This study investigates energy-efficient resource allocation for covert vehicular communications with age constraints, where vehicle-to-vehicle (V2V) links leverage spectrum sharing to conceal covert transmissions. Vehicle-to-infrastructure (V2I) links serve as friendly jammers, simultaneously disrupting detection and maintaining connectivity with the base station. To maximize V2V links’ covert energy efficiency (CEE) and V2I links’ throughput under quality of service (QoS), communication covertness, and freshness constraints, a novel matching-based resource allocation framework is proposed. Specifically, we derive the minimum error detection rate and the optimal detection threshold at warden. The transmit probability and power are jointly optimized using the successive convex approximation method. Jamming selection is then modeled as a stable marriage problem, solved via the Gale-Shapley algorithm for stable matching between V2V and V2I links. Additionally, we explore a coalition falsification strategy to further enhance the CEE of certain V2V links without hurting the performance of the rest. Extensive simulations validate the proposed approach, showing significant improvements over existing baselines.

Advanced metering infrastructure in smart grids faces critical security challenges from distributed denial of service (DDoS) attacks, while traditional traceback methods suffer from resource constraints that limit their mitigation effectiveness. To address this, we propose a reverse auction-driven fog resource allocation strategy that innovatively employs a reverse auction mechanism to coordinate distributed fog resources for collaborative DDoS attack mitigation. Specifically, the framework is formulated as a 0–1 integer programming model, for which we develop a second-price sealed-bid auction (SPSA) algorithm that reduces computational complexity from factorial to polynomial time while maintaining allocation optimality. Experimental results demonstrate that the SPSA algorithm outperforms existing baseline methods, significantly improving both the efficiency and security of DDoS defenses in smart grid environments.

The extensive local training in Hierarchical Federated Learning (HFL) imposes a substantial computational energy burden on end devices, a problem intensified by inherent system and data heterogeneity. While prior works attempt to mitigate this by using heterogeneous models or adjusting local training, they often suffer from critical drawbacks such as accuracy degradation from update biases and an inability to adapt to the dynamic nature of device resources. This paper introduces FedE2AD (Federated Energy-Efficient Adaptive Distillation), a novel cloud-network collaboration framework that leverages dynamic distillation to holistically optimize the energy-accuracy balance. At its core, FedE2AD implements this collaboration through a multi-level optimization approach. At the cloud layer, a Dynamic Model Allocation strategy intelligently assigns model architectures by assessing device status from static, dynamic, and data-centric perspectives. At the device layer, Variable Local Iterations enable real-time adaptation to fluctuating computational power. Crucially, to counteract model divergence, FedE2AD employs a Dual Knowledge Sharing mechanism at the edge layer, which uniquely combines direct aggregation of shared structures with data-free model distillation to ensure robust knowledge transfer. Experiments conducted on simulation platforms show that FedE2AD markedly outperforms existing methods. For instance, on the CIFAR-10 dataset under strong heterogeneity, it reduces single-round computation energy by 21.1% and increases final model accuracy by 1.42% compared to HDHRFL.

Federated learning (FL) has emerged as a leading methodology for facilitating collaborative edge learning (EL) across Artificial Intelligence of Things (AIoT) devices, enabling efficient model training and bolstering privacy protection. Nevertheless, current EL methods that depend on trusted servers engender apprehensions concerning potential data leakage and misuse. Moreover, the untrusted AIoT environment increases security threats in EL collaboration. In addressing these challenges, we introduce an innovative swarm reputation (SR)-based decentralized autonomous organization (DAO) autonomous FL framework, SRFL. Within SRFL, we utilize DAO nodes as autonomous units for processing local services, effectively diminishing the communication overhead attributed to frequent interactions, the SR-based DAO committee oversees the FL process and ensures model consistency. SRFL seamlessly integrates FL with the distributed consensus process and introduces an SR-based consensus mechanism to enhance the collaboration process’s trustworthiness. SR utilizes a hierarchical reward and punishment mechanism, designed to equitably reward honest participants and hammer penalize those undermining the system’s stability. Through extensive experimentation with SRFL, employing different models and datasets, we have substantiated its superior performance in efficiency and robustness.

