Diffusion probabilistic models have set a new standard for generative fidelity but are hindered by a slow iterative sampling process. A powerful training-free strategy to accelerate this process is Schedule Optimization, which aims to find an optimal distribution of timesteps for a fixed and small Number of Function Evaluations (NFE) to maximize sample quality. To this end, a successful schedule optimization method must adhere to four core principles: effectiveness, adaptivity, practical robustness, and computational efficiency. However, existing paradigms struggle to satisfy these principles simultaneously, motivating the need for a more advanced solution. To overcome these limitations, we propose the Hierarchical-Schedule-Optimizer (HSO), a novel and efficient bi-level optimization framework. HSO reframes the search for a globally optimal schedule into a more tractable problem by iteratively alternating between two synergistic levels: an upper-level global search for an optimal initialization strategy and a lower-level local optimization for schedule refinement. This process is guided by two key innovations: the Midpoint Error Proxy (MEP), a solver-agnostic and numerically stable objective for effective local optimization, and the Spacing-Penalized Fitness (SPF) function, which ensures practical robustness by penalizing pathologically close timesteps. Extensive experiments show that HSO sets a new state-of-the-art for training-free sampling in the extremely low-NFE regime. For instance, with an NFE of just 5, HSO achieves a remarkable FID of 11.94 on LAION-Aesthetics with Stable Diffusion v2.1. Crucially, this level of performance is attained not through costly retraining, but with a one-time optimization cost of less than 8 seconds, presenting a highly practical and efficient paradigm for diffusion model acceleration.

State re-sharding can reduce cross-shard transaction ratios, which improves the scalability of blockchain systems. However, unavoidable cross-shard transactions and account-locking mechanisms can lead to security risks (read-write conflicts) and performance bottlenecks (low synchronization efficiency). Therefore, this paper proposes a secure and efficient read-write synchronization model for cross-shard transactions in blockchain state re-sharding via a lightweight Global State Tree ($\mathcal{GT}$). The model consists of intra-shard and inter-shard state consistency modules. The intra-shard module includes two methods: account state read-write and account record update. The former allows local shard committees to track account state changes and prevent the use of expired account states, while the latter incorporates account records within maximum latency into an account state data structure, thereby enhancing the traceability and verification efficiency of update history. In the inter-shard module, a transaction processing method with a global takeover mechanism is proposed during the re-sharding window. By using validated data in the$\mathcal{GT}$, the method achieves non-blocking global coordination and account reallocation. Experimental results demonstrate that the proposed model increases transaction throughput and reduces transaction latency compared to bases under Byzantine conditions.

Depression is a common emotional disorder in modern society that causes growing burdens globally. This mental disorder has been frequently confirmed to be closely related to abnormalities in the prefrontal cortex (PFC). However, it remains to be fully investigated whether the neural activity in the PFC has regular quasi-steady spatiotemporal structures, and whether dynamic patterns of these structures are associated with prefrontal dysfunction in depression. To further uncover such neural correlates, we extended the traditional electroencephalography (EEG) microstates to a novel localized level and developed a variational Bayesian probabilistic generative model to decode such localized microstate patterns from few-channel prefrontal EEG signals. We validated the method with a publicly available multichannel EEG dataset obtained from 165 healthy individuals and a three-channel prefrontal EEG dataset (43 depressed and 43 healthy). The approach was finally used to examine dynamic evolution of prefrontal neural activities in depression. Our results demonstrate that the localized prefrontal microstates exhibit high cross-dataset reproducibility and were characterized by finer spatiotemporal patterns independent from traditional whole-brain microstates. The results further revealed significant emotional task-specific abnormalities in localized prefrontal microstates between the depressed and the healthy individuals, including more frequent occurrences and shorter durations in high-power bilaterally asymmetric microstates, as well as less organized low-power symmetric microstates. Our extended concept of the localized microstates and associated analytical methods provides a novel theoretical framework for elucidating prefrontal neural dynamics and also lays a theoretical foundation for uncovering prefrontal functional abnormalities in depression and developing auxiliary diagnostic tools with prefrontal few-channel EEG data.

Event-driven neuromorphic computing involves sparse and asynchronous signal activity, which leads to irregular computation patterns and fine-grained concurrency. As a result, processing architectures need to support both high parallelism and energy efficiency. Among existing architectural solutions, superscalar designs exhibit significant potential for addressing high parallelism demands. However, conventional superscalar processors, which rely on synchronous circuits, maintain high-frequency clocking at all times, leading to substantial power inefficiency in sparse computation scenarios. To address this issue, we propose an asynchronous superscalar architecture that replaces global clocking with fully local handshake-based control, implemented using a bundled-data asynchronous protocol. The design supports decoding of up to 64 scalar instructions per cycle and implements the RISC-V RV32IMC instruction set. A prototype was fabricated using a 110 nm complementary metal oxide semiconductor (CMOS) process and was evaluated through post-layout simulation. Operating at 1.2 V, the processor delivers a peak INT8 throughput of 669.4 GOPS, with a static power consumption of 421 mW.

Ethereum 2.0 (ETH2) marks a pivotal shift in blockchain technology, transitioning from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus mechanism, with Gasper at its core. While this evolution promises enhanced scalability and energy efficiency, the performance of its block proposal stage is highly sensitive to network latency and system parameters, such as slot length. This sensitivity introduces a critical trade-off between throughput and security, measured by the probability of blockchain forking. This paper reveals that network latency is not just a passive risk but an exploitable attack surface. We introduce the "adaptive latency-driven equivocation attack", a novel adversarial strategy where an attacker deliberately creates forks while mimicking the behavior of a high-latency node, thus achieving plausible deniability. To formally analyze and quantify the impact of this threat, we develop a comprehensive theoretical model by using Markov chains to analyze the fork probability and throughput of the Gasper's block proposal mechanism under both honest and adversarial conditions. Through extensive simulations, we validate the accuracy of our model in both normal and bursty traffic conditions. Our findings provide a systematic methodology for optimizing system parameters to achieve a robust balance between performance and security, offering a foundational guide for configuring ETH2 networks against sophisticated, latency-based threats.

