important to realize the content analysis before the content reaches application layer. The novel characteristics of data naming in ICN make it possible to search and analyse content during the transmission of content, which can directly get the critical content without the process of the application layer. In this paper, we propose a MapReduce enabling content analysis architecture for ICN. MapReduce framework can realize the parallelization of content collection and analysis during the routing process. For more efficient content collection, we put forward an optimal selection for mapper nodes. Moreover, Convolutional Neural Network (CNN) is deployed in the MapReduce architecture providing further analysis for ICN content. The simulation result shows the advantages of the proposed architecture.

With integration between Internet-of-Things (IoT) and industrial systems, Industrial Internet of Things (IIoT) raises more efficiency and security access for monitoring and control. The IEEE 21451 standard, gradually accepted by manufacturers and users, aims to solve the problem of unified sensor communication standard for sensor networks, especially for wireless sensor networks. Although IEEE 21451-1-5 can realize the simple network management for wireless IIoT, efficient security access during the network management continues to be an unresolved problem. In this paper, we propose an efficient secure access scheme for IEEE 21451 based wireless IIoT using optimized Transducer Electronic Data Sheet (TEDS) and Management Information Base (MIB). First, MIB is adapted to index efficiently TEDS of IIoT, which is extended to record the parameters of network status. Second, optimized MIB is reconstructed to monitor and configure the attributes of IIoT management data. Third, MIB based Dynamic Role based Access Control (DRBAC) is proposed for flexible security access in wireless IIoT. Simulation results verify the advantages of the propose scheme. The proposed scheme is momentous to realize the simplifying, extensibility and security data access in wireless IIoT.

Nowadays, cyberspace has become a vital part of social infrastructure. With the rapid development of the scale of networks, applications and services have become enriched, and the bearing function of the underlying network devices (such as switches and routers) has also been extended. To promote the dynamics architecture, high-level security, and high quality of service of the network, control network architecture forward separation is a development trend of the networking technology. Currently, software-defined networking (SDN) is one of the most popular and promising technologies. In SDN, high-level strategies are deployed by the proprietary equipment, which is used to guide the data forwarding of the network equipment. This can reduce many complicated functions of the network equipment and improve the flexibility and operability of the implementation and deployment of new network technologies and protocols. However, this novel networking technology faces novel challenges in term of architecture and security. The aim of this study is to offer a comprehensive review of the state-of-the-art research on novel advances of programmable SDN, and to highlight what has been investigated and what remains to be addressed, particularly, in terms of architecture and security.

Advanced communications and data processing technologies bring great benefits to the smart grid. However, cyber-security threats also extend from the information system to the smart grid. The existing security works for smart grid focus on traditional protection and detection methods. However, a lot of threats occur in a very short time and overlooked by exiting security components. These threats usually have huge impacts on smart gird and disturb its normal operation. Moreover, it is too late to take action to defend against the threats once they are detected, and damages could be difficult to repair. To address this issue, this paper proposes a security situational awareness mechanism based on the analysis of big data in the smart grid. Fuzzy cluster based analytical method, game theory and reinforcement learning are integrated seamlessly to perform the security situational analysis for the smart grid. The simulation and experimental results show the advantages of our scheme in terms of high efficiency and low error rate for security situational awareness.

Recently, fog computing has gained increasing attention in processing the computing tasks of the industrial Internet of things (IIoT) with different service popularity. In task-diversified fog computing-enabled IIoT (F-IIoT), the mismatch between expected computing efficiency and partitioned resources on fog nodes (FNs) may pose serious traffic congestion even large-scale industrial service interruptions. The existing works mainly studied offloading which type of computing tasks into FNs, but few studies enabled smart resource partitioning of FNs. In this paper, a service popularity-based smart resources partitioning (SPSRP) scheme is proposed for fog computing-enabled IIoT. We first exploit Zipf's law to model the relationship between popularity ranks and computing costs of IIoT services. Moreover, we propose an implementation architecture of the SPSRP scheme for F-IIoT, which decouples the computing control layer from data processing layer of IIoT through a specified SPSRP controller. Besides, a mobility and heterogeneity-aware partitioning algorithm is presented for extending SPSRP scheme to seamlessly support cross-domain resources partitioning. The simulations demonstrate that the SPSRP scheme can bring notable performance improvements on delay time, successful response rate and fault tolerance for fog computing to deal with the large-scale IIoT services.

