Pre-trained Graph Neural Networks (GNNs) have demonstrated remarkable performance in graph mining tasks, yet they often amplify societal biases against protected demographic groups. Existing fairness-aware approaches primarily address discrimination based on a single sensitive attribute (e.g., gender or race), overlooking real-world scenarios where individuals possess multiple overlapping demographic characteristics, leading to unfair treatment of underrepresented subgroups. Moreover, incorporating extra fairness constraints into pre-trained GNNs usually requires full model retraining, which is computationally expensive and often impractical. To address these limitations, we propose a novel Multi-attribute Fairness-aware Prompt Tuning framework named MultiFPT. Our approach operates in two key stages: in the graph prompt learning stage, MultiFPT injects fairness-aware structural and feature prompts into pre-trained GNN inputs; in the adapter tuning stage, a lightweight adapter regularized by the Hilbert–Schmidt Independence Criterion (HSIC) enforces statistical independence between node representations and multiple sensitive attributes. Experiments on real-world datasets demonstrate that MultiFPT significantly improves multi-attribute fairness, reducing bias by approximately 30% on average in node classification while maintaining competitive predictive performance compared to state-of-the-art baselines.

The surge in location-based services has driven the demand for accurate indoor localization techniques, with WiFi based localization emerging as a promising solution due to its extensive coverage in indoor environments. This paper presents BFI-L10N, an indoor localization method that leverages beam forming feedback information (BFI) obtained from standard multi-user multiple-input multiple-output (MU-MIMO) WiFi operations. Unlike traditional channel state information (CSI) based methods that require vendor-specific firmware patches or restricted device support, BFI leverages standardized MU-MIMO operations, enabling compatibility with off-the-shelf WiFi devices. BFI-L10N processes the BFI data collected during the beam forming process through a deep learning framework and uses a BERT model for localization. Compared to CSI-based systems, BFI-L10N offers advantages such as reduced overhead, enhanced sensitivity, compatibility with standard devices, and real-time predictions. Our experimental results in two distinct indoor environments demonstrate that BFI-L10N achieves average localization accuracies of 10.7 cm and 15.5 cm in a research laboratory and a conference room, respectively, outperforming the state-of the-art CSI techniques by 28%. Moreover, the BERT model can be fine-tuned after pre-training across multiple locations, which enhances the versatility of BFI-L10N. This paper presents a novel perspective on WiFi sensing and lays the foundation for practical indoor localization using standard WiFi infrastructure.

Wi-Fi is the de facto standard for providing wireless access to the Internet using the 2.4GHz ISM (Industrial Scientific Medical) band. LoRa (Long Range) is specially designed for Low-Power, Wide-Area Networks (LPWANs) and has a broad range of applications in Internet of Things. Tens of billions of mobile devices (e.g., smartphones) are manufactured with limited types of wireless radio, making it challenging to access the data in the heterogeneous IoT devices. To address this challenge, we propose a method that enables LoRa devices to establish connections and engage in communication with Wi-Fi networks. A key observation of this study is that when a LoRa frame collides with an ongoing Wi-Fi transmission, the Wi-Fi receiver captures and retains the LoRa data. By analyzing the decoded Wi-Fi payload, we can retrieve the LoRa data, and this method remains fully compatible with existing commodity Wi-Fi hardware. Moreover, evaluations with Universal Software Radio Peripheral (USRP) and commodity devices demonstrate reliable wireless communication from LoRa to Wi-Fi networks with a high reliability in frame reception and low frame error rates across various indoor and outdoor environments.

Cross-Technology communication (CTC) has emerged as a pivotal solution for enabling communication between heterogeneous wireless devices, particularly in scenarios involving high-power and long-range networks. This paper introduces an innovative approach called WeRa, which leverages IEEE 802.11n technology to emulate LoRa waveforms, thus achieving efficient CTC from Wi-Fi to LoRa. WeRa utilizes orthogonal frequency division multiplexing (OFDM) technology in 802.11n to emulate LoRa chirp signals. To address the unique characteristics of LoRa signals, we propose a subcarrier selection scheme to enhance the communication performance between commercial Wi-Fi devices and commercial LoRa devices. During the Wi-Fi emulation process, frequency offsets are inevitable due to technical constraints. To mitigate this, we implement a fixed frequency compensation mechanism for the emulated signals based on thorough analysis. To tackle errors introduced by the cyclic prefix (CP), we build upon the boundary-flipping method proposed in WeBee and introduce a dynamic mode-flipping technique, effectively reducing the interference of CP on the emulated signals. We successfully implemented a prototype of WeRa on commercial Wi-Fi devices, the USRP platform, and commercial LoRa devices. Extensive experiments demonstrate WeRa’s robust performance, achieving a throughput of 119.683 kbps for efficiently transmitting complete LoRa frames while maintaining a symbol error rate below 0.1.

