The Internet of Things (IoT) is a key engine of global socio-economic transformation, where data sharing stands as a central catalyst for the IoT market's growth. However, data security and privacy concerns significantly impede the advancement of IoT data sharing. Consequently, Attribute-Based Encryption (ABE), offering fine-grained access control, is increasingly favored by data users. Unfortunately, existing ABE schemes still face these drawbacks: (1) the encryption and decryption computation overhead grows linearly with attributes; (2) keyword searches within ciphertexts are intricate and inefficient; (3) the access policy is at higher risk of privacy disclosure. To address these issues, this paper presents an efficient searchable and verifiable scheme with partial hidden policy for IoT (ESVPH). This scheme not only provides flexible keyword-based search and re-encryption verification, but also achieves fixed costs for encryption, decryption, searching and verifying. Additionally, ESVPH introduces an access policy where attribute names are disclosed while their values remain concealed, thereby enhancing user privacy. In conclusion, the scheme offers outstanding performance in computation and communication, proving its feasibility for practical IoT data sharing through rigorous proofs and extensive experimentation.

The mobile health (mHealth) system, leveraging mobile edge computing, can monitor health status and provide diagnosis. However, due to the privacy of medical data and the resource limitations of mobile devices, patients are unable to access diagnostic services provided by untrusted servers in real-time. Existing schemes present significant challenges in private heterogeneous data aggregation, model training and inference in the presence of malicious participants, and expensive resource consumption. To address these issues, in this paper, we propose a non-interactive privacy-preserving system with the naive Bayesian model, i.e.,$\mathsf {RobustHealth}$, for heterogeneous mHealth diagnosis. Specifically, we extract homogeneous features from heterogeneous datasets to enable efficient encrypted aggregation. We propose a novel private model training algorithm with enhanced security to against collusion-then-differential attacks. We develop a novel non-interactive private model inference algorithm using minimal lightweight cryptographic primitives, designed for patients under unstable network environments. We provide formal security proofs for our system using the Universal Composable (UC) framework. To validate the performance of$\mathsf {RobustHealth}$, we conduct extensive experiments on real-world heterogeneous datasets, and compared with related works. The results demonstrate a$\bf {4.37\%}$improvement in model accuracy, along with significant reductions in computational and communication overheads of$\bf {21.18\times }$and$\bf {4.24\times }$, respectively.

Federated learning facilitates collaborative model training across multiple clients while preserving data privacy. However, its performance is often constrained by limited communication resources, particularly in systems supporting a large number of clients. To address this challenge, integrating over-the-air computations into the training process has emerged as a promising solution to alleviate communication bottlenecks. The system significantly increases the number of clients it can support in each communication round by transmitting intermediate parameters via analog signals rather than digital ones. This improvement, however, comes at the cost of channel-induced distortions, such as fading and noise, which affect the aggregated global parameters. To elucidate these effects, this paper develops a theoretical framework to analyze the performance of over-the-air federated learning in large-scale client scenarios. Our analysis reveals three key advantages of scaling up the number of participating clients: (1) Enhanced Privacy: The mutual information between a client’s local gradient and the server’s aggregated gradient diminishes, effectively reducing privacy leakage. (2) Mitigation of Channel Fading: The channel hardening effect eliminates the impact of small-scale fading in the noisy global gradient. (3) Improved Convergence: Reduced thermal noise and gradient estimation errors benefit the convergence rate. These findings solidify over-the-air model training as a viable approach for federated learning in networks with a large number of clients. The theoretical insights are further substantiated through extensive experimental evaluations.

The current watermark-removal neural networks (WRNNs) can effectively remove the watermarks from watermarked images without damaging their host images, which poses a significant threat to image copyright protection. As one of the most effective technologies of preventing watermarks from being removed, the watermark vaccine generally attacks the WRNNs by generating and adding the adversarial perturbations to watermarked images. However, the existing watermark vaccine schemes perturb all the pixels of watermarked images, which makes it difficult to find a good trade-off between attack efficiency and imperceptibility. To address the above issues, we propose a Dual-Population Watermark Vaccine (DPWV) scheme. In this scheme, we formulate the task of adding adversarial perturbation as a bi-objective optimization problem, and address it by decoupling the space of adversarial perturbation addition to the Intensity Population (IP)-based and Position Population (PP)-based subspaces to search for the optimal solution. The extensive experiments demonstrate that the proposed scheme significantly outperforms the state-of-the-arts in the aspects of attack efficiency and imperceptibility, simultaneously, with the improvements of 45%-55% attack efficiency and 30%-40% attack imperceptibility.

