With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target’s location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches.

Most existing RF-based vital sign monitoring systems either assume that a human subject is stationary or discard measurements when motion is detected in order to output reliable respiration rates and heart rates. Such an assumption greatly limits the usability of these systems in practice. Even during sleep, one can undergo various body states including turns and involuntary twitches in light sleep, motionlessness during deep sleep, or abnormal limb movements due to sleep disorders such as restless legs syndrome. In this work, we develop SlpRoF, a low-cost contact-free system using a commercial-off-the-shelf UWB radar that achieves high temporal coverage and high accuracy in vital sign monitoring during sleep. By classifying body states into the motionless state, limb movement state, and torso movement state, and extracting vital signs during the first two states, it directly increases effective reporting periods over nights. By analyzing high-order harmonics and leveraging spatial diversity in captured signals from multiple on-body areas, it improves the accuracy of heart rate estimations and thus indirectly increases temporal coverage through reliable assessments. Experiment results show that SlpRoF is able to achieve an average median absolute error (MAE) of 0.44 beats per minute (bpm) in respiration rates, 1.55 s in respiration intervals, and 0.9 bpm for heart rates, respectively.

Under the development of intelligent transportation systems, In-Vehicle Networks (IVNs) serve as a critical channel for both internal and external communications. However, the inherent complexity and diversity of data traffic present significant challenges for the detection of IVN anomalous flows. Meanwhile, the introduction of various novel technologies has introduced new security vulnerabilities to IVNs. These vulnerabilities significantly impact the security of IVNs and the accuracy of in-vehicle Intrusion Detection Systems (IDS). To address these issues, this paper proposes a lightweight and efficient anomaly detection method based on knowledge distillation technology, termed Knowledge Distillation from BERT to CNN-BiLSTM (KDBC). Specifically, the KDBC distills the deep semantic knowledge from the BERT model into a more lightweight CNN-BiLSTM architecture, significantly reducing computational overhead and storage requirements without substantially compromising detection performance. Experimental results demonstrate that the KDBC model enhances both security and versatility, achieving superior detection accuracy in identifying abnormal attacks across diverse IVN data, including automotive Ethernet and CAN networks. Moreover, the KDBC model has been validated for its effectiveness and robustness in actual in-vehicle gateway environments, achieving an accuracy of over 0.98 and an F1 score greater than 0.98.

Location Fingerprinting (LF) is a promising localization technique that enables enormous commercial and industrial Location-Based Services (LBS). Existing approaches either appeal to the simple Received Signal Strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer Channel State Information (CSI), whose intricate structure leads to an increased computational complexity. In this paper, we adopt Autoregressive (AR) modeling based entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while exploiting the most location-specific statistical channel information. On this basis, we design EntLoc, a CSI-based probabilistic indoor localization system using commercial off-the-shelf Wi-Fi devices. EntLoc is deployed in an office building covering over 200 m2. Extensive indoor scenario experiments corroborate that our proposed system yields superior localization accuracy over previous approaches even with only one signal transmitter.

With the rapid advancement of vehicle connectivity and intelligent technologies, an increasing number of vehicles are now connected to the Internet. However, these connected vehicles are vulnerable to malicious attacks, posing serious security events. In particular, the in-vehicle controller area network (CAN) bus has witnessed a rise in incidents involving various network attacks, such as denial of service (DoS), fuzzy attacks, and gear attacks. In response, this paper proposes an enhanced cuckoo filter-based intrusion detection system (ECF-IDS) for in-vehicle network. The ECF-IDS builds on an enhanced version of the cuckoo filter. It first utilizes the cuckoo filter to establish two lists (a normal list and an intrusion list) based on the labeled dataset using Car Hacking Dataset (CHD) and can-train-and-test dataset. Then, the input CAN traffic is sequentially compared with these two lists, where the conflicting traffic is further identified using a BERT-based model. The ECF-IDS is experimentally validated using the CHD and can-train-and-test dataset, demonstrating higher detection efficiency, lower resource consumption, and detection success exceeding 99% compared to other algorithms presented in previous studies. Furthermore, we conducted real in-vehicle environment testing on the ECF-IDS model, and its detection performance proved to be excellent.

To predict the load of the power system with a known network structure, this paper proposes a novel attention based spatial-temporal graph convolutional network (ASTGCN) model to predict the node load in the power grid. The experimental results show the good performance of ASTGCN.