Automatic sleep staging plays a vital role in assessing sleep quality and diagnosing sleep disorders. Most existing methods rely heavily on long and continuous EEG recordings, which poses significant challenges for data acquisition in resource-constrained systems, such as wearable or home-based monitoring systems. In this paper, we propose the task of resource-efficient sleep staging, which aims to reduce the amount of signal collected per sleep epoch while maintaining reliable classification performance. To solve this task, we adopt the masking and prompt learning strategy and propose a novel framework called Mask-Aware Sleep Staging (MASS). Specifically, we design a multi-level masking strategy to promote effective feature modeling under partial and irregular observations. To mitigate the loss of contextual information introduced by masking, we further propose a hierarchical prompt learning mechanism that aggregates unmasked data into a global prompt, serving as a semantic anchor for guiding both patch-level and epoch-level feature modeling. MASS is evalutaed on four datasets, demonstrating state-of-the-art performance, especially when the amount of data is very limited. This result highlights its potential for efficient and scalable deployment in real-world low-resource sleep monitoring environments.

Jia Liu, Jiaxin Luo, Weiwen Xu, Jonathan M. Garibaldi, Xiao-Kun Wu, Yixue Hao, Min Chen. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

With the evolution of sixth generation (6G) technologies and Internet of Things (IoT), base stations and IoT devices are deployed densely to achieve the ultra-high data rate, resulting in the scarcity of spectrum resource. To tackle it, we study a cell-free symbiotic radio network (CF-SRN) that consists of the cell-free network (CFN) and IoT network, and includes multiple access points (APs), multiple backscatter devices (BDs), and a single receiver. APs collaboratively transmit primary radio frequency (RF) signals to the receiver, and BDs split the energy of primary RF signals to perform backscatter communication, and energy harvesting. Existing works focus on the SRN with commensal symbiotic radio (CSR) or parasitic symbiotic radio (PSR) setup, while we design a hybrid CSR-PSR setup to balance the tradeoff between primary communication and backscatter communication in the CF-SRN. Based on the design, we formulate the sum backscatter throughput maximization problem by optimizing the time allocation vector, beamforming vectors of APs and BDs, and reflection coefficients of BDs, subject to the minimum sum primary throughput constraint. Due to the coupling relationship among high-dimensional variables, we decompose the formulated problem into time allocation optimization (TAO) subproblem, beamforming optimization (BO) subproblem, and reflection coefficient optimization (RCO) subproblem. For TAO subproblem, we use a linear programming method to obtain the optimal solution. For BO subproblem and RCO subproblem, we propose a block coordinate descent-based semi-definite relaxation and successive convex approximation (BSS) algorithm. Simulation results validate the superiority of the BSS algorithm and hybrid CSR-PSR setup.

Channel state information (CSI) is known to be crucial for both enhancing the transmission performance and ensuring physical-layer security (PLS) in wireless communication systems. To estimate a channel’s CSI, the transmitter (Tx) typically broadcasts a predetermined pilot signal, then the receiver (Rx) computes the channel coefficients based on the received pilot signal and returns the estimated CSI to the Tx. Most, if not all, of existing communication algorithms simply assume that the fed-back CSI is reliable/secure. However, in practice, a malicious terminal may send falsified CSI to the infrastructure, thus compromising the throughput and/or security of the communication over the channel. Although some researchers have already identified this vulnerability, demonstrated the feasibility of the CSI-forgery attacks, and designed countermeasures thereof, their methods either (i) are tailored to specific types of attacks, thus lacking generality, or (ii) require modifications to the pilot sequence and hence the protocol. To counter the CSI-forgery attacks and remove/mitigate the deficiencies of existing counter-measures, we first develop a comprehensive CSI-forgery model that can subsume the existing CSI-forgery attacks as special instances to facilitate the design of general countermeasures. Then, we propose a novel approach, called SecCSI, to detect potential CSI-forgery activities and identify their initiators using reconfigurable intelligent surface (RIS). SecCSI leverages the RIS to secretly and dynamically modify the wireless environment transparently to the receiver (Rx) in which the pilot signal is transmitted. The infrastructure can, therefore, detect any attempted manipulation of CSI by appropriately configuring the reflection coefficient matrix of the RIS, transmitting the pilot signal, and analyzing all CSI feedback. SecCSI can serve as a guard module for existing communication systems that simply accept the fed-back CSI without checking its trustworthiness. Our theoretical analysis, experimental and numerical evaluations have shown SecCSI to effectively detect the CSI-forgery attacks and identify the attacker.

This paper investigates a dynamic heterogeneous mobile edge computing network (HMECN), where mobile devices (MDs) could offload their full tasks to a small base station (SBS) directly or the macro base station (MBS) in direct or relay mode. As age of information (AoI) is a comprehensive and accurate metric to capture the freshness of computation results, we formulate a long-term weighted sum AoI (LWSA) minimization problem in the HMECN by jointly optimizing the offloading decisions of MDs as well as the bandwidth and computation resource allocation of all base stations, subject to energy, delay and peak AoI constraints. To address the formulated non-convex mixed integer nonlinear programming problem, we decompose it into the offloading decision optimization (ODO) top-problem and the resource allocation optimization (RAO) sub-problem. Based on the decomposition, we propose a federated learning (FL)-assisted hybrid DRL and convex approach that is comprised of a safe multi-agent DRL algorithm, convex optimization and FL. The ODO top-problem is solved by the safe multi-agent DRL algorithm, which strictly ensures that the actions of each agent do not exceed its energy constraint and then paves the way for using convex optimization to solve the RAO sub-problem. FL is used to alleviate the training instability problem aggravated by multi agent settings via breaking the limitation of partial knowledge for each individual agent. Simulation results demonstrate the superiority of the proposed approach in terms of the LWSA, convergence, scalability and robustness in dynamic environments.

Space-air-ground integrated network (SAGIN) represents a pivotal architecture for the future evolution of global mobile communications. However, its inherent high dynamics and stochastic nature pose significant challenges to conventional routing mechanisms. Moreover, the vast spatial-scale openness of SAGIN makes it particularly vulnerable to eavesdropping attacks. This paper presents a novel sleeping multi-armed bandit (SMAB) framework, designed to enable secure routing in SAGIN. Specifically, we first establish channel models for all types of links in SAGIN. Then, we theoretically analyze the statistical properties of secrecy capacity and end-to-end (E2E) delay for message transmission over arbitrary routes, and formulate the secure routing problem to maximize cumulative secure transmission throughput under the delay constraint. The uncertainty in the network state of SAGIN, along with the complexity of the optimization objective and constraint (non-convex, non-linear, and coupled), renders the solution to the secure routing problem highly intractable. To this end, we leverage the MAB model to transform the secure routing problem into a budget-constrained arm-pulling problem and introduce the “sleeping” mode to capture route unavailability due to intermittent link failures. To effectively balance route exploration and exploitation, we further apply the upper confidence bound (UCB) method to design the SMAB-based secure routing algorithm (SMAB-SR), and derive its regret upper bound theoretically. Finally, extensive simulations verify that the SMAB-SR algorithm exhibits significant advantages in E2E secure transmission throughput compared to benchmarks and can maintain highly effective across various SAGIN configurations.

The deep learning-based semantic communication system (DeepSC) is designed to improve the efficiency and accuracy of information transmission, by leveraging joint source-channel coding techniques to extract relevant semantic features. However, existing research on attack methods targeting DeepSC has primarily focused on text and image domains, leaving the speech domain largely unexplored. To this end, this paper proposes Iterative Semantic Gradient Update (ISGU), a novel approach for crafting physical layer adversarial attacks on DeepSC for speech transmission (DeepSC-ST). Specifically, we introduce a joint loss that combines the semantic similarity loss with Connectionist Temporal Classification loss to expedite the generation process of targeted attacks against the DeepSC-ST. In addition, we design an algorithm to generate adversarial examples, enhancing their imperceptibility by meticulously controlling the perturbation power added to the input speech. Extensive experiments indicate that ISGU is capable of rapidly generating highly covert adversarial examples, with a notably high attack success rate.

Semantic communication, an emerging AI-driven communication paradigm, offers great potential for multimodal data delivery in space-ground integrated networks (SGINs). However, the dynamic nature of SGINs presents severe challenges for path selection, making it difficult to ensure the quality of service (QoS) at the semantic level. To this end, we propose in this paper a novel path selection scheme in space-ground multimodal semantic communication networks based on the sleeping multi-armed bandit (MAB) approach. Specifically, we first model the approximate semantic entropy and semantic rate, formulating an optimal path selection problem that integrates link state information and semantic data transmission volume. Then, we convert the path selection problem into a sleeping MAB problem and meticulously design an upper confidence bound (UCB)-based algorithm to solve it, called Periodic Probability Sleeping Path Selection (PPSPS), which copes with the dynamic feature of SGINs. We further theoretically verify the bounded regret of the PPSPS algorithm, indicating that it can ensure good semantic communication QoS. Simulation results demonstrate the superiority of the proposed path selection scheme compared to traditional reinforcement learning methods.

