As vehicular edge computing (VEC) grows increasingly demanding, distributed task offloading is brought up as a potential solution. However, the high mobility and limited resources of vehicles, coupled with uncertain service reliability, make it difficult to guarantee timely and correct execution of computation-intensive tasks. Existing task offloading models overlook critical aspects such as punctuality and correctness, limiting their ability to select reliable service vehicles in dynamic and potentially adversarial environments. To address this gap, we propose a performance-reputation integrated task offloading scheme for reliable VEC. We introduce a dual-dimensional reputation model (D2Rep) that jointly evaluates vehicles based on the degree of punctuality, result correctness probability, and historical reputation value. Building on this, we develop the performance value-maximized vehicular computation offloading (PFVMax-VCO) scheme, which combines reputation value, link reliability, and projected success rate into a unified performance value to guide multi-objective optimization of delay, energy consumption, and reliability. To support the realistic evaluation, we implement a SUMO-Veins-OMNeT++ integrated simulation framework. Experimental results demonstrate that the proposed PFVMax-VCO scheme outperforms existing solutions in terms of execution cost, result correctness probability, and task success rate, while enabling robust and dynamic reputation management for VEC systems.

Recent advancements in computing and autonomous driving technologies have led to the integration of new functionalities into intelligent automotive, such as environmental perception, path planning, assisted driving, and entertainment services. This integration requires the rapid processing of critical tasks, including collision detection, emergency braking, lane keeping, and Vehicle-to-Everything (V2X) communication, exceeding past functional demands. Consequently, high-performance multi-core processors have emerged as the preferred hardware solution due to their superior processing speeds, energy efficiency, and parallel task execution capabilities. This paper explores various applications of multi core processors in intelligent automotive systems, systematically reviewing recent advancements in multi-core scheduling methods. It categorizes and analyzes approaches to task loading, task migration, efficiency improvement, safety assurance, communication, and resource contention in both general and automotive contexts. To objectively assess these methods, the paper establishes reference standards for evaluating scheduling methods and system architectures and provides analytical methods aligned with these standards. Finally, the paper discusses future challenges, considers trends in intelligent automotive systems and multi-core processors, and offers recommendations for future research in this field.

In cognitive radio ad-hoc networks (CRAHN), we can change the network topology through flexible spectrum allocation. In this regard, even though CRAHN is not prone to single points of failure, its network performance is entirely dependent on node connectivity. However, the existing solutions mainly focus on the connections between certain nodes, without fully considering the overall network connectivity. To this end, to avoid a large number of connection failures caused by local damage, it is necessary of improving system connectivity performance from a global perspective when allocating available spectrum. As such, we explore the relationship between spectrum allocation and global connectivity of CRAHN, and then propose a robust optimization scheme using optimal spectrum allocation (ROUOS). This scheme aims to create new connections in the connectivity vulnerable areas through spectrum allocation. Specifically, we establish a spectrum allocation model including available spectrum matrix, bandwidth benefit matrix, interference constraint matrix, communication connection matrix, alternate channel vector and optimal allocation matrix. After that, based on the connectivity quantitative index in [11], we design a network benefit function to measure the gain effect of different spectrum allocation solutions on network connectivity. Finally, we select the spectrum allocation solution (i.e., adding communication links) that most effectively improves network connectivity. Simulation results show that, compared to the benchmark scheme, our proposed ROUOS scheme increases the second smallest eigenvalue of the graph Laplacian matrix from 0.09 to 0.29, significantly enhancing the robustness of topological connectivity.

