Vehicle-to-Everything (V2X) communication faces a critical authentication dilemma: traditional public-key schemes like ECDSA provide strong security but impose 2 ms verification delays unsuitable for collision avoidance, while symmetric approaches like TESLA achieve microsecond-level efficiency at the cost of 20-100 ms key disclosure latency. Neither meets 5G New Radio (NR)-V2X's stringent requirements for both immediate authentication and computational efficiency. This paper presents SALT-V, a novel hybrid authentication framework that reconciles this fundamental trade-off through intelligent protocol stratification. SALT-V employs ECDSA signatures for 10% of traffic (BOOT frames) to establish sender trust, then leverages this trust anchor to authenticate 90% of messages (DATA frames) using lightweight GMAC operations. The core innovation - an Ephemeral Session Tag (EST) whitelist mechanism - enables 95% of messages to achieve immediate verification without waiting for key disclosure, while Bloom filter integration provides O(1) revocation checking in 1 us. Comprehensive evaluation demonstrates that SALT-V achieves 0.035 ms average computation time (57x faster than pure ECDSA), 1 ms end-to-end latency, 41-byte overhead, and linear scalability to 2000 vehicles, making it the first practical solution to satisfy all safety-critical requirements for real-time V2X deployment.

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.

With the rapid expansion of the Internet of Medical Things (IoMT) and cloud computing, ensuring secure communication in e-health systems has become increasingly critical. However, many existing authentication solutions suffer from excessive overhead and security vulnerabilities. To address these challenges, we present a lightweight, high-speed authentication protocol that relies on secure hash functions and XOR operations, facilitating efficient mutual authentication among users, trusted servers, and medical servers while establishing session keys for data exchange. We then rigorously assess our protocol's security against a comprehensive threat model, employing both informal methods and formal analyses, including Real-Or-Random (ROR) model, BAN logic, and automated verification via ProVerif. The results demonstrate that our protocol remains resilient against known attacks and satisfies e-health security standards. Furthermore, a detailed performance comparison reveals that our approach significantly reduces some costs compared to existing schemes, while reinforcing security and privacy protections.

Existing joint user association and trajectory control (JUATC) methods provide remarkably high data rates for mobile users (MUs) in unmanned aerial vehicle (UAV)-assisted multi-access edge computing systems. Nevertheless, current methods give more attention to the uplink and downlink of MU, which must be associated with the same UAV or base station (BS), ignoring the network’s heterogeneity and considerably reducing the MU’s communication efficiency. Furthermore, UAVs typically provide communication services to MUs under partial observation, leading to challenges in achieving optimal service performance due to information loss. Moreover, although existing solutions can readily reach optimal, restriction-fulfilling strategies, they frequently breach restrictions during intermediate iterations. To address these issues, we present a fully decentralized JUATC algorithm based on the Communication and Proportional-Integral-Derivative (PID) Lagrangian-based Multi-Agent Actor-Critic (CPID-MAAC). First, to improve communication efficiency, we consider that each MU can be associated with a different UAV or BS in the uplink and downlink. Second, we establish a messaging mechanism between UAVs based on autoencoding UAV’s observations to handle the information loss. Finally, to alleviate constraint-violating behavior, we incorporate the PID Lagrangian algorithm. The experiments show that CPID-MAAC improves data rate by 7.88%~16.03% and drastically reduces the number of constraint violations during UAV agent training.

With the rise of virtual communication and smart devices, speech has become the most natural medium of interaction. Yet it remains intrinsically difficult: speech is fleeting, unstructured, and disfluent, making key information prone to loss. Conventional Speech-to-Text (STT) systems attempt to acoustically reconstruct what was said. However, their frame-level alignment and rigid token-by-token decoding break down under noise, interruptions, or fragmentation. Humans, in contrast, readily grasp what was meant by exploiting syntax, discourse, pragmatics, and prosody. We argue for a paradigm shift from acoustic reconstruction to semantic transduction: inferring meaning directly from speech, abstracted from surface distortions. This shift raises two challenges: (C1) the lack of anchors between audio and meaning, and (C2) the need to maintain compositional semantics. To address these, we introduce CogTrans, a cognitively inspired speech-to-meaning framework. CogTrans tackles C1 through a Semantic Anchor Explorer, built on I-JEPA to capture higher-order regularities, prosodic rhythms, cross-frequency coarticulation, discourse continuity-providing resilient semantic scaffolds under noise and fragmentation. For C2, it designs a Lexical-Semantic Harmonizer that dynamically integrates these anchors with lexical embeddings; thereby preserving fine-grained compositional fidelity in roles, order, and entities. Extensive experiments show that CogTrans delivers consistent and substantial gains under challenging conditions. On GigaSpeech, it achieves a 6.58% relative Word Error Rate (WER) reduction, and on the multilingual VoxPopuli benchmark, the gain climbs to 12.97% at 10 dB noise-a regime where conventional models typically collapse. Beyond literal accuracy, CogTrans also boosts semantic fidelity, with a 3.40% increase in ROUGE-L and 3.45% in USE-Sim, ensuring transcripts remain faithful not only in words but also in meaning. Together, these results underscore that CogTrans is robust in noisy, unconstrained environments-precisely the conditions where reliability matters most.

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.

Global aging necessitates tele-monitoring systems to provide real-time tracking and timely assistance for older adults living independently. While pervasive wireless devices (e.g., CSI, IMU, UWB) enable cost-effective, non-intrusive monitoring, existing systems lack flexibility, limiting their adaptability to different environments. In this work, we posit that the motion dynamics of human movement are invariant across sensing modalities, inspiring the design of HARMONY—a privacy-preserving, sensor-agnostic system that supports multi-modal inputs and diverse tele-monitoring tasks. HARMONY incorporates Modality-agnostic Data Processing to uniformly encrypt multi-modal signals and Task-specific Activity Recognition for seamless tasks adaptation. A novel Encrypted-processing Engine then significantly accelerates computations on encrypted data by optimizing matrix and convolution operations. Evaluations across five different sensing modalities show that HARMONY consistently achieves high accuracy while delivering 3.5 × to 130 × speedups over state-of-the-art baselines. Our results demonstrate that HARMONY is a practical, scalable, and privacy-centric prototype for next-generation remote healthcare.