Integrated sensing and communication (ISAC) has emerged as a cornerstone technology for achieving seamless coverage in next-generation networks. Moreover, the advent of extremely large-scale multiple-input-multiple-output significantly enhances ISAC’s potential, facilitating innovative applications in near-field (NF) ISAC. Nonetheless, ISAC networks face numerous challenges, with power consumption being one of the most critical concerns. To address this issue, we propose a novel low-power beamforming approach within the coordinated multipoint (CoMP) ISAC framework. Specifically, our approach involves orchestrating base station (BS) cooperation for seamless coverage and synergistically augmenting the sensing beam with the communication beam to reduce power consumption. By utilizing the NF communication theory, we accurately model signal propagation dynamics and formulate a beamforming optimization problem aimed at minimizing transmit power while adhering to transmission rate and object detection constraints, which is a nonconvex second-order cone programming (SOCP) problem. To overcome the nonconvexity of this problem, we propose a successive convex approximation (SCA)-based beamforming optimization algorithm that ensures convergence. Moreover, we propose a fast-converging algorithm that leverages the unique characteristics of both communication channel and sensing array response vector. Simulation results validate the effectiveness of the proposed scheme for the power minimization problem and yield essential design insights.

Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Things (IoT) components enable effective artificial intelligence (AI)-based models to be built. Beyond ground data sources, autonomous aerial vehicles (AAVs)-based service providers for data collection and AI model training, i.e., Drones-as-a-Service (DaaS), have become increasingly popular in recent years. However, the stringent regulations governing data privacy potentially impede data sharing across independently owned AAVs. To this end, we propose in this article a federated learning (FL)-based architecture that enables privacy-preserving collaborative machine learning across a federation of independent DaaS providers for the development of IoT applications. Specifically, this work introduces a novel incentive mechanism based on the hedonic coalition formation game to enhance the sustainable efficiency and stability of the FL system. By establishing tailored operational rules and functions, the proposed mechanism enables IoT sensing nodes to autonomously form optimal coalitions with AAVs, thereby ensuring robust collaboration. To deal with incentive mismatches and information asymmetry, we leverage the contract theory and propose a self-disclosure mechanism that guarantees truthful reporting of AAV capabilities while optimizing the global model owner’s profits. The performance-based AAV type is also defined to offer a practical measure for heterogeneous AAVs and serve as the foundation for fair and effective contract design. Simulation results validate the superiority of the proposed approach, demonstrating significant improvements in utility optimization and system stability compared to existing benchmarks.

In mobile edge networks, federated learning (FL) has garnered substantial attention as a distributed machine learning framework with significant advantages for protecting user privacy. Due to the limited resources of wireless bandwidth, such FL-based applications are quite susceptible to Distributed Denial-of-Service (DDoS) attacks. Prior solutions either rely on centralized mechanisms that require complete information about all participants or are customized to specific systems. However, these solutions are either obsolete or ineffective given the new properties of FL. In this work, we first formulate a DDoS mitigation problem on bandwidth allocation for FL within mobile edge networks. Considering interactions between various network components and users, we propose anEvolutionaryGame andDouble-sidedAuction-based framework, termed EGDA, which consists of EG-based and DA-based mechanisms for user-bandwidth allocation (UBA) and server-bandwidth allocation (SBA), respectively. Specifically, to address DDoS attacks on UBA, we design an EG-based approach with minimum latency for FL under limited information. The proposed EG-based allocation algorithm is proven to be stable and achieve the evolutionary equilibrium. To mitigate DDoS attacks on SBA, we study an approach of DA with social welfare maximization while protecting the privacy of participants. Then, an iterative DA-based allocation algorithm is developed to be convergent and satisfy desirable economic properties. Extensive evaluation demonstrates that EGDA mitigates DDoS attacks effectively and efficiently.

Warden collusion represents a hazardous threat to wireless covert communication, where wardens can combine their observations to perform a more aggressive detection attack. This paper investigates the impact of warden collusion on covert communication in a multi-antenna wireless network consisting of one source, one destination, multiple wardens and interferers. By employing the techniques of Laplace Transform and Cauchy Integral Theorem, we first establish a framework to model the aggregate interference distribution (AID) for covert communication in the network under the typical additive white Gaussian noise (AWGN) and Rayleigh fading channels. Based on the AID results, we then develop theoretical models to reveal the inherent relationship between the collusion intensity and fundamental communication metrics in terms of the covert outage probability, connection outage probability and covert throughput. With the help of these models, we further explore the covert throughput optimization problems and present extensive numerical results to illustrate the impact of warden collusion on the covert throughput under both channel models.

With the rapid development of wireless communication technologies, Internet of Things (IoT) has emerged as one of the most important application scenarios. Due to the high density of IoT devices and the limited spectrum resources, along with the miniaturization and sustainability requirements of these devices, the development of low-cost interference management (IM) methods has become crucial for widespread use of IoT. Interference has long been known to harm network performance. Since a desired signal can be distorted by interference, and thus be incorrectly decoded at the destination, we argue that interference can also be transformed intentionally to extract the desired data from interfering signal(s). Based on this observation, we proposeInterference ReCycling(IRC) for the IoT. Under IRC, a recycling signal is generated using the interference a victim IoT device is subjected to, and then sent by the device’s associated gateway. Under the influence of the recycling signal, the desired data of the interfered/victim IoT transmission-pair can be recovered from the interference at the IoT device. We also show that the interfered user’s spectral efficiency (SE) with IRC can be optimized further by properly distributing the transmit power used for the desired signal’s transmission and the recycling signal. We validate the feasibility of IRC by implementing the method on the Universal Software Radio Peripheral (USRP) platform. Our theoretical analysis, experimental and numerical evaluation have shown that the proposed IRC can fully exploit interference, and hence can significantly improve the SE of the victim IoT device compared to other existing IM methods.

Due to the broadcast nature of wireless communications, users' data transmitted wirelessly is susceptible to security/privacy threats. The conventional modulation scheme “loads” all of the user's transmitted information onto a physical signal. Then, as long as an adversary overhears and processes the signal, s/he may access the user's information, hence breaching communication privacy. To counter this threat, we proposeIRS-DMSC, aDistributed Modulation based Secure Communication(DMSC) scheme by exploitingIntelligent Reflecting Surface(IRS). Under IRS-DMSC, two sub-signals are employed to realize legitimate data transmission. Of these two signals, one is directly generated by the legitimate transmitter (Tx), while the other is obtained by modulating the phase of the direct signal and then reflecting it at the IRS in an indirect way. Both the direct and indirect signal components superimpose on each other at the legitimate receiver (Rx) to produce a waveform identical to that obtained under traditional centralized modulation (CM), so that the legitimate Rx can employ the conventional demodulation method to recover the desired data from the received signal. IRS-DMSC incorporates the characteristics of wireless channels into the modulation process, and hence can fully exploit the randomness of wireless channels to enhance transmission secrecy. However, due to the distribution and randomization of legitimate transmission, it becomes difficult or even impossible for an eavesdropper to wiretap the legitimate user's information. Furthermore, in order to address the problem of decoding error incurred by the difference of two physical channels' fading, we developRelative Phase Calibration(RPC) andConstellation Point Calibration(CPC), to improve decoding correctness at the legitimate Rx. Our method design, experiment, and simulation have shown the proposed IRS-DMSC to cripple the eavesdropper while maintaining good performance of the legitimate transmission.

With the rapid advancement of wireless communication technologies, interference has become a key impediment to the improvement of wireless data transmission performance. Traditional interference management (IM) suppresses or adjusts interference at the cost of additional communication resourceswithoutexploiting interference effectively. Especially when the interference is strong, simply eliminating it may be inefficient and costly. Moreover, wirelessly transmitted data is susceptible to threats such as eavesdropping, which also necessitates a thorough investigation. To address these issues cooperatively, we proposeOpportunistic Parasitic Communication with Asymmetric Demodulation(OPC-AD). In particular, we consider the interference experienced by the intended/target communication (i.e., parasitic) receiver (Rx) as the host signal. The target communication constructs a selection signal carrying parasitic indication information based on the data it intends to send and the data decoded by its Rx using asymmetric demodulation from the host signal, and then sends it to its Rx. This signal is used to instruct the parasitic Rx to extract the desired information from the host signal. OPC-AD allows for the exploitation of the interference (i.e., host signal) for data transmission to an interfered Rx. Using AD can also ensure the privacy of the host communication. Since the parasitic communication is concealed within the host signal, eavesdroppers cannot compromise the confidentiality of the parasitic transmission without precisely decoding the selection signal. Furthermore, considering more practical situations, such as non-zero delay/phase difference between the host signal and the selection signal, the implementation of the proposed method under various modulation schemes, and the enhancement of the probability of successful parasitism, we extend the OPC-AD design to cover a broader range of realistic scenarios. Our experimental results validate the applicability of OPC-AD, while our in-depth simulations demonstrate that parasitic communication can effectively thwart eavesdropping and achieve higher spectral efficiency (SE) than other existing IM methods, particularly in strong interference environments.