In software-defined networks (SDNs), the abstracted control plane is its symbolic characteristic, whose core component is the software-based controller. The control plane is logically centralized, but the controllers can be physically distributed and composed of multiple nodes. To meet the service management requirements of large-scale network scenarios, the control plane is usually implemented in the form of distributed controller clusters. Cluster management technology monitors all types of events and must maintain a consistent global network status, which usually leads to big data in SDNs. Simultaneously, the cluster security is an open issue because of the programmable and dynamic features of SDNs. To address the above challenges, this paper proposes a big data analysis-based secure cluster management architecture for the optimized control plane. A security authentication scheme is proposed for cluster management. Moreover, we propose an ant colony optimization approach that enables big data analysis scheme and the implementation system that optimizes the control plane. Simulations and comparisons show the feasibility and efficiency of the proposed scheme. The proposed scheme is significant in improving the security and efficiency SDN control plane.

Information-centric networking (ICN) and software defined networking (SDN) has been perceived as a promising paradigm for integrating distributed generation (DG) into smart grid networks for flexibility and dynamic features. However, flexible, controllable and reliable time synchronization still remains an open issue for supervisory control and data sensing in smart grid. Firstly, for information-centric smart grid, smart grid entities may obtain available data from caching routers, and data delivery between caching routers with edge devices results in time synchronization requirements of on-path caching routers. Without on-path time synchronization of caching routers, it may lead to maliciously fluctuations if energy data for monitoring the energy supplying and consumption are collected and traversed at an inappropriate time. Secondly, as scale expanding and network environment of smart grid becomes complex and changeable, on-path time synchronization needs a unified and dynamic management and control. To address these issues, in this paper we propose a novel scheme of software- defined on-path time synchronization (SD-OPTS) scheme for information-centric smart grid. In proposed scheme, all on-path caching routers share the time stamps from master clock and synchronize the time of local clock during one-time synchronization process. Besides, SDN controller estimates on-path caching routers' sync error, before choosing the nearest nodes to implement accurate time synchronization. The simulation results demonstrate the efficiency of software-defined on-path time synchronization scheme. The SD-OPTS scheme supports the flexibility, controllability and reliability of time synchronization.

Fog computing, shifting intelligence and resources from remote cloud to edge networks, has the potential of providing low-latency for the end-to-end communication from data sources to users. However, it's hard to enhance resource-efficiency in the existing relatively static and proprietary framework of fog nodes due to the diversity of service requirements. With the growing deployment of fog computing, the overall resource consumption in fog will be huge without considering the efficient service provisioning in each fog node. On one hand, different fog users require diverse local services policies which are carried out within the fog nodes. Moreover, for one user, the requirements on services are time-varying. On the other hand, the processing strategies on different types of content (e.g. video, audio, etc.) are also distinct. These dynamic features impose the need for user-driven and content-based service reconstruction in order to achieve the high recycling utilization of resources of fog system. To this end, we propose a software-defined efficient service reconstruction (SDSR) scheme in fog using content awareness and weighted graph. Service reconstruction mechanism is devised to dynamically recycle modularized resources after mapping different contents to relevant operations. Weighted graph is introduced to schedule and optimize the services reconstruction in terms of resource saving during content-driven controlling. User-defined interfaces are designed to enable fog users to reconfigure the recyclable resource modules. Simulation results demonstrate that the service cost of each fog nodes is reduced significantly, thus promote efficient service provisioning for the whole fog system.

E-health is one of the domains which will need more and more IoT solutions in the future, as it requires short delays for life-dependency situations and safe local storage spaces for privacy matters. The emergency context demands efficient communication and computing capacities, and the cloud vision as well as traditional TCP/IP way of communication cannot really suit these requirements. New approaches are currently used : the fog computing paradigm, which describe the IoT environment in a more relevant way than the cloud vision, provides new performances as low latency, local storage opportunities, end-to-user services. E-health fogs have attracted a lot of attentions. To realize the efficient communication, E-Health systems also introduce Content-Centric Network (CCN) approach, whose characteristics include quick response, content distribution, IoT oriented technology. Nowadays, the existing E-health fog or CCN are seen as separate and autonomous networks, but many applications could require that those E-health fog computing systems exchange data, share processing results, or subscribe to each other information flux. In this paper we propose a solution for E-health systems to communicate by combining fog computing and CCN to provide communication efficiency and local storage opportunities. Finally we use simulation to evaluate the delay performances of our proposal.