With the rapid development of Internet of Things (IoT) technologies, Cross-Technology Communication (CTC) has become a key method for establishing direct links between heterogeneous wireless systems. Existing CTC approaches typically rely on emulating the waveform of the target signal; however, such emulation often faces precision limitations due to bandwidth differences, mismatched modulation schemes, and hardware non-idealities. This paper proposes WiBlue, a CTC scheme that enables direct wireless data transmission from Wi-Fi devices to Bluetooth Low Energy (BLE) devices. The approach is based on the IEEE 802.11n physical layer and uses cyclic prefix (CP) processing, quadrature amplitude modulation (QAM), and frequency compensation mechanisms to achieve efficient compatibility across heterogeneous physical layers. WiBlue has been implemented on the USRP B210 platform with commercial BLE devices. Experimental results show that WiBlue achieves high reliability in various indoor environments, maintaining a packet reception rate above 96 % and a symbol error rate below 0.2, demonstrating its practicality and stability for heterogeneous wireless communication.

The growing heterogeneity of wireless ecosystems calls for a unified framework that enables seamless interaction among diverse communication protocols. This paper introduces NNUT (Neural Network-based Wi-Fi Universal Transmitter), a novel approach that leverages deep learning to achieve cross-protocol data transmission without any hardware modification or firmware rewriting. Unlike conventional Cross-Technology Communication (CTC) methods that depend on customized transceivers or handcrafted signal emulation, NNUT employs interpretable neural network modules to emulate key physical-layer operations-FFT, IFFT, and QAM–through lightweight and data-driven architectures. By learning the waveform transformation behaviors of Wi-Fi, ZigBee, LoRa, and Bluetooth, NNUT dynamically generates compatible signals that facilitate direct cross-technology communication. The design integrates 1D transposed convolutional and linear layers to approximate frequency-domain transformations, combined with differentiable subcarrier selection and pilot insertion mechanisms to ensure robust performance under varying channel conditions.

Cross-Technology Communication (CTC) is a cornerstone for seamless interoperability in heterogeneous wireless environments, enabling diverse devices to coexist and cooperate effectively. In this paper, we present Wi-Fi-to-X, designed to leverage deep learning techniques to generate waveforms that are compatible with multiple communication protocols, allowing seamless data transmission between Wi-Fi and other wireless technologies such as ZigBee, LoRa, and Bluetooth. This approach enables devices operating under different wireless standards to communicate effectively without requiring hardware modifications or protocol standardization. By training a specialized neural network on simulations of Orthogonal Frequency Division Multiplexing (OFDM) and Quadrature Amplitude Modulation (QAM), we have improved the efficiency and reliability of signal processing in CTC, Wi-Fi-to-X achieves robust signal modulation and demodulation across disparate technologies, enabling communication from Wi-Fi to other IoT devices, including ZigBee, LoRa, and Bluetooth. We evaluated both USRP and commodity devices, demonstrated that Wi-Fi-to-X can achieve concurrent wireless communication from Wi-Fi to other IoT devices.

Wi-Fi is a very common means for providing wireless access to the Internet, e.g., using the 2.4GHz Industrial, Scientific, and Medical (ISM) band and more recently also the 6 GHz band via Wi-Fi 6E. Thanks to a chip recently launched by Semtech, in the same 2.4GHz band now can also operate Long Range (LoRa), which is widely used in Internet of Things (IoT) applications due to its low power consumption and wide coverage range. To allow for data interchange among these technologies, multi-radio gateways are needed, which introduce additional costs, complexities, and potential points of failure. To address this challenge, we propose the concept of Wireless to LoRa (WiLo) to make directional communication from Wi-Fi to LoRa. WiLo uses physical-layer (PHY) communication and dedicated input chips in the 2.4 GHz band to transmit information. To overcome the modulation technique differences between Wi-Fi and LoRa, WiLo leverages narrow-band communication, a technique that generates ultra-narrowband signals using single-tone sinusoidal signals by manipulating the payload of Wi-Fi devices. These signals can be detected by LoRa Wide Area Network base stations due to their high receiver sensitivity for long-range communication. Our experiments, which make use of both Universal Software Radio Peripheral (USRP) and commodity devices, demonstrate that WiLo can achieve concurrent wireless communication over a distance of 500 m, from commercial Wi-Fi chips to a LoRaWAN, with more than 96% frame reception rate. These findings show the effectiveness of WiLo in enabling reliable and efficient wireless communication over long distances, making it particularly relevant for applications such as remote monitoring systems, sensor networks, and smart cities.

