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When a wireless sensor node’s wireless communication fails after being deployed in an inaccessible area, the lost node cannot be repaired through a debugging interaction that relies on that communication. Visible light communication (VLC) as a supplement of radio wave communication can improve the transmission security at the physical layer due to its unidirectional propagation characteristic. Therefore, we implemented a VLC-based hybrid communication debugging system (HCDS) based on VLC using smartphone and sensor node. For the system’s downlink, the smartphone is taken as the VLC gateway and sends the debugging codes to the sensor node by the flashlight. To improve the transmission efficiency of the downlink, we also propose a new coding method for source coding and channel coding, respectively. For the source coding, we analyze the binary instructions and compress the operands using bitmask techniques. The average compression rate of the binary structure reaches 84.11%. For the channel coding, we optimize dual-header pulse interval (DH-PIM) and propose overlapped DH-PIM (ODH-PIM) by introducing a flashlight half-on state. The flashlight half-on state can improve the representation capability of individual symbols. For the uplink of HCDS, we use the onboard LED of the sensor node to transmit feedback debugging information to the smartphone. At the same time, we design a novel encoding format of DH-PIM to optimize uplink transmission. Experimental results show that the optimized uplink transmission time and BER are reduced by 10.71% and 22%, compared with the original DH-PIM.

The dynamic behavior of three-layer composite beams, consisting of concrete slabs and steel beams, is influenced by the structural configuration of each layer as well as the shear connectors. The interlayer shear stiffness in three-layer composite beams governs their global dynamic behavior, while interlayer slippage-induced localized vibration effects represent a key limiting factor in practical applications. Based on the dynamic test results of steel–concrete double-layer composite beams, the feasibility of a finite element solid model for composite beams, which accounts for interlayer shear connectors and beam body characteristics, has been validated. Utilizing identical modeling parameters, an analytical model for the inherent vibration characteristics of three-layer steel–concrete composite beams has been developed. This study encompasses two types of composite beams: concrete–steel–concrete (CSC) and concrete–concrete–steel (CCS). Numerical simulations and theoretical analysis systematically investigated the effects of interface shear connector arrangements and structural geometric parameters on dynamic performance. Research indicates that the natural frequency of steel–concrete three-layer composite beams exhibits a distinct two-stage increasing trend with the enhancement in interlayer shear stiffness. For CSC-type simply supported composite beams, the fundamental vertical vibration frequency increases by 37.82% when achieving full shear connection at both interfaces compared to the unconnected state, while two-equal-span continuous beams show a 38.06% improvement. However, significant differences remain between the fully shear-connected state and theoretical rigid-bonding condition, with frequency discrepancies of 24.69% for simply supported beams and 24.07% for continuous beams. Notably, CCS-type simply supported beams display a 12.07% frequency increase with full concrete-to-concrete connection, exceeding even the theoretical rigid-bonding frequency value. Longitudinal connector arrangement non-uniformity significantly impacts dynamic characteristics, while the transverse arrangement has minimal influence. Among structural parameters, steel flange plate thickness has the most significant effect, followed by concrete slab width and thickness, with steel web thickness having the least impact. Based on the observation that the first-order vertical vibration frequency of three-layer composite beams exhibits a two-stage decreasing trend with an increase in the span-to-depth ratio, it is recommended that the span-to-depth ratio of three-layer steel–concrete composite beams should not be less than 10.

Based on a continuous box girder bridge on Beijing Metro Line 5, a coupling vibration model of passenger-vehicle-bridge is established. According to the Lagrange equation, the differential equations of motion of the system are derived. The corresponding calculation program is written in Fortran language to study the dynamic response of passengers, vehicles and bridges. The difference between passenger and vehicle vibration responses are compared and then the passenger comfort is evaluated. The bridge is subjected to on-site vibration testing, and the measured data is used to verify the reliability of the calculation model. The results show that the numerical calculation results are consistent with the vibration change tendency of the measured data, and the amplitudes are relatively close. The established dynamic analysis model and calculation program have good reliability; the passenger's partial vibration response is different from the vehicle and has a certain hysteresis. According to the ISO2631 standard, the riding comfort level of the section is uncomfortable at part of the driving speed, and the passenger's comfort may be the worst at the middle position.

In the modern cloud environment, considering the cost of hardware and software resources, applications are often co-located on a platform and share such resources. However, co-located execution and resource sharing bring memory access conflict, especially in the Last Level Cache (LLC). In this paper, a lightweight method is proposed for partition LLC named by Classification-and-Allocation (C&A). Specifically, Support Vector Machine (SVM) is used in the proposed method to classify applications into the triple classes based on the performance change characteristic (PCC), and the Bayesian Optimizer (BO) is leveraged to schedule LLC to guarantee applications with the same PCC sharing the same part of LLC. Since the near-optimal partition can be found efficiently by leveraging BO-based scheduling with a few sampling steps, C&A can handle unseen and versatile workloads with low overhead. We evaluate the proposed method in several workloads. Experimental results show that C&A can outperform the state-of-art method KPart (El-Sayed et al in Proceedings of 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA) 104−117, 2018) by 7.45% and 22.50% respectively in overall system throughput and fairness, and reduces 20.60% allocation overhead.

