In this paper, we consider a large-scale Wi-Fi HaLow heterogeneous network, where numerous stations (STAs) are distributed around an access point (AP) to collect and transmit data using the restricted access window (RAW) mechanism. The AP manages channel access by adjusting and broadcasting RAW Parameter Set (RPS) and grouping messages, which include the duration and slot allocation for each RAW group. We aim to maximize the overall network throughput while ensuring fairness among STAs. Traditional methods struggle with real-time RAW optimization in practical networks. To overcome this challenge, we first divide the RAW groups heuristically according to the STAs' task type and formulate the throughput optimization problem regarding various RPS. Then, we construct a virtual twin network environment to estimate the throughput performance used for RAW optimization. Specifically, the twin environment is built on neural networks and trained by both synthetic data generated from the classic Markov model and the NS-3 simulator. Given the throughput estimate, we devise the reward function of the proximal policy optimization (PPO) algorithm to adapt the optimal RPS, without frequent interaction with the real network environment. Numerical results indicate that the twin-enhanced PPO (TE-PPO) algorithm achieves a comparable performance with the classic PPO algorithm built on the real trace of the NS3 simulator. Particularly, TE-PPO can reduce the time overhead for RPS adaptation to 1/180.

Energy constraints significantly limit the long-term operation of underwater wireless sensor networks (UWSNs) due to their battery-powered sensor nodes (SNs). In this paper, we investigate an autonomous underwater vehicle (AUV)assisted UWSN where the AUV simultaneously collects data and recharges multiple independently located SNs. We employ the value of information (VoI) to evaluate the importance of the sensing data. We propose a novel Lyapunov-guided deep reinforcement learning (LDRL) algorithm to maximize the long-term average VoI while guaranteeing the battery energy constraints of SNs. We first employ a Lyapunov optimization to decompose the multi-stage stochastic VoI maximization problem into a series of single-stage deterministic subproblems. The Lyapunov drift-pluspenalty function is designed to deal with the long-term energy queue equilibrium. Then, we employ a deep neural network (DNN) to optimize the AUV's path and integrate a sequential convex approximation (SCA) optimization module for optimal charging time allocation. Experimental results demonstrate that our algorithm significantly enhances the long-term average VoI while ensuring the SNs' battery energy constraints.

Unmanned surface vessels (USVs) are increasingly utilized for maritime sensing and data collection. With advancements in Sixth-Generation (6G) mobile network technologies, such as UAV-assisted communication, it is now possible to utilize UAVs to provide auxiliary data collection services for USVs in offshore areas. Traditionally, UAV-assisted communication involves UAVs serving as dedicated, long-term relays for USVs. However, within the 6G paradigm, UAVs operate as public communication devices, offering only temporary relay services to USVs. This paper focuses on developing opportunistic routing strategies for this scenario. For ordinary data with low time sensitivity, the objective is to maximize the benefits of opportunistic communication, which is achieved using the proposed Utilization Priority (UP) strategy. For critical data with high time sensitivity, the goal is to ensure the fastest possible delivery, addressed through the proposed Completion Priority (CP) strategy. Simulation results demonstrate that employing UAVs as relays enhances network throughput and reduces latency. The UP strategy generally achieves higher throughput in most scenarios, while the CP strategy is more effective in minimizing network latency.

Utilizing an autonomous underwater vehicle (AUV) for data collection in underwater wireless sensor networks (UWSNs) is a promising approach. However, since underwater sensor nodes are typically battery-powered and difficult to replace, effective energy management is crucial for extending the network lifetime of UWSNs. Additionally, the limited energy of the AUV presents further challenges. To address these issues, this paper proposes an energy-aware AUV-assisted data collection scheme based on dynamic clustering and cluster head selection (DCCHS) to maximize network lifetime. Specifically, we utilize the energy center to define the cluster center and employ a bottom-up hierarchical clustering approach to address the node dynamic clustering problem under the AUV movement distance constraint. Subsequently, we introduce a cluster head (CH) selection algorithm based on iterative optimization, and adds auxiliary CHs near the AUV path to reduce the energy consumption of CHs. Simulation results demonstrate that the proposed DCCHS scheme significantly extends the network lifetime compared to existing schemes, particularly in scenarios with dense node deployment.