Distributed Vehicle Sharing System (DVSS) leverages emerging technologies such as blockchain to create a secure, transparent, and efficient platform for sharing vehicles. In such a system, both efficient matching of users with available vehicles and optimal pricing mechanisms play crucial roles in maximizing system revenue. However, most existing schemes utilize user-to-vehicle (two-sided) matching and pricing, which are unrealistic for DVSS due to the lack of participation of service providers. To address this issue, we propose in this paper a novel Three-sided stable Matching with an optimal Pricing (TRIMP) scheme. First, to achieve maximum utilities for all three parties simultaneously, we formulate the optimal policy and pricing problem as a three-stage Stackelberg game and derive its equilibrium points accordingly. Second, relying on these solutions from the Stackelberg game, we construct a three-sided cyclic matching for DVSS. Third, as the existence of such a matching is NP-complete, we design a specific vehicle sharing algorithm to realize stable matching. Extensive experiments demonstrate the effectiveness of our TRIMP scheme, which optimizes the matching process and ensures efficient resource allocation, leading to a more stable and well-functioning decentralized vehicle sharing ecosystem.

The energy harvesting D2D-enabled cellular network (EH-DCN) has emerged as a promising approach to address the issues of energy supply and spectrum utilization. Most of existing works mainly focus on the throughput, while the information freshness, which is critical to the time-sensitive applications, has been rarely explored. Considering above facts, we aim to develop an optimal mode selection and resource allocation (MSRA) policy that maximizes the long-term overall throughput of a time-varying dynamic EH-DCN, subject to an age of information (AoI) constraint. As the MSRA policy involves both continuous variables (i.e., bandwidth, power, and time allocations) and discrete variables (i.e., mode selection and channel allocation), the optimization problem is proved to be nonconvex and NP-hard. To solve the nonconvex NP-hard problem, we exploit a deep reinforcement learning (DRL) approach, called MSRA twin delayed deep deterministic policy gradient (MSRA-TD3). The MSRA-TD3 employs a double critic network structure to better fit the reward function, and could effectively mitigate the overestimation of Q-value in deep deterministic policy gradient (DDPG), which is a classical DRL algorithm. It is worth noting that in the design of the MSRA-TD3, we use the throughput of user equipments (UEs) at the previous time slot as a state to bypass the channel state information estimation resulting from the time-varying dynamic environment, and take the weights of throughput and AoI penalty into the reward function to evaluate two performance. Simulations demonstrate that the established MSRA-TD3 algorithm achieves better performance in terms of throughput and AoI than comparison DRL algorithms.

With the increasing number of wireless devices connecting to networks and sharing the same spectrum resources, interference has become a significant obstacle to improving network performance. Existing interference management (IM) methods treat interference as a negative factor and mainly focus on suppressing or eliminating its impact on desired transmissions. However, this often comes at the cost of consuming communication resources. Therefore, design of low-cost IM method that can exploit interference is of research importance. To achieve this goal, we leverage the cost-effectiveness and adaptable deployment capabilities of Intelligent Reflecting Surface (IRS) to propose an IRS Empowered Interference Utilization (IRS-IU) method to realize efficient desired data transmission. By appropriately designing the reflection coefficient of the IRS, a phase shift is introduced to the incident interference, allowing the reflecting interference to interact with its direct counterpart at the interfered receiver (Rx). As a result, the interfered Rx can retrieve its desired data from the mixed interference. In this way, IRS-IU can make full use of the interference to enhance the desired data transmission. Our theoretical analysis and simulation results show that the proposed method can significantly improve the spectral efficiency (SE) of the interfered communication-pair.

With the rapid development of wireless technologies, future communication networks should not only enhance data transmission but also provide accurate and reliable location services. In complex indoor environment, traditional positioning methods encounter challenges related to accuracy and cost due to unpredictable attenuation, multipath interference, and other factors. This paper designs a Machine Learning Enhanced Indoor Positioning method that utilizes RIS-aided Channel Configuration and Analysis, called RCCA-MLEIP, for large-scale warehouse applications. This method consists of two stages: the RIS-aided Channel Configuration and Analysis (RCCA) stage, and the Machine Learning Enhanced Indoor Positioning (MLEIP) stage. In the RCCA stage, multiple RISs are deployed to create reflection paths associated with product tags. The phase coefficients of the RISs are adjusted multiple times, and the mixed signals observed by the reader after each adjustment are recorded. Based on these mixed signals, a system of equations is established to resolve both the phase fading and amplitude fading of each reflection path. In the MLEIP stage, a feature dataset is created using simulation methods, consisting of the phase fading and amplitude fading of multiple reference tags analyzed in the RCCA stage. We use the coordinates of the reference tags as the label dataset to train a Back Propagation (BP) neural network. The trained model can then output the coordinates of a target product tag based on the channel fading features associated with that tag, which are solved/obtained in the RCCA stage. The proposed RCCA-MLEIP utilizes RIS to create a multipath environment for positioning, effectively reducing the hardware costs of the positioning system. Moreover, employing machine learning techniques to estimate the target position can enhance both accuracy and response speed.

Air-ground integrated networks (AGINs) are promising to provide wide-coverage, high-capacity, and low-latency communication services, and thus have been attracting increasing attention from industry and academia recently. However, the open and dynamic nature of AGINs makes them vulnerable to eavesdropping attacks, posing a major challenge in ensuring end-to-end information transmission security. To this end, we propose in this paper a secure routing scheme in AGINs based on the multi-armed bandit (MAB) approach. Specifically, we first model the secrecy transmission performance for the ground-to-ground links and ground-to-air links. Based on this, we then formulate the end-to-end secure route selection problem and convert it into a budget-constrained MAB problem, where each arm is associated with a corresponding reward and cost. We further design a Secure Route Upper Confidence Bound (SRUCB) algorithm to solve the MAB problem, which copes with the scenario where the locations of eavesdroppers and jammers are unknown, and can be proven to have a bounded regret. Numerical results demonstrate the superiority of the proposed routing scheme compared to several online learning algorithms.

Abstract Due to the ever‐growing applications and services of the Internet of Things (IoT), designing energy‐efficient and spectral‐efficient transmission schemes to support IoT devices for the 6G space–air–ground integrated networks becomes much more challenging. Fortunately, energy harvesting (EH) and cognitive radio (CR) technologies have been proposed to alleviate these challenges. Inspired by this fact, this paper studies the issue of spectrum reuse in terms of spectrum utilization efficiency (SUE) in the energy harvesting cognitive radio network (EH‐CRN), where multiple primary transceiver pairs, one multi‐antenna secondary transmitter (ST), and one secondary base station (SBS) coexist. To characterize the impact of small‐scale fading and improve the SUE of the EH‐CRN with perfect spectrum sensing (SS), an adaptive scheme concerning SS, channel selection, EH, and data transmission (SCED) scheme are proposed, where the ST selects the channels for SS based on the residual energy, and adjusts the duration of EH and data transmission with respect to the sensing results. Then the Markov decision process problem of SUE is formulated, which is challenging due to the infinite system space and action space. To tackle the Markov decision process problem, the system space and action space are discreted, and divide the ST into the energy‐limited case and energy‐sufficient case according to specific energy condition. Moreover, theoretical results are extended to the EH‐CRN with imperfect SS. Numerical results show that the SUE under the SCED scheme in perfect SS and imperfect SS scenarios is better than that under other schemes.

In recent years, significant breakthroughs have been achieved in smart fabric technology within the healthcare sector, providing an impetus for the smart integration of wearable devices and equipment in medical applications. However, the tight coupling between fabric hardware devices and software solutions, tailored for various scenarios, has led to inefficient utilization of hardware resources and led to challenges for device upgrades and iterations. This paper focuses on the virtualization technology of smart fabric hardware resources and introduces a novel approach, termed “Big Fiber Slicing”. First, we outline the design of novel fiber devices customized for two major application scenarios: health monitoring and protection. Subsequently, we delve into the process of partitioning hardware resources into multiple “fiber slices” to better meet the unique requirements of various application scenarios and services. Next, we built a smart fabric platform, combined with 5 real multi-modal applications with different preferences, to verify the performance of the system when resources are limited and demand changes dynamically. Lastly, we explore the potential challenges that smart fabric technology may encounter in future application scenarios and provide insights into the future direction of this field.

As a vital metric of information timeliness, age of information (AoI) is important for real-time applications in Internet of Things (IoT), such as health monitoring. To satisfy these requirements, we study a wireless-powered IoT (WPIoT), where a static hybrid access point (HAP) coordinates the wireless energy transfer to mobile IoT nodes, and mobile IoT nodes transmit data to the HAP or static IoT nodes. We minimize the AoI of mobile IoT nodes by optimizing the selection of the HAP or static IoT node for transmission, the channel selection, the duration of data transmission, and the transmit power, and prove the AoI minimization problem as NP-hard. To tackle it, we propose a deep deterministic policy gradient (DDPG)-based distributed multi-node resource allocation (DDMRA) algorithm, which combines the advantages of distributed algorithms and centralized algorithms, and combines the selection of discrete actions in the DQN algorithm into the DDPG algorithm. In the DDMRA algorithm, mobile IoT nodes save the energy consumption of transmitting state information to the HAP. Numerical results validate the superior performance of the DDMRA algorithm compared with baseline algorithms.