The Internet of Things (IoT) is rapidly permeating all aspects of human life, involving a network of devices that share sensitive data. A notable application is the e-healthcare systems, which employ connected sensors, medical servers, and wearable devices. However, the public nature of communication in e-healthcare systems poses challenges such as security, privacy, and authentication of participating entities. Recently, many authentication protocols have been introduced to address these challenges. However, most of these protocols remain vulnerable to various security attacks, including device or medical server impersonation, denial of service, physical or cloning, and de-synchronization attacks. Therefore, we introduce an authenticated key-management protocol utilizing hash functions and Cipher-Block Chaining-Advanced Encryption Standard encryption (CBC-AES) encryption. The proposed protocol also employs the Physical Unclonable Function (PUF), which makes it more robust and efficient in resisting physical or cloning attacks. Additionally, the proposed scheme resists various security threats, including impersonation, session key leakage, ephemeral secret leakage, and de-synchronization attacks. We analyze the scheme’s security and reliability through formal and informal analysis. The informal analysis demonstrates that the scheme encompasses crucial security features, while the formal analysis substantiates. Moreover, performance analysis of the proposed protocol with various competing results indicates that our protocol achieves an average reduction in communication and computation overheads by 36.03.% and 41.79%, respectively.

Data sharing schemes based on the Internet of Medical Things (IoMT) have emerged as a more convenient way to monitor and manage individuals’ health. However, this scenario faces challenges such as privacy preservation, effectiveness, and practicality, which hinder its further development. To the best of our knowledge, there is no agreed-upon data-sharing method that addresses all of these problems. In this paper, we make a step ahead by designing an Efficient and Auditable Privacy-preservation Data Sharing scheme (EAPDS) based on multi-authority attribute-based encryption. EAPDS designs an auditable anonymous authentication mechanism to realize identity privacy protection, as well as an efficient multi-authority attribute-based encryption mechanism to achieve the efficiency and practicability of data-sharing. Formal security analysis demonstrates EAPDS can resist replayable chosen-ciphertext attack. Many performance evaluation experiments and functional analyses show that EAPDS not only performs better than existing medical data-sharing schemes in terms of efficiency, but also in terms of privacy protection and feasibility. Consequently, our EAPDS scheme holds promising application prospects.

Wireless Body Area Networks (WBANs) support data communication between devices around the human body and are widely used in areas such as healthcare and health monitoring. Due to the sensitivity of transmitted data in WBANs, the restriction of device resources, and the requirement of communication efficiency in emergencies, it remains a great challenge to construct an efficient and secure authentication and key agreement scheme to meet the needs of WBANs. Recently, for the secure exchange of sensitive data in WBANs, Zhang et al. (2024) designed a biometric authentication and key agreement protocol using fuzzy extractor. However, an in-depth analysis reveals that the scheme cannot effectively withstand man-in-the-middle attacks and is insufficient in stability. To address the issues, we propose an improved biometric authentication and key agreement scheme. The solution mainly uses fuzzy extraction techniques, biometrics and elliptic curve cryptography . The user is not required to store any information and only requires to send the message once to complete the authentication, which protects the user’s privacy and is more appropriate for WBANs devices with limited resources. The security of our scheme is demonstrated by formal and informal security analysis. Additionally, we comprehensively evaluate the calculation complexity and security characteristics of this scheme. The evaluation shows our scheme provides both better security as well as reduced computational and communication overheads compared with Zhang et al. (2024)’s scheme.