Volumetric videos offer an incredibly immersive viewing experience but encounters challenges in maintaining quality of experience (QoE) due to its ultra-high bandwidth requirements. One significant challenge stems from user’s spatial interactions, potentially leading to discrepancies between transmission bitrates and the actual quality of rendered viewports. In this study, we conduct comprehensive measurement experiments to investigate the impact of six degrees of freedom information on received video quality. Our results indicate that the correlation between spatial quality and transmission bitrates is influenced by the user’s viewing distance, exhibiting variability among users. To address this, we propose a spatial quality oriented rate control system, namely sparkle, that aims to satisfy spatial quality requirements while maximizing long-term QoE for volumetric video streaming services. Leveraging richer user interaction information, we devise a tailored learning-based algorithm to enhance long-term QoE. To address the complexity brought by richer state input and precise allocation, we integrate pre-constraints derived from three-dimensional displays to intervene action selection, efficiently reducing the action space and speeding up convergence. Extensive experimental results illustrate that sparkle significantly enhances the averaged QoE by up to 29% under practical network and user tracking scenarios.

Reliable and timely data collection is an important and challenging problem for underwater wireless sensor networks (UWSNs), partly due to the very slow underwater communication and the difficulty in recharging the sensors. In this paper, we exploit the use of autonomous underwater vehicles (AUVs) for UWSN data collection task. Specifically, we investigate how to maximize the value of information (VoI) for data collection through joint optimization of cluster head (CH) selection and multi-AUV path planning. We formulate the joint optimization problem for the task, taking into account the energy constraints of sensor nodes. To solve the problem, we propose a deep reinforcement learning algorithm based on an encoder-decoder architecture. The entire UWSN system is fed into the encoder network, followed by a composite decoder consisting of an AUV selection decoder and a cluster access sequence decoder to obtain the cluster access sequence for each AUV. Based on the determined sequences, we further utilize the dynamic programming algorithm to achieve optimal CH selection. Finally, we obtain the sequence of AUVs accessing the selected CHs. Simulation results demonstrate that the proposed learning-based approach converges and achieves a higher VoI than the existing benchmark algorithms.

In this paper, we consider a multiple unmanned aerial vehicles (UAVs)-assisted wireless sensing network, where low-power ground users (GUs) periodically sense the environmental information and upload the recent sensing information to a base station (BS). The GUs firstly backscatter their information to the UAVs and then the UAVs transmit the information to the BS by the non-orthogonal multiple access (NOMA) transmissions. Our goal is to minimize the long-term age-of-information (AoI) by jointly optimizing the UAV's sensing scheduling, transmission control, and trajectories. To solve this problem, we propose the Lyapunov-driven hierarchical proximal policy optimization framework, named Lya-HPPO, to decouple the multi-stage AoI minimization problem into several control subproblems. In each control subproblem, the UAVs' sensing scheduling and transmission control are firstly determined by the outer-loop deep reinforcement learning (DRL) approach, and then the inner-loop optimization module is to update the UAVs' trajectories. Simulation results verify that the proposed Lya-HPPO framework converges very fast to a stable value and can make online decisions in real time, while guaranteeing the long-term data buffer and AoI stability.

Passive traffic monitoring can be used for network diagnosis and management in wireless networks by deploying wireless sniffers to monitor abnormal data traffic on different channels and locations. This motivates the spatial sniffer-channel assignment (SSCA) problem, i.e., assigning each wireless sniffer a proper operating channel and location to detect the target signals or data packets. The existing approaches for SSCA problems are usually designed for the scenarios where the behavior features of the target users are known. In this paper, we focus on a cognitive monitoring system without information about the users' activities. The wireless sniffers can be deployed to patrol different locations and meet a desirable detection probability requirement. Considering a dynamic network environment with a huge state space, we propose a novel deep reinforcement learning (DRL) approach to adapt the patrolling route for each wireless sniffer. Moreover, we employ Bayesian optimization to help explore the action space and thus improve the learning efficiency. Via numerical simulations, we show that the Bayesian optimization enhanced DRL approach can improve the detection performance and fast adapt the wireless sniffers' actions according to the environmental changes.

