Sharded blockchain offers scalability, decentralization, immutability, and linear improvement, making it a promising solution for addressing the trust problem in large-scale collaborative IoT. However, a high proportion of cross-shard transactions (CSTs) can severely limit the performance of decentralized blockchain. Furthermore, the dynamic assemblage characteristic of collaborative sensing in sharded blockchain is often ignored. To overcome these limitations, we propose HMMDShard, a dynamic blockchain sharding scheme based on the hidden Markov model (HMM). HMMDShard leverages fine-grained blockchain sharding and fully embraces the dynamic assemblage characteristic of IoT collaborative sensing. By integrating the HMM, we achieve adaptive dynamic incremental updating of blockchain shards, effectively reducing CSTs across all shards. We conduct a comprehensive analysis of the security issues and properties of HMMDShard, and evaluate its performance through the implementation of a system prototype. The results demonstrate that HMMDShard significantly reduces the proportion of CSTs and outperforms other baselines in terms of system throughput and transaction confirmation latency.

The rapid growth of wireless sensor networks (WSNs) has opened new doors to realize remote monitoring in various areas. However, designing authentication protocol for resource-constrained WSNs is a challenging task. Recently, Aghili et al. introduced an efficient three-factor authentication scheme for WSNs using hash function, and argued that the scheme is immune to known attacks. However, we discover that their scheme suffers from a few serious weaknesses, such as session key disclosure attack, desynchronization attack, sensor node impersonation attack, and session-specific temporary information attack, and does not provide forward secrecy. In order to overcome the deficiency of this scheme, we propose an enhanced biometric-based authentication scheme for WSNs using Chebyshev chaotic map. We demonstrate that the proposed scheme is provably secure in the random oracle model. Furthermore, Burrows–Abadi–Needham logic analysis shows that the proposed scheme achieves mutual authentication and session key agreement. The informal analysis demonstrates that the proposed scheme is resistant to various attacks and has desirable attributes such as forward secrecy and three-factor secrecy. Finally, the security and performance comparison show that the proposed scheme is more practical.

To provide secure communication, the authentication-and-key-agreement scheme plays a vital role in multi-server environments, Internet of Things (IoT), wireless sensor networks (WSNs), etc. This scheme enables users and servers to negotiate for a common session initiation key. Our proposal first analyzes Amin et al.’s authentication scheme based on RSA and proves that it cannot provide perfect forward secrecy and user un-traceability, and is susceptible to offline password guessing attack and key-compromise user impersonation attack. Secondly, we provide that Srinivas et al.’s multi-server authentication scheme is not secured against offline password guessing attack and key-compromise user impersonation attack, and is unable to ensure user un-traceability. To remedy such limitations and improve computational efficiency, we present a multi-server two-factor authentication scheme using elliptic curve cryptography (ECC). Subsequently, employing heuristic analysis and Burrows–Abadi–Needham logic (BAN-Logic) proof, it is proven that the presented scheme provides security against all known attacks, and in particular provides user un-traceability and perfect forward security. Finally, appropriate comparisons with prevalent works demonstrate the robustness and feasibility of the presented solution in multi-server environments.

Feature maps of different scales in convolutional neural networks (CNNs) can be regarded as image pyramids. In classification tasks, only the last layer of feature maps is used for making decision. However, in tasks such as road crack detection, the target objects are so small that some original information might lose during the downsampling process in CNN. The authors propose a structure that uses the information that is contained in different layers of feature maps, so all the information could contribute to the classification. This process is managed by adding the weighted values of pixels in corresponding regions of different layers in feature maps and using the sum of these values as the output. The authors apply this structure on a residual network and use it to learn the features of road cracks. Experiments have shown that with the authors’ structure, the network performs better than others at understanding and detecting road cracks.

Smart grid (SG) achieves more convenient and efficient power transmission through the rapid development of the information and communication technology. In addition to the transmission of electrical energy, the SG system also involves privacy and control information of service providers, which is closely related to people's livelihood. However, at present, the data in the SG system are transmitted in the form of plaintext, which does not have complete protection measures for entity privacy and communication data security. Focusing on preserving entity privacy and data security, this article proposes an anonymous secure authentication protocol (ASAP-SG) for the SG environment. Based on elliptic curve cryptography and physical unclonable function, ASAP-SG can realize authentication and key agreement between smart meters and service providers. The security of ASAP-SG has been verified with proverif tool and proven strictly under the real-or-random model. Furthermore, heuristic analysis manifests that ASAP-SG can successfully resist known security attacks. Finally, a fully comparison with the other five most advanced schemes reveals that ASAP-SG consumes the optimal computation cost on both smart meters and service providers and reduces the communication overhead by 33.3%–45.8%, on the basis of satisfying ten security objectives. These features make ASAP-SG more suitable for resource-constrained SG environment.

With the development of Internet of Things (IoT) technologies, Internet-enabled devices have been widely used in our daily lives. As a new service paradigm, cloud computing aims at solving the resource-constrained problem of Internet-enabled devices. It is playing an increasingly important role in resource sharing. Due to the complexity and openness of wireless networks, the authentication protocol is crucial for secure communication and user privacy protection. In this paper, we discuss the limitations of a recently introduced IoT-based authentication scheme for cloud computing. Furthermore, we present an enhanced three-factor authentication scheme using chaotic maps. The session key is established based on Chebyshev chaotic-based Diffie–Hellman key exchange. In addition, the session key involves a long-term secret. It ensures that our scheme is secure against all the possible session key exposure attacks. Besides, our scheme can effectively update user password locally. Burrows–Abadi–Needham logic proof confirms that our scheme provides mutual authentication and session key agreement. The formal analysis under random oracle model proves the semantic security of our scheme. The informal analysis shows that our scheme is immune to diverse attacks and has desired features such as three-factor secrecy. Finally, the performance comparisons demonstrate that our scheme provides optimal security features with an acceptable computation and communication overheads.