With the rapid evolution of Web3.0, cryptocurrency has become a cornerstone of decentralized finance. While these digital assets enable efficient and borderless financial transactions, their pseudonymous nature has also attracted malicious activities such as money laundering, fraud, and other financial crimes. Effective detection of malicious accounts is crucial to maintaining the security and integrity of the Web 3.0 ecosystem. Existing malicious account detection methods rely on large amounts of labeled data and suffer from low generalization. Label-efficient and generalizable malicious account detection remains a challenging task. In this paper, we propose ShadowEyes, a framework for detecting malicious accounts by leveraging interaction feature learning with only a small labeled dataset. Specifically, We first propose a generalized account representation named TxGraph, which captures the universal interaction features of Ethereum and Bitcoin. Then we carefully design an account representation augmentation method tailored to simulate the evolution of malicious accounts to generate positive pairs. We conduct extensive experiments using public datasets to evaluate the performance of ShadowEyes. The results demonstrate that it outperforms state-of-the-art (SOTA) methods in four typical scenarios. Specifically, in the scenario of acrossplatform malicious account detection, ShadowEyes maintains an F1 score of around 90%, which is 10% higher than the SOTA method. In the zero-shot learning scenario, it can achieve an F1 score of 79.56% for detecting gambling accounts, surpassing the SOTA method by 10.44%.

The Industrial Internet of Things (IIoT) has been widely studied, which dramatically enhanced the manufacturing efficiency and service elasticity. However, how to ensure the data confidentiality and security in the event-driven publish/subscribe communication model becomes a cumbersome problem. To address this concern, ring signatures have been researched deeply. Nevertheless, existing solutions have large computational burdens and neglect to incorporate reporting and tracing features, which makes it impractical for IIoT. In this way, research focus on designing an efficient report and traceable ring signature is still far-reaching. In this article, we propose RAT ring, a novel report and traceable ring signature, which provides publisher authentication, anonymous communication, reporting, and tracing. To achieve this, we adopt the zero knowledge proof to verify the authenticity of publisher data, and the signature of knowledge to trace the signature. Then, we formalize and prove the security of our scheme. Eventually, through comprehensive performance evaluation, our scheme outperforms prior works by approximately up to 51 times in terms of total computational overhead. These results demonstrate that our design is practical and effective for data privacy-preserving in IIoT.

Employing the caching resources of end users via Device-to-Device (D2D) communication to assist the edge server in microservice caching is promising to further alleviate the network congestion of the Internet of Things (IoT). However, significant extra energy consumption prevents the caching system from maximizing cache utility if all end users cache simultaneously. In this paper, we propose two novel end-edge collaborative microservice caching algorithms in D2D-assisted networks. First, we construct a D2D caching sharing link graph from the aspects of physical and social attributes of end users and introduce the Entropy-based Partitioning Around Medoid (EPAM) algorithm to identify critical users. Second, to address the challenges posed by unknown time-varying user preferences, we model the end-edge collaborative caching problem as a Multi-Agent Multi-Armed Bandit (MAMAB) problem, thus developing two caching decision schemes, i.e, Edge-Centric Scheme (ECS) and User-Centric Scheme (UCS), to accommodate different decision sequences. The simulation results show that the EPAM-ECS and EPAM-UCS have at least 29.2% and 39.3% improvement compared with other baseline algorithms.

Crowdsensing Data Trading (CDT) has emerged as a novel data trading paradigm, where market stability is crucial during the transaction matching process. However, most existing CDT systems usually assume that the preferences of both parties are known and the third-party trading platform is trustworthy, which is impractical in real-world scenarios and leads to significant challenges in reliability and privacy preservation. To address these challenges, we propose a Privacy-Preserving and Stable Data Trading for Unknown Market based on Blockchain and Bilateral Reputation (PPSDT-UMBBR) scheme in the decentralized CDT system. First, a privacy-preserving bilateral preference initialization method is designed to achieve the initial matching of buyers and sellers without exposing their location and attribute privacy. Then, a stable matching method based on dynamic bilateral preference updating is proposed, integrating Differential Privacy, Stable matching theory, and a strategy based on Asymmetric Bilateral Preferences with Multi-Armed Bandits (DPS-ABPMAB). Finally, we theoretically analyze the security and prove that the market outcome is$\delta$-stable. Furthermore, compared to other benchmark methods based on real datasets, our proposed DPS-ABPMAB algorithm improves the average accumulative reward by at least 4.22%, and reduces the average accumulative regret and the mean evaluation error rate by at least 66.86% and 7.35%, respectively.