In this paper, we consider a hybrid computation offloading scheme that allows edge users to offload workloads to the edge servers by using active RF communications and backscatter communications. We aim to maximize the overall energy efficiency by jointly optimizing the beamforming of access point (AP) and the users’ offloading decisions. Considering a dynamic environment, we propose a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework to solve this problem. The high-level agent resides in the AP and optimizes the beamforming strategy, while the low-level user agents learn and adapt individuals’ offloading strategies. To further improve the learning efficiency, we propose a novel optimization-driven learning algorithm that allows the AP to estimate the low-level users’ actions by solving an approximate problem efficiently. Then, the action estimation can be shared with all users and drive them to update individuals’ actions independently. Simulation results reveal that our algorithm can improve the reward performance by 50%. The learning efficiency and reliability are also enhanced comparing to the conventional model-free learning methods.

In recent years, an increasing amount of new social applications have been emerging and developing with the profound success of deep learning technologies, which have been significantly reshaping our daily life, e.g., interactive games and virtual reality. Deep learning applications are generally driven by a huge amount of training samples collected from the users’ participation, e.g., smartphones and watches. However, the users’ data privacy and security issues have been one of the main restrictions for a broader distribution of these applications. In order to preserve privacy while utilizing deep learning applications, federated learning becomes one of the most promising solutions, which gains growing attention from both academia and industry. It can provide high-quality model training by distributing the training tasks to individual users, relying on on-device local data. To this end, we model the users’ participation in social model training as a training service market. The market consists of model owners (MOs) as consumers (e.g., social applications) who purchase the training service and a large number of mobile device groups (MDGs) as service providers who contribute local data in federated learning. A two-layer hierarchical dynamic game is formulated to analyze the dynamics of this market. The service selection processes of MOs are modeled as a lower level evolutionary game, while the pricing strategies of MDGs are modeled as a higher level differential game. The uniqueness and stability of the equilibrium are analyzed theoretically and verified via extensive numerical evaluations.

In this paper, we employ multiple wireless-powered relays to assist information transmission from a multi-antenna access point to a single-antenna receiver. The wireless relays can operate in either the passive mode via backscatter communications or the active mode via RF communications, depending on their channel conditions and energy states. We aim to maximize the overall throughput by jointly optimizing the transmit beamforming and the relays’ radio modes and operating parameters. Due to the non-convex and combinatorial problem structure, we develop a novel optimization-driven hierarchical deep deterministic policy gradient (H-DDPG) approach to adapt the beamforming and relay strategies. The optimization-driven H-DDPG algorithm firstly decomposes the binary relay mode selection into the outer-loop deep$Q$-network (DQN) algorithm and then optimizes the continuous beamforming and relaying strategies by using the inner-loop DDPG algorithm. Secondly, to improve the learning efficiency, we integrate the model-based optimization into the inner-loop DDPG framework by providing a better-informed target estimation for DNN training. Simulation results reveal that these two special designs ensure a more stable learning performance and achieve a higher reward, up to 20%, compared to the conventional model-free DDPG approach.

Symbiotic radio (SR) has recently emerged as a promising technology to boost spectrum efficiency of wireless communications by allowing reflective communications underlying the active RF communications. In this paper, we leverage SR to boost physical layer security by using an array of passive reflecting elements constituting the intelligent reflecting surface (IRS), which is reconfigurable to induce diverse RF radiation patterns. In particular, by switching the IRS’s phase shifting matrices, we can proactively create dynamic channel conditions, which can be exploited by the transceivers to extract common channel features and thus used to generate secret keys for encrypted data transmissions. As such, we firstly present the design principles for IRS-assisted key generation and verify a performance improvement in terms of the secret key generation rate (KGR). Our analysis reveals that the IRS’s random phase shifting may result in a non-uniform channel distribution that limits the KGR. Therefore, to maximize the KGR, we propose both a heuristic scheme and deep reinforcement learning (DRL) to control the switching of the IRS’s phase shifting matrices. Simulation results show that the DRL approach for IRS-assisted key generation can significantly improve the KGR.