Benefiting from the booming of Big Data and artificial intelligence (AI) technologies, data-as-a-service is gradually transforming into knowledge-as-a-service. Extracting knowledge from massive raw data is becoming a popular paradigm to save network resources and improve efficiency, and establishing knowledge markets is receiving increasing attention from academia and industry. In this paper, we propose a one-stop knowledge acquisition ecosystem termed BWKA that covers the whole process from upper-layer knowledge trading to underlying knowledge generation. In the knowledge trading process, the knowledge-as-a-service platform (KSP) is the buyer and publishes knowledge demands to multiple local knowledge sellers (LKSs). In the knowledge generation process, each LKS aggregates data from its sensors and then trains data into knowledge according to the KSP's requirements. We resort to blockchain technology and provide a series of tailored operating rules and functions to protect the truthfulness of data gathering and the fairness of knowledge trading. In addition, we introduce incentive mechanisms to stimulate selfish and rational entities in the BWKA ecosystem to participate in knowledge acquisition. To analyze the strategic interactions among entities theoretically, we develop a nested hierarchical game model, where the upper-layer knowledge trading is evaluated based on the Contract Theory, and the lower-layer knowledge generation is formulated as a two-stage Stackelberg game. By solving the nested hierarchical game in a backward inductive way, we identify the optimal strategy for each entity in closed form. Experiments on the Ethereum blockchain and simulation results demonstrate the practical operability and outstanding performance of the BWKA ecosystem.

With the rapid development of wireless communication technologies, interference management (IM) and security/privacy in data transmission have become critically important. On one hand, due to the broadcast nature of wireless medium, the interference superimposed on the desired signal can destroy the integrity of data transmission. On the other hand, malicious receivers (Rxs) may eavesdrop a legitimate user’s transmission and thus breach the confidentiality of communication. To counter these threats, we propose a novel encoding method, called immunizing coding (iCoding), which handles both IM and physical-layer security simultaneously. By exploiting both channel state information (CSI) and data carried in the interference, an iCoded signal is generated and sent by the legitimate transmitter (Tx). The iCoded signal interacts with the interference at the desired/legitimate Rx, so that the intended data can be recovered without the influence of disturbance, i.e., immunity to interference. In addition, since the data carried in the iCoded signal which is obtained via encoding the desired data and interference cooperatively, is different from the original desired data, the eavesdropper cannot access unauthorized information by wiretapping the desired signal, thus achieving immunity to eavesdropping. Our theoretical analysis, experimental and numerical evaluation have shown iCoding to effectively manage interference while preventing potential eavesdropping, hence enhancing the legitimate user’s transmission and secrecy thereof.

With the rapid deployment and wide use of mobile services and applications, more and more sensitive user information is being transmitted wirelessly. Due to the broadcast nature of wireless transmissions, they are exposed to all surrounding entities and thus vulnerable to eavesdropping. To counter this vulnerability, we propose a new physical-layer secure transmission scheme, called DDM-Sec, based on decomposed and distributed modulation (DDM). We show that a high-order modulation can be decomposed into multiple quadrature phase shift keying (QPSK) modulations, each of which can be further represented by two mutually orthogonal binary phase shift keying (BPSK) modulations. Therefore, traditional modulation can be realized by two cooperative transmitters (Txs), each generating a BPSK signal, in a distributed manner. The legitimate receiver (Rx) can decode the desired/intended information from the mixed two received BPSK signals while preventing the eavesdropper from accessing the legitimate user's information. DDM-Sec can effectively exploit the randomness of wireless channels to secure data transmission, enrich the spatial signatures of the legitimate user's transmission by employing two cooperative Txs, and then distribute the user's information to two transmissions so that none of the decomposed signals alone carry the legitimate user's full information. Moreover, due to random deployment of the two Txs and Rx, delay difference of the two transmissions is introduced. This can be further utilized to make eavesdropping difficult. Our theoretical analysis and simulation have shown that DDM-Sec can effectively prevent the eavesdropping, and hence guarantee the secrecy of the legitimate user's data transmission.

Mobile crowdsensing (MCS) has emerged as a popular and promising paradigm for solving challenging problems by utilizing collective wisdom and resources. However, the system architecture and operational rules for MCS have not been well-defined, and obtaining accurate and reliable results from conflicting data collected by workers is difficult due to discrepancies in sensor quality and privacy protection requirements. In this paper, we combine the methodologies of Dynamic Truth Discovery (DTD), Combinatorial Multi-Armed Bandit (CMAB), and Multi-Attribute Reverse Auction to develop a novel MCS ecosystem, with the objective of maximizing the sensing accuracy-aware utility under the budget constraint. We first establish the data collection model by jointly considering the task completion duration as well as the deviation caused by both endogenous errors and privacy protection-oriented injected noise. Then, we theoretically evaluate the accuracy of truth discovery and quantify the contribution of each worker to MCS to form the worker selection criterion. As the qualities of workers are initially unknown, the platform faces the exploration-exploitation dilemma. Therefore, we apply CMAB to transform the worker recruitment problem into a combinatorial arm-pulling problem and elaborately design an Upper Confidence Bound (UCB) algorithm to achieve a desirable exploration-exploitation tradeoff. Moreover, we design an auction-based payment method for the platform, stimulating workers to provide their quoted price honestly while enabling individual rationality. Extensive simulations and comparison results demonstrate the feasibility and effectiveness of our proposed MCS ecosystem.

Immersive service caching, based on the intelligent edge cloud, can meet delay-sensitive service requirements. Although numerous service caching solutions for edge clouds have been designed, they have not been well explored. Moreover, to the best of our knowledge, there is no work to consider the immersive service caching scheme under the supply of renewable energy. In this article, we investigate the service caching problem under the renewable energy supply to minimize service latency while making full use of renewable energy. Specifically, we formulate the service caching and renewable energy harvesting problem, which considers the dynamic renewable energy, unknown service requests, and limited capacity of the edge cloud. To solve this problem, we propose an effective algorithm, called OSCRE. Our algorithm first uses Lyapunov optimization to convert the time-average problem into time-independence optimization and thus realizes optimal renewable energy harvesting. Then, it realizes the service caching scheme using data-driven combinatorial multi-armed bandit learning. The simulation results show that the OSCRE scheme can save service latency while making sufficient use of renewable energy.

With the increasing number of wireless devices connecting to networks and sharing spectrum resources, interference has become a major obstacle to improving network performance. Existing interference management (IM) methods treat interference as a negative factor and focus on suppressing or eliminating its impact on the transmission of intended signals. However, this often comes at the cost of consuming communication resources and degrading desired transmission performance. Therefore, the design of a cost-effective IM method that “exploits” interference is important. To achieve this goal, we proposeIntelligent Reflecting Surface Assisted Interference Exploitation(IRS-IE) to realize efficient desired data transmission. IRS-IE leverages the low-cost and adaptive deployment capabilities of IRS to gather and reflect interference towards the interfered receiver (Rx). By appropriately designing the reflection coefficient of IRS, a phase shift is introduced to the incident interference, allowing the reflected interference to interact with its direct counterpart at the interfered Rx. As a result, the interfered Rx can retrieve its desired data from the mixed interference. This way, IRS-IE can make use of the interference to facilitate the desired data transmission. Our theoretical analysis and simulation results show that IRS-IE significantly improves the spectral efficiency (SE) of the interfered communication pair over the other IM methods.

Due to the broadcast nature of wireless transmissions, they are exposed to all surrounding entities and thus vulnerable to eavesdropping. To counter this vulnerability, we propose a new physical-layer secure transmission scheme, called DDM-Sec, based on decomposed and distributed modulation (DDM). DDM-Sec realizes traditional QPSK modulation by using two cooperative transmitters (Txs), each generating a BPSK signal, in a distributed manner. The legitimate receiver (Rx) can decode the desired/intended information from the mixed received signal while preventing the eavesdropper from accessing the legitimate user's information. DDM-Sec can effectively exploit the randomness of wireless channels to encrypt data transmission, enrich the spatial signatures of the legitimate transmission by employing two cooperative Txs. Moreover, DDM-Sec distributes user's information to two transmissions so that none of the decomposed signals alone carry the legitimate user's full information. Our theoretical analysis, hardware experiment, and simulation have shown that DDM-Sec can effectively prevent the eavesdropping, and hence guarantee the secrecy of the legitimate user's data transmission.

As a new distributed data training framework, federated learning (FL) has attracted increasing attention owing to its advantages of preserving data privacy and low communication cost. However, in a wireless FL network, due to the limited bandwidth resources, when a large number of clients participate in FL, the communication burden is too heavy. Therefore, efficient bandwidth allocation is critical to facilitate the application of wireless FL. In this paper, we investigate bandwidth allocation in wireless FL networks with the objective of minimizing the latency of FL services. We first analyze and formulate the latency minimization problem. Then, considering that the computing and transmitting capabilities of each client cannot be completely and truly acquired, we develop an evolutionary game (EG) framework to model the dynamic process of bandwidth allocation in wireless FL. We further show the optimal bandwidth allocation solution is equivalent to the evolutionary equilibrium (EE) obtained by the replicator dynamics in the EG model, and prove the EE is asymptotically stable. With the help of these results, we propose the EG-based bandwidth allocation algorithm, which enables the latency of FL services to be reduced by performing the replicator dynamics iteratively. Numerical simulations are provided to demonstrate the evolutionary behaviors in the EG-based bandwidth allocation Algorithm.