The Artificial Intelligence of Things (AIoT) is driving human society from “information” to “intelligence”, and the information technology industry is undergoing tremendous changes. However, AIoT data faces security threats such as leakage and illegal access when assisted by third parties. Therefore, some scholars use attribute-based proxy re-encryption (ABPRE) for secure sharing of data. However, the existing ABPRE schemes suffer from high computational overhead and inefficient attribution revocation, which seriously hinders practical application. To solve these problems, in this paper, we propose an efficient attribute-based proxy re-encryption scheme with fast attribute revocation (EPREAR). We design a non-interactive zero-knowledge proof protocol based on blockchain to ensure the verifiability of the key during attribute revocation. Furthermore, we devise a boundless encryption and decryption mechanism to enable the system's encryption and decryption with a fixed computation overhead, regardless of the size of the attribute set. And EPREAR possesses the ability to add infinite attributes without re-initializing the system. Finally, we perform theoretical and experimental analyses that show EPREAR has excellent computational performance. As a consequence, it has better application value in AIoT.

Task offloading is a key technology in fog computing, which allows resource-intensive tasks to be offloaded from terminal devices to fog nodes with higher computing capabilities. However, traditional task offloading methods usually use heuristic methods, which are highly dependent on the mode. They cannot effectively optimize the delay, resulting in a decline in service quality. To solve this problem, a task offloading method based on deep reinforcement learning (DRL) is proposed in this paper. We applied various methods to improve the Deep Q-Network (DQN) and utilized it for task offloading to enhance system performance in fog computing.

Electronic health records (EHRs) based on the Internet of Things (IoT) can provide real-time health data for quick intelligent medical services and give convenience to many data-sharing scenarios. However, EHRs also face various security threats since they are highly private. To the best of our knowledge, no recognized data-sharing work can satisfy the stringent privacy requirements of EHRs. Motivated by this, we propose a blockchain-based personalized access control EHR-sharing scheme with data verifiability, which can safeguard the interests of data owners (DOs) and users simultaneously. First, we take ciphertext-policy attribute-based encryption to achieve personalized access control for DOs. Second, we design an interactive zero-knowledge proof protocol between DOs and users, which can provide authenticity verification of EHR for users and prevent EHR away from forgery. In addition, smart contracts and the interplanetary file system are used to reduce the computation and storage costs of patients. Finally, the security analysis shows that the proposed scheme meets the predefined security goals. The performance analysis demonstrates that the proposed scheme is efficient and can be applied to practical electronic medical record sharing scenarios.

In mobile crowd sensing (MCS), how to obtain accurate truth estimation under privacy preservation has gained much attention. It is important to prevent the leakage of the sensing data, weight, estimated truth, and intermediate truth to third parties to avoid attacks from adversaries when aggregating a large amount of data collected by workers. In addition, dishonest or malicious workers may report false or malicious data. Therefore, we propose a weight-based trust identification for privacy-preserving truth discovery (WTIPPTD) scheme to enhance the accuracy of truth discovery by identifying the trust and data qualities of workers, and then recruiting trusted high-quality workers. First, the garbled circuit (GC) is used for weight update in the encrypted state, and a trust evaluation scheme is proposed based on weight credibility. Second, a data quality evaluation scheme for workers is designed, and the trusted high-quality workers are recruited to improve the accuracy of truth discovery while reducing the recruitment cost. Finally, we conduct experiments with a large number of real and synthetic data sets, and the results show that our proposed scheme significantly improves the accuracy of truth discovery by 17.80%–98.61% and substantially reduces worker recruitment cost by 16.43%–26.50%.