In this article, we employ multiple energy harvesting relays to assist information transmission from a multiantenna hybrid access point (HAP) to a receiver. All the relays are wirelessly powered by the HAP in the power-splitting (PS) protocol. We introduce the novel concept of hybrid relay communications, which allows each relay to switch between two radio modes, i.e., the active RF communications and the passive backscatter communications, according to its channel and energy conditions. We aim to jointly optimize the HAP's beamforming, individual relays' radio modes, PS ratios, and the relays' collaborative beamforming strategies to enhance the throughput performance at the receiver. The resulting formulation becomes a combinatorial and nonconvex problem. We first propose a convex approximation to the original problem, which serves as a lower bound of the relay performance. Then, we design an iterative algorithm that decomposes the binary relay mode optimization from the other operating parameters. In the inner loop of the algorithm, we exploit the structural properties to optimize the relay performance with the fixed relay mode by using alternating optimization. In the outer loop, different performance metrics are derived to guide the search for a set of passive relays to further improve the relay performance. The simulation results verify that the hybrid relaying communications can achieve 20% performance improvement compared to the conventional relay communications with all active relays.

In this work, we consider multiple energy harvesting relays to assist information transmission from a hybrid access point (HAP) to a distant receiver. The multi-antenna HAP also beamforms RF power to the relays by using a power-splitting protocol. We aim to maximize the throughput by jointly optimizing the HAP's beamforming strategy as well as individual relays' energy harvesting and collaborative beamforming strategies. With dense user devices, the throughput maximization takes account of the direct links from the HAP to the receiver as they are short and contribute considerably to the overall throughput. Moreover, we introduce the concept of hybrid relaying communications which allows the energy harvesting relays to switch between two radio modes. In particular, the relays can operate either in RF communications or backscatter communications, depending on their channel conditions and energy status. This results in a non-convex and combinatorial throughput maximization problem. With the fixed relay mode, we can find a feasible lower performance bound via convex approximation, which further motivates our algorithm design to update the relay mode in an iterative manner. Simulation results verify that the proposed hybrid relaying strategy can achieve significant performance improvement compared to the conventional relaying strategy with all relays operating in the RF communications mode.

In this paper, we consider a backscatter-aided hybrid data offloading scheme for a battery-less wireless sensor network. All sensor devices on the edge are coordinated by a hybrid access point (HAP), while also provides power for them via wireless power transfer. Co-located with the HAP, an edge computing server is set up to provide the computation and caching capabilities for the edge devices with insufficient power and computation resources. Each node is allocated a fixed time- slot for data offloading via either the conventional active communications or the passive backscatter communications. Such a hybrid data offloading scheme can flexibly control the trade- off between power consumption and data rate in offloading. We aim to minimize the total energy consumption by optimizing the offloading strategy of each edge device and the HAP's wireless power allocation over different edge devices. We show that the energy minimization problem exhibits a convex reformulation. For practical consideration, we devise a distributed algorithm to solve the problem. The numerical results demonstrate that the distributed algorithm can achieve a near- optimal performance. With a fixed transmit power at the HAP, our proposed hybrid offloading scheme provides a higher offloading throughput compared to the state-of-the-art data offloading schemes.

In this work, we exploit the signal and energy cooperation in wireless powered multi-user networks. In particular, multiple relays are employed to assist data transmissions from a multi-antenna hybrid access point (HAP) to a distant receiver. The HAP also transfers wireless power to the relays in either a power-splitting (PS) or time-switching (TS) protocol. With dense user deployment, the direct links from the HAP to the receivers are short and can contribute considerably to the overall throughput. To account for the direct links, we propose a throughput maximization problem by jointly optimizing the HAP's beamforming strategy to control the information and power transfer to the relays as well as individual relays' energy harvesting and collaborative beamforming strategies. The main challenge lies in that the direct links require the beamforming design to balance the performances of relay and direct transmissions. Though the throughput maximization problem is non-convex and the globally solution may not be available, we obtain two feasible lower performance bounds corresponding to the PS and TS protocols. Our simulation results also verify that the new design with direct links achieves significant performance improvement compared with the conventional scheme that ignores the direct links.