Vehicular Ad-hoc Networks (VANETs) face significant security and privacy challenges in modern intelligent transportation systems. This paper analyzes vulnerabilities in Al-Shareeda et al.'s vehicle authentication protocol (doi: 10.1109/TDSC.2025.3553868) and proposes an enhanced ECC-based scheme using short-lived pseudonymous certificates. We identify two critical weaknesses in Al-Shareeda et al.'s protocol—a desynchronization attack causing potential denial-of-service and an identity linking attack compromising vehicle privacy. Our protocol establishes mutual authentication between vehicles and roadside units, ensuring message integrity, anonymity, and perfect forward secrecy. Unlike existing approaches, it eliminates the need for online third-party authenticators. Formal security proofs demonstrate that the scheme's security is reducible to the hardness of the ECDLP and CDH problems. Performance analysis shows our approach achieves an optimal security-efficiency balance with competitive communication overhead (4608 bits) and computation costs (5.02 ms) compared to state-of-the-art alternatives, while uniquely satisfying all twelve evaluated security properties.

LoRa, designed for Low-Power, Wide-Area Networks (LPWANs), is widely used in the Internet of Things (IoT). In contrast, Wireless Personal Area Network (WPAN) technologies like ZigBee struggle to connect directly to LPWANs due to their limited communication range and differing modulation schemes. ZigBee uses Offset Quadrature Phase-Shift Keying (OQPSK) modulation, while LoRa employs Chirp Spread Spectrum (CSS) modulation, complicating cross-technology communication. To address this challenge, we propose a novel approach for seamless physical-layer cross-technology communication between ZigBee and LoRa networks, bridging the gap between short-range and long-range communication technologies. We introduce ZigRa, a communication method that leverages neural networks for efficient modulation translation between ZigBee's IEEE 802.15.4 standard and LoRa's CSS modulation. The core of ZigRa is a deep learning model that adapts and optimizes the transformation of ZigBee signals into ultra-narrowband single-tone sinusoidal signals, which can be reliably detected by LoRaWAN base stations. Our solution enables ZigBee devices to seamlessly connect to LoRa-based LPWANs, overcoming modulation mismatches and providing long-range connectivity. Extensive evaluations with both USRP hardware and commercial devices demonstrate that ZigRa achieves a frame reception rate exceeding 85% at distances up to 500 meters, significantly enhancing the interoperability and coverage of heterogeneous IoT networks.

Wi-Fi is the de facto standard for providing wireless access to the Internet in the 2.4 GHz ISM band. Tens of billions of Wi-Fi devices (e.g., smartphones) have been shipped worldwide with limited types of wireless radios operating only when Wi-Fi connectivity is available, making it challenging to access data in heterogeneous IoT devices. However, the direct connection between Wireless Personal Area Network (WPAN) technologies, such as Bluetooth, and Wi-Fi presents challenges due to the inherent distinct physical layer. In our work, a novel communication method called BlueWi has been introduced, which serves as a cross technology communication method that enables BLE devices to establish connections and engage in communication with Wi-Fi based WPAN networks. We let BLE signals hitchhike on ongoing Wi-Fi signals, enabling Wi-Fi to recognize specific BLE signal waveforms in the frequency domain. By analyzing the decoded Wi-Fi payload, BlueWi can retrieve the BLE data, ensuring this method remains fully compatible with existing commodity Wi-Fi hardware. The direct sequence spread spectrum scheme is appended to handle general BLE frames and can be considered as “COPY” operation, which allows for better correlation and detection of the signal at the receiver. Evaluations conducted using both USRP and commodity devices have demonstrated that BlueWi can achieve concurrent wireless communication from BLE commercial chips to Wi-Fi networks with a frame reception rate exceeding 96%.

