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In mountainous area, earthquake is an inevitable factor during the construction of tunnel. Earthquake can cause great damage to faults and mountain slopes, and at the same time, the deformation and failure of mountain tunnels are closely related to faults and landslides. This study analyzes the impacts of faults and landslides on the mountain tunnel by numerical modelling, model test and field investigation, and discusses the corresponding engineering countermeasures. Mountain tunnel through fault tends to be damaged severely because it may be shorn by fault dislocation. However, the fractured tunnel may undergo even severer damage due to earthquake wave. Various factors, including earthquake wave, condition of surrounding rock, width of fault, relative position of fault and tunnel, fault friction velocity, fault activity and lining section type, affect the seismic performance of tunnel through fault. In addition, buffer layer, grouting, shock absorption gap, sectional tunnel lining with flexible joints, fiber reinforced concrete lining, and ultra-excavating are all available aseismic or anti-dislocation measures. The damage mode and degree of landslide to tunnel are related to the type of tunnel, and the different relative positions of the tunnel and the sliding surface controls damages. Landslide prevention and control engineering measures mainly include weight loss, drainage, construction of retaining works, improvement and reinforcement of soil and rock properties of sliding zone, etc.

Covert channels are a method of communication that is used to exfiltrate information from computing devices and break the security policy of computer systems. Any shared resource can be potentially leveraged as a covert channel, and conventional wisdom of cyber-security believes that air-gapped computing devices, disconnected from the Internet, are highly secured. Recent studies show that advanced covert channel attacks using acoustic, thermal, and electromagnetic effects can only work under a limited proximity constraint (e.g., within 2 meters). In this work, we present SpiralSpy, a new covert channel to attack air-gapped computing devices through millimeter-wave (mmWave) sensing technologies. SpiralSpy can be stealthily launched and circumvent strongly isolated computing devices from a practical distance (up to 8 meters). Specifically, we demonstrate that ordinal cooling fans can be leveraged for covert channel attacks. A malicious software inside air-gapped computing devices can saliently encode confidential data into the fan control signals, and modulated status on fan motions can be remotely decoded by a commodity mmWave sensor. SpiralSpy can be adopted on multiple-fan systems and enable a scalable capacity for multi-channel and high-speed information transfer. We evaluate SpiralSpy with 71 computing devices with cooling fans. Experimental results demonstrate that SpiralSpy can achieve up to 6 bps that is 6-24X faster than existing covert channels on air-gapped computing devices. We evaluate the usability and robustness of SpiralSpy under different real-world scenarios. Moreover, we conduct in-depth analysis and discussion on countermeasures for SpiralSpy-based covert channel attacks to improve computer and information security.

With the fast-growing demands for efficient and scalable content distribution, information-centric networking (ICN) can be a promising candidate for future networks. ICN promises direct retrieval of the content using its unique, persistent, and location-independent name. In ICN, all nodes work together to scale up content delivery, but security issues caused by malicious behaviors of ICN nodes cannot be avoided. To this end, in this article we develop a trust-enhanced blockchain based ICN (BICN) architecture for content delivery. In our proposed architecture, the whole process of content delivery is first traced in an implicitly trusted way by exploiting the excellent properties of the blockchain, so that the malicious ICN nodes can be located. Second, we leverage transactions to record the mapping between human-readable name and self-certifying name in BICN, which supports convenient and trusted alteration for users' requirements. Moreover, we present a case study in BICN based smart grid for energy data delivery, where we perform security analysis and conduct experiments. Numerical results show the superior performance of our proposal. Finally, we present open issues for future work.

The emergence of edge computing has witnessed a fast-growing volume of data on edge devices belonging to different stakeholders which, however, cannot be shared among them due to the lack of the trust. By exploiting blockchain’s non-repudiation and non-tampering properties that enable trust, we develop a blockchain-based big data sharing framework to support various applications across resource-limited edges. In particular, we devise a number of novel resource-efficient techniques for the framework: (1) the PoC (Proof-of-Collaboration) based consensus mechanism with low computation complexity which is especially beneficial to the edge devices with low computation capacity, (2) the blockchain transaction filtering and offloading scheme that can significantly reduce the storage overhead, and (3) new types of blockchain transaction (i.e., Express Transaction) and block (i.e., Hollow Block) to enhance the communication efficiency. Extensive experiments are conducted and the results demonstrate the superior performance of our proposal.

Accurately predicting users’ perceived stress is beneficial to aid early intervention and prevent both mental illness and physical disease during the COVID-19 pandemic. However, the existing perceived stress predicting system needs to collect a large amount of previous data for training but has a limited prediction range (i.e., next 1-2 days). Therefore, we propose a perceived stress prediction system based on the history data of micro-EMA for identifying risks 7 days earlier. Specifically, we first select and deliver an optimal set of micro-EMA questions to users every Monday, Wednesday, and Friday for reducing the burden. Then, we extract time-series features from the past micro-EMA responses and apply an Elastic net regularization model to discard redundant features. After that, selected features are fed to an ensemble prediction model for forecasting fine-grained perceived stress in the next 7 days. Experiment results show that our proposed prediction system can achieve around 4.26 (10.65% of the scale) mean absolute error for predicting the next 7 day’s PSS scores, and higher than 81% accuracy for predicting the next 7 day’s stress labels.

Voice-user interface (VUI) has become an integral component in modern personal devices (\textite.g., smartphones, voice assistant) by fundamentally evolving the information sharing between the user and device. Acoustic sensing for VUI is designed to sense all acoustic objects; however, the existing VUI mechanism can only offer low-quality speech sensing. This is due to the audible and inaudible interference from complex ambient noise that limits the performance of VUI by causing denial-of-service (DoS) of user requests. Therefore, it is of paramount importance to enable noise-resistant speech sensing in VUI for executing critical tasks with superior efficiency and precision in robust environments. To this end, we investigate the feasibility of employing radio-frequency signals, such as millimeter wave (mmWave) for sensing the noise-resistant voice of an individual. We first perform an in-depth study behind the rationale of voice generation and resulting vocal vibrations. From the obtained insights, we presentWaveEar, an end-to-end noise-resistant speech sensing system.WaveEar comprises a low-cost mmWave probe to localize the position of the speaker among multiple people and direct the mmWave signals towards the near-throat region of the speaker for sensing his/her vocal vibrations. The received signal, containing the speech information, is fed to our novel deep neural network for recovering the voice through exhaustive extraction. Our experimental evaluation under real-world scenarios with 21 participants shows the effectiveness ofWaveEar to precisely infer the noise-resistant voice and enable a pervasive VUI in modern electronic devices.