Various wireless technologies have been utilized for sensing. Although promising, these wireless sensing technologies have inherent limitations. Prior research mainly focuses on overcoming the limitations of an individual wireless sensing technology, and little attention has been paid to the potential benefits of sensing with more than one wireless technology. In this paper, we introduce the concept of cross-technology sensing for the first time, and propose LoFiSen to enable LoRa-to-WiFi sensing. LoFiSen leverages the strengths of both LoRa and WiFi—combining LoRa's long-range capability with WiFi's pervasiveness. The chirp characteristic of LoRa signal significantly improves the sensing range of WiFi, and the widespread availability of WiFi devices makes LoRa sensing more pervasive. LoFiSen is fully compatible with LoRa and WiFi protocols, and can work on commodity LoRa and WiFi hardware. The key component of our design is enabling the WiFi receiver to capture fine-grained LoRa signal variations for sensing. Real-world experiments demonstrate that LoFiSen improves the WiFi sensing range for respiration monitoring from 8 m to 41 m, and pushes the walking sensing range from 16 m to 73.5 m. Through-wall passive respiration monitoring, previously infeasible with state-of-the-art WiFi sensing, is now possible with LoFiSen.

Time series anomaly detection is a pivotal task in data analysis, yet it poses the challenge of discerning normal and abnormal patterns in label-deficient scenarios. While prior studies have largely employed reconstruction-based approaches, which limit the models’ representational capacities. Moreover, existing deep learning-based methods are not sufficiently lightweight. Addressing these issues, we present PatchAD, our novel, highly efficient multiscale patch-based MLP-Mixer architecture that utilizes contrastive learning for representation extraction and anomaly detection. With its four distinct MLP Mixers and innovative dual project constraint module, PatchAD mitigates potential model degradation and offers a lightweight solution, requiring only0.403 Mparameters. Its efficacy is demonstrated by state-of-the-art results across8datasets sourced from different application scenarios, outperforming over30comparative algorithms. PatchAD significantly improves the classical F1 score by6.84%, the Aff-F1 score by4.27%, and the V-ROC by2.49%. Simultaneously, an in-depth analysis of the mechanisms underlying PatchAD has been conducted from both theoretical and experimental perspectives, validating the design motivations of the model.

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.

Vehicular Ad Hoc Networks (VANETs) facilitate real-time information exchange through Vehicle-to-Vehicle (V2V) and Vehicle-to-Roadside (V2R) communications. While V2R communication plays a crucial role, it faces significant security challenges due to the transmission of sensitive data, leaving the system vulnerable to man-in-the-middle, replay, and impersonation attacks. Previous attempts to enhance security, such as dynamic anonymization and trusted key management, have introduced new challenges, including complex authentication processes, high resource demands, and inadequate privacy protection. To overcome these issues, we propose a lightweight and efficient authentication scheme that enhances vehicle privacy and security through a combination of signatures, pseudonyms, batch verification, and flexible certificate management. Our approach also employs Bloom filters to improve authentication efficiency, addressing the limitations of traditional certificate management systems that suffer from large lists and slow query times. The evaluation results demonstrate that the proposed scheme ensures comprehensive security by providing two-way authentication and guaranteeing anonymity. It effectively prevents replay attacks, DoS attacks, and other potential threats. Moreover, the scheme significantly reduces both communication and computational overhead, offering an efficient and secure solution for V2R communication in VANET.

LoRa, as a typical representative of Low Power Wide Area Networks (LPWAN), has been widely used to connect massive IoT devices. However, in mobile applications, there is significant packet loss in LoRa transmission due to link performance degradation. Existing studies take little account of end-devices' movement, particularly when the movement pattern is unknown. We propose LMLoRa to enhance theLink Performance forMobile LoRa networks in general scenarios for both single-gateway and multi-gateway applications. The key observation is that, due to LoRa's unique feature, repeating the original packet content enables the use of smaller, more energy-saving transmission parameters, which not only enhances link performance but also reduces energy consumption. Technically, we propose a link performance estimation model based on packet content repetition for both single-gateway and multi-gateway mobile networks. Then, we propose the corresponding channel frequency selection model to avoid transmission collisions. Finally, we design low-overhead communication mechanisms to operate the system. To evaluate the performance of LMLoRa in various scenarios, we design and implement real-world testbeds and a simulation platform for both single-gateway and multi-gateway scenarios. Extensive results show that LMLoRa improves packet delivery ratio by an average of 33.4% to 69.2% compared with the state-of-the-art.

The explosive growth of the Internet of Things (IoT) has dramatically increased the demand for secure mechanisms to protect against unauthorized access and attacks. Traditionally, expensive Software-Defined Radios (SDRs) have been utilized to gather IoT physical features, which are critical for reliable authentication. However, the high cost of SDRs makes them impractical for widespread deployment across the vast and diverse IoT ecosystem. In contrast, this paper presents AUTHFi, a novel cross-technology device authentication framework that transforms the SDR approach for collecting and authenticating IoT device signals (e.g., ZigBee and Bluetooth) by utilizing commercial WiFi devices. Specifically, AUTHFi leverages the recent advances in Cross-Technology Communication (CTC) to reconstruct the partial waveform of IoT transmission, thus eliminating the requirement for expensive SDRs. AUTHFi requires us to address several unique challenges. First, AUTHFi compensates for signal losses of the partial waveform to get more signal information. Then, it introduces an enhanced Carrier Frequency Offset (CFO) estimation and a fusion neural network that combines CFO and the reconstructed waveform for accurate device authentication. We implement AUTHFi based on RTL8812au (commodity WiFi) and CC2652P (commodity ZigBee/Bluetooth). Our thorough evaluation confirms that AUTHFi offers reliable authentication under various settings, achieving a maximum accuracy of 94.2%.

The increasing integration of Internet of Things (IoT) devices in Wireless Local Area Networks (WLANs) necessitates robust and efficient authentication mechanisms. While existing IoT authentication protocols address certain security concerns, they often fail to provide comprehensive protection against threats such as perfect forward secrecy violations, insider attacks, and key compromise impersonation, or impose significant computational and communication overhead on resource-constrained IoT systems. This paper presents a novel Extensible Authentication Protocol (EAP) based scheme for IoT-WLAN environments that addresses these security challenges while maintaining cost-effectiveness. Our approach utilizes elliptic curve cryptography and incorporates advanced features including perfect forward secrecy, strong identity protection, and explicit key confirmation. We provide a thorough security analysis using informal heuristics, formal methods (Random Oracle Model and BAN Logic), and automated verification with ProVerif. Performance evaluations demonstrate that our protocol achieves lower communication, storage, and computational costs compared to state-of-the-art solutions, with an average 79.6% reduction in computation time. A detailed comparison with existing schemes highlights the efficiency and enhanced security features of our proposed authentication mechanism for IoT-WLAN deployments.

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.