As the proliferation of 5th Generation Mobile Communication Technology (5G) accelerates the adoption of Internet of Things (IoT) applications, building robust and secure communication channel becomes increasingly crucial with the exponential growth of connected devices. The 3rd Generation Partnership Project (3GPP) has established security standards for 5G systems, including mechanisms such as the 5G-Authentication and Key Agreement (5G-AKA), which enables establish secure sessions in untrustworthy participants or insecure channels. The private key which untrustworthy parties have independently or transmitted through insecure channels, may involve risk of information leakage in 5G-AKA. Motivated by this challenge, we propose a practical and secure dual-server key agreement scheme based on password authentication for IoT devices in 5G networks. The scheme ensures secure reliable key storage and key transmission, mitigating risks associated with key information leakage through a dual-server architecture and three-lock security policy. Importantly, we avoid ownership of the complete key by any untrustworthy entity in insecure 5G network to ensure key security. The scheme can resilience to various security threats prevalent in 5G networks through rigorous formal security. We analyze the communication and computational loads to illustrate the protocol’s practicality and efficacy.

Mobile Crowdsourcing (MCS) has become a novel paradigm for enabling data collection by worker recruitment, and the reputation plays a crucial role in achieving high-quality data. Although identity, data, and bid privacy preserving have been thoroughly investigated with the advance of blockchain technology, existing literature barely focuses on reputation privacy, which prevents malicious workers from submitting false data that could affect truth discovery for data requester. Therefore, we propose a Blockchain-Based Reputation Privacy Preserving for Quality-Aware Worker Recruitment Scheme (BRPP-QWR). First, we design a lightweight privacy preserving scheme for the whole life cycle of the worker’s reputation, which adopts sub-address retrieval technique combined with Pedersen Commitment and Compact Linkable Spontaneous Anonymous Group (CLSAG) signature to enable fast and anonymous verification of the reputation update process. Subsequently, to tackle the unknown worker recruitment problem, we propose a Reputation, Selfishness, and Quality-based Multi-Armed Bandit (RSQ-MAB) learning algorithm to select reliable and high-quality workers. Lastly, we implement a prototype system on Hyperledger Fabric to evaluate the performance of the reputation management scheme. The results indicate that the execution latency for the reputation score verification and retrieval latency can be reduced by an average of 6.30%–56.90% compared with ARMS-MCS. In addition, experimental results on both real and synthetic datasets show that the proposed RSQ-MAB algorithm achieves an increase of at least 20.05% in regard to the data requester’s total revenue and a decrease of at least 48.55% and 3.18% in regret and Multi-round Average Error (MAE), respectively, compared with other benchmark methods.

Virtual machines (VMs) have been increasingly deployed in real-time systems to enhance heterogeneous resource sharing. Despite being isolated for security and prevention of failure propagation, VMs need to communicate with each other to complete certain tasks jointly. A real-time inter-VM communication framework has been proposed based on VirtIO, which is in essence a mechanism of message passing with high latency and low scalability on data amount. In contrary, IVSHMEM implements inter-VM communication with shared memory, which is generally fast and large in throughput. Unfortunately, IVSHMEM does not resolve resource contention between flows and hence cannot be applied in real-time scenarios. In this work, we propose a real-time inter-VM communication framework RTISM built upon shared memory and IVSHMEM. RTISM provides priority-based scheduling and supports mixed-criticality flows. Worst-Case Response Time (WCRT) analysis is reported to bound the end-to-end communication delay and a Limited Priority Assignment (LPA) algorithm is developed to enhance schedulability. Experimental evaluation shows that (i) RTISM has about 4 times higher throughput than the VirtIO-based inter-VM communication framework; (ii) LPA improves schedulability by over 25% compared to the state-of-the-art; (iii) WCRT produces a tight bound.

With the continuous improvement of computation and communication capabilities, the Internet of Things (IoT) plays a vital role in many intelligent applications. Therefore, IoT devices generate a large amount of data every day, which lays a solid foundation for the success of machine learning. However, the strong privacy requirements of the IoT data make its machine learning very difficult. To protect data privacy, many privacy‐preserving machine learning schemes have been proposed. At present, most schemes only aim at specific models and lack general solutions, which is not an ideal solution in engineering practice. In order to meet this challenge, we propose an efficient and privacy‐preserving machine learning training framework (ePMLF) in a fog computing environment. The ePMLF framework can let the software service provider (SSP) perform privacy‐preserving model training with the data on the fog nodes. The security of the data on the fog nodes can be protected and the model parameters can only be obtained by SSP. The proposed secure data normalization method in the framework further improves the accuracy of the training model. Experimental analysis shows that our framework significantly reduces the computation and communication overhead compared with the existing scheme.