Data integrity verification in geographically distributed edge systems remains a critical unsolved challenge. While centralized verification introduces bottlenecks and single points of failure, existing decentralized alternatives suffer from inefficiency due to their lack of batch verification capabilities. This limitation leads to prohibitive communication and computational overheads that scale poorly as data volume grows. This paper introduces EdgeBatch, the first decentralized protocol designed for efficient batch integrity verification, reducing communication rounds from$\mathcal {O}(n)$to$\mathcal {O}(1)$a small, constant number. At its core is a reputation-aware Combination Selection Algorithm (CSA), a polynomial-time heuristic that identifies near-optimal peer server combinations, balancing verifier group size against servers' historical trustworthiness through intelligent pruning strategies. This process is orchestrated through distributed ledger technology and smart contracts, ensuring a secure, transparent, and trustless verification environment. The protocol's design is underpinned by rigorous theoretical analysis, including formal proofs of security and correctness, and a probabilistic model for optimizing key system parameters. Extensive simulations show that EdgeBatch drastically outperforms state-of-the-art methods; it improves computational efficiency by an average of 518.60× over EdgeWatch and 1030.93× over CooperEDI, while also reducing communication overhead by 296.68× and 62.66×, respectively. A concluding ablation study confirms the vital role of our reputation mechanism, demonstrating it reduces the required verification rounds by 73% and is the key to the protocol's efficiency.

Depression detection using Electroencephalogram (EEG) signals obtained from wearable medical-assisted diagnostic systems has become a well-established approach in the field of affective disorders. However, despite recent advancements, on-board Artificial Intelligence (AI) models still demand substantial computational resources, presenting significant challenges for deployment on resource-constrained wearable medical devices. Embedded Multi-core Processors (MPs) offer a promising solution for accelerating these models. However, the limited computational capabilities of embedded MPs, combined with the structural diversity of AI models, complicate resource allocation and increase associated costs. To address these challenges, we propose a Memory-Aware Multi-Objective Iterative Local Search (MAMILS) algorithm to optimize task scheduling, thereby improving the efficiency of AI model deployment on wearable EEG devices. Experimental results across seven AI models demonstrate that, the MAMILS approach yields substantial improvements in key performance indicators: Total Energy Consumption ($\bm {TEC}$) with an average reduction of 47.57%,$\bm {Makespan}$with an average reduction of 48.75%, and$\bm {Throughput}$with an average increase of 198.37%, all while maintaining satisfactory classification performance for both Machine Learning (ML) and Deep Learning (DL) models. Especially, on-board deployment of EEGNeX achieves an accuracy of 93.4%, sensitivity of 91.6%, and specificity of 95.8%. Further analysis indicates that, when integrated with wearable EEG sensors and executable on-board AI models, the proposed MAMILS optimization strategy shows significant promise in facilitating the widespread adoption of low-power, real-time diagnostic systems for depression detection.

Quality of Service (QoS) prediction faces challenges from location-dependent variability and sparse user-service interactions. Existing methods often struggle to integrate location information (e.g., using fixed weights for spatial attributes) or learn representative features from sparse matrices. This paper proposes a method for Decoupling Location and Preference via a dual-branch architecture for robust QoS prediction under extreme sparsity, called DLP. It integrates location and preference features to address the challenges of sparsity and contextual variability. Unlike conventional single-stream or simple concatenation methods, DLP features a novel dual-branch architecture that decouples heterogeneous features and specializes in processing them: Location context and user-service preferences. The first branch, a location feature extraction network, processes user and service geographical and network information. It utilizes an attention mechanism to dynamically weight spatial attributes (instead of fixed weights) based on their actual impact on QoS and selects the most salient co-location features to model spatial interactions. The second branch, a preference feature extraction network, constructs high-dimensional feature representations from similarity-based user-service vectors derived from the sparse QoS matrix. It employs a multi-layer feature extraction block that hierarchically aggregates intermediate features to compensate for information loss during transformation, thereby capturing richer user/service preferences. Finally, a feature fusion prediction network integrates the learned location and preference features to generate accurate QoS predictions. Ablation studies and analysis validate that each component contributes significantly to performance gains. Extensive experiments on the WS-DREAM dataset show that DLP outperforms 22 baselines across 2.5%–20% sparsity, excelling in throughput prediction (achieving reductions up to 9.07% in Mean Absolute Error and 28.86% in Root Mean Squared Error at 2.5% sparsity) and validating its superior QoS prediction accuracy.

Prolonged mental fatigue poses significant risks to both individual health and societal productivity. Traditional methods of detecting mental fatigue often neglect the importance of timely, accessible monitoring. Although blue-enriched light (BEL) is effective at mitigating mental fatigue, prolonged exposure to it can result in visual fatigue and impairment. To address these limitations, we propose a portable fatigue monitoring and BEL feedback system. The system uses three-lead prefrontal Electroencephalogram (EEG) signals to detect mental fatigue in real time and dynamically adjusts BEL exposure based on the user's current fatigue state. This prevents visual issues associated with prolonged BEL exposure. In this work, we develop an onboard executable Convolutional Neural Network (CNN) model using interpretable two-dimensional (2D) convolution implemented with TensorFlow Lite. Our model strikes a favorable balance between classification accuracy (85 %) and computational efficiency, requiring only 50.98 K Floating-Point Operations (FLOPs) and a parameter size of 7.28 KB. When deployed on an EEG sensor, the model operates with just 79.98 KB of RAM and 378.98 KB of ROM. It performs a single inference in 130 ms and consumes 232.31 mW of power per inference. Experimental results demonstrate that the lightweight, onboard executable model integrated with the custom-designed hardware system shows potential for effectively modulating mental fatigue.