The integration of 5G/6G networks with intelligent healthcare systems has enabled early disease detection through patient data monitoring. However, the Internet of Medical Things (IoMT) and remote healthcare services introduce significant privacy and security risks. In this paper, we propose LiteCrypt, which addresses these challenges by introducing an optimized Homomorphic Convolutional Neural Networks (HCNN) structure for secure inference and a lightweight Threshold Signature Scheme (TSS) based dual-authorization mechanism. To enhance the practicality of Homomorphic Encryption (HE)-based secure inference in telemedicine applications, LiteCrypt presents an optimized HCNN framework that ensures efficient and adaptable operations across multiple datasets. A high-performance GPU-accelerated HE engine is developed to address the computational demands of HE operations, enabling real-time processing of encrypted patient data. Besides, LiteCrypt introduces a novel TSS-based dual-authorization protocol, requiring consent from both the patient and the hospital to access patient data, thereby mitigating unauthorized access risks. The system adapts to a flexible 2-out-of-3 authorization scheme for emergencies, ensuring timely data retrieval while maintaining security. To overcome the initial challenge of prolonged computation time due to compute-intensive operations, In LiteCrypt, we utilized the lightweight TSS protocol, based on Oblivious Transfer (OT), which is designed for resource-constrained IoMT devices, reducing computation time from 11.9 to 0.11 seconds. Empirical validation demonstrates LiteCrypt’s superior performance, achieving a 233-fold increase in processing speed, a $96 \%$ reduction in encrypted message size, and a 28-fold speed increase using GPUs.

Keystroke dynamics has proven to be highly effective, with its applications expanding significantly over the years in areas such as preventing transaction fraud, account takeovers, and identity theft. Key-positioning and feature-learning methods are commonly used to identify keystroke signals. However, the existing methods face challenges in detecting overlapping keystrokes and environmentally changed signals. We propose a solution called Chameleon to address these limitations. Unlike previous signal separation and deep learning methods that are ineffective in keystroke signals and computationally demanding, Chameleon employs a low-computation Ranking Model to separate overlapping keystroke signals. Moreover, our experiments demonstrate that Chameleon separated signals can be recognized with an average accuracy of 92.69%, surpassing the commonly used FastICA method, which only reaches 25% accuracy. To account for environmental changes, we utilize the Fréchet Inception Distance (FID) as a guiding metric for model migration. Additionally, we introduce the Inductive Vector, which enables our key-identifying model to adapt to altered environmental conditions such as environment, phone location, and user variety. The Inductive Vector adjusts the model parameters based on the shift in FID. In scenarios with various phone locations, the Inductive Vector significantly improves recognition accuracy from 61% to 98%, outperforming the best existing keystroke recognition algorithm. In other dynamic environmental conditions, our approach achieves an average accuracy rate of 81.7%, which is at least 1.6 times better than the current state-of-the-art keystroke recognition algorithm.

With the advancement of the Internet of Medical Things (IoMT) and cryptographic technologies, remote healthcare services have become more widespread, presenting new challenges for patient privacy and data security. Conventional security mechanisms, such as centralized authentication and key distribution systems, are susceptible to single points of failure and significant management burdens, potentially leading to compromised authentication centers and internal security threats. In response, this study presents a threshold signature algorithm, it uses Distributed Key Generation (DKG) that distributes private keys without the need for a trusted key distributor, requiring the cooperative signature of at least two nodes for authentication. This approach not only circumvents the risk of single points of failure but also enhances the system’s robustness and efficiency. The experimental results validate its prospective utility in safeguarding remote healthcare data.

Traffic sign recognition (TSR) is vital for vehicle safety and navigation, especially in the era of autonomous cars. Internet of Vehicles (IoV) provide a promising infrastructure for vehicular networks due to their agility and interoperability. However, privacy concerns and network restrictions hinder the collection of massive data from distributed automotive sensors in IoV. To address these challenges, this article proposes the application of federated learning (FL) and model sparsification to optimize traffic sign recognition (TSR) in autonomous vehicles. FL enables decentralized learning while preserving data privacy, and model sparsification significantly reduces communication costs. Furthermore, we incorporate the Adam optimizer for local training, ensuring efficient model optimization on each vehicle. Experimental results demonstrate the effectiveness of our approach, with improved TSR performance while mitigating privacy risks and enhancing communication efficiency. This research contributes to the advancement of TSR in IoV by introducing FL, model sparsification, and the use of the Adam optimizer for local training, facilitating efficient and privacy-preserving vehicular network learning.

With the rapid increase of data from unmanned aerial vehicles (UAVs), the security and privacy of data presents a severe challenge for UAV-based applications. Moreover, UAVs with constrained resources cannot be equipped with strong but complicated cryptographic primitives for authentication protocol design. Although some attempts have been made to deal with security and privacy issues for UAVs, most of the existing studies have been found numerous security vulnerabilities or own extreme communication/computation overheads. This article offers a lightweight and practical mutual authentication protocol solely comprised of bitwise XOR operations and one-way hash functions. Moreover, blockchain technology is utilized to alleviate the centralized trusted party (TA) issue. Then, security of our proposed authentication protocol is proved by widely adopted formal security proof—Real-or-Random model and informal security proof. The experimental results prove that the proposed protocol can achieve better security requirements (e.g., decentralized TA, replay attack defense, and session key security) with less communication cost (i.e., reduced by around 58.7% at most) and computation cost (i.e., reduced by around 98.9% at most) than related UAV authentication schemes.

In recent years, the cloud infrastructure has been developed as a promising sharing system for the Internet of Vehicles (IoV) communication. During the information exchange, search service over ciphertext called searchable encryption (SE) is an extraordinary method to prevent data breaches. However, two open problems still need to be solved for ranked search, which hinders the application practicality in IoV. First, each data owner must store the extra information to distribute weight values dynamically. Second, ranking in the cloud has not been supported by most existing schemes. In this article, to address the above problems and fit the characteristics of real-time data exchange in IoV, we present a multikeyword-ranked search scheme supporting extreme environments for the IoV. Specifically, our system designs a unique encrypted index tree structure to realize the multikeyword-ranked retrieval, the weight value dynamic adaptive calculation, and dynamic updating in IoV. Moreover, we use a primary–secondary dual-server model to cope with extreme environments and propose a “greedy breadth-first search” algorithm to achieve an effective sublinear search. Finally, comprehensive security analysis and experimental simulation for the proposed system prove that our system can guarantee user privacy and acceptable efficiency.