As a data-free knowledge transfer paradigm, federated learning (FL) provides a novel solution for knowledge fusion in smart cities, especially in the field of Internet of Vehicles (IoV). However, the bandwidth bottleneck in the IoV limits the efficiency of federated collaboration, while trust issues associated with aggregation servers reduce users’ willingness to collaborate. To address these challenges, this article proposes a multiple collaborative iterative distillation-based swarm learning (CISL) framework for IoV. CISL leverages multiple collaborative iterative distillations to transform federated collaboration into serverless cross-device and cross-decentralized autonomous organization (DAO) knowledge transfer and fusion, enabling trustworthy swarm collaboration under bandwidth-constrained conditions. Moreover, it adaptively adjusts the inheritance and elimination of shared knowledge (SK) to enhance model adaptability and improve single-vehicle performance. Specifically, CISL proposes a collaborative iterative distillation mechanism that progressively integrates knowledge of other vehicles within the DAO, achieving cross-device SK fusion. Meanwhile, CISL introduces a multisage collaborative distillation mechanism, enabling each DAO to collaboratively distill and integrate SK from other DAOs, thereby expanding its knowledge domain. Additionally, CISL employs a dynamic balancing strategy to adaptively regulate the inheritance and elimination of SK, optimizing local models and enhancing their performance. Comprehensive experiments conducted on six benchmarks across two scenarios demonstrate that, compared to state-of-the-art methods, CISL exhibits superior adaptability and robustness across different datasets and task scenarios.

Unmanned aerial vehicles (UAVs) are viewed as a key component of 5G, 6G and beyond wireless networks to receive, store and forward information. Benefiting from swift deployment, low cost and high mobility, it has become a promising trend to leverage the UAVs to assist the covert communicator (Alice) against the detector’s (Willie’s) detection in a covert communication network. Nevertheless, once Willie notices the covert transmission process, the covert information is exposed to the risk of being cracked. In this paper, we propose a UAV-assisted covert communication asymmetric information game model (UCCAIG) that focuses on the covert communication countermeasure process based on information asymmetry. We introduce the UAV into covert communication relay assistant Alice and interference assistant Willie respectively, analyze the interaction between Alice and Willie, and derive the optimal strategies for both sides. We further prove the existence of a Bayesian Nash equilibrium (BNE) in the UCCAIG. In addition, we evaluate our proposals on a covert communication testbed with software radio, and the numerical results show that our proposed strategy increases the payoffs of Alice, reduces the payoffs of Willie in a variety of scenarios, and improves the covert communication rate compared with UAV-relayed game (URG).

Industrial Internet of Things (IIoT) is experiencing rapid developments in the era of Industry 4.0. However, the ever-increasing applications put forward higher requirements for authentication. Facing such a problem, researchers combine two cutting-edge techniques, i.e., physical unclonable function (PUF) and blockchain. In detail, PUF can generate multiple challenge–response pairs (CRPs) for IIoT devices by leveraging their unique physical features. Moreover, blockchain platforms are employed for storing/synchronizing CRPs, thereby resisting the single-point failure. Although realizing the unclonable authentications, the existing works ignore the device heterogeneity of IIoT and fail to develop the specified blockchain platform for supporting PUF. In this article, we present a hybrid-PUF-based consortium blockchain for IIoT authentication, named HPCchain. Specifically, we first present the notion of hybrid-PUF, which assigns different devices to generate different types of PUFs, and then employs them to play different roles in HPCchain. In this way, we can overcome the IIoT heterogeneity. Moreover, we propose the PUF-empowered credit scheme for HPCchain and realize the dynamic endorsement with which we develop a PUF-based consensus mechanism for HPCchain. Finally, we design the registration and authentication schemes for IIoT nodes, atop HPCchain. Extensive experiments demonstrate the validity of our proposals.