The CAN FD emerges as a promising CAN technology inside the ACPS due to its advantages of high data-phase bit-rate and message payload. HSM based security solution is recommended by auto industry to protect CAN FD from potential security attacks, but it induces new challenges on timing analysis of CAN FD messages, which is left open in the literature. This article develops the first security-aware system model to describe the processing of CAN FD messages, and presents a new WCRT analysis to bound the interference induced by security-critical messages. We give the theoretical proof that our WCRT analysis can upper bound the response time of CAN FD messages. Using a small message set, we show that the WCRT computed by our new analysis is only 14% percent higher than the true WCRT obtained from an exhaustive search based simulator. By comparing with existing method, the number of impacted messages increases along with the increasing number of security critical messages, and for the two typical CAN FD systems, the percentage of WCRT increase varies from 12.43% to 14.57% and 7.0% to 10.89%, respectively; the percentage of WCRT decrease varies from 3.29% to 6.04% and 4.13% to 7.93%, respectively.

Real-time data access based on wireless sensor networks (WSNs) is an indispensable and essential foundation for real-time control of industrial processes, especially for the smart industry. But the transmission of real-time data between participants faces many security issues in WSNs, such as sensitive data leakage and unauthorized access. Many researchers have made efforts to solve such a problem. But there are no secure and efficient schemes because security schemes based on public key cryptography have excessive computation and communication cost, while non-public key security schemes are vulnerable to various security attacks. We design a secure and efficient anonymous authentication key agreement scheme to balance security and efficiency by combining physical unclonable function (PUF) and elliptic curve cryptography (ECC). It only uses simple cryptographic calculations to resist identity impersonation attacks based on PUF completely. It also achieves the consistency and randomness of the session key with only a small amount of ECC operations and can resist the attack of the session key. We present a formal security analysis based on Mao-Boyd logic and demonstrate that our scheme satisfies the security requirements for real-time data access in smart industry networks. And performance analysis shows that our scheme has a higher security level and also has greater advantages in terms of computation and communication cost.

Industrial wireless sensor networks (IWSNs) are indispensable for Industry 4.0. However, limited to the resource of sensor nodes, designing an effective and secure authentication protocol for real-time data access in IWSNs is full of challenges. In addition, existing protocols fail to balance security and performance for real-time access to IWSNs simultaneously. Thus, we design an efficient authentication key agreement protocol using the physical unclonable function (PUF), elliptic curve cryptography (ECC), and smart contract. We perform formal security analysis under the random oracle model and demonstrate that our protocol meets the basic requirements. Rinkeby, an online Ethereum test network, is used to test the smart contract. Finally, we evaluate computation, communication and storage costs with several similar protocols. The results show that our protocol can provide a higher level of security and the lowest storage cost.

Summary Mobile social networks (MSNs) are developed from mobile ad hoc networks. Nodes in such networks usually have social characteristics. In recent years, researchers are trying to use the social characteristics of the network to propose new data forwarding metrics, so as to design more efficient routing algorithms. However, most of the proposed algorithms only consider local context information, which leads to the performance of the routing is not optimized enough. In this paper, we introduce two key metrics, namely, social relationship and social activity. The metrics will be used to search the best data forwarding nodes to improve the probability of data delivery. We propose a prediction‐based social‐aware opportunistic routing (PSOR). In the proposed method, node's social profiles are used to search relay candidates set, and the discrete‐time semi‐Markov prediction model is used to find the probability distribution of node transition between communities. Many simulation experiments based on real traces show that the proposed PSOR algorithm is more efficient to maximize the packet delivery probability than other state‐of‐the‐art algorithms.

Internet of Things (IoT) has been widely used in many fields, bringing great convenience to people’s traditional work and life. IoT generates tremendous amounts of data at the edge of network. However, the security of data transmission is facing severe challenges. In particular, edge IoT nodes cannot run complex encryption operations due to their limited computing and storage resources. Therefore, edge IoT nodes are more susceptible to various security attacks. To this end, a lightweight mutual authentication and key agreement protocol is proposed to achieve the security of IoT nodes’ communication. The protocol uses the reverse fuzzy extractor to acclimatize to the noisy environment and introduces the supplementary subprotocol to enhance resistance to the desynchronization attack. It uses only lightweight cryptographic operations, such as hash function, XORs, and PUF. It only stores one pseudo-identity. The protocol is proven to be secure by rigid security analysis based on improved BAN logic. Performance analysis shows the proposed protocol has more comprehensive functions and incurs lower computation and communication cost when compared with similar protocols.