Proper planning of charging infrastructure can significantly facilitate the popularization of electric vehicles and alleviate users’ mileage anxiety. Charging station siting and sizing are two key challenges in the planning with each of them being a complex optimization problem. In this paper, a multi-objective optimization approach is proposed to solve them together. First, considering that accurate charging demand estimation is crucial for planning, a traffic road network is established for this purpose. A Monte Carlo method is used to estimate the spatiotemporal distribution of charging demand in a region based on the probabilistic characteristics of user trips. Since uncoordinated charging not only increases the load but also leads to unstable operation of the local power system, a heuristic algorithm is proposed to coordinate charging scheduling. Then, based on the scheduled demand, this paper proposes a framework for the siting and sizing of charging stations to optimize the benefits for both operators and users by minimizing the construction, operation and maintenance costs, and the user’s detour time. As the given problem is a complex multi-objective combinatorial optimization problem, it is easy to fall into local optimum if traditional evolutionary algorithms are employed. Therefore, a multi-objective dynamic binary particle swarm optimization method is designed to solve this problem effectively. Finally, experimental simulations show that the proposed method outperforms the other comparative algorithms in terms of solution quality. A case study is presented to demonstrate the applicability and effectiveness of the proposed method in optimizing the location and capacity of charging stations.

Monitoring driver's behavior is the foundation for ensuring road safety. However, identifying unsafe behavior of the driver during the driving process still faces many challenges. In this paper, we propose a recognition network based on graph convolution, aimed at effectively identifying various driver's unsafe behaviors during the driving process. The SDRM-CSTJM graph convolutional network (SC-GCN) comprises two modules: the stacked deconvolution residual module (SDRM) and the channel-spatial-temporal joint module (CSTJM). The SDRM extracts information about the driver's unsafe actions across various scales by stacking multiple unit structures and introducing multi-level residual blocks. The CSTJM extracts features from different dimensions by integrating channel, spatial, and temporal information. Integrating SDRM and CSTJM into the spatial temporal graph convolutional network (ST-GCN), and a two-stream network is introduced to handle joint data and skeletal data separately. Subsequently, integrating the outputs of the two streams. Achieving recognition of various behaviors exhibited by the driver based on their body posture features. Finally, we evaluated the proposed method on the dataset. Experiments demonstrate that the proposed method achieves the highest recognition accuracy on the driver action dataset.

As a special ring signature, a traceable ring signature (TRS) provides limited anonymity and traceability, and has been shown useful in many practical applications such as anonymous voting. On the other hand, DualRing, a novel generic construction of ring signature introduced in CRYPTO 2021, can reduce computation and communication costs. Recently, building upon DualRing, Ye et al. proposed a lattice-based TRS that introduced TripleRing construction to ensure traceability. However, the proposed TRS encounters issues with certificate management and linear signature size. To address these concerns, we introduce a certificateless TRS with logarithmic size based on SM2-DualRing (SDR-CTRS) in the discrete logarithm setting. The SDR-CTRS leverages the TripleRing construction and non-interactive sum argument. Security analysis indicates that our SDR-CTRS satisfies tag-linkability, anonymity, and exculpability under the discrete logarithm hypothesis. Theoretical analysis shows that the SDR-CTRS features logarithmic communication cost and acceptable computation cost. Additionally, the SDR-CTRS does not require a fully trusted party during the tracking phase. Finally, to illustrate the practicality of the ring signature, we present a blockchain-based data sharing system with conditional privacy protection based on it.

Existing studies on post-disaster road network repair strategies have ignored the impact of different levels of road damage and recovery on the efficiency of network repair. To solve this issue, this study integrates the construction material distribution (CMD) with the repair crew scheduling and routing problem (RCSRP), and determine the level of road recovery through the CMD. Then, a bi-level optimization model is proposed with network performance resilience and recovery speed resilience as the optimization objectives. A two-stage optimization algorithm (TSOA) composed of a genetic algorithm with an improved coding method (ICM-GA) and the Frank-Wolfe algorithm (FW) is then employed to solve this model. Finally, the effectiveness of the model and algorithm is validated through simulation experiments. The results indicate that, under given material and time constraints, the optimal repair strategy proposed in this study outperforms the repair strategy without considering road recovery level by 17.51% and 5.42% in terms of network performance resilience and recovery speed resilience, respectively. This demonstrates the positive significance of considering road recovery level in formulating road network repair strategies. Besides, this strategy can be applied to optimize the configuration of workstation count for different-scale networks.