Leveraging watchtowers to monitor payment channel networks (PCNs) is regarded to be a promising option to ensure off-chain transaction security and boost cryptocurrency scalability. However, existing solutions have two major limitations: First, since the watchtower’s inaction or collusion with counterparties, the deposits in off-chain transactions will be threatened; Second, due to occasional false positives, the efficiency of the single watchtower in monitoring the payment channels for fraud is questionable. To solve this, we present anti-collusion multiparty smart contracts for distributed watchtowers in PCNs. Specifically, we first design the distributed watchtower mechanism to solve the false positive problem in regulating PCNs. In addition, we utilize smart contracts to constrain and force counterparties to relinquish collusion in the distributed watchtower mechanism, thus making collusion impossible for rational parties. We further offer a mathematical proof and contract implementation in Solidity. Finally, extensive experiments and contracts executed on Ethereum under various benchmarks with baseline comparison demonstrate the validity of our proposals. Specifically, our scheme can both improve the throughput and accuracy by up to 20-25% and 10-15%, respectively, and reduce the false positive rate by up to 10% compared with existing single watchtower mechanism.

Software-defined networking (SDN) has become a promising trend for managing the Industrial Internet of Things (IIoT) devices. As the core of sensitive data storage and business interaction, the SDN is vulnerable to advanced persistent threats (APTs) attacks, while honeypots have shown great promise against APT attacks. In this article, we propose a new SDN-based dynamic bounded rational honeypot-APT game model in IIoT. Specifically, the defender maximizes the utility by chossing the period strategy of honeypot collecting and analyzing the data, while the attacker maximizes utility by choosing the period strategy of its latency and attack. To describe the bounded rationality, we model the simultaneous dynamic attack and defense process through the prospect theory, in which the Prelec function and the value function are both introduced. Experiment results show that bounded rationality affects strategy selection and reduces defender and attacker’s utilities. Furthermore, our strategy outperforms the existing work in defensive performance.

Quantization aims to form new vectors or matrices with shared values close to the original. In recent years, the popularity of scalar quantization has been soaring as it is found huge utilities in reducing the resource cost of neural networks. Popular clustering-based techniques suffers substantially from the problems of dependency on the seed, empty or out-of-the-range clusters, and high time complexity. To overcome the problems, in this paper, scalar quantization is examined from a new perspective, namely sparse least square optimization. Specifically, several quantization algorithms based on l1l1 least square are proposed and implemented. In addition, similar schemes with l1+ l2l1+l2 and l0l0 regularization are proposed. Furthermore, to compute quantization results with given amount of values/clusters, this paper proposes an iterative method and a clustering-based method, and both of them are built on sparse least square optimization. The algorithms proposed are tested under three data scenarios and their computational performance, including information loss, time consumption, and distribution of values of sparse vectors are compared. The paper offers a new perspective to probe the area of quantization, and the algorithms proposed are superior especially under bit-width reduction scenarios, where the required post-quantization resolution (the number of values) is not significantly lower than the original scalar.

In the transportation industry, task offloading services of edge-enabled Internet of Vehicles (IoV) are expected to provide vehicles with the better Quality of Experience (QoE). However, the various status of diverse edge servers and vehicles, as well as varying vehicular offloading modes, make a challenge of task offloading service. Therefore, to enhance the satisfaction of QoE, we first introduce a novel QoE model. Specifically, the emerging QoE model restricted by the energy consumption: 1) intelligent vehicles equipped with caching spaces and computing units may work as carriers; 2) various computational and caching capacities of edge servers can empower the offloading; and 3) unpredictable routings of the vehicles and edge servers can lead to diverse information transmission. We then propose an improved deep reinforcement learning (DRL) algorithm named PS-DDPG with the prioritized experience replay (PER) and the stochastic weight averaging (SWA) mechanisms based on deep deterministic policy gradients (DDPG) to seek an optimal offloading mode, saving energy consumption. Specifically, the PER scheme is proposed to enhance the availability of the experience replay buffer, thus accelerating the training. Moreover, reducing the noise in the training process and thus stabilizing the rewards, the SWA scheme is introduced to average weights. Extensive experiments certify the better performance, i.e., stability and convergence, of our PS-DDPG algorithm compared to existing work. Moreover, the experiments indicate that the QoE value can be improved by the proposed algorithm.