Traffic sign recognition is a crucial aspect of autonomous vehicle research, and deep learning techniques have significantly contributed to its progress. Nevertheless, the distribution of traffic sign information in natural complex road conditions is long-tailed, and traffic sign identification in complex road conditions has become a significant barrier to autonomous vehicle applications. The imbalanced distribution of information on the dataset migrates to the feature space during training, resulting in imbalanced classifier prediction. In this paper, we propose the gradient rebalanced traffic sign recognition (GRTR) method to address this problem for the first time. GRTR first evaluates the prediction and classification bias of the classifier using the fitted deviation between the model’s output probability and the ground-truth distributions. Then, GRTR dynamically adjusts the correction and compensation factors following the classifier’s prediction and classification biases. GRTR rebalances the positive and negative sample gradients for each category based on the synergistic effect of the correction and compensation factors to prevent the transfer of distribution imbalance and to significantly enhance the performance of the traffic sign classifier under difficult road conditions. Experimental results demonstrate that our GRTR achieves state-of-the-art performance on long-tailed traffic sign and multilabel datasets.Note to Practitioners—Most traffic sign recognition algorithms are still designed based on the assumption of a balanced distribution of traffic signs in the dataset. Real-world autonomous vehicles require traffic sign recognition on datasets with severely imbalanced distributions. This paper proposes a general approach to solving the long-tailed traffic sign recognition problem.

The Industrial Internet of Things (IIoT) has been introduced in an era of increasingly broad potentials in the medical industry. In recent years, IIoT-based healthcare applications have grown in popularity, with the majority of them relying on Wireless Body Area Network (WBAN) for flexibility. There have been a few recent works that have investigated SDN-based fog architecture for constructing smart healthcare systems. However, the best fog node from the fog layer must be identified and limit the transmission of unnecessary data. To address this issue, the Intelligent Software-defined Fog Architecture (i-Health) is developed in this work. Based on the prior data pattern of each patient, the controller will decide whether to send the data to the fog layer. Furthermore, we introduced the Fog Ranking Service (FRS) and Fog Probing Service (FPS) to select the best fog node. The performance comparison reveals that the proposed i-Health outperforms existing benchmark approaches.

In the undertaken study, we have used a customized dataset termed ``Cardiac-200'' and the benchmark dataset ``PhysioNet.'' which contains 1500 heartbeat acoustic event samples (without augmentation) and 1950 samples (with augmentation) heartbeat acoustic events such as normal, murmur, extrasystole, artifact, and other unlabeled heartbeat acoustic events. The primary reason for designing a customized dataset, ``cardiac-200,'' is to balance the total number of samples into categories such as normal and abnormal heartbeat acoustic events. The average duration of the recorded heartbeat acoustic events is 10-12 s. In the undertaken study, we have analyzed and evaluated various heartbeat acoustic events using audio processing libraries such as Chromagram, Chroma-cq, Chroma-short-time Fourier transform (STFT), Chroma-cqt, and Chroma-cens to extract more information from the recorded heartbeat sound signals. The noise removal process has been carried out using local binary pattern (LBP) methodology. The noise-robust heartbeat acoustic images are classified using long short-term memory (LSTM)-convolutional neural network (CNN), recurrent neural network (RNN), LSTM, Bi-LSTM, CNN, K-means Clustering, and support vector machine (SVM) methods. The obtained results have shown that the proposed InfusedHeart Framework had outclassed all the other customized machine learning and deep learning approaches such as RNN, LSTM, Bi-LSTM, CNN, K-means Clustering, and SVM-based classification methodologies. The proposed Knowledge-infused Learning Framework has achieved an accuracy of 89.36% (without augmentation), 93.38% (with augmentation), and a standard deviation of 10.64 (without augmentation), and 6.62 (with augmentation). Furthermore, the proposed framework has been tested for various signal-to-noise ratio conditions such as SignaltoNoiseRatio0, SignaltoNoiseRatio3, SignaltoNoiseRatio6, SignaltoNoiseRatio9, SignaltoNoiseRatio12, SignaltoNoiseRatio15, and SignaltoNoiseRatio18. In the end, we have shown a detailed comparison of texture and without texture approaches and have discussed future enhancements and prospective ways for future directions.

Mobile smart devices provide convenience for people’s daily life with the users’ data, but also put consumers’ privacy and security at risk. Privacy-enhancing technologies (PETs), including secure two/multi-party computation, have emerged as solutions to alleviate privacy concerns in mobile cloud computing (MCC). However, cloud servers, although capable of easing the burden of PETs, introduce potential risks by being malicious and colluding with computation parties to access additional private data. In this article, we propose a privacy-preserving cloud-assisted two-party computation scheme and the optimized variant with the half-gate method in MCC with a higher security level. To the best of our knowledge, the work is the first cloud-assisted two-party computation, designed to resist all collusion attacks in the malicious model. This is achieved by distributing circuit generation tasks among the parties and separately processing private inputs based on authenticated garbled circuits. Security analysis demonstrates that our scheme ensures correctness and fairness. Performance comparison results indicate the efficiency of our work, even with stronger security against malicious servers and any collusion attack. It outperforms the state-of-the-art scheme, particularly in terms of the server’s communication cost in the online phase, achieving a remarkable reduction of approximately 96.8%.

The concept of the metaverse, first introduced in science fiction, is rapidly becoming a technological reality with profound implications for various sectors, including healthcare. By merging virtual reality (VR), augmented reality (AR), artificial intelligence (AI), and advanced communication technologies, the metaverse promises to create immersive, interactive environments that can transform medical practice, education, and patient care [1].

Real-time healthcare indicates monitoring people's health status in a timely manner. In this regard, wireless techniques can be used to provide immediate access to bio-sensing information, facilitating monitoring and instant communication between healthcare providers [1]. Such a scheme aims to realize real-time decision-making and intervention, improving patient outcomes and efficiency in healthcare delivery.

In the above paper, Tu et al. proposed an efficient and anonymous authenticated key exchange scheme optimized for wearable computing environments, utilizing lightweight cryptographic primitives like XOR, ASCON, and hash functions. They claimed the employed Authenticated Key Exchange (AKE) scheme is robust against prevalent security threats. However, our analysis reveal a critical vulnerability to replay attacks that could undermine the protocol's security; specifically, an attacker could intercept messages and induce unauthorized server-side password updates, effectively blocking further legitimate user communications. Upon dissecting the root causes of this vulnerability, we offer targeted recommendations to mitigate such attacks and reinforce the protocol's defenses.

Green communication has attracted lots of attention recently, where NOMA (Non-Orthogonal Multiple Access) is one of the most promising technologies to realize energy-efficient communication. Specifically, by making massive wireless devices connect to the same time-frequency resource, NOMA can enhance the spectrum efficiency. In this paper, to investigate the freshness of the sensing data, we analyze the Age of Information (AoI) performance in NOMA-enabled Mobile Crowdsensing (MCS) circumstance, where the stochastic geometry theory is adopted. Firstly, we focus on the data submission process between mobile workers (MWs) and service provides (SPs), which drives to establish a model of the NOMA-enabled MCS. Then, given the transmission schemes of NOMA and OMA (Orthogonal Multiple Access), the mathematical expressions of the AoI metric are derived in the closed form respectively. Furthermore, simulation experiments are conducted to obtain AoI numerical results under various parameter settings (e.g., power strategies, queue models, and transmission protocols). Finally, the evaluation results not only prove the validness of the established models, but also provide some efficient solutions to achieve the optimal AoI value under the considered NOMA-enabled MCS scenario.