In the era of big data, the unprecedented growth of data has spawned the commercial application of data trading markets in the Internet of Vehicles (IoV), while also posing challenges to their economic feasibility. In this paper, we propose a data-energy trading architecture in IoV consisting of a market operator, electric vehicles (EVs), and roadside units (RSUs), where RSUs exchange energy for data collected by EVs, and the market operator solves the data/energy allocation problem to maximize social welfare. However, due to the information asymmetry and fragmentation in the market, it is difficult to determine the optimal data and energy trading amount. To this end, we design an iterative double-sided auction (IDA) mechanism to regulate the interactive behaviors among the trading entities, where the market operator gathers local information from RSUs and EVs, and gradually adjusts the submitted bids of two sides to reach the desired payment and reward rules. The proposed IDA-based data-energy trading algorithm is convergent and satisfies the economic properties of efficiency, incentive compatibility, individual rationality, and budget balance. Numerical results demonstrate the performance of the proposed IDA-based data-energy trading architecture in IoV.

The emergence of mobile edge computing (MEC) technology has deployed edge clouds with strong computing capabilities closer to Internet of Thing (IoT) devices, which can effectively meet the demands for computing power and latency. However, in addition to the stringent latency requirements, more and more emerging IoT applications also have higher standards for the freshness and timeliness of collected information. In order to ensure the freshness and high-information value in IoT system, we propose an Age of Information (AoI)-based optimization strategy for computation offloading and transmission scheduling. The strategy considers the AoI during the transmission phase and the execution phase, respectively, under the constraints of delay and remaining energy. Then, a joint optimization model is established based on the comprehensive benefits of AoI and computation rate. To address the strong coupling between the offloading decision and the transmission decision, the original optimization problem is divided into two stages. By the use of the deep deterministic policy gradient (DDPG) algorithm and the dueling double deep$Q$network (D3QN) algorithm, the solution is obtained in terms of the offloading decision and transmission scheduling decision, respectively. The proposed joint optimization strategy considers the impact of the transmission decision on the offloading decision and is adaptable to the dynamic changes in the channel connection between the edge cloud and the user due to user mobility. Experimental results show that compared with other offloading and transmission strategies, the proposed approach has higher overall system revenue and lower AoI.

Nowadays, backscatter, radio-frequency (RF) energy harvesting (EH), and cognitive radio (CR) technologies have been widely applied in Internet of Things (IoT) to address the issues of energy supply and spectrum scarcity. This article focuses on the throughput maximization problem of backscatter-aided wireless-powered heterogeneous CR networks (WPHetCRNs), where two types of secondary transmitters (STs), i.e., the STs with backscatter units (STBs) and the STs with RF-EH units (STEs), coexist. The STBs operate in the ambient backscatter (AB) mode, and the STEs operate in the harvest-then-transmit (HTT) mode. Inspired by the potential benefits of cooperations between different users, we propose a backscatter-aided cooperative relay transmission (BaCRT) strategy to improve the sum-throughput of the secondary users (SUs). The main idea is that when the licensed spectrum of the primary users (PUs) is busy, the STBs first help to relay the primary data via the passive relay mode, and then transmit the secondary data via the AB mode, while the STEs harvest energy in the HTT mode. With the help of relaying, the target throughput of the PUs could be met in shorter duration and the licensed spectrum could become idle more quickly. When the licensed spectrum becomes idle, the STEs transmit data in the HTT mode. The goal of this article is to identify the optimal time allocation among the passive relay mode, AB mode, and data transmission of HTT mode that maximizes the sum-throughput of the SUs. To reach this goal, we first investigate the single-ST case for each type and derive the closed-form solution of the optimal time allocation. We then extend to the multiple-ST case for each type, where three scenarios are classified with respect to the fairness issue of the STBs. We prove that the sum-throughput maximization problem is convex in each scenario and employ the block coordinate descent and gradient descent iterative algorithms to solve the problem. Numerical results show that the proposed BaCRT strategy significantly improves the sum-throughput of the SUs compared with other strategies.

In the era of big data, the unprecedented growth of data has been regarded as an important asset and the commercial application of data acquisition markets has emerged accordingly. With the advancement of vehicle manufacturing and sensor technologies, a large amount of data can be collected and stored in electric vehicles (EV), making the data acquisition scenario gradually extend to the Internet of Vehicles (IoV), and thus the corresponding operational rules and economic feasibility need to be fully investigated there. In this paper, we focus on a general IoV-oriented data acquisition market that consists of a data center, multiple EVs, multiple roadside units (RSUs), and a market operator (broker), with the objective of social welfare maximization (SWM) by identifying the optimal data task allocation. However, due to the inherent information asymmetry and fragmentation in such a market, it is not feasible to solve the SWM problem directly. To this end, we propose an iterative double-sided auction (IDA) mechanism, which leverages the self-interested feature of RSUs and EVs to decompose the SWM problem, enabling every participant to make decisions in a distributed manner under the broker’s coordination. A complete set of operational rules covering the data task allocation, bidding, payment, and reimbursement are elaborately designed to achieve SWM, and energy is adopted as the pricing “currency”, such that an IDA-based Data-Energy Transaction (IDADET) ecosystem is established in IoV. We verify the economic feasibility of the proposed IDADET ecosystem by showing its convergence and desirable properties of individual rationality, budget balance, incentive compatibility, and economic efficiency. In addition, considering the psychological effects of practical market participants, we make amendments to the operational rules of the IDADET ecosystem from the behavioral economics perspective, aiming to ensure its long-term well-functioning. Extensive numerical results are presented to show the performance of the IDADET ecosystem and demonstrate its advantages in terms of economic properties, operational feasibility, fast convergence, and market social welfare.

Motivated by the benefits of cognitive radio (CR), energy harvesting (EH), and backscatter communication (BC) technologies to support Internet of Things (IoT) systems, we investigate the backscatter-aided EH CR networks (EH-CRNs) in a multichannel scenario. To achieve high throughput on various channels, we propose a novel hybrid communication scheme that the secondary transmitter (ST) selects one channel for spectrum sensing, and performs multiple actions based on the sensing result. To be specific, if the selected channel is detected as busy, the ST potentially performs underlay mode transmission, ambient BC (AmBC), or radio frequency (RF) EH. Otherwise, the ST performs interweave mode transmission. Based on the ST’s knowledge of the channel availability and the amount of the available energy, the decisions of channel and specific action selections are made. Furthermore, the sequential decision problem is formulated as a mixed observability Markov decision process (MOMDP), and addressed by the classic value iteration algorithm. The proposed scheme could be flexibly adapted to the changes in energy and channel availabilities. Simulations demonstrate the superiority of this scheme in terms of throughput, and show that even without channel selection, the proposed scheme conducted on the channels with different idle probabilities always achieves high throughput.

Achieving higher spectrum utilization, auction-based mechanisms has been regarded as a popular tool in dynamic spectrum access (DSA). Recently, Sybil attacks in auction-based DSA mechanisms have been investigated, where a cheating bidder can manipulate an auction by submitting bids under multiple fake identities. Existing Sybil-proof mechanisms in DSA are limited to prevent Sybil attacks from primary users (PUs) or secondary users (SUs). However, both of PUs and SUs may perform Sybil attacks in DSA, i.e., double Sybil attacks. The challenge of solving the double Sybil attacks is that fictitious identities and fake bids can directly affect allocation results, but the malicious bidders cannot be straightforwardly distinguished from all bidders. To resist the double Sybil attacks, we propose STEAM, the first double Sybil-proof and two-dimensional Truthful spEctrum Auction Mechanism for DSA. Specifically, STEAM merges suspicious buyers based on geographic characteristics and sorts sellers by a bid-independent sorting method to minimize the impact of untruthful bids and Sybil attacks on the allocation results. Theoretical analysis and extensive evaluations prove that STEAM is double Sybil-proof, two-dimensional truthful, individual rational and budget-balanced, while the performance loss in various metrics within 8% compared to the existing auction-based mechanisms.

In this paper, we focus on a multi-hop decentralized wireless network consisting of legitimate nodes, adversary wardens, and friendly but selfish jammers, and investigate the routing design for achieving covert communication. For a pair of source and destination nodes, we first provide theoretical analysis for a given route between them to reveal how the covertness performance is related to the jamming power of jammers in the network. Then, we design an incentive mechanism that stimulates selfish jammers to supply artificial jamming to protect communication covertness, by granting them rewards from the source. A two-stage Stackelberg game framework is developed to analyze the strategic interactions between the source and jammers, and so as to determine the optimal settings of rewards and jamming power. Based on these results, we formulate a shortest weighted path-finding problem to identify the optimal route for covert communication between the source and destination, which can be solved efficiently by employing Dijkstra's algorithm. Simulation results demonstrate the performance of the proposed incentive routing scheme.