Music therapy has been shown to be effective in treating depression, as supported by numerous clinical studies and randomized controlled trials utilizing subjective reports and psychometric scales. However, a more efficient and objective approach is needed to complement these assessments and to investigate the neural effects of music therapy. This study designed a validity assessment framework based on the dot-probe paradigm, which uses participants' attentional biases toward emotional faces as an objective marker of music therapy effectiveness. To validate this framework, we collected 64 -channel electroencephalogram (EEG) signals from patients undergoing music therapy during a dot-probe task, and explored the dynamic cognitive processes in depressed patients before and after treatment using event-related potentials (ERPs). Our results indicate modulated attentional biases toward negative emotional faces in depressed patients following music therapy. Specifically, we observed shorter response times and higher ERP amplitudes for negative faces compared to positive faces before treatment. These abnormalities were ameliorated after treatment and showed significant correlations with pre- and post-treatment scale measurements. These findings lay a foundation for future artificial intelligence-based systems that could automate the assessment of music therapy effectiveness using neurophysiological markers, potentially enabling personalized treatment approaches and real-time therapeutic adjustments.

Heroin addiction represents a chronic neuropsychiatric disorder characterized by profound alterations in the brain's reward circuitry, particularly within the medial prefrontal cortex (mPFC), ventral tegmental area (VTA), and nucleus accumbens (NAc). Despite mounting evidence that addiction involves dysregulated neural oscillations, frequency domain analysis of reward processing in deeper brain structures remains critically understudied. We collected$\mathbf{6 4}$-channel EEG data from heroin addicts and healthy controls during a monetary incentive delay task with three conditions: positive (potential monetary gain), neutral (no gain or loss), and negative (potential monetary loss). Using advanced source localization techniques with fine realistic head models, we reconstructed brain source signals from key reward circuitry regions (mPFC, VTA, and NAc), analyzing neural responses across three temporal stages: reward anticipation, reward expectation, and reward outcome through event-related desynchronization/synchronization (ERD/ERS) analysis and time-frequency analysis. Heroin addicts exhibited significantly altered neural oscillatory patterns compared to healthy controls across delta, alpha, beta, and gamma frequency bands within the reward circuitry, with frequency-specific abnormalities observed in both cortical and subcortical reward-related regions during different stages of reward processing. This study provides the first comprehensive characterization of frequency-specific neural dysfunction spanning the entire reward circuitry in heroin addiction, offering novel insights into the oscillatory mechanisms underlying reward processing abnormalities and informing the development of frequency-targeted therapeutic interventions.

The widespread adoption of WiFi has made throughput efficiency a critical concern in wireless networks. While Full-Duplex (FD) technology promises to double network capacity by enabling simultaneous transmission and reception, existing FD-WiFi designs primarily focus on the data transmission phase, leaving the fundamental inefficiencies in channel contention unaddressed. This paper presents CollFree, a novel WiFi protocol that exploits FD capabilities during both contention and data transmission phases. At its core, CollFree introduces a Slotwise Arbitration (SA) mechanism that enables each node to simultaneously transmit contention signals and sense channel status in each contention slot. This dual-mode operation significantly reduces contention time and facilitates collision-free data transmissions through a unique winner-determination process. We then develop theoretical models to analyze CollFree’s contention performance and throughput efficiency under both perfect and imperfect Clear Channel Assessment (CCA) conditions, providing guidelines for parameter optimization in practical deployments. Extensive simulations demonstrate that CollFree enhances throughput efficiency by over 20% compared to state-of-the-art FD-WiFi systems while maintaining distributed control and compatibility with current WiFi standards. These results suggest that CollFree represents a significant step toward realizing the full potential of FD technology in next-generation WiFi networks.

The advancement of wearable electronic technology has facilitated the integration of smart wearable devices into artificial intelligence (AI)-driven medical assisted diagnosis. Embedded multi-core processors (MPs) have gradually emerged as pivotal hardware components for smart wearable medical diagnostic devices due to their high performance and flexibility. However, embedded MPs face the challenge of balancing performance, power consumption, and load-balancing. In response, we introduce a Pareto-based iterated local search (PILS) algorithm for task scheduling, which systematically optimizes multiple objectives, alongside a task list model to reduce the dimension of the decision space and enhance scheduling performance. In addition, we present a two-stage discretization scheme to ensure that the proposed algorithm offers meaningful guidance throughout the scheduling process. Simulation and on-board testing results show that the proposed algorithm effectively optimizes energy consumption, task execution time, and load balancing in embedded MPs task scheduling, indicating the potential of the proposed algorithm in enhancing the performance of smart wearable medical diagnostic devices powered by embedded MPs.

In this article, a structure-reconfigurable series resonant DC-DC converter is proposed for a wide gain on-board charger application. The proposed converter consists of a dual-bridge structure on the primary side which can realize 0.5 to 1 voltage gain by using a reconfigurable half/full bridge structure, and a hybrid rectifier on the secondary side which can realize 1 to infinite voltage gain by replacing two diodes with active switches. Moreover, the proposed converter employs a control scheme based on fixed frequency PWM, with the operating frequency being identical to the series resonant frequency. Accordingly, magnetizing inductance of the transformer is independent of the converter gain characteristics, which simplifies the consideration of the resonance parameters design. In addition, soft switching can be realized during the entire charging process, and high efficiency can be achieved. To avoid the voltage spike and current impact in the transition between two operation modes, a unified switching modulation strategy is applied to achieve a smooth transition and improve the control stability. Finally, a 2.5 kW prototype with an output voltage range of 200V - 500 V is established and tested to verify the effectiveness and feasibility of the proposed converter.

