With the thriving artificial intelligence-generated content (AIGC), it is becoming increasingly appealing to exploit generative AI to generate wireless signals for facilitating wireless sensing. However, this is a challenging task, as wireless signals are highly random in general and contain rich physical information. To tackle these challenges, we propose a novel signal disentanglement and generation framework termed SigGen, which is inspired by the Fourier Transform (FT) that converts signals to the frequency domain and accordingly separates objectives by distinct frequency bands. In our proposed framework, we first disentangle the features of objects embedded in the signal and subsequently modify these features to generate the desired signals. Specifically, we devise a neural network based on the vision transformer (ViT) to extract effective features for signal generation. In this neural network, we incorporate both local and global frequency attention modules to adaptively leverage frequency features, and introduce a hybrid patch embedding module to enhance information interaction for the ViT architecture. Furthermore, we propose a novel sequential training method to improve the disentanglement and generation capability of the neural network. Finally, extensive experiments on two benchmark public wireless sensing datasets demonstrate that our framework can effectively decouple wireless signals and generate diverse signals closely resembling real ones, surpassing state-of-the-art methods by 30.83%. A practical case study further demonstrates that our framework can be used as a data augmentation method to improve gesture recognition accuracy by 12.74%.

Edge computing, as a novel computing paradigm, is expected to significantly enhance the productivity of the Industrial Internet of Things (IIoT). However, in a practical IIoT environment consisting of multiple edge servers, the supply of computing services (edge servers) may involve different business strategies or pricing schemes, while the demand side (user terminals) can choose the service that best suits their needs based on quotes from different suppliers. The competition between the supply and demand sides for their respective interests leads to strategic games between service suppliers and users. Therefore, how to design reasonable pricing mechanisms and offloading strategies to balance service supply and resource demand has become an urgent issue. Furthermore, during these games, self-interested service providers may collude to form mutually beneficial cartels. To address the intricacies of these games, this paper integrates deep reinforcement learning (DRL) with game theory. Specifically, a heterogeneous system model is first constructed in the Stackelberg game framework. Subsequently, we leverage the Double-Dueling Deep Q-Network (D3QN) and Deep Deterministic Policy Gradient (DDPG) algorithms to formulate users’offloading decisions and service providers’ pricing strategies, respectively. Consequently, a joint D3QN-DDPG and Stackelberg algorithm is proposed to maximize the benefits for both users and servers. Moreover, to disrupt the formation of cartels among service providers, we introduce an intervention mechanism and present an intervention-based benefit maximization algorithm. Finally, simulation experiments demonstrate that the total system utility with intervention surpasses that without intervention by approximately 16%, validating the necessity of the intervention. Comparisons with other benchmark algorithms confirm that the proposed algorithm achieves superior system benefits.

Human-centered wireless sensing has been significantly advanced by artificial intelligence (AI) technologies. To enhance AI model performance, signal preprocessing, as a fundamental procedure, is widely employed for improving signal quality. However, existing methods are time-consuming, labor-intensive, and exhibit limited generalization. To address this issue, we first investigate the frequency spectrum of various signals. The results demonstrate that, in human-centered wireless applications, human activities significantly affect the low-frequency components in the signal spectrum. Motivated by this observation, we propose Sense+, a concise and versatile signal preprocessing module that can be seamlessly integrated into existing models to enhance sensing performance. Specifically, we transform the raw signals into a unified frequency domain, apply a learnable filter to process their frequency spectra, and then convert them back to the original signal domain. To accurately extract low-frequency features, we further propose a low-pass weight initialization method for the filter. Extensive experiments are conducted across various sensing tasks and signal types, including IR-UWB signals for person identification, mmWave radar signals for gesture recognition, and Wi-Fi signals for action recognition. The results highlight the effectiveness of Sense+ in enabling preprocessing across diverse wireless signals. Specifically, when equipped with Sense+, the average accuracy improves by 21.84% compared to conventional preprocessing methods. Additionally, Sense+ accelerates convergence and exhibits consistent generalization across different neural network models.