This paper investigates secure communication in a two-hop relaying system based on physical layer security. The relay employs time-switching simultaneous wireless information and power transfer (SWIPT) to harvest energy and receive information from the source, and then transmits the source’s information and its own information to the destination. A passive eavesdropper exists and wiretaps information transmission over both hops. Under the general system configuration, we first provide performance modeling to reveal the secrecy rate of source and relay as well as identify their utilities. Then, we analyze the hierarchical competition behaviors between the source and relay from a game-theoretic perspective. In particular, we develop a Stackelberg game-based analytical framework to determine the optimal strategies for the source and relay by deriving the Stackelberg equilibrium. Furthermore, we summarize the feasible conditions of utilizing SWIPT-enabled relaying for secure communication and propose the end-to-end transmission scheme accordingly. Extensive numerical results are presented to demonstrate the performance of the proposed SWIPT-enabled relaying system.

Abstract This paper focuses on the issue of joint time and power allocation in multi‐channel energy harvesting CR networks (EH‐CRNs), where the multi‐antenna secondary transmitter (ST) opportunistically accesses the licensed subchannels by a hybrid overlay/underlay transmission approach. To improve spectrum efficiency and energy efficiency of the EH‐CRNs, the ST scavenges energy from the radio‐frequency signal radiated by the primary transmitter, and exploits the harvested energy for data transmission through subchannels of different states in overlay/underlay mode simultaneously. Moreover, under the interference power constraint, energy constraint, and maximum power constraint, the secondary throughput is improved by optimising the allocation of subchannels, the time scheduling between energy harvesting and data transmission, and the power allocation of the ST among different subchannels. A subchannel allocation scheme with low time complexity is proposed, and the secondary throughput optimisation problem is formulated with respect to the time scheduling and power allocation of the ST. Then it is proved the problem is convex, and the problem is solved by a proposed joint time and power allocation algorithm. Numerical results show that the proposed scheme has an advantage of secondary throughput over the other schemes. Finally, the impacts of key relevant factors on the secondary throughput are explored.

The efficient buffer space management in intermittently connected Internet of Things (IC-IoT) is of great importance for data delivery performance guarantee in such networks. This article considers two typical buffer space management policies for IC-IoT, i.e., buffer-space sharing (BS) and buffer-space allocation (BA). The BS policy allows the buffer space of each device to be fully shared by the exogenous packets and the packets from other devices, while the BA policy divides the buffer space into the source buffer and relay buffer for storing the two kinds of packets separately. With the help of the queueing theory and Markov chain theory, we develop a theoretical framework to capture the sophisticated queueing processes for the buffer space under either BS or BA policy, which enables the limiting distribution of the buffer occupation state to be determined. We then provide theoretical modeling for throughput and expected end-to-end delay to evaluate the fundamental performance of the IC-IoT under the BS and BA policies. Finally, extensive simulation and numerical results are presented to validate theoretical models and to demonstrate the effects of BS and BA policies on the IC-IoT performance.

This paper explores the joint secrecy and delay guarantees based on opportunistic jamming and link selection in a wireless relay system consisting of a source, a destination, multiple buffer-aided relays and a passive eavesdropper wiretapping over both hops. Based on the information of link state and buffer status, we design a novel transmission scheme based on link selection and jammer selection, which dynamically grants transmission links different priorities for packet delivery, such that the constraints on both secrecy outage probability and packet delay are jointly satisfied. To understand the performance of the new scheme, we then apply the bitmap technique and Markov chain theory to develop a complete theoretical framework for the modelling of three fundamental metrics, namely reliability outage probability, packet discarding probability and secrecy/delay constrained throughput (SDT). Finally, we provide extensive simulation and numerical results to validate our theoretical modelling, as well as to demonstrate that the proposed scheme is superior to the benchmarks in terms of SDT.

In recent years, depression has become an increasingly serious problem globally. Previous studies of automatic depression recognition based on functional near-Infrared spectroscopy (fNIRS) or other brain imaging techniques have shown potential to serve as auxiliary diagnosis methods that provide assistance to medical professionals. Recently, some studies have found that, besides directly using the data themselves (temporal data), the use of functional connectivity among channels (spatial data) also can be effective. In this paper, we propose a method based on Graph Neural Network (GNN) that combines both temporal and spatial features of fNIRS data for automatic depression recognition. Specifically, fNIRS data of 96 subjects were collected and pre-processed. Basic statistical metrics of each channel were extracted as temporal features, and channel connectivity (coherence and correlation) were calculated as spatial features. Point-biserial analysis was conducted on these features and depression labels as a data-driven motivation. For classification, we considered data of each subject as a graph, with temporal features as node features and spatial features as edge weights. The graphs were fed into GNNs for training and testing. Experimental results showed that our GNN-based methods realized the best depression recognition performance compared with classical machine-learning methods regarding accuracy, F1 score, and precision, especially in F1 score for over 10%.

The significant achievements have been made in crowd detection and tracking due to the advancement of artificial intelligence in the autonomous driving. However, the image-based methods have strict requirements for the collection conditions of video, and the development of the new generation of flexible fabrics has become potential sensors to perceive context. In this paper, an intelligent fabric space enabled by multi-sensing sensors is established to track the motion objects. We propose a behavior analysis pipeline including the modules of data preparation, trajectory coupling, motion scenario segmentation, and motion pattern measurement to capture the crowd information from micro-level and macro-level over the intelligent fabric space. After making preprocess for the multi-sensing data, a coupling mechanism is formulated to fuse the video-based trajectory and fabric-based trajectory. And an automatic motion scenario segmentation model divides the surrounding scenario into main-crowd, sub-crowd, and background according to the motion behavior. Further, we define measurement metrics to analyze the motion pattern for the different crowds. Extensive experiments prove that our proposed methods effectively fuse multiple trajectories and realize the crowd segmentation and the motion description. This will greatly help autonomous vehicles and control system perceive the surrounding pedestrians and the environment to make precise driving decisions.

Blockchain technology is a promising approach for solving the security and personal privacy problems in Internet applications. The successful commercial deployment of Blockchain markets relies on a comprehensive understanding of the economic and strategic interactions among different entities involved. In this paper, we focus on a blockchain market consisting of a blockchain platform (BP), multiple miners, and blockchain users (BUs), and formulate their interactions as a three-stage Stackelberg game. In Stage I, the BP strategizes the rewards granted to the miners, so as to attract the miners to contribute more computing power used for improving the security and privacy of the blockchain. In Stage II, each miner strategizes its computing power individually for winning the mining compe-tition, which is modeled as a non-cooperative game. In Stage III, the BUs strategize the transaction fee to acquire a corresponding service experience. With the objective of utility maximization, we develop a theoretical framework to analyze the hierarchical interactive behaviors among the entities in a backward inductive way. By solving the Stackelberg equilibrium, we determine the optimal strategies of entities in closed-form. Numerical results are provided to demonstrate the performance of the strategic interactions in the blockchain market.

This article focuses on the secure routing problem in the decentralized Internet of Things (IoT). We consider a typical decentralized IoT scenario composed of peer legitimate devices, unauthorized devices (eavesdroppers), and selfish helper jamming devices (jammers), and propose a novel incentive jamming-based secure routing scheme. For a pair of source and destination, we first provide theoretical modeling to reveal how the transmission security performance of a given route is related to the jamming power of jammers in the IoT. Then, we design an incentive mechanism with which the source pays some rewards to stimulate the artificial jamming among selfish jammers, and also develop a two-stage Stackelberg game framework to determine the optimal source rewards and jamming power. Finally, with the help of the theoretical modeling as well as the source rewards and jamming power setting results, we formulate a shortest weighted path-finding problem to identify the optimal route for secure data delivery between the source-destination pair, which can be solved by employing the Dijkstra's or Bellman-Ford algorithm. We prove that the proposed routing scheme is individually rational, stable, distributed, and computationally efficient. Simulation and numerical results are provided to demonstrate the performance of our routing scheme.

This paper focuses on the multiple-input-multiple-output (MIMO) two-way relay system with an untrusted relay and investigates its secure and energy-efficient precoding design issue based on the physical layer security technology. We first provide theoretical modeling for the index of secrecy energy efficiency (SEE) and formulate the optimal precoding design for SEE maximization (SEEM) as a high-dimensional non-convex programming problem. By exploring the techniques like fractional programming, alternate optimization and semi-definite programming, we then develop a hierarchical theoretical framework to solve the SEEM problem and thus to identify the optimal precoding designs for the source and relay. Furthermore, we demonstrate the proposed theoretical framework is also applicable to the problem of precoding design for secrecy sum rate maximization. Finally, with the help of generalized singular value decomposition, we propose a sub-optimal relay precoding design scheme with significantly lower computational complexity. Extensive numerical results provided in the paper indicate that the proposed schemes can remarkably improve the SEE performance in MIMO two-way untrusted relay systems.