Since Facebook officially changed its name to Meta in Oct. 2021, the metaverse has become a new norm of social networks and three-dimensional (3D) virtual worlds. The metaverse aims to bring 3D immersive and personalized experiences to users by leveraging many pertinent technologies. Despite great attention and benefits, a natural question in the metaverse is how to secure its users' digital content and data. In this regard, blockchain is a promising solution owing to its distinct features of decentralization, immutability, and transparency. To better understand the role of blockchain in the metaverse, we aim to provide an extensive survey on the applications of blockchain for the metaverse. We first present a preliminary to blockchain and the metaverse and highlight the motivations behind the use of blockchain for the metaverse. Next, we extensively discuss blockchain-based methods for the metaverse from technical perspectives, such as data acquisition, data storage, data sharing, data interoperability, and data privacy preservation. For each perspective, we first discuss the technical challenges of the metaverse and then highlight how blockchain can help. Moreover, we investigate the impact of blockchain on key-enabling technologies in the metaverse, including Internet-of-Things, digital twins, multi-sensory and immersive applications, artificial intelligence, and big data. We also present some major projects to showcase the role of blockchain in metaverse applications and services. Finally, we present some promising directions to drive further research innovations and developments toward the use of blockchain in the metaverse in the future.

Internet of Things (IoT) has led to significant advancements in communication technologies, specifically, concerning IoT-based sustainable information systems. Lately, industry-academic communities have made great strides for the development of security in IoT-based applications, such as traffic management, industrial automation systems, military surveillance systems, transportation, parking, etc. The sustainable IoT converges AI and blockchain technologies for enhancing quality of individual’s life. As a result, emerging IoT applications operate a distributed ledger technology to provide robust-level of encryption and execution for contractual agreement that resolves interoperability and security issues. Thus, this article proposes a blockchain-based remote mutual authentication (B-RMA) that considers smart devices and cloud networks to offer security and privacy. The proposed B-RMA can coexist with the IoT-based smart environment to decentralize the processing of user authentication requests. The prominence of the proposed strategies including security efficiency and privacy protection, is evaluated using informal security analysis. Moreover, a runtime platform “Node.js” was used to analyze the communication metrics, such as execution time, throughput, and overhead ratio, over the concurrent requests. The investigation results prove that the B-RMA achieves a scalable environment, accordingly.

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.

6G-based wireless communication system is poised to redefine the next-generation network landscape by enabling novel services and applications, such as intelligent link establishment, power control, data collection, transmission, and distribution. However, security issues, particularly recently revealed channel access attack (CAA), present significant challenges to performance optimization tasks in the heterogeneous wireless networks of 6G, namely, Age of Information (AoI) oriented Network (AoN), Throughput oriented Network (ToN), and Latency oriented Network (LoN). To address these challenges, this article presents a game theory-based smart optimization solution to enable unmanned aerial vehicles (UAV) to resist CAA within a 6G-based heterogeneous network. Our methodology begins by outlining the advantages and challenges associated with UAV usage, followed by the design of performance indicators and intelligent resource allocation schemes under the influence of CAA. Subsequently, we introduce definitions and categories within game theory, encompassing the concept and equilibrium of three typical game models. The efficacy of our proposed framework is validated through simulation results, which demonstrate the achievement of optimal AoI, enhanced throughput, and reduced latency compared with baseline methodologies when countering CAA in a UAV-assisted heterogeneous network.

The Internet of Medical Things (IoMT) has a bright future with the development of smart mobile devices. Information technology is also leading changes in the healthcare industry. IoMT devices can detect patient signs and provide treatment guidance and even instant diagnoses through technologies, such as artificial intelligence (AI) and wireless communication. However, conventional centralized machine learning approaches are often difficult to apply within IoMT devices because of the difficulty of large-scale collection of patient data and the potential risk of privacy breaches. Therefore, we propose a blockchain-based two-stage federated learning approach that allows IoMT devices to train a global model collaboratively without gathering the data to a central server. Specifically, to address the problem of poor training performance on non-independent identically distributed (non-IID) data, we design a blockchain-based data-sharing scheme that can significantly improve the model’s accuracy without threatening user privacy. We also design a client selection mechanism to further improve the system’s efficiency. Finally, we validate the feasibility and effectiveness of our system through simulation experiments on three popular datasets (i.e., MNIST, Fashion-MNIST, and CIFAR-10).

The advancement of sensory technologies proliferates the development of low-cost electronics systems to operate the environmental features of smart cities. Global urbanization integrates networking systems to offer computing-based practical solutions for improving the quality of application-oriented services. Few existing studies have primarily focused on a single-point vulnerability for decentralized IoT applications. However, very few mechanisms address the issues concerning privacy-preserving and trust-aware authentication for IoT-enabled industrial applications. Moreover, the existing schemes are in fact not applicable to real-time scenarios, such as decentralized networks and long-term evolution advanced networks. Thus, this article presents a trust-aware blockchain-based seamless authentication with privacy-preserving (TAB-SAPP) to resolve the critical things, such as privacy, security, and packet delivery ratio. In the proposed TAB-SAPP, a novel data traffic pattern is utilized using identity management to show that the proposed mechanism can be more functional in expanding users’ connectivity to improve the communication metrics, such as packet delivery ratio and mobility speed.

The use of aerial base stations, AI cloud, and satellite storage can help manage location, traffic, and specific application-based services for vehicular social networks. However, sharing of such data makes the vehicular network vulnerable to data and privacy leakage. In this regard, this article proposes an efficient and secure data sharing scheme using community segmentation and a blockchain-based framework for vehicular social networks. The proposed work considers similarity matrices that employ the dynamics of structural similarity, modularity matrix, and data compatibility. These similarity matrices are then passed through stacked autoencoders that are trained to extract encoded embedding. A density-based clustering approach is then employed to find the community segments from the information distances between the encoded embeddings. A blockchain network based on the Hyperledger Fabric platform is also adopted to ensure data sharing security. Extensive experiments have been carried out to evaluate the proposed data-sharing framework in terms of the sum of squared error, sharing degree, time cost, computational complexity, throughput, and CPU utilization for proving its efficacy and applicability. The results show that the CSB framework achieves a higher degree of SD, lower computational complexity, and higher throughput.