This paper proposes a high reliability push-pull forward resonant converter for space power supply systems. The primary side of the converter consists of two series-connected forward converter units, and the secondary side incorporates a resonant tank in the semiactive variable-structure rectifier (SA-VSR), which adjusts the voltage gain range through fixed-frequency PWM control. By controlling the duty cycle of the bidirectional switch in SA-VSR, the proposed converter can operate in three modes: voltage doubling rectification (VDR) mode, full bridge rectification (FBR) mode, and PWM control mode, which can achieve a regulation of twice the voltage gain range. This paper provides a detailed working principle and voltage gain analysis of the converter. All switches of the proposed converter can achieve ZVS, and there is no issue of direct short-circuiting through the switches, making it highly reliable and appropriate for applications in aerospace. Finally, a prototype with a rated input voltage of 90-180V, output voltage of 400V, and output power of 800W is built to verify the effectiveness of the proposed converter.

Cross-technology communication (CTC) enables direct communications among devices with heterogeneous wireless technologies, e.g., Bluetooth, WiFi, and ZigBee, thereby reducing the cost and complexity of their interconnections. Yet CTC is unreliable due to the technology heterogeneity, and most existing CTC designs do not provide acknowledgment (ACK) feedback to ensure reliable data transmission. Few ACK designs are only applicable to feedback for ZigBee-WiFi pair and vulnerable to sampling offsets that inherently exist in CTC. In this work, we propose a General and Offset-resistant Physical-layer ACK approach, called GOP-ACK, to support reliable communications. Its core idea lies in encoding ACK messages with offset-resistant signal that has two benefits: 1) it can be adapted to a wide range of CTC scenarios with minimal adjustment, and 2) it can be effortlessly and robustly detected even in the presence of sampling offsets. We offer practical guidelines to tackle key deployment challenges related to signal construction, efficient and robust transmission, and effective firmware module reuse, enabling the application of GOP-ACK to specific CTC scenarios. Based on them, we implement two designs: ZigBee-to-BLE and ZigBee-to-WiFi feedback, and propose a theoretical model to analyze their performance. We then conduct experiments and simulations to verify GOP-ACK’s feasibility and superiority over the state of the art, thereby enhancing the practicality of CTC greatly.

With the rapid evolution of Web3.0, cryptocurrency has become a cornerstone of decentralized finance. While these digital assets enable efficient and borderless financial transactions, their pseudonymous nature has also attracted malicious activities such as money laundering, fraud, and other financial crimes. Effective detection of malicious accounts is crucial to maintaining the security and integrity of the Web 3.0 ecosystem. Existing malicious account detection methods rely on large amounts of labeled data and suffer from low generalization. Label-efficient and generalizable malicious account detection remains a challenging task. In this paper, we propose ShadowEyes, a framework for detecting malicious accounts by leveraging interaction feature learning with only a small labeled dataset. Specifically, We first propose a generalized account representation named TxGraph, which captures the universal interaction features of Ethereum and Bitcoin. Then we carefully design an account representation augmentation method tailored to simulate the evolution of malicious accounts to generate positive pairs. We conduct extensive experiments using public datasets to evaluate the performance of ShadowEyes. The results demonstrate that it outperforms state-of-the-art (SOTA) methods in four typical scenarios. Specifically, in the scenario of acrossplatform malicious account detection, ShadowEyes maintains an F1 score of around 90%, which is 10% higher than the SOTA method. In the zero-shot learning scenario, it can achieve an F1 score of 79.56% for detecting gambling accounts, surpassing the SOTA method by 10.44%.

Recently, Li et al. proposed an identity-based linearly homomorphic network coding signature (IB-HNCS) scheme for secure data delivery in Internet of Things (IoT) networks, and they claimed that the IB-HNCS scheme can resist pollution attacks. However, this paper shows that the IB-HNCS scheme is vulnerable to pollution attacks, as anyone who only has the public parameter can forge a new file identifier or a valid signature on a corrupted data packet to pollute legitimate sensor data. To enhance security and performance in network coding-based IoT networks, we propose a secure and efficient certificateless linearly homomorphic network coding signature scheme for IoT data delivery, which is free of burdensome certificate management and key escrow issue. In addition, our scheme is proved to be secure against adaptive chosen identity and adaptive chosen subspace attacks under two types of adversaries in the algebraic group model and random oracle model. Therefore, our scheme can verify the validity of data packets and allow data packets to be computed, so as to resist pollution attacks. The performance evaluation demonstrates that our scheme is more efficient and practical than existing secure schemes. Specifically, for a 73-dimensional data vector, the costs of signature generation and verification in our scheme are reduced by 38.588%-86.076% and 38.570%-85.664% respectively under the symmetric bilinear pairing setting, and the costs of signature generation and verification in our scheme are reduced by 17.740%-49.752% and 29.697%-58.645% respectively under the asymmetric bilinear pairing setting.

