With the ever-increasing requirements of delay-sensitive and mission-critical applications, it becomes a popular research trend to incorporate edge computing in the Internet of Things (IoT) to mitigate the pressure of traditional cloud-based IoT architecture. Edge computing delivers real-time computations and communications for IoT devices by leveraging edge servers deployed close to users, which creates a collaborative edge computing (CEC) paradigm. The capacity of edge servers is beneficial but risky, as vulnerable servers can be exploited to conduct surveillance or perform other nefarious activities. Besides, fake IoT devices would bring security threats and compromise the IoT system. This highlights the necessity of designing a secure and efficient mutual authentication scheme for CEC. In this direction, related works have proposed various authentication mechanisms, but most of them are found unfit due to the absence of decentralization, anonymity, and mobility. Motivated by this fact, we propose a blockchain-based mutual authentication scheme that bridges these gaps. Specifically, blockchain, certificateless cryptography, elliptic curve cryptography, and pseudonym-based cryptography are integrated into our scheme to provide mutual authentication between edge servers and IoT devices. Except for static conditions, both intraedge and interedge authentication are considered. Besides, we elaborate on the key generation procedures and design a session key negotiation mechanism. Extensive experiments and security analyses have been conducted to show the feasibility of the proposed scheme.

With the emergence of the sixth-generation (6G) communication technologies, the Internet of Vehicles (IoV) is rapidly developing with the coordination between intelligent networked vehicles, road infrastructures, and the cloud. However, the openness and dynamic nature of the IoV raise significant security and privacy concerns, highlighting the need for efficient authentication schemes. Conventional authentication schemes are no longer suitable for 6G-enabled IoV due to high latency, single point of failure, and heavy management costs. Additionally, existing literature on multi-domain authentication mainly investigates vehicle mobility, ignoring the challenges posed by vehicle heterogeneity. To fill this gap, we propose a multi-domain authentication scheme with conditional privacy preservation (MACPP) that considers administrative domains ( AD ) and geographic domains ( GD ) in the IoV. In MACPP, we design a novel identity-based signature scheme without requiring bilinear pairing for efficient authentication. Additionally, we propose a blockchain-assisted pseudonym management scheme (BAPM) to further improve system security by designing a dynamical sparse Merkle tree structure (DSMT). We demonstrate that the proposed MACPP satisfies the security requirements through an in-depth security analysis. Moreover, the experimental results demonstrate the effectiveness and efficiency of both MACPP and BAPM.

The property of Internet of Things (IoT) applications is their capability to execute tasks through the collaboration of interconnected IoT objects. However, IoT collaborations face significant challenges due to security threats that undermine their reliability. An uncertified task publisher may deceive IoT devices into executing illegal tasks, while malicious attackers may intercept and modify transmitted data. Existing works on IoT trusted management issues tend to concentrate on individual aspects, such as authentication, privacy protection, and access control. However, trusted management for IoT collaboration is a multifaceted and intricate endeavor that necessitates a comprehensive approach. To fill this gap, we propose a lightweight authentication-driven trusted management framework that includes a novel authentication and key agreement scheme to guarantee the validity of task publishers, with greatly reduced overheads compared to recent works. The framework also incorporates a distributed data storage scheme and a fine-grained access control mechanism. We record the interactive messages on the blockchain to ensure behavior traceability. We evaluate the authentication scheme through comparative experiments and formal security analysis, demonstrating its efficiency and effectiveness. The experimental results of data storage and acquisition in real-world IoT environments indicate that the proposed framework is a feasible solution for reliable IoT collaboration.

Computing ecosystems are shifting toward a computing continuum paradigm designed to handle the diverse and dynamic nature of computing resources spread across various locations. It demonstrates significant potential in providing high-bandwidth and low-latency services for users. However, as a large number of users request services from distributed computing continuum systems, it is critical to schedule numerous delay-sensitive, fractional workloads and maximum parallelism-bound jobs to appropriate backend resources, e.g., cloud container instances. In addition, the scheduling strategy also needs to maximize the social welfare that incorporates the utilities of jobs and the revenue of service providers. However, current workload scheduling algorithms are based on simple heuristics and lack performance guarantees. Due to the unpredictability of online requests, the distribution of requests should not be assumed. Therefore, designing an online workload scheduling strategy without assumptions on request distributions is essential for balancing the online workload. This work first establishes a spatiotemporal integrated resource pool to reflect the computational resources provided by distributed computing continuum systems. Then, several pseudo-social welfare functions and marginal cost functions are constructed, where the latter is used to estimate the marginal cost of provisioning services to each newly arrived job based on the current resource surplus. We propose an online workload scheduling strategy named OnSocMax to solve the above problems. It operates by following the solutions to several convex pseudo-social welfare maximization problems and is proven to be $\alpha$-competitive for some $\alpha$ with a value of at least 2. The evaluation results demonstrate that OnSocMax outperforms several benchmark strategies in maximizing social welfare.

Blockchain and smart contracts are one of the key technologies promoting Web 3.0. However, due to security considerations and consistency requirements, smart contracts currently only support simple and deterministic programs, which significantly hinders their deployment in intelligent Web 3.0 applications. To enhance smart contracts intelligence on the blockchain, we propose SMART, a plug-in smart contract framework that supports efficient AI model inference while being compatible with existing blockchains. To handle the high complexity of model inference, we propose an on-chain and off-chain joint execution model, which separates the SMART contract into two parts: the deterministic code still runs inside an on-chain virtual machine, while the complex model inference is offloaded to off-chain compute nodes. To solve the non-determinism brought by model inference, we leverage Trusted Execution Environments (TEEs) to endorse the integrity and correctness of the off-chain execution. We also design distributed attestation and secret key provisioning schemes to further enhance the system security and model privacy. We implement a SMART prototype and evaluate it on a popular Ethereum Virtual Machine (EVM)-based blockchain. Theoretical analysis and prototype evaluation show that SMART not only achieves the security goals of correctness, liveness, and model privacy, but also has approximately 5 orders of magnitude faster inference efficiency than existing on-chain solutions.