Federated learning (FL) is a promising solution to overcome data island and privacy issues in intrusion detection systems (IDSs) for the Industrial Internet of Things (IIoT). However, the heterogeneity of various IIoT devices poses formidable challenges to FL-based intrusion detection, especially the training cost relating to delay and energy consumption. In this article, we propose a delay and energy-efficient asynchronous FL (AFL) framework for intrusion detection (DEAFL-ID) in heterogeneous IIoT. Specifically, we address the shortcomings of low efficiency and high energy consumption in existing FL-based solutions involving all idle IIoT devices. To do so, we formulate an AFL-based optimal device selection problem which aims to select high-quality training devices in advance by exploring the device advantages in detection accuracy, delay reduction, and energy saving. Subsequently, a deep Q-network (DQN)-based learning algorithm is developed to quickly solve the above high-dimensional problem. In addition, to further improve the detection performance, we build a hybrid sampling-assisted convolutional neural network (CNN)-based IDS model, which can eliminate the imbalance of IIoT data and enable the selected devices to fully extract data features. Through simulations, we demonstrate that DEAFL-ID achieves a significant improvement in training cost and detection performance compared with existing IDS schemes.

This paper considers computation offloading for mobile applications with task-dependency requirements in mobile edge computing (MEC) systems. Based on the online arrival patterns and various delay constraints of practical applications, we focus on minimizing the system deadline violation ratio (DVR) to improve the overall reliability performance. Specifically, we propose a DVR minimization computation offloading scheme with task migration and merging, in which the task migration and merging model is designed to construct an overall directed acyclic graph (DAG) for all currently dependent tasks. We consider a multi-slot MEC system where applications arrive slot-by-slot without prior knowledge of future arrivals. Then given the number of application arrivals at each time slot, we equivalently transform the DVR minimization problem into a problem that maximizes the number of completed applications in a finite time horizon. The above problem is challenging to determine the optimal task execution order for different applications with various task dependencies and delay constraints. To address this, we develop a migration-enabled multi-priority task sequencing algorithm, which creatively introduces several task priority metrics and determines the optimal task execution order. Then, a deep deterministic policy gradient (DDPG)-based learning algorithm is developed to find the optimal offloading policy. Experimental results demonstrate that the proposed scheme can reduce the system DVR by 60.34%~70.3% compared with existing benchmark schemes under various network scenarios.

Double auctions play a pivotal role in stimulating active participation of a large number of users comprising both task requesters and workers in mobile crowdsourcing. However, most existing studies have concentrated on designing offline two-sided auction mechanisms and supporting single-type tasks and fixed auction service models. Such works ignore the need of dynamic services and are unsuitable for large-scale crowdsourcing markets with extremely diverse demands (i.e., types and urgency degrees of tasks required by different requesters) and supplies (i.e., task skills and online durations of different workers). In this article, we consider a practical crowdsourcing application with an on-demand service strategy. Especially, we innovatively design three online service models, namely, online single-bid single-task (OSS), online single-bid multiple-task (OSM), and online multiple-bid multiple-task (OMM) models to accommodate diversified tasks and bidding demands for different users. Furthermore, to effectively allocate tasks and facilitate bidding, we propose a truthful online double auction mechanism for each service model based on the McAfee double auction. By doing so, each user can flexibly select auction service models and corresponding auction mechanisms according to their current interested tasks and online duration. To illustrate this, we present a three-demand example to explain the effectiveness of our on-demand service strategy in realistic crowdsourcing applications. Moreover, we theoretically prove that our mechanisms satisfy truthfulness, individual rationality, budget balance, and consumer sovereignty. Through extensive simulations, we show that our mechanisms can accommodate the various demands of different users and improve social utility, including platform utility and average user utility.