Cross-technology communication (CTC) facilitates seamless interaction between different wireless technologies. Most existing methods use reverse engineering to derive the required transmission payload, generating a waveform that the target device can successfully demodulate. However, traditional approaches have certain limitations, including reliance on specific reverse engineering algorithms or the need for manual parameter tuning to reduce emulation distortion. In this work, we present NNCTC, a framework for achieving physical layer cross-technology communication through explainable neural networks, incorporating relevant knowledge from the wireless communication physical layer into the neural network models. We first convert the various signal processing components within the CTC process into neural network models, then build a training framework for the CTC encoder-decoder structure to achieve CTC. NNCTC significantly reduces the complexity of CTC by automatically deriving CTC payloads through training. We demonstrate how NNCTC implements CTC in WiFi systems using OFDM and CCK modulation. On WiFi systems using OFDM modulation, NNCTC outperforms the WEBee and WIDE designs in terms of error performance, achieving an average packet reception ratio (PRR) of 92.3% and an average symbol error rate (SER) as low as 1.3%. In WiFi systems using OFDM modulation, the highest PRR can reach up to 99%.

The underwater Internet of Things (UIoT) is becoming a significant means to explore marine areas, and compared to IoT, UIoT is deployed in a three-dimension marine space characterized by water depth. In this article, we propose a reliable data forwarding framework for UIoT, and aim to leverage the named data networking to reduce delays and costs of underwater data forwarding. The main idea behind this framework is to integrate the push-based data forwarding mode with the pull-based mode to realize in-networking caching and aggregation. That is, the push-based mode implements in-networking caching to shorten distances between users and marine data while the pull-based mode exploits aggregation to share data among users from the closest provider through one data forwarding process. The experimental results show that the framework achieves the above objectives.

Low-Power Wide-Area Networks (LPWANs) have emerged as a promising communication technology for the Internet of Things (IoT). However, frequency overlap among wireless networks using different radio technologies creates significant interference, compromising communication reliability. This challenge is particularly urgent in LoRa networks, which coexist in the 2.4 GHz ISM band with other IoT transmitters capable of transmitting at much higher power levels. In our study, we begin by providing a comprehensive understanding of the LoRa physical layer (PHY), including insights into modulation and demodulation mechanisms. Leveraging this knowledge, we successfully implemented a real-time LoRa PHY on the GNU Radio Software-Defined Radio platform. To address cross-technology interference during peak detection, we introduce a spectrum merging technique that maintains phase coherence between superimposed peaks, minimizing spectral leakage artifacts. Beyond that, our analysis actively enhances the performance of commercial LoRa devices. Furthermore, we systematically explore the interference dynamics between LoRa and IEEE 802.15.4g networks. Our rigorous investigation reveals LoRa’s ability to achieve high packet reception rates, even in the presence of strong IEEE 802.15.4g interference.

LoRa networks operating in a star topology, this configuration creates a single point of failure and may limit scalability and reliability in areas that are large and geographically dispersed. In order to improve the overall transmission capabilities of the network, recent studies show that adding LoRa to the ZigBee devices effectively disseminates network management. By doing so, the strengths of both technologies can be leveraged, with LoRa serving as the long-range transmitter and ZigBee functioning as the mesh network. In this study, we present LoBee, a novel bidirectional communication method between LoRa and ZigBee that relies on Physical-Layer Cross-Technology Communication. Despite the fact that LoRa and ZigBee utilize different modulation techniques, ZigBee devices can detect and recognize LoRa chirps through the process of sampling the received signal strength. For the transmissions from ZigBee to LoRa devices, we carefully select the input chips to generate specific waveforms, where LoBee detects the preamble of a ZigBee frame based on the locations of the repeated peaks. Our evaluation, which was conducted using USRP and commodity devices, demonstrates that LoBee is capable of achieving concurrent bidirectional wireless communications, with a data rate of approximately 639.38 bits per second from LoRa to ZigBee and from ZigBee to LoRa with more than 90% frame reception rate in the 2.4 GHz frequency band.