The papers in this special section focus on federal learning applications for the Internet of Medical Things. Due to to the advancements in Internet of Medical Things (IoMT), wearable devices, remote monitoring of patients is possible like never before. Machine learning and deep learning techniques help the doctors immensely in remotely diagnosing the patients by learning the patterns from the data generated through these devices [1]. The main problem with traditional machine learning (ML)/deep learning (DL) models is that the data from the individual devices, sensors, wearables of patients have to be transferred to the central servers to train the data using the ML/DL models. Due to the sensitive nature of the healthcare data, the aforementioned approach of transferring the patients’ data to the central servers may create serious security and privacy issues.

Trajectory design is of great significance for the intelligent Unmanned Ground Vehicle (UGV) when performing various ground tasks. Though obstacle avoidance, speed control and other movement issues in the UGV navigation have been considered by the current research, the UGV path planning against malicious radio source is off the beaten path. To address such a research gap, we propose a reinforcement learning-based scheme to design UGV trajectory against malicious radio source as well as minimize the movement cost. Firstly, the malicious radio source detection and localization models are introduced after the Received Signal Strength Indicator (RSSI) map establishment. Then, the RSSI Map-based UGV trajectory design problem is formulated, where the movement cost and security risk are both concerned. To solve the formed problem, we propose a reinforcement learning-based trajectory design scheme, whose complexities are analyzed in detail. Finally, experiments are conducted under various parameter settings, where the simulation results evaluate the correctness and effectiveness of the proposed algorithm.

Internet of things (IoT) has emerged as a promising technology that can be widely used in various industries to realize real-time information collection, so as to improve production efficiency and reduce running costs. By combining the technology of IoT, maritime transportation systems (MTS) can prevent vessels collision, improve the efficiency of maritime transportation and reduce the loss of revenue for ports and shipbuilders. The large amount of real-time data generated in IoT-enabled MTS can be efficiently utilized to predict the future trajectories and hotspots of vessels on the sea combined with historical data. However, the maritime traffic data in MTS cannot be effectively processed in traditional big data analysis methods, and the integrity of it needs to be checked before being used to achieve the prediction of trajectories and high-density areas of vessels. In this paper, we propose a flexible data integrity checking scheme with original data recovery in IoT-enabled MTS. In the proposed scheme, the data blocks of vessels are encoded based on the technology of erasure coding. To ensure the availability of the historical data, the existence and the integrity of the data elements stored in the cloud can be checked. Moreover, the original data blocks can be recovered efficiently if the encoded data elements have been corrupted or deleted. Security analysis demonstrates that the proposed scheme can be proved to be correct and is secure against malicious attacks. Performance analysis shows that our scheme is more efficient than the previous schemes.

As a promising enabler for edge intelligence, Unmanned Aerial Vehicles (UAV) have become more and more important in Mobile Edge Computing Networks (MECN), such as communication, computation, collection and control service supply. Although Age of Information (AoI) minimization is indispensable for fresh information collection and computation in the UAV-aided MECN, some attackers can launch attacks to deteriorate the availability of precious channel resources, such as revealed channel access attacks (CAAs). Moreover, recent research has not considered the system’s active probability and security issues concurrently, e.g., CAA, in the average AoI minimization process. In this paper, to deal with this problem, we consider an AoI-oriented channel access problem under CAA with a game theory viewpoint. Firstly, to obtain a MECN-based AoI indicator under CAA, the system model with active probability consideration is established. Next, the channel access-based AoI minimization problem is formulated from the viewpoint of the Ordinary Potential Game (OPG). Furthermore, two algorithms called AACSD and DCASD are proposed to determine channel access strategies, by which the Nash Equilibrium (NE) solution of the OPG could be reached. Finally, experiments are conducted under homogeneous and heterogeneous parameter settings, and the simulation results evaluate the correctness and effectiveness of our proposals.

Intelligent Vehicle Systems (IVSs) devote to integrating the data sensing, processing, and transmission in the Vehicle to Everything (V2X) scenarios, where the Unnamed Aircraft Vehicle (UAV)-aided traffic monitoring network is one of the most significant applications. Moreover, since the central premise to support the IVS is timely and effectively sensing data processing, Age of Information (AoI) can precisely reflect the timeliness and effectiveness of the communication process in the UAV-aided traffic monitoring network. However, recent researches pay little attention to AoI minimization issue, especially when the malicious attacker attempts to deteriorate the network performance. The accurately modelling of the adversarial relationship between legitimate UAVs and attacker is not fully investigated. To make up this research gap, we start from the Stackelberg game viewpoint to investigate the AoI optimization problem in the UAV-aided traffic monitoring network under attack. Firstly, the system model and three-layer Stackelberg game-based optimization goal are established. Secondly, based on the Backward Induction (BI) analysis, the follower’s data sensing rate, transmission power, and the leader’s attacking power are determined by the Lagrange duality optimization technology successively. Moreover, the sub-gradient update-based optimization technology is used to achieve the Stackelberg Equilibrium (SE). Finally, simulations are performed under various parameters. The evaluation results present better performance of our proposed approach when compared with the typical baselines.

The rapid growth of artificial intelligence (AI), blockchain technology, and edge computing services have enabled the Internet of Medical Things (IoMT) to provide various healthcare services to patients, including neural network-based disease diagnosis, heart rate monitoring, and fall detection. Generally, end devices should transmit the collected patient data to a centralized server for further model training, but at the same time, the patient's privacy may be at risk. In addition, due to the diversity of patient conditions, a one-size-fits-all model cannot meet personalized healthcare needs. To address the above challenges, we propose a blockchain-based personalized federated learning (FL) system that enables clients to participate in personalized model training without directly uploading private data. We further realize the decentralized FL by combining blockchain technology, which improves the security level of the system. Finally, we verify the reliable performance of our system on different datasets through simulation experiments.

To efficiently and reliably obtain the target data, mobile crowdsensing (MCS) is widely used to provide the sensing data collection service. Currently, despite concerns of sensing network scale and mobility can be addressed to some degree in the MCS manner, the freshness and privacy goals of the sensing data are still not considered simultaneously. We combine the Age of Information (AoI) with security-enhanced technique to establish a novel MCS system, which can guarantee data freshness and security, respectively. Hence, the problem is formatted as a joint AoI optimization and privacy-preservation process. To solve this problem, we utilize game theory to achieve AoI-oriented spectrum access, and homomorphic encryption to encrypt communication data. Finally, security analysis and numerical results show that our proposed approach can effectively ensure the security level and improve the AoI performance at the same time in MCS.