Convoy driving has great potential in the development of autonomous driving industry, which can bring great improvement to the utilization rate of roads and the travel experience of passengers. However, the emergence of convoy driving also brings some new challenges to the location privacy of autonomous vehicles. In this paper, a novel dynamic mixed zone establishment scheme is proposed to protect the location privacy of autonomous vehicles in convoy driving context. As the convoy is a closed group, the request for the establishment of the mix zone will be broadcast first within the convoy, followed by outside the convoy. In order to prevent pseudonym syntactic join attacks within the convoy, the proposed scheme allows autonomous vehicles to use multiple valid pseudonyms if only itself change the pseudonym in the convoy. In order to trace the real identity of the offending vehicle, the proposed scheme specifies the distribution method of the pseudonym, the legitimate vehicle can trace the real identity of the offending vehicle by submitting the pseudonym of the offending vehicle. Compared with the scheme to protect location privacy in the traditional vehicular network, the proposed scheme has less overhead and a higher level of security.

Federated learning is a distributed machine learning algorithm that enables collaborative training among multiple clients without sharing sensitive information. Unlike centralized learning, it emphasizes the distinctive benefits of safeguarding data privacy. However, two challenging issues, namely heterogeneity and backdoor attacks, pose severe challenges to standardizing federated learning algorithms. Data heterogeneity affects model accuracy, target heterogeneity fragments model applicability, and model heterogeneity compromises model individuality. Backdoor attacks inject trigger patterns into data to deceive the model during training, thereby undermining the performance of federated learning. In this work, we propose an advanced federated learning paradigm called Federated Mutual Distillation Learning (FMDL). FMDL allows clients to collaboratively train a global model while independently training their private models, subject to server requirements. Continuous bidirectional knowledge transfer is performed between local models and private models to achieve model personalization. FMDL utilizes the technique of attention distillation, conducting mutual distillation during the local update phase and fine-tuning on clean data subsets to effectively erase the backdoor triggers. Our experiments demonstrate that FMDL benefits clients from different data, tasks, and models, effectively defends against six types of backdoor attacks, and validates the effectiveness and efficiency of our proposed approach.

Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things (IoT). In this paper, a device management system is proposed to track the devices by using audio-based location distinction techniques. In the proposed scheme, traditional cryptographic techniques, such as symmetric encryption algorithm, RSA-based signcryption scheme, and audio-based secure transmission, are utilized to provide authentication, non-repudiation, and confidentiality in the information interaction of the management system. Moreover, an audio-based location distinction method is designed to detect the position change of the devices. Specifically, the audio frequency response (AFR) of several frequency points is utilized as a device signature. The device signature has the features as follows. (1) Hardware Signature: different pairs of speaker and microphone have different signatures; (2) Distance Signature: in the same direction, the signatures are different at different distances; and (3) Direction Signature: at the same distance, the signatures are different in different directions. Based on the features above, a movement detection algorithm for device identification and location distinction is designed. Moreover, a secure communication protocol is also proposed by using traditional cryptographic techniques to provide integrity, authentication, and non-repudiation in the process of information interaction between devices, Access Points (APs), and Severs. Extensive experiments are conducted to evaluate the performance of the proposed method. The experimental results show that the proposed method has a good performance in accuracy and energy consumption.

Key agreement is one the most essential steps when applying cryptographic techniques to secure device-to-device (D2D) communications. Recently, several PHY-based solutions have been proposed by leveraging the channel gains as a common randomness source for key extraction in wireless networks. However, these schemes usually suffer a low rate of key generation and low entropy of generated key and rely on the mobility of devices. In this paper, a novel secret key extraction protocol is proposed by using interference in wireless D2D fading channel. It establishes symmetrical keys for two wireless devices by measuring channel gains and utilizing artificial jamming sent by the third party to change the measured value of channel gains. We give a theoretically reachable key rate of the proposed scheme from the viewpoint of the information theory. It shows that the proposed scheme can make hundred times performance gain than the existing approaches theoretically. Experimental results also demonstrate that the proposed scheme can achieve a secure key distribution with a higher key rate and key entropy compared with the existing schemes.

The Internet of Things (IoT) offers a wide variety of benefits to our daily lives in many ways, ranging from smart wearable devices to industrial systems. However, it also brings well-known security and compliance concerns, especially in the physical layer. In addition, due to numerous IoT architectures which have been developed and deployed based on the cloud, the security and compliance of IoT depend on the cloud thoroughly. In this paper, a secure and compliant continuous assessment framework (SCCAF) is proposed to evaluate the security and compliance levels of cloud services in life-cycle. The SCCAF facilitates cloud service to customers to select an optimal cloud service provider (CSP) which satisfies their desired security requirements. Moreover, it also enables cloud service customers to evaluate the compliance of the selected CSP in the process of using cloud services. To evaluate the performance and availability of SCCAF, we carry out a series of experiments with case study and real-world scenario datasets. Experimental results show that SCCAF can assess the security and compliance of CSPs efficiently and effectively.

The wireless body area networks (WBANs) have emerged as a highly promising technology that allows patients’ demographics to be collected by tiny wearable and implantable sensors. These data can be used to analyze and diagnose to improve the healthcare quality of patients. However, security and privacy preserving of the collected data is a major challenge on resource-limited WBANs devices and the urgent need for fine-grained search and lightweight access. To resolve these issues, in this paper, we propose a lightweight fine-grained search over encrypted data in WBANs by employing ciphertext policy attribute based encryption and searchable encryption technologies, of which the proposed scheme can provide resource-constraint end users with fine-grained keyword search and lightweight access simultaneously. We also formally define its security and prove that it is secure against both chosen plaintext attack and chosen keyword attack. Finally, we make a performance evaluation to demonstrate that our scheme is much more efficient and practical than the other related schemes, which makes the scheme more suitable for the real-world applications.