Since Deep Convolutional Neural Network (DCNN) training involves complex computations and data transmissions, the previous DCNN processors hard to achieve ideal energy efficiency. This paper proposed a DCNN processor supports both inference and training for the embedded system. The processor contains three heterogeneous cores to provide distinct computation patterns and dataflow for different training phases. In addition, since inference takes up more than 90% of the workload of the DCNN application, the three cores of the processor can be reconfigured to efficiently support the inference to achieve leading resources Utilization. The processor is fabricated in 55nm CMOS technology, post-layout simulation shows the processor achieving 1.36 Tops/w energy efficiency for training and 2.44 Tops/w for the inference.

Satellite Internet of Things (S-IoT), which integrates satellite networks with IoT, is a new mobile Internet to provide services for social networks. However, affected by the dynamic changes of topology structure and node status, the efficient and secure forwarding of data packets in S-IoT is challenging. In view of the abovementioned problem, this paper proposes an adaptive routing strategy based on improved double Q-learning for S-IoT. First, the whole S-IoT is regarded as a reinforcement learning environment, and satellite nodes and ground nodes in S-IoT are both regarded as intelligent agents. Each node in the S-IoT maintains two Q tables, which are used for selecting the forwarding node and for evaluating the forwarding value, respectively. In addition, the next hop node of data packets is determined depending on the mixed Q value. Second, in order to optimize the Q value, this paper makes improvements on the mixed Q value, the reward value, and the discount factor, respectively, based on the congestion degree, the hop count, and the node status. Finally, we perform extensive simulations to evaluate the performance of this adaptive routing strategy in terms of delivery rate, average delay, and overhead ratio. Evaluation results demonstrate that the proposed strategy can achieve more efficient and secure routing in the highly dynamic environment compared with the state-of-the-art strategies.

Abstract The ever‐evolving trip mode of human being leads the automobiles moving toward connected, autonomous, sharing, and electrified vehicles rapidly. But the connection introduces new cybersecurity problems on in‐vehicle networks, which poses great challenges for safety guarantee of distributed automotive cyber‐physical systems. This article first analyzes the cybersecurity vulnerabilities and defines the security requirements for in‐vehicle networks, and then introduces the architecture evolution of in‐vehicle network. Based on the definition on architecture of in‐vehicle networks, this article defines a security protection framework for it. And then, it surveys the state‐of‐the‐art works for availability protection, integrity protection, and confidentiality protection of in‐vehicle networks, respectively, and detailed analysis and comparisons are given about the proposed cybersecurity protection mechanisms. Finally, it summarizes the future challenges for cybersecurity protection of in‐vehicle networks, and proposes possible solutions for these challenges.

Extensibility is an important optimization objective for the E/E architecture of automotive cyber-physical systems (ACPS), while little attention has paid to the extensibility-aware design of in-vehicle network. To address this problem, this paper formulates a trade-off problem that balances the bandwidth utilization and the extensibility from the initial design of CAN FD. We firstly propose a new extensibility model and the related evaluation metric, and then two optimization algorithms, namely, the mixed integer linear programming (MILP) approach and the simulated annealing (SA) based heuristic approach, are proposed to resolve the trade-off problem for mid-sized and industry sized signal sets, respectively. The experiment results show the efficiency of the proposed extensibility metric and the optimization algorithms. By comparing with state-of-the-art algorithm, the MILP reduces the increase range of the bandwidth utilization of the extended signal set by 18.17% to 57.64% averagely, and 49.22% to 89.40% maximally, with only 0.06% to 0.79% bandwidth utilization overhead; the SA approach can reduces the increase range of the bandwidth utilization of the extended signal set by 12.71% to 58.33% averagely, and 40.08% to 89.40% maximally, with only 0.06% to 0.8% bandwidth utilization overhead.