In this paper, we consider wireless powered Internet of Things (IoT) by a power beacon station (PBS). Each IoT device can be a sensor node that has continuous data transmission using a dual-mode radio, which operates in either active radio frequency (RF) communications or passive backscatter communications. The flexibility in the radio mode switching provides an additional degree of freedom to improve the overall network performance. To exploit the radio's diversity gain, we formulate the sum throughput maximization by jointly optimizing the transmission strategy of each node, the time allocation, and beamforming strategies of the PBS. Besides, capitalizing the fact that two nodes in different modes can complement each other, we propose the passive relaying scheme to exploit the user's cooperation gain that leverages the passive radios to relay for active RF communications. Though the backscatter-aided throughput maximization is nonconvex due to the coupling among different nodes, we design the passive relaying game to balance energy harvesting and relay performance. The simulation results verify that it can significantly enhance the sum throughput of a hybrid radio network, along with the optimal time allocation and beamforming strategies at the PBS.

In this paper, we consider a wireless powered multi-relay network in which a multi-antenna hybrid access point underlaying a cellular system transmits information to distant receivers. Multiple relays capable of energy harvesting are deployed in the network to assist the information transmission. The hybrid access point can wirelessly supply energy to the relays, achieving multi-user gains from signal and energy cooperation. We propose a joint optimization for signal beamforming of the hybrid access point as well as wireless energy harvesting and collaborative beamforming strategies of the relays. The objective is to maximize the network throughput subject to probabilistic interference constraints at the cellular user equipment. We formulate the throughput maximization with both the time-switching and power-splitting schemes, which impose very different couplings between the operating parameters for wireless power and information transfer. Although the optimization problems are inherently non-convex, they share similar structural properties that can be leveraged for an efficient algorithm design. In particular, by exploiting monotonicity in the throughput, we maximize it iteratively via customized polyblock approximation with reduced complexity. The numerical results show that the proposed algorithms can achieve close to optimal performance in terms of the energy efficiency and throughput.

In this paper, we consider a multi-user device-to-device network capable of harvesting energy from radio frequency (RF) signals. We assume that each user has a dual-mode radio architecture and can operate in either conventional active RF communications or passive backscatter communications. In the latter, the data transmission relies on the reflection of ambient RF signals. We capitalize the fact that the two communications technologies can complement each other in a hybrid radio network, by proposing the passive relaying scheme for active RF communications. Specifically, each active RF radio is allocated a fixed time slot for its data transmission. It is also assisted by a set of passive radios via backscattering the RF signals of the active radio. Though passive relaying is energy efficient as it consumes minuscule amount of energy in backscatter communications, it indeed competes the channel time that can be used by the passive radios to harvest RF energy. We propose a game-theoretic approach to balance energy harvesting and relaying performance, by allowing each passive radio to iteratively optimize its reflection coefficients. The simulation results verify that the passive relaying scheme significantly enhances the sum throughput of a hybrid radio network.

The integration of wireless power transfer (WPT) with the backscatter communications provides a promising way to sustain batteryless wireless networks. In this paper, we consider a backscatter communication network, in which the passive radio uses the harvested energy from a power beacon station (PBS) to supply its data transmissions, while some other radios can help as the wireless relays. To improve the throughput performance of a distant transceiver pair, we propose a two-hop backscatter relay model and formulate a throughput maximization problem to jointly optimize WPT and the relay strategies. Noting that the proposed problem is non-convex, an iterative algorithm with reduced complexity is proposed to decompose the original problem into a power allocation subproblem in the outer loop and an optimization of the relay strategy in the inner loop. Numerical results reveal that the power allocation converges to the optimum and the relay strategy significantly improves the throughput when the radios' power demand is low.