As a promising enabler for edge intelligence, Unmanned Aerial Vehicles (UAVs) are playing a more and more important role in Mobile Edge Computing Networks (MECN), such as ground sensor communication assistance, user data collection, edge computation offloading and remote control services. In UAV-aided MECN, the timeliness of exchange data is a key factor that influences the real-time data-driven decisions at the server-side. Simultaneously, the Age of information (AoI) is also an indicator that reflects the freshness of data in terms of the destination during the communication process. Hence, AoI minimization is a vital goal in the MECN. The most recent work overlooks the possible security issues in the AoI minimization process, especially the revealed channel access attacks (CAAs), which aim to deteriorate network performance from ground to air channels. To overcome this research gap, in this paper, we improve the AoI-oriented channel access problem under CAA from the perspective of game theory. Firstly, a system model with active probability consideration is established to obtain a MECN-based AoI indicator under CAA. Subsequently, by utilizing Ordinary Potential Game (OPG), we formulate the AoI-based channel access optimization problem. Then, to reach the Nash Equilibrium (NE) of the OPG, a learning algorithm called Distributed Channel Access Strategy Determination (DCASD) is proposed to determine the channel access strategies. Finally, we conduct experiments under different parameters to present the better performance of our algorithm as compared with related work.

Malicious code attacks have severely hindered the current development of the Internet technologies. Once the devices are infected with virus, the damages to companies and users are unpredictable. Although researchers have developed malware detection methods, the analysis result still cannot achieve the desired accuracy due to complicated malicious code families and fast-growing variants. In this paper, to solve this problem, we combine Convolutional Neural Networks (CNNs) with Generative Adversarial Networks (GANs) to design an efficient and accurate malware detection method. First, we implement a code visualization method and utilize GAN to generate more samples of malicious code variants in the role of data augmentation. Then, the lightweight AlexNet originated from CNN to classify malware families. Finally, simulation experiments are conducted to evaluate that our CNN plus GAN model can achieve a higher classification accuracy (i.e., 97.78%) compared with some related work.

Due to the emergence of heterogeneous Internet of Medical Things (IoMT) (e.g., wearable health devices, smartwatch monitoring, and automated insulin delivery systems), large volumes of patient data are dispatched to central cloud servers for disease analysis and diagnosis. Although this direct mode brings a lot of convenience for both patients and medical professionals (MPs), the open communication channel between them also incurs several security and privacy issues, such as man-in-the-middle attacks, eavesdropping attacks, and tracking attacks. Based on the unsolved challenges in wireless medical sensor networks (WMSNs), several researchers have proposed various authentication and key agreement (AKA) protocols for this type of healthcare system recently. However, most of these protocols do not perceive physical-layer security and over-centralized server problem in WMSN. In this article, to address these two open problems, we propose a lightweight and reliable authentication protocol for WMSN, which is composed of cutting-edge blockchain technology and physically unclonable functions (PUFs). In addition, a fuzzy extractor scheme is introduced to deal with biometric information. Subsequently, two security evaluation methods are used to prove the high reliability of our proposed scheme. Finally, performance evaluation experiments illustrate that the proposed mutual authentication protocol requires the least computation and communication cost among the compared schemes.

Given the explosive growth of portable devices, mobile crowdsensing (MCS) is becoming an essential approach that fully utilizes pervasive idle resources to accomplish sensing tasks. The traditional MCS relies on the centralized server for task handle is susceptible to a single point of failure. Targeting this security issue, researchers have proposed a series of blockchain-based MCS. However, nodes in the blockchain suffer from high computation cost for data processing. Simultaneously, most blockchain-based MCS systems lack an efficient incentive mechanism for service requesters and workers. In this work, we integrate the smart contract and mobile devices to establish a secure, interactive, and fair blockchain-based MCS system called BSIF. To prevent illegitimate participants, BSIF requests all users to verify their identities using private keys from the registration phase. In the case of worker location privacy leakage, the location-based symmetric key generator is adopted to coordinate a session key for target range worker selection. Besides, we transfer the data evaluation process to the requester side (e.g., a personal computer), reducing computation cost in the blockchain nodes. Due to the homomorphic feature of the Paillier Cryptosystem and common interest, the requester cannot violate the directives from the blockchain. Subsequently, the Stackelberg game is adopted to investigate the participation level of the workers and the fair reward mechanism for the requesters to achieve a dynamic balance. Finally, the security analysis and performance evaluation demonstrate that our BSIF can defend against possible adversaries while significantly cutting overhead and giving participants the utmost incentive.

Since the outbreak of COVID-19 pandemic in 2020, a dramatic loss of human life has occurred and this trend presents an unprecedented challenge to public health, economic systems and social operations. Hence, it is urgent for us to take some countermeasures to restrain and dispel epidemic diffusion to the uttermost. Data freshness plays an inevitable role in timely infestor determination during this process. However, existing works pay little attention to optimizing this indicator in health monitoring. To make up this research gap, in this paper, we propose a mixed game-based Age of Information (AoI) optimization scheme, where the edge-based wireless technologies and AI-empowered diagnostic bots are adopted. Firstly, we establish the system model for Epidemic Prevention and Control Center (EPCC)-based health state monitoring network, where ultimate biosensing data is transmitted from AI bots via edge servers. Then, upon deriving AoI expression with a closed form, the minimization goal between edge servers and bots is specified. Simultaneously, we reformulate the AoI optimization problem from the mixed game viewpoint (i.e., coalition formation game and ordinary potential game), and then propose two algorithms for cooperative order-based bot deployment and stochastic learning-based channel selection. Finally, compared with the typical baselines, the experiment result shows our scheme can reach the lower AoI value for biosensing data transmission under different parameter settings.

Cognitive-inspired Internet of Medical Things (CI-IoMT) combines cognitive science and artificial intelligence to interact with humans and ubiquitous digital environments. The Internet of Things devices generate massive amounts of data and process it with cognitive computing to perform efficient analysis at the edge nodes. Internet of Medical Things (IoMT) uses the said analysis to design smart communication systems to facilitate ubiquitous services. However, the protocols used in IoMT use conventional number theory systems that are vulnerable to quantum-computer attacks. Therefore, an efficient CI-IoMT scheme is required to handle access privacy, preservation, and trust guarantee. This article presents an identity-based seamless privacy preservation (IB-SPP) for CI-IoMT to authorize smart device communications. It is entirely based on fast user authentication to shorten access timing in an emergency situation. The simulation analysis shows that the proposed IB-SPP scheme consumes less response time and minimum data volume than other existing schemes.

Integrating lightweight web application programming interfaces (APIs) into mobile Apps is a promising way for quick and cost-effective development of mobile Apps with desired functions. Web APIs, on the other hand, are created by distinct enterprises or organizations, making it challenging to develop compatible and diverse mobile Apps by combining existing web APIs. It has been demonstrated that this process is an NP-hard problem. In mobile Apps development, it is often necessary to read confidential information, leading to the business privacy leakage of enterprises. Thus, we devise a novel efficient web APIs recommendation (E-WAR) approach based on locality-sensitive hashing for recommending desirable web APIs to developers. Through analyzing industrial enterprises’ expected needs, E-WAR efficiently makes compatible and diverse web APIs recommendations while guaranteeing privacy protection. Finally, extensive experiments on real-world web APIs datasets are conducted. The results show that E-WAR can achieve significant performance improvements over the existing approaches.