Guaranteeing safety is always a prerequisite in the process of realizing various automotive applications. However, the automotive functional safety design has been challenged by multiple factors, such as the complexity of the new generation automotive electrical and electronic (E/E) architecture, the continuous release and update of automotive functional safety standard International Standardization Organization (ISO) 26262, the release of new AUTOSAR adaptive platform standard, and the increase in different types of costs. In this article, we summarize the recent advances of automotive functional safety design methodologies through analysis, design, optimization, and runtime phases, respectively: 1) functional safety analysis; 2) functional safety guarantee; 3) safety-aware cost optimization; and 4) safety-critical multifunctional scheduling. Then, we provide the future trends in functional safety design methodologies that will be directly oriented to autonomous vehicles and adapt to the next generation functional safety standard ISO 21448.

To realize the optimized design of the automotive cyber-physical system, it is required to consider the interplay between the communication and computation. However, the existing research about the design of the controller area network (CAN) with flexible data rate (CAN FD) ignores this, it only considers the minimization of the bandwidth utilization and neglects the fact that it would trigger too many unnecessary message receiving interrupts (MRIs) on message receiving electronic control units. To address this problem, this article formulates a tradeoff problem that balances the bandwidth utilization and the number of unnecessary MRIs during the design of the CAN FD. We first propose an algorithm to analyze the number of unnecessary MRIs triggered by the packed messages, and then, two heuristic algorithms, namely, the Top-Down approach and the Hybrid approach, are proposed to resolve the tradeoff problem for midsized and large signal sets, respectively. The experiment results show that compared with the state-of-the-art algorithm, the Top-Down approach reduces the unnecessary MRIs by 10.48%-99.89% with only 0.02%-1.32% bandwidth utilization overhead, the Hybrid approach reduces the unnecessary MRIs by 23.15%-99.63% with only 0.13%-2.07% bandwidth utilization overhead.

The controller area network with flexible data-rate (CAN FD) is the new generation of the CAN technology to meet the daily increasing bandwidth requirement for automotive cyber-physical systems (ACPS). However, ACPS is a security-critical system, an efficient security/timing-aware design space exploration (DSE) method is required to fully utilize CAN FD's high data phase data rate. In this paper, we propose an AUTOSAR-compliant system model that integrates both timing and security constraint, an integrated mixed-integer linear programming formulation (i-MILP) for the optimal DSE of CAN FD, and a divide-and-conquer approach to the i-MILP (dc-MILP) to address its timing complexity problem. The experiment results show that dc-MILP scales well for industrial-size systems and saves 1.94%-4.76% bandwidth utilization and guarantees the schedulability for more signal sets by comparing with the state-of-the-art algorithm.

Automotive cyber-physical systems are energy-aware and safety-critical systems where energy consumption should be controlled from a perspective of design constraints and reliability should be enhanced toward functional safety goal assurance. In this paper, we solve the problem of reliability enhancement of an automotive function (i.e., functionality or application) under energy and response-time constraints based on the dynamic voltage and frequency scaling technique. The problem is solved by a two-stage solution, namely, response-time reduction under energy constraint and reliability enhancement under energy and response-time constraints. The first stage is solved by proposing average energy preallocation, and the second stage is solved by proposing a reliability-enhancement technique based on the first stage. Examples and experiments show that the proposed solution can not only assure energy and response-time constraints, but also enhances reliability as much as 16.66% compared with its counterpart.

Vehicular cloud network (VCN) is deemed as the most promising platform for providing transportation safety, road optimization, and valued‐added application services. Because VCN is of distinguishing feature with super‐large scale and unstable communication, it is a challenging task to study efficient authentication scheme for VCN without losing security and conditional privacy‐preserving. To meet the challenge, a new efficient message authentication scheme is proposed in this paper. A batch message verification and signature aggregation are included in the proposed scheme to improve the authentication efficiency and decrease the communication cost. Compared with the similar conditional privacy‐preserving authentication schemes, the proposed scheme has superior performance in computation and communication cost. Simulation analysis further proves that the proposed scheme has better advantages in reducing the verification loss rate and message delay in the application of VCN.