In this paper, we consider a mobile edge computing (MEC)-based secure computation offloading system, and design a practical multi-eavesdropper model including two specific scenarios of non-colluding and colluding eavesdropping. Furthermore, we design a requirement satisfaction model by exploring practical variations in user request patterns for security provisioning, delay reduction and energy saving. Based on these, we propose a satisfaction-maximized secure computation offloading (SMax-SCO) scheme, and then formulate an optimization problem aiming at maximizing users’ requirement satisfactions subject to secrecy offloading rate, tolerable delay, task workload and maximum power constraints. Since the optimization problem is nonconvex, we present an efficient successive convex approximation (SCA)-based algorithm to obtain suboptimal solutions. We demonstrate that the proposed SMax-SCO scheme achieves a significant improvement in security performance and requirement satisfaction compared with existing schemes. Moreover, we conclude that SMax-SCO can resist eavesdropping attacks of multiple eavesdroppers and even colluding eavesdroppers.

The combination of pervasive edge computing and blockchain technologies opens up significant possibilities for industrial Internet of Things (IIoT) applications, but there are several critical limitations regarding efficient storage and rapid response for large-scale low-delay IIoT scenarios. To address these limitations, in this article we propose a hierarchical edge-cloud blockchain called LayerChain. Specifically, to promote scalability, we design a layered structure to hierarchically store the blockchain data in multiple distributed clouds and edge nodes. Next, we propose a node classification method to accommodate differences between the edge nodes when deploying the blockchain. Moreover, to mitigate lengthy delays during block propagation, we propose a tree-based clustering algorithm where blocks are propagated through different clusters with a compressed tree depth. Simulation results show that our LayerChain efficiently reduces the system's resource requirements and block propagation time, making it well-suited for large-scale low-delay IIoT applications.

Analyzing network vulnerability, especially connectivity vulnerability, is vital for network security planning. Traditionally, network vulnerability analysis methods separate the studies of global connectivity vulnerability and critical area vulnerability, and thus ignore joint failure of network connectivity and critical-area integrity that may cause grave damage to a network. To this end, this paper proposes a prioritizing critical area approach for connectivity analysis to identify the corresponding vulnerable elements. Specifically, we consider the worst-case scenario of a network and aim at finding the minimum disruption-cost set of elements whose removal not only severely damages network connectivity but also disrupts the critical-area integrity. Since the above optimization problem is NP-hard, a heuristic algorithm based on spectral partitioning is developed to solve it. Simulation results validate the effectiveness of our proposed scheme in accurately identifying the vulnerable elements in critical areas to prevent significant loss in the overall network connectivity and performance.

In this paper, we consider a multiuser multiple-input multiple-output (MIMO) downlink communication system with simultaneous wireless information and power transfer (SWIPT). In particular, we focus on a realistic and efficient multi-receiver multi-eavesdropper MIMO SWIPT system, in which the channel state information (CSI) of each legitimate receiver and energy receiver (i.e., potential eavesdropper) is partially known to the transmitter. Based on this, we propose a robust artificial noise (AN)-aided secure transmission scheme for the system, where the channel uncertainties are modeled by the worst-case model. In the proposed scheme, we aim to maximize the worst-case achievable secrecy rate under the transmit power constraint and the energy harvesting (EH) constraint, by jointly optimizing the transmit precoding matrix and the AN covariance matrix. We utilize the S-Procedure and Taylor series approximation to transform the non-convex problem. Then, we apply the interior point method to tackle the transformed convex problem, obtaining the approximate optimal matrices and the corresponding maximum worst-case secrecy rate. Simulation results show that our proposed scheme achieves significant performance improvements in terms of convergence and the worst-case achievable secrecy rate.

Blockchain has unique advantages in data privacy protection and data integrity. We can solve many security problems, such as the single point of failure and data sharing in the current centralized system through blockchain approach. Existing studies have demonstrated that the application of blockchain in the medical system could improve the patient's medical experience. However, we found that the blockchain-based medical systems didn't consider the problems of long insurance claim cycle and complicated procedures. There are also very few healthcare systems that provide targeted sharing protocols for medical data and personal health data. In this paper, we propose a multi-role healthcare data sharing system framework based on blockchain. In this system, we reduce the storage cost through the collaborative storage of blockchain and IPFS. Then we design a smart contract on insurance to help patients achieve automatic insurance claim. In addition, we design two different sharing protocols to realize the fine management of personal data. The system analysis shows that our proposed blockchain-based multi-role healthcare data sharing system can effectively address the actual needs of users and has perfect performance in data storage, privacy protection, insurance claims and personal data management.