Nowadays, the Industrial Internet of Things (IIoT) has remarkably transformed our personal lifestyles and society operations into a novel digital mode, which brings tremendous associations with all walks of life, such as intelligent logistics, smart grid, and smart city. Moreover, with the rapid increase of IIoT devices, a large amount of data is swapped between heterogeneous sensors and devices every moment. This trend increases the risk of eavesdropping and hijacking attacks in communication channels, so maintaining data privacy and security becomes two notable concerns at present. Recently, based on the mechanism of the Schnorr signature, a more secure and lightweight certificateless signature (CLS) protocol is popular for the resource-constrained IIoT protocol design. Nevertheless, we found most of the existing CLS schemes are susceptible to several common security weaknesses such as man-in-the-middle attacks, key generation center compromised attacks, and distributed denial of service attacks. To tackle the challenges mentioned previously, in this article, we propose a novel pairing-free certificateless scheme that utilizes the state-of-the-art blockchain technique and smart contract to construct a novel reliable and efficient CLS scheme. Then, we simulate the Type-I and Type-II adversaries to verify the trustworthiness of our scheme. Security analysis as well as performance evaluation outcomes prove that our design can hold more reliable security assurance with less computation cost (i.e., reduced by around 40.0% at most) and communication cost (i.e., reduced by around 94.7% at most) than other related schemes.

In the cloud-assisted Internet of things (IoT), most of the data are sent to the cloud for storage and processing. Data privacy and security are extreme concerns since retrieving data from the cloud will yield privacy disclosure risk due to the cloud’s openness. To this end, this article proposes PERT, a privacy-enhanced retrieval technology for cloud-assisted IoT. This architecture is designed through an implicit index maintained by edge servers and a hierarchical retrieval model that preserves data privacy by hiding the information of data transmission between the cloud and the edge servers. For the hierarchical retrieval model, we designed a data partition strategy. The edge server stores partial data. In this way, data privacy is preserved since the attacker must get the data maintained by both cloud and edge servers. The detailed performance analysis and extensive experiments have displayed the effectiveness of the technology for data privacy. It is tested that the architecture can efficiently and securely retrieve the stored data while the computation cost is reduced through operation downsizing. Compared with the benchmark cloud encrypted storage model, the time cost of this method is significantly reduced when the number of users is relatively large.

The blockchain-enabled internet of medical things (IoMT) is an emerging paradigm that could provide strong trust establishment and ensure the traceability of data sharing in the IoMT networks. One of the fundamental building blocks for Blockchain is Elliptic Curve Digital Signature Algorithm (ECDSA). Nevertheless, when processing a large number of transactions, the verification of multiple signatures will incur cumbersome overhead to the nodes in Blockchain. Although batch verification is able to provide a promising approach that verifies multiple signatures simultaneously and efficiently, the upper bound of batch size is limited to small-scale and the efficiency will drop rapidly as the batch size grows in the state-of-the-art ECDSA batch schemes. Meanwhile, most of the existing researches only focus on improving the efficiency of batch verification algorithms in various cryptosystem while ignoring the identification of invalid signatures, which could cause severe performance degradation when the batch verification fails. Motivated by these observations, this paper proposes an efficient and large-scale batch verification scheme with group testing technology based on ECDSA. The application of the presented protocols in Bitcoin and Hyperledger Fabric has been analyzed as supportive and effective. When the batch verification returns a false result, we utilize group testing technology to improve the efficiency of identifying invalid signatures. Comprehensive simulation results demonstrate that our protocol outperforms the related ECDSA batch verification schemes.

The timeless and efficient vehicle data transmission are the two common requirements for the Internet of Vehicles (IoV), especially the Unnamed Aircraft Vehicle (UAV)-aided Vehicular Edge Computing (VEC) network. Moreover, since the Age of Information (AoI) performance greatly influences these two indicators, data quality should be guaranteed in vehicle communication. However, few researchers pay attention to the AoI performance optimization issue regarding wireless resource constraint, transmission interference, and vehicle cooperation in recent years. To close this research gap, we propose an AoI-oriented channel access strategy in the UAV-aided VEC network from the game theory viewpoint. Firstly, the UAV-aided VEC network model and edge computing-based AoI expression are established and derived in the closed form, respectively. Subsequently, we transform the AoI minimization problem into an AoI-based channel access issue from the game theory viewpoint. Moreover, the stochastic learning-based algorithm is proposed to find the Nash Equilibrium (NE) solution of the formulated problem. Finally, simulation results evaluate the correctness and effectiveness of the proposed algorithms, where our scheme can achieve the better AoI value compared with baselines.

Federated learning can collaboratively train a global model without gathering clients’ private data. Many works focus on reducing communication cost by designing kinds of client selection method or averaging algorithm. But they all consider whether the client will participant or not, and the training time could not be reduced as data size of update for each client is not changed. We proposed COFEL, a novel federated learning system which can both reduce the communication time by layer-based parameter selection and enhance the privacy protection by applying local differential privacy mechanism on the selected parameters. We present COFEL-AVG algorithm for global aggregation and designed layer-based parameter selection method which can select the valuable parameters for global aggregation to optimize the communication and training process. And it can reduce the update data size as only selected part will be transferred. We compared with traditional federated learning system and CMFL which also applies a parameter selection method but model-based and performed experiments on MNIST, Fashion-MNIST and CIFAR-10 to verify the effectiveness of COFEL. The results denoted that it can improve at most 22.8% accuracy compared with CMFL on CIFAR-10 and reduce around 20% and 48% training time to reach an accuracy of 0.85 compared with traditional FL and CMFL on Fashion-MNIST dataset.

Due to intelligent electronic management, the smart grid has recently played a significant role in modern energy infrastructure. However, along with widespread deployment of the smart grid, many potential security threats (e.g., impersonation attack, replay attack, man-in-the-middle attack) rise to the surface. To defend against these possible attacks, numerous cryptography-based authentication schemes have been proposed for the smart grid. Most of the schemes investigate the secret key distribution problem, but the requirement of decentralized registration authority is neglected. In addition, over-complicated cryptographic primitives also strengthen the burden of authentication system. In contrast with previous researches, our proposed BlockSLAP utilizes cutting-edge blockchain technology as well as smart contract to decentralize the registration authority and reduce the interaction process to 2 steps. Moreover, our protocol is proved secure under computational hard assumption and informal security analysis. Finally, experimental results show that smart grid authentication performance in our protocol has been improved compared to the other existing ECC-related schemes.