Node identification is the first line of defense for the security of wireless Internet-of-Things (IoT), which prevents illegal devices from accessing the network and launching attacks. Hardware features originating from innate hardware manufacturing imperfections are considered promising fingerprints for identification; among which, the hardware clock feature has been put under the spotlight due to its practicality and ease of extraction. However, current extractions of hardware clock features over wireless networks rely on the transmissions of time information, which, per se, enable significant vulnerabilities such as spoofing and replay attacks. In this paper, we propose a covert method to extract the hardware clock features, which does not rely on the insecure time information transmissions that are adopted in most existing schemes. We also analyze the security of the proposed covert extraction. We further propose SNICK, a secure node identification scheme based on our tailored implementation of covert clock feature extraction and machine learning. We implement and evaluate the proposed approach on a real IoT testbed consisting of a Long Range (LoRa) gateway and heterogeneous end nodes. We conduct experiments to prove the security of the proposed scheme and evaluate the proposed scheme under three scenarios: short-term, long-term, and cross-environment. Experimental results of three scenarios demonstrate average identification accuracies of 98.53%, 85.9%, and 88.3%. We further reveal the identification performance under parameter and environmental variations.

Wireless key generation is an emerging key sharing solution for Internet-of-Things (IoT) devices, which heavily relies on wireless signal fluctuations. However, most IoT devices have remained static ever since being deployed, where their stable wireless signals seriously deteriorate the effectiveness of key generation. In this article, we proposea new wireless Key Generation approach based on Power Tuning (KGPT) for statice IoT. Unlike the existing methods, KGPT does not require any additional helpers or any hardware modifications, thus, suiting practical static IoT deployments. We analyze the possibility of eavesdropping on the existing wireless key generation. We propose three power tuning strategies to generate wireless signal fluctuations for key generation in static IoT while defending against eavesdropping. In a static IoT scenario, we conduct experimental evaluations to first verify the eavesdropping on multi-antenna wireless key generation and then demonstrate the effectiveness of the proposed KGPT in producing sufficient signal fluctuations for key generation. Results show that KGPT can achieve a correlation value up to 0.97 for legitimate links while resisting the correlation of eavesdropping links down below 0.4.

Concerns surrounding privacy leakages caused by prevalent vision-based person identifications are countless. A promising privacy-preserving solution is to identify the wireless signals reflecting persons, which, however, faces a major challenge of losing efficacy in multi-environments. In this paper, we work on person identification based on wireless signals using transfer learning, toward tackling the performance deterioration across environments. We investigate the feature variations induced by environmental shifts based on data measurements. Lay our foundation on the feature alignment concept, we propose a novel wireless-based person identification framework using transfer learning. In the framework, we integrate a series of signal processing methods including signal selection, pre-processing, and augmentation, where the first includes a reference environment to assist the feature extraction while the latter two respectively reduce the data noise and improve the data diversity. We also propose a model generalization method where a neural network is employed to align features from different environments, which facilitates the extraction of environment-independent features while incorporating both person and environment information. On a real wireless testbed consisting of an Impulse Radio Ultra-WideBand (IR-UWB) radar, we build and publicly release a dataset with 22,264 samples of ten individuals from three environments, varying in testing distance and obstruction condition. Extensive experimental evaluations demonstrate that the proposed framework can improve the identification accuracy across environments, and surpasses state-of-the-art methods by up to 18.06%.

Radio frequency fingerprint (RFF) identification has become a promising security solution for resource-constrained Internet-of-Things (IoT) devices, which relies on hardware impairments-induced radio frequency features for identification; among which, a hotspot feature is the carrier frequency offset (CFO). Existing research, however, advocates contradictory perspectives on the usage of CFO: For identification and for compensation; the former employs CFO in the feature space while the latter eliminates the CFO from the feature space, both for improving the RFF identification accuracy. In this review, we first discuss the RFF identification procedures and investigate the origination of the CFO and further its relationship with the clock skew of the crystal oscillator. We then provide a review of the state-of-the-art RFF identification schemes, in two categories respectively employing CFO for identification and compensation. Finally, on a real testbed, we experimentally investigate the impact of the usage of CFO on RFF identification accuracy. Experimental results reveal that, the stabilities of CFOs are quite different on hardware platforms from different manufacturers; CFOs can be used for identification when they are relatively distinguishable; compensating CFO alone is inadequate for long-term identification.