With the widespread applications of the Internet of Things (IoT), e.g., smart city, business, healthcare, etc., the security of data and devices becomes a major concern. Although blockchain can effectively enhance the network security and achieve fault tolerance, the huge resource consumption and limited performance of data processing restrict its deployments in IoT scenarios. Observing the heterogeneity and resource constraints, we intend to make blockchain accommodate both wimpy and brawny IoT devices. In this article, we present Tornado, a high-performance blockchain system based on space-structured ledger and corresponding algorithms, to enable blockchain in IoT. Specifically, we first design a space-structured chain architecture with novel data structures for promoting the network scalability. To address the huge heterogeneity of IoT, a novel consensus mechanism named collaborative-proof of work is developed. Moreover, we propose the space-structured greedy heaviest-observed subtree (S2GHOST) protocol for improving the resource efficiency of IoT devices. Additionally, a dynamic weight assignment mechanism in S2GHOST contributes to reflect the trustworthiness of data and devices. Extensive experiments demonstrate that Tornado can achieve a maximum throughput of 3464.76 transactions per second. The optimizations of propagation latency and resource efficiency are 68.14% and 30.56%, respectively.

In edge-enabled Internet of Things (IoT), computation offloading service is expected to offer users with better Quality of Experience (QoE) than traditional IoT. Unfortunately, the growing multiple tasks from users are occuring with the emergence of the IoT environment. Meanwhile, the current computation offloading with QoE is solved by deep reinforcement learning (DRL) with the issue of instability and slow convergence. Therefore, improving the QoE in edge-enabled IoT is still the ultimate challenge. In this article, to enhance the QoE, we propose a new QoE model to study the computation offloading. Specifically, the emerged QoE model can capture three influential elements: 1) service latency determined by local computing latency and transmission latency; 2) energy consumption according to local calculation and transmission consumption; and 3) task success rate based on the coding error probability. Moreover, we improve the deep deterministic policy gradients (DDPG) algorithm and propose a algorithm named the double-dueling-deterministic policy gradients (D3PG) based on the proposed model. Specifically, the actor network highly relies on the critic network, which makes the performance of the DDPG sensitive to the critic and thus leads to poor stability and slow convergence in the computation offloading process. To solve this, we redesign the critic network by using Double Q -learning and Dueling networks. Extensive experiments verify the better stability and faster convergence of our proposed algorithm than existing methods. In addition, experiments also indicate that our proposed algorithm can improve the QoE performance.

With the popularity of smart devices and the widespread use of machine learning methods, smart edges have become the mainstream of dealing with wireless big data. When smart edges use machine learning models to analyze wireless big data, nevertheless, some models may unintentionally store a small portion of the training data with sensitive records. Thus, intruders can expose sensitive information by careful analysis of this model. To solve this privacy issue, in this paper, we propose and implement a machine learning strategy for smart edges using differential privacy. We focus our attention on privacy protection in training datasets in wireless big data scenario. Moreover, we guarantee privacy protection by adding Laplace mechanisms, and design two different algorithms Output Perturbation (OPP) and Objective Perturbation (OJP), which satisfy differential privacy. In addition, we consider the privacy preserving issues presented in the existing literatures for differential privacy in the correlated datasets, and further provided differential privacy preserving methods for correlated datasets, guaranteeing privacy by theoretical deduction. Finally, we implement the experiments on the TensorFlow, and evaluate our strategy on four datasets, i.e., MNIST, SVHN, CIFAR-10 and STL-10. The experiment results show that our methods can efficiently protect the privacy of training datasets and guarantee the accuracy on benchmark datasets.