The integration of wireless power transfer (WPT) with the low-power backscatter communications provides a promising way to sustain battery-less wireless networks. In this paper, we consider a backscatter communication network wirelessly powered by a power beacon station (PBS). Each backscatter radio uses the harvested energy to power its data transmissions, in which some other radios can help as the wireless relays with an aim to improve throughput performance by cooperative transmission. Under this setting, we formulate a throughput maximization problem to jointly optimize WPT and the relay strategy of the backscatter radios. An iterative algorithm with reduced complexity and communication overhead is proposed to decompose the original problem into two sub-problems distributed at the PBS and the backscatter receiver. Moreover, we take uncertain channel information into consideration and formulate robust counter-parts of the throughput maximization problem when either the backscatter or relay channel is subject to estimation errors. The difficulty of the robust counter-part lies in the coupling of the PBS' power allocation and relay strategy in matrix inequalities, which is addressed by alternating optimization with guaranteed convergence. Numerical results reveal that the cooperative relay strategy of the backscatter radios significantly improves the throughput performance.

Multiple-choice (MC) question is an important form of test to assess the students' academic achievement, especially in the e-learning applications. However, the classical evaluation metrics on MC questions (such as the correctness ratio) only consider the correctness of the final selection but ignore the solving progress of the testee. In the existing literature, the eye-tracking based visual attention was studied to infer the testee's cognitive progress towards a specific MC question. However, there is little work on the visual attention based evaluation of one complete MC test. In this paper, we measure the eye movement data of a group of students in an online test, which consists of forty more MC questions. We divide the screen area into five AOIs (area of interests), including one for the question and four for the candidate options. The fixation duration as well as the gaze sequence on these AOIs are recorded and studied. In the case study on the most difficult question, we observe the great differences among the eye movement of the testees in different academic levels. A new metric, namely Visual-Attention-assisted Score (VAS), is proposed to assess the student's performance with the bias of his fixations on the correct options. Experiment results show that, this metric can reflect the difference of gaze movement of testees, and thus it is helpful for the teachers to infer the real level of the students' academic achievement.

Classroom observations have been widely used in education over the past couple of decades to measure effective teaching practice. The traditional observation methods rely on human observers, which are short of scalability and objectivity. In this paper, we implement a kind of automatic behavior measurement system, which utilizes the Microsoft Kinect devices to record the students' performance in classroom. Several Kinect devices are installed under the ceiling of one classroom. The facial images of attended students are collected and recognized. The typical gestures of students (such as sitting, raising hand, standing, sleeping and whispering) are also detected and recorded. A queue-based analysis engine is proposed to distinguish the meaningful learning behaviors from those pointless actions. Experiment results show that this system can be utilized to measure the students' active behaviors in typical learning processes, which will be helpful for the analysis of behavioral engagement in classroom teaching.

Backscatter communications allows the wireless radio to work in passive mode that transmits information by reflecting incident radio frequency signals. It consumes significantly less power compared to the conventional active radio that modulates information on self-generated carrier signals. However, the active radio is deemed more reliable as it can adapt to the varying channel conditions via transmit power control. In this paper, we aim to maximize the throughput of a multi-user network wirelessly powered by a power beacon station (PBS), assuming that each transceiver can switch between the passive and active radio modes. The joint optimization of the radios' mode selection, the PBS' energy beamforming and time allocation is formulated into a mixed integer nonlinear program (MINLP). Relying on an approximate upper bound of the MINLP, we employ a heuristic mode selection algorithm to determine each user's radio mode under uncertain channel state information. Simulation reveals that passive mode is preferred by the radios with better channel conditions and the active mode will be preferred if we ensure higher system reliability when the channels are subject to uncertainties.

Data capture is important for some critical network applications, such as network diagnosis and criminal investigation. In multi-channel wireless networks, the fundamental challenge for data capture is how to assign operation channels to wireless sniffers. The existing approaches make some impractical assumptions, such as the prior knowledge on network traffic and the perfect conditions of data capture. In this paper, we relax these assumptions and investigate the sniffer-channel assignment problem in multi-hop scenarios. Especially, sniffer redundancy deployment is discussed, which enables multiple sniffers to monitor one traffic. This problem is formulated as a combinatorial multi-arm bandit (MAB) problem, and a cooperative distribute learning policy is proposed. We analyze the regret of our policy in theory, and validate its effectiveness through numerical simulations.