Wireless key generation is a promising security solution for Internet-of-Things (IoT) networks to share identical secret keys between communication pairs, whose foundation is wireless channel randomness and reciprocity. Its security, however, affects not only the generated keys but more importantly, the security of the IoT networks. So far, a number of major attacks threatening the wireless key generation have been studied in the literature, but not the replay attack. In this paper, we reveal the replay attack can penetrate conventional defense measures and invade the wireless key generation through both analysis and experiments, which can deteriorate the channel measurement correlation and result in a high key disagreement rate. We propose a wireless key generation approach named Kerra where we integrate a synchronized time measurement to defend against the replay attack on it. On a real IoT testbed composed of Long Range (LoRa) nodes, we implement the proposed Kerra and evaluate it in terms of both key generation performance and replay attack defense. Experimental results demonstrate first the impact of the replay attack on both channel measurement correlation and key disagreement rate, then the effects of quantization and pre-processing on key disagreement rate under replay attacks, and finally, the effectiveness of the proposed Kerra whose key disagreement rates under replay attacks are maintained to a similar level as without attacks.

Deep learning has significantly advanced wireless sensing technology by leveraging substantial amounts of high-quality training data. However, collecting wireless sensing data encounters diverse challenges, including unavoidable data noise, limited data scale due to significant collection overhead, and the necessity to reacquire data in new environments. Taking inspiration from the achievements of AI-generated content, this paper introduces a signal generation method that achieves data denoising, augmentation, and synthesis by disentangling distinct attributes within the signal, such as individual and environment. The approach encompasses two pivotal modules: structured signal selection and signal disentanglement generation. Structured signal selection establishes a minimal signal set with the target attributes for subsequent attribute disentanglement. Signal disentanglement generation disentangles the target attributes and reassembles them to generate novel signals. Extensive experimental results demonstrate that the proposed method can generate data that closely resembles real-world data on two wireless sensing datasets, exhibiting state-of-the-art performance. Our approach presents a robust framework for comprehending and manipulating attribute-specific information in wireless sensing.

Long Range (LoRa) network has been thriving in the IoT era due to its long-range coverage and energy efficiency. Prevalent LoRa applications rely on time synchronization to achieve accurate data ordering and coordination among the LoRa network. The energy-efficient nature of the LoRa network where end nodes in sleep scheduling always initiate communications, however, contradicts most conventional time synchronization methods based on message exchanges. In this paper, we propose a one-way time synchronization scheme tailored for the energy-efficient LoRa network based on the reverse asymmetric framework. We first discuss the delay minimization and compensation for the reverse one-way time synchronization in the LoRa network. We then propose a time synchronization scheme consisting of two time translation methods with different computational complexities and error bounds, respectively for resource-abundant and -constrained LoRa gateway and end nodes. Experiment results on a real LoRa testbed consisting of LoRa gateway and end nodes demonstrate that the proposed scheme could achieve microsecond-level synchronization accuracy in both scenarios between the end node and gateway and between the end node and end node; the latter scenario is advocated in the recent multi-hop LoRa network research.

Energy-efficient time synchronizations for wireless sensor networks (WSNs) have been put under the spotlight for years. A promising technique among which is the timestamp-free approach where no timestamps are required to establish the synchronization, thereby sparing the transmissions of the timing messages for conserving significant transmission energy. In this paper, we first investigate the feasibility of adopting timestamp-free time synchronization in practical resource-constrained WSNs; we then identify the issue of inaccuracy in maintaining the pre-defined response interval which affects the foundations of most existing timestamp-free schemes. Based on the investigation and our previously proposed reverse asymmetric time synchronization framework, we further propose an asymmetric timestamp-free time synchronization scheme with two estimation methods tailored for resource-constrained WSNs. We as well introduce the centralized and distributed multi-hop extension methods for the proposed scheme to cover diverse multi-hop scenarios. Experimental results on a real WSN testbed consisting of TelosB motes running TinyOS demonstrate that the proposed scheme achieves high energy efficiency while maintaining microsecond-level time synchronization accuracy compared to three other conventional schemes.