This paper focuses on the QoS-aware secure routing design based on the physical layer security technology for a multi-hop wireless network consisting of legitimate nodes, malicious eavesdroppers, and selfish jammers. We first provide theoretical modeling for a given route to reveal how its end-to-end security/QoS performance is related to the transmitting power of legitimate nodes along the route and the jamming power of jammers in the network. We then design an incentive mechanism that stimulates jammers to generate artificial jamming for security enhancement, and also develop a non-cooperative game framework to resolve the jamming power setting issue here. Based on the security/QoS performance modeling of the route and jamming power setting, we further propose a theoretical framework to determine the optimal transmitting power of nodes along the route such that its optimal transmission security can be achieved under a QoS constraint. Finally, with the help of the power setting results of the given route, we formulate a shortest weighted path-finding problem to identify the optimal route for data delivery in the network, which can be solved by employing the Bellman-Ford or Dijkstra's algorithm. It is demonstrated that the proposed routing scheme is individually rational, stable, distributed and computationally efficient.

Dynamic spectrum access (DSA) is a promising platform to solve the spectrum shortage problem, in which auction based mechanisms have been extensively studied due to good spectrum allocation efficiency and fairness. Recently, Sybil attacks were introduced in DSA, and Sybil-proof spectrum auction mechanisms have been proposed, which guarantee that each single secondary user (SU) cannot obtain a higher utility under more than one fictitious identities. However, existing Sybil-poof spectrum auction mechanisms achieve only Sybil-proofness for SUs, but not for primary users (PUs), and simulations show that a cheating PU in those mechanisms can obtain a higher utility by Sybil attacks. In this paper, we propose TSUNAMI, the first Truthful and primary user Sybil-proof aUctioN mechAnisM for onlIne spectrum allocation. Specifically, we compute the opportunity cost of each SU and screen out cost-efficient SUs to participate in spectrum allocation. In addition, we present a bid-independent sorting method and a sequential matching approach to achieve primary user Sybil-proofness and 2-D truthfulness, which means that each SU or PU can gain her maximal utility by bidding with her true valuation of spectrum. We evaluate the performance and validate the desired properties of our proposed mechanism through extensive simulations.

This article investigates the secure communication in a two-hop cooperative wireless network, where a buffer-aided relay helps forward data from the source to destination, and a passive eavesdropper attempts to intercept data transmission from both the source and relay. To ensure the transmission security and communication quality of service (QoS) of the system, we design novel link selection policies for two cases that the instantaneous channel state information is available or unavailable at the source node. For evaluating the system performance, we then derive the closed-form expressions of end-to-end secrecy outage probability, system throughput and secrecy throughput, respectively. Based on the theoretical performance analysis, we further explore the performance optimization issues, revealing the insightful tradeoffs between the transmission security and QoS. An iterative algorithm is developed to identify the optimal setting of link selection parameters, which is helpful for the practical configuration of link selection policies to satisfy various system performance requirements. Finally, we conduct simulations to validate our theoretical performance analysis, and also provide extensive numerical results to illustrate the efficiency of the proposed link selection policies for ensuring the secure communication in a two-hop cooperative network.

This paper focuses on a full-duplex multiple-input multiple-output two-way untrusted relay system, and investigates the optimal beamforming design of such system to maximize its secrecy sum rate (SSR) based on the physical layer security technology. We first provide the modeling of SSR under a general beamforming setting as well as the theoretical formulation of the optimal beamforming design problem. Based on the ideal assumption that the full channel state information is available, we then develop a novel theoretical framework to solve the optimal design problem and thus establish an upper bound on SSR, where the techniques of alternate optimization, fractional programming, semi-definite programming and barrier function method are jointly employed. With the consideration of the constraints in practical implementations, we further propose two sub-optimal beamforming design solutions based on either Wiener prediction or asymptotic approximation. The issue of how to reduce the computational complexity in the optimal relay beamforming design is also discussed in this paper. Finally, we present extensive numerical results to illustrate our theoretical findings and to demonstrate the performance of the proposed sub-optimal beamforming design solutions.

Real-time surgery workflow automatic detection as computer-assisted surgery systems has become an emerging trend due to improving patient safety during surgery. Currently, the convolutional neural networks can show the best performance for content-based video analysis of surgical workflow. In this paper, a novel solution of surgery workflow detection during the procedure was presented, the edge information of original phases from video frames was extracted and then employed to train together with original phases by using a ResNet. Finally, the methods were evaluated on cataract-101 dataset, a publicly available dataset for surgical phase analysis, on which a maximum accuracy of 90.1% was reached. Additionally, the accuracy of 3% improvement was achieved when compared with the method of no processing the data by edge detection. It is shown that using the edge information of original images could improve the performance of surgical phase recognition, because it can be complementary information for original images to recognize the surgical workflow. This paper shows valuable potential to develop modern medical diagnosis and treatment in automating workflow recognition, and the edge processing of original phases for recognition images can also produce new features to assist the network to recognize the original images. Furthermore, the technology studied in this paper can also be used in other video analysis tasks, or classification of image tasks.

A lot of works have been done to demonstrate that buffer-aided relaying can achieve a significant performance gain in cooperative wireless networks. However, the additional delay introduced by buffer has been largely neglected in available works, which is of significant importance for delay-sensitive networks. In this paper, we consider a two-hop buffer-aided relaying system suffering from eavesdropping, where every packet owns a limited lifetime. In order to satisfy a specific secrecy rate of the system, this paper proposes a novel security and lifetime (SELI)-aware relay selection scheme by balancing the security and lifetime constraints. Furthermore, to address the problem of the heterogeneous packets queuing in the buffer, the approach of Markov chain is embedded to model the packet occupancy process. With the help of this complete framework, we derive the exact expressions of performance metrics, including reliable outage probability, packet discarding probability and secrecy throughput. Finally, extensive simulation and numerical results are provided to validate our analysis and illustrate the proposed scheme can efficiently reduce the packet discarding ratio.

Fog computing extends the cloud computing paradigm by allocating substantial portions of computations and services towards the edge of a network, and is, therefore, particularly suitable for large-scale, geo-distributed, and data-intensive applications. As the popularity of fog applications increases, there is a demand for the development of smart data analytic tools, which can process massive data streams in an efficient manner. To satisfy such requirements, we propose a system in which data streams generated from distributed sources are digested almost locally, whereas a relatively small amount of distilled information is converged to a center. The center extracts knowledge from the collected information, and shares it across all subordinates to boost their performances. Upon the proposed system, we devise a distributed machine learning algorithm using the online learning approach, which is well known for its high efficiency and innate ability to cope with streaming data. An asynchronous update strategy with rigorous theoretical support is applied to enhance the system robustness. Experimental results demonstrate that the proposed method is comparable with a model trained over a centralized platform in terms of the classification accuracy, whereas the efficiency and scalability of the overall system are improved.

With the rapid development of wireless communication technologies, the demand for higher data rate and more concurrent transmissions has been continually increasing. Due to the widespread deployment of various wireless technologies, interference has become a key roadblock to the improvement of network performance. Interference has long been known to be harmful, leading to development of numerous interference management (IM) mechanisms based on resource segmentation or signal processing to mitigate or suppress interference. Since a desired signal can be distorted by interference, and thus be incorrectly decoded at the destination, we argue that interference can also be transformed intentionally to extract the desired data from interfering signal(s). Based on this observation, we propose Interference ReCycling (IRC). Under IRC, a recycling signal is generated with the interference a victim receiver (Rx) is subjected to, and then sent by the Rx's associated transmitter (Tx). Under the influence of the recycling signal, the desired data of the victim Tx-Rx pair can be recovered from the interference at the victim Rx. That is, by exploiting the interactions among multiple signals, i.e., recycling signal and interference, useful data information can be extracted from interference, or the unintended data carried in interference can be artificially converted to the desired one. Our theoretical analysis and numerical evaluation have shown that the proposed IRC can fully exploit interference, and hence can significantly improve the spectral efficiency (SE) of the victim Rx compared to the other existing IM methods.

In this paper, we investigate energy-efficient secure communications in an untrusted two-way relay network, where two source nodes exchange messages via an untrusted relay. Considering both security threats and energy limitation, the performance metric secure energy efficiency is defined as the ratio of the secrecy sum rate to the total power consumption. Our objective is to maximize the secure energy efficiency by jointly designing the source and relay beamformers. We first derive the expression of secure energy efficiency under a general beamforming configuration. Then, an iterative algorithm based on the techniques of alternate optimization, fractional programming, barrier function method and semi-definite programming, is proposed to find the optimal solutions of the source and relay beamformers. Our analysis shows that the proposed scheme can not only solve the problem of secure energy efficiency maximization but also be applicable for solving the problem of secrecy sum rate maximization. Simulation results demonstrate that the proposed scheme achieves a good gain in terms of secure energy efficiency.