Practical Byzantine Fault Tolerance-based consensus mechanisms in consortium blockchains face challenges in scalability and communication efficiency. While recent approaches like HotStuff and Kauri have attempted to address these issues through star and tree communication structures, they still encounter limitations in security, communication costs, and node workload distribution. This article presents CTT, a novel consensus mechanism with a three-layer tree communication structure for consortium blockchains. CTT incorporates three key innovations: 1) A fixed three-layer architecture that reduces communication complexity between any two nodes toO(1), compared toO(logn) in existing tree-based approaches; 2) specialized role distribution among nodes at different layers to optimize workload and enhance system security; 3) an improved Borda counting method for efficient consensus node selection based on multiple attributes including verification rate, propagation rate, and storage space. The mechanism features dual middle-node communication paths with bottom nodes, providing enhanced fault tolerance and security compared to existing approaches. Experimental results demonstrate CTT's effectiveness in improving scalability and security while reducing communication overhead in consortium blockchain systems. The findings have the potential to significantly advance the performance and applicability of consortium blockchains in critical areas such as finance, supply chain, and healthcare.

Deep graph convolutional networks (GCNs) have shown promising performance in traffic prediction tasks, but their practical deployment on resource-constrained devices faces challenges. First, few models consider the potential influence of historical and future auxiliary information, such as weather and holidays, on complex traffic patterns. Second, the computational complexity of dynamic graph convolution operations grows quadratically with the number of traffic nodes, limiting model scalability. To address these challenges, this study proposes a deep encoder-decoder model named AIMSAN, which comprises an auxiliary information-aware module (AIM) and a sparse cross-attention-based graph convolutional network (SAN). From historical or future perspectives, AIM prunes multi-attribute auxiliary data into diverse time frames, and embeds them into one tensor. SAN employs a cross-attention mechanism to merge traffic data with historical embedded data in each encoder layer, forming dynamic adjacency matrices. Subsequently, it applies diffusion GCN to capture rich spatial-temporal dynamics from the traffic data. Additionally, AIMSAN utilizes the spatial sparsity of traffic nodes as a mask to mitigate the quadratic computational complexity of SAN, thereby improving overall computational efficiency. In the decoder layer, future embedded data are fused with feed-forward traffic data to generate prediction results. Experimental evaluations on three public traffic datasets demonstrate that AIMSAN achieves competitive performance compared to state-of-the-art algorithms, while reducing GPU memory consumption by 41.24%, training time by 62.09%, and validation time by 65.17% on average.

This study explores the performance optimization of uplink orthogonal frequency division multiple access (OFDMA)-based random access (UORA) in IEEE 802.11ax networks. UORA supports multi-user transmission via two methods, where users transmit either fixed-size or variable-size aggregated MAC protocol data units. However, three critical issues arise. 1 Existing studies only focus on the fixed-size method with low practicality, and overlook the impact of traffic load which leads to inaccurate evaluation of the network performance. 2 The variable-size method has never been studied due to a complex scenario, where user frames append padding bits to fulfill the transmission opportunity constraint. 3 In realistic networks, the variable-size method sacrifices throughput to achieve high practicality and low latency. To address the first two issues, we proposed two novel models based on queueing theory that accurately capture the impact of these transmission methods and various parameters (e.g., the traffic load and padding bits) on throughput, packet loss rate, and latency. To address Issue 3, we design aDynamicSelectionAlgorithm ofTransmissionMethods (DSATM), which dynamically switches between the two transmission methods to enhance practicality, maximize throughput, and minimize latency. Finally, we conducted extensive simulations to verify the accuracy of our models and DSATM.

Depression detection via wearable Electroencephalogram (EEG) sensor-assisted diagnosis system demands computationally efficient models compatible with resource-constrained edge devices. Spiking Neural Networks (SNNs) offer inherent advantages for processing the spatio-temporal patterns of EEG through event-driven manner. In this study, we innovatively present LSNNet, a lightweight SNN model specifically designed for wearable EEG sensors. The model exhibits low computational complexity with 7.18 K parameters and 67.68 M Floating-Point Operations (FLOPs). It requires only 246.88 KB of Random Access Memory (RAM) and 57.33 KB of Read-Only Memory (ROM) for on-board execution, and has been validated on both the single-core STM32U535CET6 and the multi-core GAP8 microcontrollers. Despite its minimal computational and memory requirements, LSNNet achieves impressive performance metrics, with a classification accuracy of 89.2%, specificity of 92.4%, and sensitivity of 86.4% in independent tests conducted on EEG data collected from 73 depressed patients and 108 healthy controls using our three-lead EEG sensor. Especially, when running on the GAP8 microcontrollers, the LSNNet model has a low power consumption of 21.43 mW and a satisfactory inference time of 0.63 s while maintaining a classification accuracy of 87.5% (only with a reduction of 1.98%). These results underscore the potential of integrating wearable EEG sensors with the LSNNet model for depression detection in the Internet of Things (IoT) era.

Granger causality (GC) effective connectivity (EC) calculated from electroencephalogram (EEG) signals has been widely used in mental disorder detection. However, the existing methods only take into account linear dynamics or nonlinear dynamics within a single sample, ignoring the nonlinear dynamics shared by the same class of subjects. In this article, a model combining graph neural networks (GNNs) and variational autoencoders (VAEs) is proposed to construct shared latent nonlinear EC from raw EEG signals for depression detection. Several convolution modules and fully connected layers are used in the graph encoding network to learn the embeddings of the connectivity connected by every two EEG channels. In the graph decoding network, a class-specific Gaussian mixture model (GMM) is introduced in the VAEs to model shared dynamics in EC of the same class of subjects, and the shared dynamics combine the encoded embeddings of the EC and the past time series to restore raw EEG signals. Through a node-to-edge encoding process and an edge-to-node decoding process, the shared latent nonlinear EC in EEG signals can ultimately be learned by gradually optimizing the model's loss function. The performance of the proposed method is verified on several open-accessed datasets. The excellent results prove that the proposed neural networks can learn more generalized nonlinear EC representations, and shared latent dynamics discovery can also help to identify depression better. The code is available at https://github.com/william-yuan2012/DSLNEC-tscausality.