To enforce surrounding surveillance efficiently and reduce the heavy cost to deploy various infrastructures, mobile sinks are perceived to have potentials for utilization by wireless multimedia sensor networks (WMSNs). However, since high-mobility usually causes communication disconnections and the high re-transmission rate will consume more network resources, quality of experience (QoE) monitoring and control is a must that WMSNs with mobile sinks (MS-WMSNs) should provide satisfactory services with constrained resources. In this paper, we propose a novel QoE-named content-centric network paradigm for MS-WMSNs, which supports location independent networking and low redundancy data aggregation. Each network node constructs a hierarchical content naming tree (HCNT) negotiated by QoE parameters. The MS prioritizes the sensing data and caches them differentially by identifying these QoE parameters based content names. Simultaneously, to verify the feasibility, we design a stochastic network calculus model to analyse the performances of our proposed network paradigm at worst-case situation. Simulation results show that the proposed paradigm reduces end-to-end communication delay.

With the increasing number of mobile applications and the popularity of cloud computing, the combination of these two techniques that named mobile cloud computing (MCC) attracts great attention in recent years. A promising public key encryption scheme, Attribute-Based Encryption (ABE), especially the Ciphertext Policy Attribute-Based Encryption (CP-ABE), has been used for realizing fine-grained access control on encrypted data stored in MCC. However, the computational overhead of encryption and decryption grow with the complexity of the access policy. Thus, maintaining data security as well as efficiency of data processing in MCC are important and challenging issues. In this paper, we propose an efficient encryption method based on CP-ABE, which can lower the overhead on data owners. To further reduce the decryption overhead on data receivers, we additionally propose a verifiable outsourced decryption scheme. By security analysis and performance evaluation, the proposed scheme is proved to be secure as well as efficient.

As a shared economy platform, Airbnb provides collaborative practices for customers and guides them to match with hosts' rooms. Based on the records and ratings, there is great significance attached to inferring the satisfaction between users and rooms. Several essential problems arise when evaluating satisfaction and matching. Data confidence and prediction bias influence the inference performance of the satisfaction. When two users stay in a room, the two users' joint satisfaction also deserves particular research because of the roommate effect. In this paper, the satisfaction is inferred considering confidence and prediction uncertainties. The satisfaction with the confidence uncertainty is modeled using a normalized variance of the Beta distribution. The algorithms for inferring satisfaction with the prediction uncertainties are divided into two parts: a weighted matrix factorization-based algorithm for individuals and a preference similarity-based algorithm for pairs. The problem can be reduced to a matching problem. Finally, extensive experiments show the effectiveness and accuracy of the proposed method.

Cloud-supported Internet of Things (Cloud-IoT) has been broadly deployed in smart grid systems. The IoT front-ends are responsible for data acquisition and status supervision, while the substantial amount of data is stored and managed in the cloud server. Achieving data security and system efficiency in the data acquisition and transmission process are of great significance and challenging, because the power grid-related data is sensitive and in huge amount. In this paper, we present an efficient and secure data acquisition scheme based on ciphertext policy attribute-based encryption. Data acquired from the terminals will be partitioned into blocks and encrypted with its corresponding access subtree in sequence, thereby the data encryption and data transmission can be processed in parallel. Furthermore, we protect the information about the access tree with threshold secret sharing method, which can preserve the data privacy and integrity from users with the unauthorized sets of attributes. The formal analysis demonstrates that the proposed scheme can fulfill the security requirements of the Cloud-IoT in smart grid. The numerical analysis and experimental results indicate that our scheme can effectively reduce the time cost compared with other popular approaches.

As an extension for Internet of Things (IoT), Internet of Vehicles (IoV) achieves unified management in smart transportation area. With the development of IoV, an increasing number of vehicles are connected to the network. Large scale IoV collects data from different places and various attributes, which conform with heterogeneous nature of big data in size, volume, and dimensionality. Big data collection between vehicle and application platform becomes more and more frequent through various communication technologies, which causes evolving security attack. However, the existing protocols in IoT cannot be directly applied in big data collection in large scale IoV. The dynamic network structure and growing amount of vehicle nodes increases the complexity and necessary of the secure mechanism. In this paper, a secure mechanism for big data collection in large scale IoV is proposed for improved security performance and efficiency. To begin with, vehicles need to register in the big data center to connect into the network. Afterward, vehicles associate with big data center via mutual authentication and single sign-on algorithm. Two different secure protocols are proposed for business data and confidential data collection. The collected big data is stored securely using distributed storage. The discussion and performance evaluation result shows the security and efficiency of the proposed secure mechanism.