Cross-technology communication (CTC) enables seamless interactions between diverse wireless technologies. Most existing work is based on reversing the transmission path to identify the appropriate payload to generate the waveform that the target devices can recognize. However, this method suffers from many limitations, including dependency on specific technologies and the necessity for intricate algorithms to mitigate distortion. In this work, we present NNCTC, a Neural-Network-based Cross-Technology Communication framework inspired by the adaptability of trainable neural models in wireless communications. By converting signal processing components within the CTC pipeline into neural models, the NNCTC is designed for end-to-end training without requiring labeled data. This enables the NNCTC system to autonomously derive the optimal CTC payload, which significantly eases the development complexity and showcases the scalability potential for various CTC links. Particularly, we construct a CTC system from Wi-Fi to ZigBee. The NNCTC system outperforms the well-recognized WEBee and WIDE design in error performance, achieving an average packet reception rate (PRR) of 92.3% and an average symbol error rate (SER) as low as 1.3%.

Cross-technology communication (CTC) enables seamless interactions between diverse wireless technologies. Most existing work is based on reversing the transmission path to identify the appropriate payload to generate the waveform that the target devices can recognize. However, this method suffers from many limitations, including dependency on specific technologies and the necessity for intricate algorithms to mitigate distortion. To address these challenges, we present NNCTC, a Neural-Network-based Cross-Technology Communication framework which can achieve reliable and interpretable Cross-Technology Communication through a training process with an example of WiFi (OFDM and CCK) to both known and unknown modulation schemes.

Cross-technology communication is essential for the Internet of Multimedia Things (IoMT) applications, enabling seamless integration of diverse media formats, optimized data transmission, and improved user experiences across devices and platforms. This integration drives innovative and efficient IoMT solutions in areas like smart homes, smart cities, and healthcare monitoring. However, this integration of diverse wireless standards within cross-technology multimedia communication increases the susceptibility of wireless networks to attacks. Current methods lack robust authentication mechanisms, leaving them vulnerable to spoofing attacks. To mitigate this concern, we introduce DeepSpoof, a spoofing system that utilizes deep learning to analyze historical wireless traffic and anticipate future patterns in the IoMT context. This innovative approach significantly boosts an attacker's impersonation capabilities and offers a higher degree of covertness compared to traditional spoofing methods. Rigorous evaluations, leveraging both simulated and real-world data, confirm that DeepSpoof significantly elevates the average success rate of attacks.

Time synchronization is a fundamental requirement for wireless communication systems to work properly. Most of the existing studies focus on time synchronization among homogeneous devices. This work investigates time synchronization with heterogeneous technologies (e.g., WiFi, ZigBee, and Bluetooth) which is important for the rising Internet of Thing (IoT) scenarios where heterogeneous devices coexist. Recent advances in cross-technology communication (CTC) break the wall between heterogeneous wireless devices. In this work, we propose a new time synchronization strategy based on the CTC technique and provide a technique called TimeBee, which takes the advantage of coordination from a WiFi device to assist ZigBee devices for time synchronization. An effective method is employed so that ZigBee nodes are coordinated for time synchronization based on the received timestamps from WiFi devices. The experimental results show that TimeBee achieves global time synchronization with low time errors.

Federated learning (FL) is a machine learning technique that allows for on-site data collection and processing without sacrificing data privacy and transmission. Heterogeneity is a key challenge in federated settings. Recently, cross-technology communication (CTC) has emerged as a solution for Internet of Things (IoT) heterogeneity, enabling direct communication between different wireless devices without the need for hardware modifications or gateway intervention. For example, a sophisticated WiFi device can serve as a central coordinator for other heterogeneous devices, such as LoRa, ZigBee, Bluetooth, and LTE, leading to more efficient and ubiquitous cross-network information exchange. However, heterogeneous wireless technologies present different data transmission rates and computing resources, making it difficult to achieve high accuracy in predictions due to large amounts of multidimensional data, communication delays, transmission latency, limited processing capacity, and data privacy concerns. In this work, we propose an FL framework based on CTC for heterogeneous IoT applications, called FLCTC. To demonstrate the usability of FLCTC, we implemented FLCTC and a specific solution for forest fire prediction. FLCTC was concretely implemented as a federal deep learning based on long and short-term memory and used for forest fire prediction, addressing the challenge of data characterization in heterogeneous IoT networks. FLCTC promises to improve communication efficiency and prediction accuracy. Our platform-based evaluation results show that FLCTC is feasible, with a recall of 96% and an accuracy of 88%, offering valuable insights into the use of FL with CTC for heterogeneous IoT applications.