Internet of Things (IoT) is a new paradigm that involves the interconnection of thousands of devices and home appliances. Due to the scarcity of the spectrum suitable for wireless electromagnetic transmission, many communication systems and devices are close to each other, or even overlapping in spectrum, thus incurring complicated interference situations. Therefore, interference in IoT is worthy of thorough investigation and should be well addressed. There have been numerous interference management (IM) proposals at the interfering transmitter or the interfered transmitter/receiver separately or cooperatively. Moreover, the existing IM schemes rely mainly on the use of channel state information (CSI). However, in some communication scenarios, the option to adjust the interferer is not available, and, in the case of downlink transmission, it is always difficult or even impossible for the interfered receiver to acquire necessary information for IM. Based on the above observations, we first propose a novel IM technique, called interference steering (IS). By making use of both CSI with respect to and data carried in the interfering signal, IS generates a signal to modify the spatial feature of the original interference, so that the steered interference at the interfered receiver is orthogonal to its intended signal. We then apply IS to a wireless local area network (WLAN)-based IoT in which the same frequency band is reused by adjacent basic service sets (BSSs) with overlapping areas. With IS, multiple nearby access points (APs) could simultaneously transmit data on the same channel to their mobile stations (STAs), thus enhancing spectrum reuse. Our in-depth simulation results show that IS significantly improves network SE over existing IM schemes.

Both interference alignment (IA) and interference neutralization (IN) are exploited for coordinated multi-point (CoMP) communications. With the cooperation of the base station (BS), a transmit precoder and a receive filter are jointly designed, and concurrent transmissions of multiple data streams are then enabled. In the design of a precoder, the IN is applied to the interferences carrying the same data so as to align the interfering signals in the opposite direction in a subspace. On the other hand, for interferences carrying different information, IA is employed to align them in the same direction in a subspace, thus reducing the interference signal observed at the receiver side. Based on different precoding schemes at the transmitter's side, receivers adopt zero forcing (ZF) so as to recover the desired data. The proposed IA- and IN-based CoMP (IAN-CoMP) mechanism can achieve effective interference cancellation and suppression by exploiting limited and flexible collaboration at the BS side. It can also make a flexible tradeoff between the cooperation overhead and the system's achievable degrees of freedom (DoFs). We extend the mechanism to general cases where the antenna configurations at both the transmitter and receiver sides, the number of transmitters participating in CoMP, and that of simultaneously served users are variable. Moreover, we discuss both single-location and multi-location-based realizations of the IAN-CoMP. Finally, by defining the average transmit and receive cooperation order, we analyze the upper bound for IAN-CoMP. Our in-depth simulation shows that the IAN-CoMP can significantly improve the spectral efficiency (SE) for cell-edge users.

With the rapid development in wireless communication technologies, interference has become a main impediment to network performance, making interference management (IM) a critical issue. Interference steering (IS) is emerging as a novel way of IM, which can steer the spatial feature of interference to a target subspace by exploiting the interactions of multiple signals over the air to avoid the disturbance to the interfered receiver. In reality, there always exists multiple interferences from single or multiple sources. We propose three ways to realize IS. The first two of them are categorized as individual interference steering (IIS), in which multiple interfering signals are separately adjusted to either an identical direction (a single-target IS, STIS) or different directions (multi-target IS, MTIS), incurring single or multiple degrees of freedom (DoFs) overheads, respectively. Furthermore, by recognizing that the goal of IM is to limit the effect of interference-i.e., the effective portion of interference imposed on the intended transmission- we propose aggregated interference steering (AIS) that exploits the constructive or destructive effects of multiple interfering signals. By considering the overall effect of multiple interferences, the DoF cost of AIS is reduced to one regardless of the number of interference to be managed. Finally, the proposed three IS realizations are adapted to minimize the power cost of steering interference. Our theoretical analysis and in-depth simulation results have shown that the proposed IS schemes can effectively manage multiple interference via better utilizing the DoF and transmit power. The AIS is shown to be advantageous in both power cost and DoF consumption.

In recent years, the dramatically rising mobile data traffic has required an ever-increasing data rates. Ultra-dense network (UDN) is a promising technique to significantly enhance the network capacity by densely deploying small cells. The large amount of traffic also has been characterized by asymmetry and variations in both time and space. Dynamic time-division duplex (DTDD) has been taken into account to accommodate the traffic due to its advantage in the dynamic adjustment of UL/DL configuration. In this work, we propose an analytical framework to investigate the coverage performance of a UDN operating DTDD scheme, where impacts of line-of-sight (LOS)/non-line-of-sight (NLOS) propagation in large-scale fading and small-scale fading are both incorporated. Our results show that the LOS propagation in small-scale fading can substantially improve the DL and UL coverage probabilities in the low-to-middle density region, while the enhancement vanishes in the ultra-dense region where the network coverage is dominated by the LOS propagation in large-scale fading.

To satisfy the growing capacity demand of explosive traffic, ultra-dense network (UDN) is proposed as a key technology, where small cells are densified to fully exploit the spatial spectrum reuse gain. The decreasing coverage area of a small cell also leads to the obviously increasing traffic asymmetry in UL and DL within the same cell and among different cells. To adapt to the dynamic and asymmetric traffic within UDN, dynamic time-division duplex (DTDD) can be seen as a promising scheme due to its capability in the flexible adjustment of ratio of UL to DL subframes. In this paper, we propose a load-aware analytical framework to accurately characterize the network performance of a DTDD UDN with the emphasis on the effect of idle mode cells. With a practical multi-slope path loss model, we first derive the void probability of a random SAP and obtain the coverage probability and area spectral efficiency (ASE). Then we evaluate how the SAP void probability, UL/DL configuration and network density affect the network performance. Our numerical results show that the idle mode cells have significant effect on the network performance, which alters the variation tendency of coverage probability and ASE with the increasing network density.

This paper study the secure optimal QoS routing (SOQR) in wireless ad hoc networks (WANETs) based on the physical layer security techniques. Specifically, we consider a multi-hop WANET with malicious eavesdroppers and cooperative jammers, and formulate the SOQR as an optimization problem. To deal with this problem, we first derive the closed-form expressions of connection outage probability (COP) and secrecy outage probability (SOP) for any given end-to-end path, which serve as the performance metrics of communication QoS and transmission security, respectively. Then, we explore the minimum COP conditioned on that SOP is below a pre-specified threshold and obtain the corresponding achievable power allocation strategy. With the help of analysis of a given path, we further propose the SOQR algorithm which selects the secure path between a pair of source and destination nodes in a distributed manner to achieve the optimal QoS performance. Finally, numerical simulations are conducted to validate the efficiency of our theoretical results, as well as to illustrate the QoS-security tradeoffs.

Recently extensive works have been devoted to the performance analysis of physical layer security in wireless communication systems. However, the combination of physical layer security and quality of service (QoS) for route selection in multi-hop wireless ad hoc networks (WANETs) still remains an open technical challenge. As an initial step towards this end, this paper focuses on a multi-hop WANET with two typical transmission schemes amplify-and-forward (AF) and decode-and-forward (DF), and explores the route selection with the consideration of both security and QoS. We first derive the closed-form expressions of secrecy outage probability (SOP) and connection outage probability (COP) for a single hop link, and further extend the results to an end-to-end route. Then we conduct the performance comparison between the AF scheme and DF scheme. Finally, based on both the SOP and COP of a route, we formulate the route metric and propose a flexible route selection algorithm which enables us to select the suitable route for message delivery according to different security and QoS requirements.

This paper focuses on a class of important two-hop relay mobile ad hoc networks (MANETs) with limited-buffer constraint and any mobility model that leads to the uniform distribution of the locations of nodes in steady state, and develops a general theoretical framework for the end-to-end (E2E) delay modeling there. We first combine the theories of fixed-point (FP), quasi-birth-and-death process, and embedded Markov chain to model the limiting distribution of the occupancy states of a relay buffer, and then apply the absorbing Markov chain theory to characterize the packet delivery process, such that a complete theoretical framework is developed for the E2E delay analysis. With the help of this framework, we derive a general and exact expression for the E2E delay based on the modeling of both packet queuing delay and delivery delay. To demonstrate the application of our framework, case studies are further provided under two network scenarios with different MAC protocols to show how the E2E delay can be analytically determined for a given network scenario. Finally, we present extensive simulation and numerical results to illustrate the efficiency of our delay analysis as well as the impacts of network parameters on delay performance.

Since the seminal work of Grossglauser and Tse [1], the two-hop relay algorithm and its variants have been attractive for mobile ad hoc networks (MANETs) due to their simplicity and efficiency. However, most literature assumed an infinite buffer size for each node, which is obviously not applicable to a realistic MANET. In this paper, we focus on the exact throughput capacity study of two-hop relay MANETs under the practical finite relay buffer scenario. The arrival process and departure process of the relay queue are fully characterized, and an ergodic Markov chain-based framework is also provided. With this framework, we obtain the limiting distribution of the relay queue and derive the throughput capacity under any relay buffer size. Extensive simulation results are provided to validate our theoretical framework and explore the relationship among the throughput capacity, the relay buffer size and the number of nodes.

An analytical approach to quantifying the routing overhead in wireless ad hoc networks is presented in this paper. We find that in addition to the traditional control overhead and sub-optimal routing overhead, the retransmissions of discarded packets due to buffer overflow in receiver nodes on a route will consume extra bandwidth, which increasing the routing overhead. In this paper, we focus on the impact of packet loss process, analytical expressions for routing overhead and minimal packet loss rate are also derived. A simulation comparing retransmission-aware routing and a hypothetical optimal reactive routing protocol is used as a supplement of our theory, which shows that there still has a great potential to reduce the overhead to improve the network capacity.