With the development of blockchain applications, demand for cross-chain technology has been increasing. Relay chain mode is the state-of-the-art and mainstream solution nowadays. However, the relay chain mode suffers from poor performance, which stems from its core facility – the relay chain. Therefore, guiding its improvement and parameters configuration is vital. Currently, there is no specialized performance model of the relay chain. The cross-chain scenario involves receiving transactions from blockchains and uniformly verifying them, while general blockchain models are not applicable for it. Relay chains are characterized by the following features: transaction arrival in batches with uncertain sizes, updating block headers for simplified payment verification (SPV), Byzantine fault tolerance (BFT) type protocol, and different packaging rules. This work first proposes an analytical framework for relay chain performance. It captures the mentioned features by constructing a batch-arrival and bulk-service model. We give a concrete calculation of the relay chain with practical BFT (PBFT) consensus and develop a method to arrive at the computational forms of two essential performance descriptors: system throughput and cross-chain transaction confirmation delay. Through this model, we can judge accurately whether the relay chain is overloaded, and eliminate the overload state by tuning the parameters; and we can evaluate the system performance under different traffic and design parameters. Finally, we verify the model through experiments. With our study, operators can configure the system parameters effectively and improve the relay chain to meet the requirements of practical use.

With social development, the demand for automatic sleep quality assessment in wearable devices is increasing, especially as sleep quality is closely related to the diagnosis of psychiatric disorders. However, existing automatic sleep stage classification models are mostly designed for unimodal signals, with an emphasis on increasing parameter scale and model depth. As a result, it is challenging to meet the requirements for both lightweight models and high accuracy in wearable device-based automatic sleep staging tasks. To address this problem, this study introduces a novel lightweight model for sleep staging, MultimodalSleepNet, which is based on multi-modal physiological signals. Specifically, the model is designed to capture the temporal dynamics of physiological signals and the spatial interactions between multimodal signals. Additionally, an inflationary convolution mechanism is incorporated to accelerate temporal feature extraction. We validate the model using the publicly available Sleep-EDF-Expanded dataset. Compared to similar studies, our model achieves outstanding performance, with accuracies of 93.1% and 90.2% in the three-stage and five-stage sleep recognition tasks, respectively. Notably, the three-stage classification results show an 11.9% improvement in modal fusion accuracy compared to unimodal signals, with an 8.9% improvement in multiclass F1 score and a 20.8% increase in Cohen’s kappa coefficient. In conclusion, our study offers a reference for the design of lightweight models for sleep staging and provides new insights into feature extraction and fusion of multimodal signals.

Previous studies have shown that the resting-state electroencephalogram (EEG) of depressed patients exhibits abnormal dynamic activation of large-scale networks in microstate analysis. However, the problem of heterogeneous results in microstate features as physiological markers for depressive disorders has not been addressed. An important factor contributing to this problem is that previous studies have overlooked the effects of eye-opening and eye-closing on resting-state EEG microstates in depression. To address this gap, the present study proposes a new microstate delineation method to accurately identify EEG microstates in both open-eye and closed-eye resting states, and further analyzes the differential performance of the depressed group and the control group in the two states. We validated our method on a dataset containing 64-channel EEG data (55 cases in the depressed group and 55 cases in the control group). The results showed that the classical seven microstate topographies were present in both open-eye and closed-eye conditions, which may explain why other studies have overlooked the impact of eye state on resting-state microstates in depression. In addition, the microstate characteristics of depression were differentially expressed in the open-eye and closed-eye conditions, with the depressed group showing more significant results in the closed-eye condition. Overall, our study demonstrates that eye state affects depression microstates and provides new insights to address the problem of heterogeneity in the results of EEG microstate studies of depression.

Depressive affective dysfunction could be manifested by dynamic reorganization processes of functional brain networks under emotional tasks. However, such emotional task-specific reorganizations remain not fully understood in terms of spatiotemporal organization of oscillation dynamics. The insufficiency of approaches for quantifying such dynamic reorganization limits the effective extraction of dynamic neuromarkers of depressive affective dysfunction. To address this gap, this study presents a neural oscillation-dependent microstate approach to quantify the dynamic reorganization process of functional networks at high temporal resolution. The approach was tested by analyzing a 64-channel electroencephalography (EEG) dataset with 110 participants (55 depressed patients and 55 normal controls) collected during emotional tasks with four affective polarities (positive, neutral, negative, and resting state). Our analyses revealed oscillation-dependent microstates that reflected abnormalities in the networks associated with external information processing and interoception in depression. Our analyses further suggest that such abnormalities may be caused by dysfunction in a limited number of brain regions, which then dynamically affects functional brain networks. These findings may reflect increased self-focus and deficits in the perception of external information in depression. In summary, our study further explores source-level evidence related to abnormalities in microstate features of depression based on the generalization of depression microstate research to a multi-band framework. Our study provides a direction for expanding the application of dynamic reorganization analyses of functional networks with high temporal resolution, and provides new support and insights into the neural mechanisms of affective dysfunction in depression.

Sleep is a daily activity essential for well-being, yet many modern individuals experience sleep problems, and prolonged poor sleep negatively impacts both physiological and psychological health. Aromatherapy, an emerging complementary alternative medicine, has shown promise as a sleep aid, but it lacks objective and reliable monitoring and control methods. To address this gap, we propose a wearable, portable sleep monitoring and aromatherapy system that utilizes single-channel electroencephalogram (EEG) signals from the prefrontal lobe for sleep detection and provides aromatherapy feedback based on the detected sleep state. Our system incorporates a lightweight model based on convolutional neural networks (CNN) and long short-term memory (LSTM) networks, enabling on-board execution and classification. Trained on the publicly available Sleep-EDF dataset, the model achieves a classification accuracy of 85.1% and a macro-F1 score of 79.5%. The combination of our developed EEG sensor and the proposed model presents a promising solution for effective sleep monitoring and intervention, aiming to enhance sleep quality.