With the rapid growth of the Internet, the current TCP/IP based network cannot well satisfy the requirements such as scalable content distribution, mobility, security and so on. The new networking architectures which aren't based on TCP/IP have been a trade of next generation networking such as Information-Centric Networking (ICN). In smart grid, parts of communication protocol in IEC 61850 are also not based on TCP/IP architecture such as Sampled Value (SV) and Generic Object-Oriented Substation Event (GOOSE). IEC 61850 based smart substation employing wireless network has significantly improved the interoperability and interconnection of substation devices. However, with the amount and openness growth of Intelligent Electronic Devices (IED) nodes, these changes introduce new efficiency, reliability and security challenges especially in wireless network. The strict time requirement has limited the use of heavyweight security protocols to fight against cyber- attacks. To address these issues, a name-based security mechanism using ICN is proposed for the non TCP/IP based SV and GOOSE communication. Publish/ subscribe (pub/sub) based access control and lightweight encryption algorithm are utilized to secure the decentralized large-scale smart grid data sharing. The results show the proposed mechanism is secure and efficient for IEC 61850 communication employing wireless network.

Software defined networks (SDN) is perceived to have specific capabilities for utilization by network infrastructures automatically. The success of OpenFlow protocol is to decouple control plane from data plane completely. However, current SDN still regards the network as a group of devices rather than a holistic resource, and traffic monitoring and control only relies on network states but not including traffic behaviours. Although speed of packet forwarding is improved significantly, QoS demands can not be satisfied when network congested, unavailability of SDN in some resource constrained scenes does not present well. To address this, we propose an application-aware traffic control scheme, in which both network states and traffic behaviours are exploited cooperatively. Deep Packet Inspection (DPI) is introduced into SDN controller. Meanwhile, a mechanism for packet classification and behaviour matching is designed. To perform information exchange between components, a publish/subscribe based middle ware is designed. Besides, mathematical models for analysing network throughput and latency are established. Simulation results show that proposed scheme can facilitate the improvement of throughput and reduce latency time of end-to-end communication.

The space information network (SIN) has attracted more and more attention due to its extensive applicability and great expanding access services. The complicated properties of SIN, such as the dynamic and unstable topology, the highly exposed links and so on, make it necessary to design an appropriate key management scheme to ensure the security of communication. In this paper we propose an anonymous and distributed certificate-less key management scheme (aCL-KMS) for SIN. It mainly adopts the strategy of the distributed key generation, update and agreement instead of the complex centralized key management. Based on the certificate-less public key cryptosystem (CL-PKC), this scheme not only avoids the high cost of complicated certificate management, but also overcome the key-escrow problem of the certificate-based or identity-based public key cryptosystem. Also, due to the fact that the anonymous authentication mechanism adopts the temporary identification of members, this scheme can efficiently protect the members' privacy and ensure the confidentiality of communications. The security properties discussion and the computational overhead analysis show that the proposed key management system is secure enough to meet the security requirements of SIN, and it is of less computing cost at the same time.

Software-defined Home Networks (SDHN) is a key development trend of smart home. Security is still an important issue in SDHN. In this paper, a multi-stage attack mitigation mechanism is proposed for SDHN using Software-Defined Networking (SDN) and Network Function Virtualization (NFV). Firstly, an evidence-driven security assessment method using SDN factors and NFV- based detection is designed to perform security assessment along with observed security events. Secondly, an attack mitigation countermeasure selection method is proposed. The evaluation shows that the proposed mechanism is effective for multi-stage attack mitigation in SDHN.

Industrial Wireless sensor networks (IWSNs) are emerging as a promising technique for industrial applications. With limited energy resources, prolonging the lifetime of IWSNs is a fundamental problem for industrial applications. At the same time, Software- Defined Networking (SDN) and Network Function Virtualization (NFV) are future network techniques which make the underlying networks and node functions programmable. SDN and NFV have inherent advantages to control topology and node mode in IWSNs. In this paper, we propose a mechanism improving energy efficiency in industrial wireless sensor networks using SDN and NFV named M-SEECH for industrial application. Firstly, we propose a new architecture based on traditional IWSNs and operation mechanism using SDN and NFV. In the architecture, the global view and central control properties of SDN are utilized to monitor IWSNs. Also, the programmability of SDN and instant deployment capability of NFV are utilized to control the topology of IWSNs and the modes of nodes in IWSNs. Thirdly, we propose advanced algorithms for controller in IWSNs taking the advantages of SDN and NFV. By this way, the average lifetimes of IWSNs are prolonged. Finally, the case study and evaluation show the advantages of the proposed energy efficient scheme comparing with traditional methods.