In linear cooperative spectrum sensing, the weights of secondary users and detection threshold should be optimally chosen to minimize missed detection probability and to maximize secondary network throughput. Since these two objectives are not completely compatible, we study this problem from the viewpoint of multiple-objective optimization. We aim to obtain a set of evenly distributed Pareto solutions. To this end, here, we introduce the normal constraint (NC) method to transform the problem into a set of single-objective optimization (SOO) problems. Each SOO problem usually results in a Pareto solution. However, NC does not provide any solution method to these SOO problems, nor any indication on the optimal number of Pareto solutions. Furthermore, NC has no preference over all Pareto solutions, while a designer may be only interested in some of them. In this paper, we employ a stochastic global optimization algorithm to solve the SOO problems, and then propose a simple method to determine the optimal number of Pareto solutions under a computational complexity constraint. In addition, we extend NC to refine the Pareto solutions and select the ones of interest. Finally, we verify the effectiveness and efficiency of the proposed methods through computer simulations.

Near Field Communication (NFC) is a promising short distance radio communication technology for many useful applications. Although its communication range is short, NFC alone does not guarantee secure communication and is subject to security attacks, such as an eavesdropping attack. Generating a shared key and using symmetric key cryptography to secure the communication between NFC devices is a feasible solution to prevent various attacks. However, conventional Diffie-Hellman key agreement protocol is not preferable for resource constrained NFC devices due to its extensive computational overhead and energy consumption. In this paper, we propose a practical, fast and energy-efficient key agreement scheme, which uses random bits transmission with waveform shaking, for NFC devices by exploiting its off-the-shelf full-duplex capability. In the proposed method, two devices send random bits to each other simultaneously without strict synchronization or perfect match of amplitude and phase. On the contrary, the method randomly introduces synchronization offset and mismatch of amplitude and phase for each bit transmission in order to prevent a passive attacker from determining the generated key. A shared bit can be established when two devices send different bits. We conduct theoretical analysis on the correctness and security strength of the method, and extensive simulations to evaluate its effectiveness. We build a testbed based on USRP software defined radio and conduct proof-of-concept experiments to evaluate the method in a real-world environment. It shows that the proposed method achieves a high key generation rate of about 26 kbps and is immune to eavesdropping attack even when the attacker is within several centimeters from the legitimate devices. The proposed method is a practical, fast, energy-efficient, and secure key agreement scheme for resource-constrained NFC devices.

Sniffer channel assignment (SCA) is a fundamental building block for wireless data capture, which is essential for traffic monitoring and network forensics. Most of the existing SCA approaches for cognitive radio networks (CRNs) adopt optimization-based methods and rely on the prior knowledge of the secondary user (SU) activities. To relax this constraint, learning-based methods have been recently developed; however, there is still insufficient theoretical understanding within the learning framework for SCA. In this paper, we aim to maximize the total amount of the captured SU traffic, and we formulate the SCA problem as a nonstochastic/adversarial multiarmed bandit problem. Moreover, the inherent error in wireless capturing, i.e., imperfect monitoring, is considered in our model. We propose two online learning algorithms for the SCA scenarios with and without channel switching costs, respectively, and their regret performances are proved uniformly sublinear in time and polynomial in the number of channels. The numerical evaluation shows, in addition to their robust regret performances, the proposed algorithms greatly outperform the existing SCA approaches in the amount of effectively captured SU traffic.