Based on the reverse asymmetric time synchronization framework, we have proposed several schemes with a major focus on the energy efficiency and computational complexity of a large number of battery-powered, low-cost sensor nodes in wireless sensor networks (WSNs). To address the cumulative end-to-end synchronization error, we have also introduced an idea of compensating for the processing delays at packet-relaying gateways as an energy-efficient way of multi-hop extension of WSN time synchronization schemes. In this paper, we present a comprehensive analysis of the multi-hop extension of WSN time synchronization schemes based on packet-relaying gateways with the per-hop delay compensation and the results of extensive experiments for the energy-efficient time synchronization schemes based on the reverse asymmetric time synchronization framework together with the flooding time synchronization protocol as a representative of existing schemes. Experimental results based on a real testbed demonstrate that the multi-hop extension based on packet-relaying gateways with the per-hop delay compensation greatly improves the performance of time synchronization of all the schemes considered compared to the multi-hop extension based on the conventional time-translating gateways.

Node identification based on unique hardware features like clock skews has been considered an efficient technique in wireless sensor networks (WSNs). Spoofing attacks imitating unique hardware features, however, could significantly impair or break down conventional clock-skew-based node identification due to exposed clock information through broadcasting. To defend against Spoofing attacks, we propose a new node identification scheme callednode identification against Spoofing attack(NISA). It utilizes the reverse time synchronization framework, where sensor nodes’ clock skews are estimated at the head of a WSN, and the spatially-correlated radio link information to achieve simultaneous node identification and attack detection. We further provide centralized and distributed NISA for covering both single-hop and multi-hop scenarios, the former of which employs a single-input and multiple-output convolutional neural network. With a real WSN testbed consisting of TelosB sensor nodes running TinyOS, we investigate the identifiability of clock skews under temperature and voltage variations and evaluate the performance of both centralized and distributed NISA. Experimental results demonstrate that both centralized and distributed NISA could provide accurate node identification and Spoofing attack detection.

The ever-increasing number of WSN deployments based on a large number of battery-powered, low-cost sensor nodes, which are limited in their computing and power resources, puts the focus of WSN time synchronization research on three major aspects of accuracy, energy consumption, and computational complexity. In the literature, the latter two aspects haven't received much attention compared to the accuracy of WSN time synchronization. Especially in multi-hop WSNs, intermediate gateway nodes are overloaded with tasks for not only relaying messages but also a variety of computations for their offspring nodes as well as themselves. Therefore, not only minimizing the energy consumption but also lowering the computational complexity while maintaining the synchronization accuracy is crucial to the design of time synchronization schemes for resource-constrained sensor nodes. In this paper, focusing on the three aspects of WSN time synchronization, we introduce a framework of reverse asymmetric time synchronization for resource-constrained multi-hop WSNs and propose a beaconless energy-efficient time synchronization scheme based on reverse one-way message dissemination. Experimental results with a WSN testbed based on TelosB motes running TinyOS demonstrate that the proposed scheme conserves up to 95% energy consumption compared to the flooding time synchronization protocol while achieving microsecond-level synchronization accuracy.

Accurate detection of the channel quality degradation is crucial for applying effective remedial actions to ensure the reliability of IEEE 802.15.4 links. Without knowing the channel quality is degraded, remedial actions may lead to more packet losses, e.g., increasing transmission power may cause even more interference. In this work, we aim to detect the channel quality degradation that turns a good link into a bad one, based on the received signal strength of radio links. The detection should be accurate and robust to diverse channel characteristics and dynamic environmental changes. To achieve this, we propose RADIUS, a lightweight approach that lays its foundation on a thresholding technique based on Bayesian decision theory and combines it with techniques for adapting to environmental changes. Extensive evaluation of RADIUS on a testbed shows that the employed Bayes thresholding technique outperforms two relevant state-of-the-art thresholding techniques by providing a higher accuracy consistently for all links across the network. Besides, RADIUS is able to keep a low error rate of detection (5.78% on average) in a 72-hour experiment, adapting to environmental changes. Furthermore, we developed an exemplary application of RADIUS to show how an existing transmission power tuning scheme can benefit from using RADIUS as an accurate and robust trigger for taking remedial actions.