Social Peer-to-Peer (P2P) is a novel model to organize sensor networks, which can establish social relationships in an autonomous way with the benefits of extending the network boundaries and enhancing the network scalability. However, the complexity and time dependence characteristics introduced by social P2P model raise difficulties for assessing and selecting security services accurately and effectively in sensor networks. To address this, we propose a chance discovery based security service selection scheme for social P2P based sensor networks. We firstly establish the security assessment model for services in social P2P based sensor networks, regarding the security factors of exploitability, credibility, severity, confidentiality, integrality, availability and importance weight. More importantly, time dependence characteristics introduced by social P2P are considered during security assessment. Next, a security service selection scheme is proposed based on KeyGraph construction as well as the computation of its connection and tightness values. Finally, the service request forwarding model is established. The simulation results show the effectiveness and accuracy of the proposed security service selection scheme, which improves the feasibility and security of integrated sensor network and social networks.

As one of core technologies of smart grid, demand response (DR) helps improving electricity efficiency and optimizing power consumption way. In addition to having advanced technologies and applications of communication and computing infrastructures, the security of DR is still an issue deserving research to defend against evolving cyber attacks. In this paper, a security mechanism for DR is proposed to solve the security problem using Role Based Access Control (RBAC)and Publish/subscribe (Pub/sub). Different from traditional security schemes in DR, not only real existed entities such as the electricity suppliers and users but also logical subjects such as DR event are taken into consideration. It realizes the logical separation of users and permissions and grants role with secure analysis based on Support Vector Machines (SVM). A Pub/sub based communication scheme acquires greater network scalability and a more dynamic network topology with the help of group key distribution algorithm.

The introduction of device-to-device (D2D) into cellular networks poses many new challenges in the resource allocation design due to the co-channel interference caused by spectrum reuse and limited battery life of user equipments (UEs). In this paper, we propose a distributed interference-aware energy-efficient resource allocation algorithm to maximize each UE's energy efficiency (EE) subject to its specific quality of service (QoS) and maximum transmission power constraints. We model the resource allocation problem as a noncooperative game, in which each player is self-interested and wants to maximize its own EE. The formulated EE maximization problem is a non-convex problem and is transformed into a convex optimization problem by exploiting the properties of the nonlinear fractional programming. An iterative optimization algorithm is proposed and verified through computer simulations.

In this paper, we consider the application scenario where multiple Base Stations (BSs) cooperate to transmit signals to a mobile terminal in the same frequency and any of the cooperative BSs is allowed to enter into sleeping mode to save energy. The mobile terminal employs the Joint Maximum Likelihood Sequence Estimation (JMLSE) based Joint Signal Detection (JSD) to simultaneously detect multiple co-channel signals and judge whether a cooperative BS is active or not. The detection error probability of JSD is analyzed in this paper. For the case of BS sleeping, the error probability is computed based on a tentative modulation scheme which incorporates the M constellation points of conventional M-QAM and the additional constellation point 0. For the case of BS cooperation, the error probability bounds are derived based on a genie-aided receiver, and a new Tighter Lower Bound (TLB) is derived by replacing the genie with a less generous one. Simulation results have verified that the computed error probability can provide a rapid and accurate estimation of the Symbol Error Rate (SER) performance.

In the implementation of turbo‐like decoder, the size of state metrics cache (SMC) has a predominant impact on the core area and the overall power dissipation. Different from previous reported decoding schemes, in the proposed decoding schemes, a compressing module and a regeneration module are added to the decoder. The compressing module sorts the forward state metrics from the minimum to the maximum, by which an index sequence and the corresponding increase metrics are calculated, and subsequently are stored in the SMC. In the regeneration module, the forward state metrics are estimated with the index sequence and the increase metrics that accessed from the SMC. With the cost of dummy calculation that is performed by the compressing and the regeneration modules, two decoding schemes are proposed. For an eight‐state turbo codes, the linear and the nonlinear estimation based decoding schemes reduce the SMC size by 62.5% and 57.5%, respectively. The bit error rate (BER) simulation is performed for both binary turbo code and duo binary convolutional turbo code, and shows BER of the linear estimation‐based scheme is superior to that of the enhanced max‐log‐MAP (the maximum a posteriori probability) algorithm, whereas BER of the non‐linear estimation‐based decoding scheme is very close to that of the near optimal decoding scheme.

Security access is one of the key concerns for wireless sensor networks (WSNs). The secure authentication protocols of the most current security access schemes are complex. Moreover, the access control models of existing schemes have no concept of attribute mutability and can not perform continuous access decisions. In this paper, we propose a new security access scheme that deal with the requirements of low-complexity authentication and continuous access decision. The authentication protocol and access control mechanism is designed based on security token and usage control (UCON) respectively. An instance of UCON is implemented. The implementation results support the feasibility of using UCON in WSNs. The authentication protocol in our scheme presents several advantages including the low expenses in calculation, storage and communication. Moreover, our scheme also can perform access control with attribute mutability and decision continuity.