Leveraging high-performance software-defined networks (SDNs) to manage industrial Internet of Things (IIoT) devices has become a promising trend; the SDN is expected to be the next generation as a unified and virtualized network platform that provides unprecedented automation, flexibility, and efficiency. As the core of business applications and sensitive data storage, the SDN is vulnerable to distributed denial-of-service (DDoS) attacks in IIoT environment that numerous requests are sent to the SDN to interrupt its services. In the traditional defense systems, honeypots have shown great promises in resisting DDoS attacks. In this paper, we reveal a new attack that can identify honeypots to invalidate their protection. In addition, we analyze the optimal strategies of attackers, so that they can find the best time to carry on attacks. To protect SDN from such a kind of anti-honeypot attacks, we propose a pseudo-honeypot game (PHG) strategy with theoretical performance guarantee. We prove several groups of Bayesian-Nash Equilibrium in the PHG strategy. Moreover, we show that these strategies can achieve the optimal equilibrium between legitimate users and attackers. The proposed honeypot strategies can provide dynamic protection for SDN. Hence, malicious attacks under our strategies can be effectively controlled. Finally, we evaluate our proposals on a testbed, and experimental results show that our proposals can effectively resist DDoS attacks with lower energy consumption compared with the existing methods.

With the rapid development of machine-to-machine (M2M) mobile smart terminals, M2M services can be used in a wide range of industries, including such as telemedicine, remote meter reading and public security. Since different industries and enterprise users have different requirements for M2M specific applications, the security identity authentication of M2M mobile terminals is particularly worthy of attention. Existing methods can effectively solve the unsustainable problem of one-time verification, however they cannot address the dynamic relevance characteristics of user behavior sufficiently. Thus, the accuracy of user identity authentication needs to be further improved. In this paper, we propose a terminal identity authentication technology based on user association behavior analysis. In order to identify the abnormal login during each behavior process of authenticated user, we take largest coincident part of the user behavior sequence and short coincide into consideration. In addition, we propose a Similarity-based Behavior Sequence Similarity Algorithm (BCS-SSA) based on the traditional sequence pattern of Behavior Common Subsequence. The experimental results demonstrate that the proposed method can effectively improve the accuracy of user's behavioral sequence, and prove the uniqueness of different sequences on the other hand.

With the increasing computation and storage capabilities of mobile devices, the concept of fog computing was proposed to tackle the high communication delay inherent in cloud computing, and also improve the security to some extent. This paper concerns with the privacy issue inherent in the sustainable fog computing platform. However, there is no universal solution to the privacy problem in fog computing due to the device heterogeneity. In this paper, we proposed a differential privacy-based query model for sustainable fog computing supported data center. We designed a method that can quantify the quality of privacy preserving through rigorous mathematical proof. The proposed method uses the query model to capture the structure information of the sustainable fog computing supported data center, and the datasets for the query result are mapped to real vectors. Then, we implemented the differential privacy preserving by injecting Laplacian noise. The experiment results demonstrated that the proposed method can effectively resist various popular privacy attacks, and achieve relatively high data utility under the premise of better privacy preserving.

Machine-to-Machine (M2M) mobile networks are distributed systems which include various actuators and sensors. In terms of the security of M2M mobile networks, one very significant issue is the security of Sensor Networks (SNs). Particularly, the security of transferring data from sensors to their destinations is very critical. In this paper, focusing on intrusion detection techniques, we propose an attack-defense game model to detect malicious nodes using a repeated game approach. In the proposed game model, attackers and defenders make different strategies to achieve optimal payoffs. The existences of pure nash equilibrium and mixed nash equilibrium are analyzed and proved. In the Intrusion Detection System (IDS), a game tree model is introduced to solve the error detection and missing detection problems. Simulation results present that the proposed model can reduce energy consumption by up to 50% compared with the All Monitor (AM) model, and improve the detection rate by up to 10-15% compared with the Cluster Head (CH) monitor model.

In order to quantitatively and systematically explain the accident occur process and assess the risk for the complex system, this paper proposes a new accident-causing analysis model, i.e. perturbation-safety region (P-SR) model.In this model, the safety region definition is introduced for the quantitative description of the system safe status; also the change process of the system risk is analyzed.The four relative parts included in this model are described in details, such as the risk resource part, the perturbation part, the alarm and system change part, and the accident part.Finally, the proposed model is applied to railway transportation system, and the Wenzhou train collision is systematically analyzed, also the specified control measure for the train emergency dispatch is demonstrated.