Passive network monitoring is important for the critical applications of network diagnosis and criminal investigation. As in multi-channel wireless networks, the sniffer-channel assignment problem faces a tradeoff between exploitation and exploration. In this paper, we investigate this problem in a practical scenario. Different from the existing literature, we assume that the knowledge of the users' activities is not known a priori, and there exists capture uncertainty due to unreliable monitoring conditions. Furthermore, we consider the case of sniffer redundancy deployment, which enables multiple sniffers to monitor one channel to enhance capture reliability. Our problem is then formulated as a combinatorial multi-arm bandit problem. We propose an online learning policy, in which sniffer-channel assignment is dynamically decided based on the learning results of the users' activities. We further develop a greedy algorithm to achieve the channel assignment decision in polynomial time. Our solution is evaluated by both theoretical analysis and numerical simulations. Simulation results show that our policy achieves logarithmic regret in time and outperforms the learning policy without consideration of sniffer redundancy deployment.

Near Field Communication (NFC) is a promising short distance radio communication technology for many useful applications. Although its communication range is short, NFC alone does not guarantee secure communication and is subject to security attacks, such as eavesdropping attack. Generating a shared key and using symmetric key cryptography to secure the communication between NFC devices is a feasible solution to prevent various attacks. However, conventional Diffie-Hellman key agreement protocol is not preferable for resource constrained NFC devices due to its extensive computational overhead and energy consumption. In this paper, we propose a practical, fast and energy-efficient key agreement scheme, called RIWA (Random bIts transmission with Waveform shAking), for NFC devices by exploiting its full-duplex capability. In RIWA, two devices send random bits to each other simultaneously without strict synchronization or perfect match of amplitude and phase. On the contrary, RIWA randomly introduces synchronization offset and mismatch of amplitude and phase for each bit transmission in order to prevent a passive attacker from determining the generated key. A shared bit can be established when two devices send different bits. We conduct theoretical analysis on the correctness and security strength of RIWA, and extensive simulations to evaluate its effectiveness. We build a testbed based on USRP software defined radio and conduct proof-of-concept experiments to evaluate RIWA in a real-world environment. It shows that RIWA achieves a high key generation rate about 26kbps and is immune to eavesdropping attack even when the attacker is within several centimeters away from the legitimate devices. RIWA is a practical, fast, energy-efficient, and secure key agreement scheme for resource-constrained NFC devices.

Secret key generation by extracting the shared randomness in wireless fading channel is a promising way to ensure wireless communication security. Previous works only consider key generation in static networks, but real-world key establishments are usually dynamic. In this work, for the first time we investigate the pairwise key generation in dynamic wireless networks with a center node (eg. access point (AP)) and random arrival users. We establish the key generation model for this kind of networks. We propose a method based on discrete Markov chain to calculate the average time a user will spend on waiting and completing the key generation (average key generation delay, AKGD). Our method can tackle both serial and parallel key generation scheduling under various conditions. We conduct extensive simulations to show the effectiveness of our model and method. The analytical and simulation results match to each other.

Recently, the variable channel-width (VW) scheme was proposed to improve the performance of WLANs. Cooperative channel allocation has been studied in some existing literature under the assumption that the traffic demands of cooperative access points (APs) are constant. In fact, the traffic demands may vary when the corresponding stations change their AP association decisions. Hence, this work jointly considers the channel allocation and AP association, aims to maximize the system performance in terms of throughput and fairness. The problem is formulated as a constrained Integer Non-Linear Programming (INLP) problem, which is NP-hard. Two penalty functions are introduced to relax the constraints, and a discrete particle swarm optimization (DPSO) algorithm is then proposed to solve the problem. The simulation results show that our algorithm can improve the performance by about 20% compared to the fixed traffic scheme.

Spectrum sensing is an essential functionality of cognitive radio networks (CRN). Among existing spectrum sensing methods, cooperative spectrum sensing is the best one which can achieve superior sensing performance by introducing spatial diversity of sensing data sources. Such cooperation also introduces additional information exchanging which leads to extra power consumption and reporting delay. In this paper, the optimal sensing performance problem is formulated as a nonlinear binary integer programming problem to find suitable cooperative nodes minimizing the average detection Bayesian risk. The binary particle swarm optimization (BPSO) algorithm is adopted to obtain suboptimal solutions to cooperative nodes. Computer simulations show that the proposed scheme can significantly improve the sensing performance compared with the case that all neighboring nodes participate in sensing without discrimination under different scenarios.