Crowdsourcing is a novel distributed problem-solving mechanism that can provide the collection and share of marine data in the Internet of Things for the smart ocean. Nevertheless, the privacy leakage issue caused by multirole information interaction in crowdsourcing brings a serious challenge to the smart ocean. In this article, we propose a crowdsourcing privacy protection scheme based on multiauthority ciphertext-policy attribute-based encryption to enhance privacy protection in the data sharing environment. In this scheme, we design an independent key component distribution approach through multiple authorities, which could effectively disperse the security responsibility from the crowdsourcing platform. Then, we present the idea of partial decryption on the platform to reduce the computing cost of mobile users and prevent the platform from snooping on users' data. Moreover, we put forward an efficient attribute revocation mechanism and a task search function in the scheme to achieve dynamic on-demand services while ensuring the forward and backward security of tasks. Theoretical analysis proves the correctness of both decryption and keywords matching, and the security of each involved entity. The simulation results show that our proposed scheme achieves a significant improvement in reducing time consumption compared with several related schemes.

Mobile crowdsourcing is a promising strategy for trusted data collection in Internet-of-Things (IoT) applications. In this article, we propose a new fog-blockchain distributed approach for crowdsourcing reputation management to prevent user's privacy leakage, malicious users' participation, and reputation tampering in wireless IoT systems. To protect the user's privacy, we design a cross-layer privacy protection model to separate the user's identity and tasks flexibly by means of a hierarchical structure based on fog computing. Moreover, considering the multiconstraint requirement of crowdsourcing tasks, we present a multifactor reputation evaluation method to accurately identify malicious users. Furthermore, to solve the multi-identity problem of users on multiple fog nodes, we propose an adaptive fog-blockchain reputation storage method, which efficiently reduces the system resource consumption by analyzing the adaptive classification of fog nodes. Exhaustive experimental simulation results validate the security and efficiency of our proposed reputation management system.

With the rapid increase in service requirements driven by Internet of Things (IoT) networks, mobile crowdsourcing has become a compelling paradigm that can efficiently solve complex tasks in the physical world. Nevertheless, we found that most IoT tasks have constraints on deadline, location, and resource consumption, which limit the application of crowdsourcing platforms in the IoT networks. In this article, we innovatively propose a reliable fog-based temporal-spatial crowdsourcing for serving the above tasks. In this scenario, the key point is to achieve the best match of the attributes among tasks, fog nodes, and workers. As the bridge of the other two parts, fog nodes determine the orientation of tasks. Therefore, we present a temporal-spatial task allocation (TS-TA) scheme in the fog layer, aiming to make task results more reliable. In this scheme, we build a temporal-spatial attribute learning model based on the user behaviors. Then, we use the users' interest attribute matching model to identify the candidate fog nodes that satisfy the requirements of temporal-spatial tasks. We choose the fog nodes with low spatial correlation that is benefit to defense the attack on the nodes in the intensive area. Meanwhile, we assign the redundancy nodes for intrusion response through replacing the attacked/negative node. Both theoretical and real-topology simulation results validate that the proposed scheme can get better performance in system resource consumption and system robustness compared with other benchmark schemes.

By considering the Simultaneous Wireless Information and Power Transfer (SWIPT) schemes, this paper focuses on secure transmission model design in multiple-input-single-output (MISO) channels. In these channels, the channel state information is assumed to be perfect. Our objective is to maximize the worst-case secrecy rate with respect to both potential eavesdroppers and obvious eavesdroppers under the constraints of energy-harvesting and total transmission power. We present an optimization model to indirectly obtain maximum security rate in a single receiver system. Due to the high computational complexity of the solution process caused by the formulated non-convex optimization problem, we propose a novel indirect method to handle this issue. Then, a Semi-Definite Programming (SDP) relaxation method is used to approach the optimal solution. Moreover, we reveal the conditions for ensuring that the above semi-definite relaxation is compact. Simulation results demonstrate that the gained performance in our system is much better than those of the existing competing schemes.