Benefiting from the pay-as-you-go business model, cloud-based software applications are becoming more and more popular. A composite cloud system can be constructed by integrating existing component cloud services available over the internet as its system components. In order to fulfill the service-level agreements (SLAs), as well as users’ quality of experience (QoE), a stable execution of the constructed system is desirable in the long term. To achieve this goal, system components at high risk of failing must be identified and fault-tolerated. This is extremely challenging in the dynamic cloud environment that host the component cloud services. However, existing approaches are constrained by their lack of modeling and analysis of system components’ fluctuating reliability time series. To systematically address these issues, in this article, we propose PARS, a perturbation-aware approach, for measuring the reliability sensitivity of component cloud services. It first analyzes the negative perturbations in component cloud services’ historical reliability time series. Then, it calculates the reliability sensitivity of the component cloud services by analyzing how their reliability perturbations impact the reliability of the entire cloud system. Based on PARS, we propose a proactive adaptation approach for constructing and operating composite cloud systems with 1-out-of-2 N-version Programming fault-tolerance. This approach takes the reliability sensitivity of component cloud services estimated by PARS as input to assure the reliability of the cloud system. The results of experiments conducted on two widely used datasets demonstrate the effectiveness and efficiency of the proposed approaches in ensuring the reliability of composite cloud systems.

Benefiting from the pay-as-you-go business model, cloud computing has significantly promoted service computing techniques in real-world industrial applications. Software applications based on cloud computing are becoming more and more popular. By integrating existing component cloud services through the internet, composite cloud systems can be built to meet sophisticated application logic. Stable execution of such systems is desirable in the long term so that the service-level agreements (SLAs), as well as users’ quality of experience (QoE), can be fulfilled. To achieve this goal, it is critical to identify and fault-tolerate system components at high risks of failing. This is extremely challenging due to the dynamic and uncertainty of the cloud environment that hosts the component cloud services. Nevertheless, existing approaches pay little attention to the modeling and analysis of system components’ reliability time series. To address the above issues, we first present a reliability evaluation method for component cloud services based on the reliability model and their failure probability under continuous client-side invocation tests. Then, we propose a perturbation-aware reliability sensitivity measurement approach (named PARS) for measuring the reliability sensitivity of component cloud services. It first analyzes the negative perturbations in component cloud services’ historical reliability time series based on the Markov chain rule. Then, it calculates the reliability sensitivity of component cloud services by analyzing how their reliability perturbations impact the reliability of the entire cloud system. To guarantee the execution quality of the composite cloud system, we further propose a proactive adaptation approach named PA-PARS that enables 1-out-of-2 N-version Programming fault-tolerance for composite cloud systems based on PARS. PA-PARS takes the reliability sensitivity of component cloud services estimated by PARS as input to assure the reliability of the cloud system. It consists of four parts: 1) risky system component identification; 2) adaptation trigger; 3) candidate component cloud service selection; and 4) NVP-based system construction as the proactive adaptation for the composite cloud system. The results of experiments conducted on two widely-used datasets demonstrate the effectiveness and efficiency of the proposed approaches in ensuring the reliability of composite cloud systems.

Cross-technology communication (CTC) technique can realize direct communication among heterogeneous wireless devices (e.g., WiFi, ZigBee, and Bluetooth in the 2.4 G ISM band) without gateway equipment for forwarding, which makes heterogeneous wireless communication more convenient and greatly reduces communication costs. However, compared with the traditional homogeneous network model, CTC technique also makes it easier to implement spoofing attacks in heterogeneous networks. WiFi devices with long communication distances and sufficient energy supply can directly launch spoofing attacks against ZigBee devices, which brings severe security concerns for heterogeneous wireless communications. In this paper, we focus on the CTC spoofing attack, especially spoofing attacks from WiFi to ZigBee and propose a machine learning-based method to detect spoofing attacks for heterogeneous wireless networks by using physical-layer information. First, we model the received signal strength (RSS) data of legitimate ZigBee devices to construct a one-class support vector machine (OSVM) classifier for detecting CTC spoofing attacks depending on the obtained training samples. Then, we simulated CTC spoofing attacks in a live testbed and evaluated the performance of our detection method. Results show that our approach is highly effective in spoofing detection. Even if the distance between the legitimate ZigBee device and WiFi attacker is near each other (i.e., less than 2 m) and does not require a large number of samples, the detection rate and precision of our method are both over 90%. Finally, we employ the OSVM classifier to obtain samples of spoofing attacks and then explore using SVM to further improve the performance of the classifier.