As blockchain technology continues to advance, smart contracts, a core component, have increasingly garnered widespread attention. Nevertheless, security concerns associated with smart contracts have become more prominent. Although machine-learning techniques have demonstrated potential in the field of smart-contract security detection, there is still a lack of comprehensive review studies. To address this research gap, this paper innovatively presents a comprehensive investigation of smart-contract vulnerability detection based on machine learning. First, we elucidate common types of smart-contract vulnerabilities and the background of formalized vulnerability detection tools. Subsequently, we conduct an in-depth study and analysis of machine-learning techniques. Next, we collect, screen, and comparatively analyze existing machine-learning-based smart-contract vulnerability detection tools. Finally, we summarize the findings and offer feasible insights into this domain.

In recent years, the rise of location-based service applications such as cashier-less shopping, mobile advertisement targeting, and geo-based augmented reality (AR) has been remarkable. These applications offer convenient and interactive experiences by utilizing indoor localization technology. One popular research area in indoor localization is passive fingerprinting localization based on Channel State Information (CSI), which uses general-purpose Wi-Fi platforms and “unconscious cooperative sensing” to achieve device-free localization. However, existing studies face challenges related to inadequate fingerprint richness, limited distinguishability, and inconsistent fingerprint features in real-world dynamic environments. To address these challenges, we prpose MFFLoc in this paper. MFFLoc extracts and processes amplitude and phase information from CSI in a 2D manner. It then fuses the amplitude and phase information using multimodal fusion representation, resulting in rich and distinguishable fused fingerprint features. This approach allows MFFLoc to achieve satisfactory accuracy with just one communication link, reducing deployment costs. To overcome the issue of inconsistent fingerprint features in dynamic environments, MFFLoc proposes an unsupervised domain adaptation method. It employs a dual-flow structure, with one flow operating in the source domain and the other in the target domain. The adaptation layer, with correlated weights, remains unshared between the two flows. Meta-learning is also used to automatically determine the most suitable adaptation layer. Through extensive 6-day experiments conducted in a dynamic indoor environment, MFFLoc showcases superior performance compared to state-of-the-art systems. It demonstrates higher localization accuracy and robustness, making it a promising solution for indoor localization applications.

With various industrial wireless networks greeting booming development, modern industrial devices configured with several network interfaces increasingly become the norm. Such multihomed industrial devices can increase application throughput by making use of multiple network paths, enabled by the multipath transmission control protocol (MTCP) (MPTCP). However, MPTCP might be challenged in the heterogeneous industrial networks because concurrent transmitting industrial application data over asymmetric network paths with different delays is almost bound to the receive buffer blocking problem, which is caused by out-of-order packet arrival and is harmful to the performance of the multipath transmission. The existing MPTCP solutions generally use static mathematical models to evaluate path quality and prohibit transmission on paths with poor quality, which are unable to perform efficiently under highly dynamic and complex network environments. Therefore, in this article, we propose a learning-driven latency-aware MPTCP variant, called ${l}\,^2$ -MPTCP, which seeks to possibly mitigate the out-of-order packet arrival and receive buffer blocking problems associated with the network heterogeneity in the industrial Internet. ${l}\,^2$ -MPTCP accurately computes each MPTCP path’s forward delay and assigns application data to multiple paths according to their calculated forward delay differences by using a novel multiexpert learning-enabled forward delay estimator. ${l}\,^2$ -MPTCP dynamically manages path usage and chooses the optimal path collection for bandwidth aggregation and multipath transmission by using a promising reinforcement learning-empowered multipath manager. Experimental results demonstrate that ${l}\,^2$ -MPTCP outperforms the current MPTCP solutions in terms of multipathing service quality.

The parking assist system is an essential application of the car’s active collision avoidance system in low-speed and complex urban environments, which has been a hot research topic in recent years. Parking space detection is an important step of the parking assistance system, and its research object is parking spaces with symmetrical structures in parking lots. By analyzing and investigating parking space information measured by the sensors, reliable detection of sufficient parking spaces can be realized. First, this article discusses the main problems in the process of detecting parking spaces, illustrating the research significance and current research status of parking space detection methods. In addition, it further introduces some parking space detection methods, including free-space-based methods, parking-space-marking-based methods, user-interface-based methods, and infrastructure-based methods, which are all under methods of parking space selection. Lastly, this article summarizes the parking space detection methods, which gives a clear direction for future research.

The Internet Engineering Task Force (IETF) developed a standard wireless communication protocol stack called 6TiSCH to provide low-power and high-reliability communications in harsh industrial environments. However, the current IETF 6TiSCH protocol lacks adequate consideration of inter-device authentication and key management, exposing the IETF 6TiSCH-based wireless network to security risks such as key leakage and malicious attacks. Meanwhile, existing authentication and key management schemes cannot be directly applied to the resource-constrained IETF 6TiSCH industrial wireless network. To address this challenge, we propose a novel scheme called SAKMS, which is tightly integrated with the IETF 6TiSCH network and incorporates the following key components: (i) improved elliptic curve cryptography (ECC) operation, a regular window method is proposed to accelerate the computation of the ECC-related operations; (ii) secure authentication process, SAKMS distributes implicit certificates for each device in the network and adopts the improved ECC algorithm to achieve secure and trustworthy authentication between devices, and the process only consist of hashing, XOR, and a few ECC multiplication operations; (iii) dynamic key update, after successful authentication, devices can negotiate and dynamically update link keys, ensuring resilience against potential key leakage issues. We perform formal and informal security analyses to demonstrate the resilience of SAKMS against various known attacks. Finally, we extensively evaluate the performance of SAKMS in a real 6TiSCH wireless sensor network. The experimental results show a 37% improvement in the computational efficiency of ECC operations compared to existing works, with link key establishment taking only 0.9 s on the OpenMoteSTM platform.