Wireless sensing is an emerging technology with a wide range of applications, but most existing systems capture only the motion of a single point, such as in respiration monitoring. This limitation is critical for tasks requiring multi-point data, such as respiratory volume measurement, where different body points provide distinct information, and a single point cannot represent them all. In this paper, we propose LiSen, a smartphone-integrated LiDAR system for multi-point wireless sensing, and demonstrate its contact-free capability for measuring respiratory volume. LiSen uses smartphone LiDAR to track multiple chest and abdominal points, enabling the first ranging-based spirometer system that captures the full volume curve without new-user calibration. We leverage the unique feature of multi-point sensing to address challenges such as body interference, diverse breathing patterns, and pressure differences. Tests with 35 examinees show that LiSen accurately estimates both instantaneous forced expiratory and inspiratory volume, achieving mean absolute errors below 0.24 L and 0.30 L, respectively, and an 8.93% error for four common pulmonary function indices.

Eavesdropping poses a critical threat to the confidentiality and integrity of voice communications. In recent years, techniques have advanced beyond traditional microphone-based methods toward more intelligent approaches, such as leveraging millimeter-wave sensing to detect the subtle vibrations induced by speakers and reconstruct voice information without direct audio capture. Despite their technical feasibility, these methods remain constrained by limited working ranges—typically only several meters—rendering them impractical for real-world stealthy eavesdropping. In this work, we propose MoiréEar, the first long-range passive eavesdropping system based on moiré patterns. The key idea is to exploit the amplification capability of moiré patterns, which amplify the minute vibrations induced by acoustic signals by hundreds of times, enabling long-range eavesdropping. To make the proposed method even more practical and stealthy, we develop new theoretical foundations that relax the strict requirements for generating moiré patterns. Specifically, our approach enables the use of irregular stripe structures (e.g., commonly seen barcodes) instead of standard moiré gratings to generate moiré patterns. We implement our design using a low-cost photodiode instead of cameras, achieving real-time eavesdropping with lightweight signal processing. Comprehensive experiments show that the system can extract intelligible audio at a distance of up to 90 m, outperforming the state of the art by an order of magnitude in range. We believe this new eavesdropping modality can inspire a wide range of IoT applications.

Neighbor discovery poses significant challenges in Underwater Optical Wireless Sensor Networks (UOWSNs) due to the unique characteristics of directional transceivers, line-of-sight communication, and mobility induced by water currents. Traditional methods typically rely on prerequisites and prior knowledge, such as centralized coordination, time synchronization, and information about the number of neighbors, which are often unavailable or impractical in underwater environments. In this paper, we make the first attempt to address the issue ofRobust andEfficientNeighborDiscovery (termed the REND problem) in UOWSNs with zero-knowledge. Here, zero-knowledge refers to the capability that enables sensors to identify neighbors in dynamic underwater optical channel conditions without prerequisites or prior knowledge. We design a zero-knowledge distributed directional neighbor discovery scheme inspired by gear meshing. We then propose a deterministic algorithm for the REND problem based on theoretical analysis. Additionally, to further reduce the discovery delay for the periodic REND problem, we develop a greedy-based approximation algorithm with a performance guarantee. Finally, extensive simulations demonstrate that the proposed scheme reduces the discovery delay by 34.9% on average and achieves an additional 54.4% reduction for periodic neighbor discovery. Furthermore, test-bed experiments are carried out to verify the applicability of our zero-knowledge scheme in real-world scenarios.

The convenience of QR codes has made them increasingly popular in the field of mobile payments, with many payment service providers (e.g., PayPal, Alipay, WeChat) offering QR code payment services. However, due to the openness of QR code scanning process, attackers can capture the code and perform fraudulent transactions before the original QR code is used, posing a significant security threat. In this paper, we design UniQR (Unique QR), which embeds device pose information as “physically unclonable” fingerprints into the QR code to prevent attackers from unauthorized use of it. We propose a pose matching method based on Perspective-n-Point (PnP) algorithm and the integrated sensors of the phone to bind the QR code to the payment device. This information binding effectively enhances the security of QR code payments. Additionally, we modify the QR code encoding using a segmented hybrid encoding method, allowing secure authentication information to be embedded by utilizing only a portion of the space originally designated for dummy data. We implemented UniQR on six different commercial phones and conducted experiments with 23 participants simulating both legitimate and illegitimate payment scenarios. The 97.46% success rate in legitimate user scans demonstrates the feasibility and robustness of UniQR.

With the vigorous development of 5G communication technology, massive Internet of Things (IoT) devices generate data incrementally. Different data owners control different private domains of the IoT through edge devices and hope to achieve credible data sharing. Most of the existing solutions are based on blockchain to realize cross-domain IoT data sharing. However, incremental IoT data sharing has dual requirements for the consensus mechanism to be efficient and Byzantine fault tolerant. The independent use of the existing consensus mechanism cannot meet the above requirements simultaneously. Therefore, we propose an efficient and fault-tolerant blockchain consensus transform (BCT) mechanism for IoT. In addition, we design two consensus algorithms, namely, detectable RAFT (DRAFT) and double-layer parallel BFT (DPBFT), to improve the efficiency and fault tolerance of the data-sharing process. Extensive experiments have been conducted to show the efficiency and tolerance of our BCT mechanism.