Network coding, as a routing technology to improve network throughput and robustness, is widely used in various scenarios. However, network coding is vulnerable to pollution attacks where nodes may maliciously modify transmitted data packets. Recently, certificateless linearly homomorphic signature schemes have been proposed to resist pollution attacks in network coding, which avoids burdensome certificate management and the key-escrow issue. In this paper, we show that Wu et al.’s certificateless homomorphic network coding signature (CHNCS) scheme, Chang et al.’s CHNCS scheme, and Li et al.’s CHNCS are not secure against pollution attacks in network coding. Then we present a secure and efficient CHNCS scheme and prove it is unforgeable against adaptive chosen identity-and-subspace attacks under two types of adversaries. Finally, performance analysis illustrates that our scheme is more efficient in practical application.

Cross-technology communication (CTC) is a promising technique that enables direct communications among incompatible wireless technologies without needing hardware modification. However, it has not been widely adopted in real-world applications due to its inefficiency and unreliability. To address this issue, this paper proposes a deep joint source-channel coding (DJSCC) scheme to enable efficient and reliable CTC. The proposed scheme builds a neural-network-based encoder and decoder at the sender side and the receiver side, respectively, to achieve two critical tasks simultaneously: 1) compressing the messages to the point where only their essential semantic meanings are preserved; 2) ensuring the robustness of the semantic meanings when they are transmitted across incompatible technologies. The scheme incorporates existing CTC coding algorithms as domain knowledge to guide the encoder-decoder pair to learn the characteristics of CTC links better. Moreover, the scheme constructs shared semantic knowledge for the encoder and decoder, allowing semantic meanings to be converted into very few bits for cross-technology transmissions, thus further improving the efficiency of CTC. Extensive simulations verify that the proposed scheme can reduce the transmission overhead by up to 97.63% and increase the structural similarity index measure by up to 734.78%, compared with the state-of-the-art CTC scheme.

Neural-enhanced video streaming systems have successfully addressed the challenge of limited network bandwidth by utilizing super-resolution (SR) techniques to enhance video quality. However, mobile users often face constraints in terms of computational resources required for SR operations. To overcome this, the integration of mobile edge computing becomes crucial. The main challenge lies in efficiently allocating GPU resources on the edge server to multiple users as the available GPU resources are typically insufficient to process all video chunks. In this paper, we formulate the problem of an edge server assisting multiple users in selecting bitrate levels and performing SR inference, and prove its NP-hardness. Subsequently, we propose an edge-assisted video streaming framework named Edge-Assisted SR (EASR) for high-definition neural-enhanced video streaming. This framework is built upon the theory of model predictive control (MPC). EASR addresses the variable reward of SR enhancement across different video chunks by making joint decisions on bitrate, SR, and GPU allocation for each chunk to maximize the average quality of experience (QoE) for all users. We evaluate the performance of EASR on four videos and real network traces. Extensive experiments reveal that EASR outperforms other baselines by $18.76 \%$ to $58.37 \%$ in terms of average QoE and 0.005 to 0.017 in terms of structural similarity index (SSIM).

Variable bitrate (VBR) encoding has gained considerable interest due to its capacity to enhance video quality and mitigate transmission congestion in contrast to constant bitrate (CBR) encoding. However, adaptive bitrate (ABR) streaming faces challenges when dealing with VBR-encoded videos, primarily stemming from the significant variability in chunk size and the consequent bitrate fluctuations. This paper proposes CoarseUCB, a context-aware online learning algorithm for bitrate adaptation in VBR-encoded videos. CoarseUCB considers important aspects of VBR-encoded video streaming and uses the upper confidence bound (UCB) method for bitrate selection. The UCB method does not require precise bandwidth estimation and balances the exploration and exploitation of each action effectively. Additionally, CoarseUCB accounts for the impact of multiple future video chunks when making the bitrate decision for the current chunk. To evaluate the effectiveness of CoarseUCB, we conduct experiments to assess its efficiency. The results show that CoarseUCB delivers a higher average user quality of experience (QoE) compared to state-of-the-art ABR algorithms, resulting in an improvement of up to 9.81%.

Ensuring data integrity in edge computing environments presents significant challenges, primarily due to the distributed architecture of edge servers and the inherent risk of data corruption. Traditional edge data integrity (EDI) verification methods predominantly rely on sampling techniques, provide only probabilistic integrity assurances and often struggle with scalability and efficient corruption localization. To overcome these limitations, we introduce the deterministic integrity assurance and rapid corruption localization EDI (DL-EDI) verification scheme, a novel approach that combines extended Merkle grid (EM-Grid) with Boneh–Lynn–Shacham (BLS) signatures. DL-EDI leverages EM-Grid for comprehensive data integrity verification, ensuring deterministic integrity validation across all data blocks while facilitating rapid block corruption localization. Additionally, we incorporate a hierarchical signature aggregation method using BLS signatures to optimize verification efficiency and minimize communication and computational overhead. A thorough performance analysis of DL-EDI is conducted, evaluating its verification accuracy, communication and computational efficiency, and resilience against various security threats. Comparative experimental evaluations of DL-EDI against four established EDI schemes highlight its superior effectiveness and efficiency in addressing the challenges of EDI.