Although combining the Internet of Things (IoT) and industrial scenarios has brought about a technological revolution, it has also caused equipment security issues. Due to the characteristics of Industrial Internet of Things (IIoT) devices with a wide distribution, complex application scenarios, considerable differences in node performance, and device heterogeneity, spoofing attacks and third-party attacks are common. Identity authentication for IIoT devices can solve this dilemma. However, most existing authentication technologies involve a tradeoff between traditional centralized certificate issuance and sacrificing device storage resources, resulting in lower efficiency of IIoT device authentication, and the process is complicated. Therefore, ensuring the security and trustworthiness of device identities in the IIoT is imminent. In this article, we propose an IIoT device authentication scheme based on an editable blockchain, that can solve the problem of the device’s low energy while satisfying the usage needs of large-scale scenarios. In particular, we created a suite of secure, efficient, and innovative technical solutions for this protocol. First, to solve the problem of the authentication difficulty between industrial devices, we propose a lightweight identity authentication protocol called BLMA. Moreover, we propose the validate-practical Byzantine fault tolerance algorithm and introduce the online and offline signature algorithm to reduce communication overhead and resource consumption between devices. Finally, considering the top security and dynamics of the IIoT environment, we use the chameleon hash function to build a hash chain of authentication results. Extensive simulation and experimental results demonstrate the reliability of our protocol.

Due to the vigorous development of wireless communication technology, massive sensors have been gradually connected to the Internet of Things (IoT) and generate a massive quantity of valuable IoT data from large-scale wireless sensor networks (WSNs) controlled by different owners. Massive IoT data need to be collected and circulated among multiple data owners and data users. However, existing data collection frameworks may cause heavy computational overhead or rely on trusted third parties, since sensors have constrained resources. Consequently, massive IoT data are transformed among different parties, causing severe trust and security issues. In this article, we propose a blockchain-assisted massive IoT data collection (MIDC) intelligent framework to support the security, trust and efficiency of massive data collection for large-scale heterogeneous WSNs. In particular, we propose a series of novel technologies for the framework: 1) we design a large-scale heterogeneous WSNs collaborative identity verification protocol to ensure reliable data sources; 2) we build a hierarchical massive data aggregation scheme to collect massive IoT data efficiently and securely; and 3) we depict a blockchain-based massive IoT data management method to construct trust among different parties. Extensive simulation and prototype experimental results prove the effectiveness of our framework.

There is an urgent need to solve the problems of secure storage, reliable sharing, access control and privacy protection in medical industry. In this paper, we propose EHRChain, a blockchain-based EHR system using attribute-based and homomorphic cryptosystem to solve the above problems. First, we design a medical record storage scheme to realize secure high capacity medical data storage and reliable sharing based on blockchain technology and IPFS. Second, we propose an improved cryptographic primitive called SHDPCPC-CP-ABE. Our SHDPCPC-CP-ABE realizes the functions of semi-policy hiding and dynamic permission changing based on partial ciphertext simultaneously. Furthermore, our program achieves the neutrality of the subject of judicial identification in medical disputes and fine-grained access control of medical data. Third, our system applies an additive homomorphic cryptosystem, Paillier cryptosystem with optimized parameters on patients’ privacy protection during the process of the medical insurance claim. After analysis and experiment, we have proved that the SHDPCPC-CP-ABE is indistinguishable under chosen plaintext attack and takes one third of the time of CP-ABE when changing access policy. Our system has higher performance than other EHR systems based on blockchain.

Gene expression profiling has played a significant role in the identification and classification of tumor molecules. In gene expression data, only a few feature genes are closely related to tumors. It is a challenging task to select highly discriminative feature genes, and existing methods fail to deal with this problem efficiently. This article proposes a novel metaheuristic approach for gene feature extraction, called variable neighborhood learning Harris Hawks optimizer (VNLHHO). First, the F-score is used for a primary selection of the genes in gene expression data to narrow down the selection range of the feature genes. Subsequently, a variable neighborhood learning strategy is constructed to balance the global exploration and local exploitation of the Harris Hawks optimization. Finally, mutation operations are employed to increase the diversity of the population, so as to prevent the algorithm from falling into a local optimum. In addition, a novel activation function is used to convert the continuous solution of the VNLHHO into binary values, and a naive Bayesian classifier is utilized as a fitness function to select feature genes that can help classify biological tissues of binary and multi-class cancers. An experiment is conducted on gene expression profile data of eight types of tumors. The results show that the classification accuracy of the VNLHHO is greater than 96.128% for tumors in the colon, nervous system and lungs and 100% for the rest. We compare seven other algorithms and demonstrate the superiority of the VNLHHO in terms of the classification accuracy, fitness value and AUC value in feature selection for gene expression data.