Node-assisted WiFi backscatter communication (NWB) is a promising technology that allows backscatter tags to communicate over long distances and achieve high throughput by using WiFi nodes as relays and enabling concurrent transmissions. However, NWB lacks an accurate theoretical model to evaluate and optimize its network performance, which is challenging to develop due to the location and contention randomness of both WiFi nodes and backscatter tags. Existing backscatter models that only account for one type of randomness are not suitable for NWB. To address this issue, we propose a novel stochastic geometry-based model that captures Location and Contention Randomness as well as the involved dependency and interference (named LoCoR). We use the Matérn hard-core point process and Matérn cluster process to model the repulsive and clustering attributes of the locations of WiFi nodes and backscatter tags, respectively. We also introduce a unified time unit to analyze the randomness and dependency of WiFi and backscatter contentions. Our model factors in various design parameters (e.g., the density and transmission power of tags) and can be used to evaluate their impacts on system throughput. We conduct extensive simulations to validate the accuracy of our model. With our accurate model, one can easily configure the optimal design parameters to maximize system throughput.

Conventional wireless networks are half-duplex and most of them use contention-based protocols. These protocols usually adopt a principle of contention with collision avoidance and infer a collision occurrence very late from the absence of an acknowledgment after data transmission, causing low network performance. Wireless full-duplex (FD) enables simultaneous transmission (TX) and reception (RX) on the same channel. Exploiting this functionality, this article proposes the first design that enables contention with collision detection (CCD) to improve the network performance. We call the proposed design FD-CCD. With FD-CCD, in contention, a node exploits the TX antenna to transmit a signal for channel contention, while exploiting the RX antenna to sense if other nodes are transmitting too. By checking the status of the TX and RX antennas, the node can detect the contention collision before data transmission and, hence, obtain an opportunity to avoid the data collision effectively. FD-CCD also supports priority-based contentions, is of very low contention overhead, and is compatible with conventional 802.11 networks. This article then develops a theoretical model to analyze the system performance and optimize protocol parameter settings. Extensive simulations verify the effectiveness of our design and the accuracy of our model. This study is very helpful in designing efficient FD protocols.

Currently, the integration of artificial intelligence (AI) techniques with multimodal physiological signals represents a pivotal approach to detect affective disorders (ADs). With the increasing complexity and diversity of physiological signal modalities, researchers have introduced various AI methods using multimodal physiological signals to improve model classification performance and explainability to increase trust and facilitate clinical adoption. Among these methods, spiking neural networks (SNNs) stand out as a promising avenue due to their alignment with the operating principles of the human brain, robust biological explainability, and adeptness in processing spatial–temporal information in an efficient event-driven manner with low power consumption. Furthermore, the emergence of neuromorphic computing (NC) chips based on SNNs has greatly bolstered the field of NC, enabling effective support for objective, pervasive, and wearable AI-assisted medical diagnostic devices for ADs and other diseases. This article presents a review of recent achievements in multimodal AD detection and points out the associated challenges in utilizing multimodal physiological signals and NC based on SNNs for AD detection. Building upon this foundation, we give perspectives on future work. The intended readership for this review consists of researchers in the fields of cognitive computing, computational psychophysiology, affective computing, NC, and brain-inspired computing. We hope that this survey not only garners increased attention from the scientific community but also serves as a valuable guide for future studies in this field.

Vehicular Ad-Hoc Networks (VANETs), as the crucial support of Intelligent Transportation Systems (ITS), have received great attention in recent years. With the rapid development of VANETs, various services have generated a great deal of data that can be used for transportation planning and safe driving. Especially, with the advent of Coronavirus Disease 2019 (COVID-19), the transportation system has been impacted, thus novel modes of transportation planning and intelligent applications are necessary. Digital twins can provide powerful support for artificial intelligence applications in Transportation Big Data (TBD). The features of VANETs are varying, which arises the main challenge of digital twins applying in TBD. Network traffic prediction, as part of digital twins, is useful for network management and security in VANETs, such as network planning and anomaly detection. This paper proposes a network traffic prediction algorithm aiming at time-varying traffic flows with a large number of fluctuations. This algorithm combines Deep Q-Learning (DQN) and Generative Adversarial Networks (GAN) for network traffic feature extraction. DQN is leveraged to carry out network traffic prediction, in which GAN is involved to represent Q-network. Meanwhile, the generative network can increase the number of samples to improve the prediction error. We evaluate the performance of our method by implementing it on three real network traffic data sets. Finally, we compare the two state-of-the-art competing methods with our method.

Mobile edge computing allows for high-performance and low-latency applications by delegating computation and data processing tasks to edge servers. However, ensuring the integrity of cached data on these servers can be challenging due to their limited resources. Current designs often use a per-edge multi-round approach, which necessitates multiple communication rounds between each edge server and the application vendor (AppVend). This approach results in high communication and computational costs, as well as the stragglers effect during batch verification. To address these inefficiencies, we propose a Hierarchical Signature Aggregation for Edge Data Integrity (HSA-EDI) verification design. Our design adopts a novel per-edge one-round approach, which significantly reduce the number of communication rounds to one for each edge server, while mitigating the impact of stragglers. Furthermore, it remarkably reduces computational costs through a hierarchical aggregation mechanism. This mechanism supports intra-edge signature aggregation at the edge server level, followed by inter-edge aggregation at the AppVend, which enhances overall efficiency. We then conduct a theoretical analysis of HSA-EDI’s correctness, security, and communication, computation, and storage efficiency. Experimental results validate its superior performance over state-of-the-art designs.

Predicting quality of service (QoS) is an important issue in the field of service recommendation that has been widely studied in the past few years. Many current methods predict QoS values based on the historical invocation records of services, but most of them ignore the time-varying characteristics of these values. Capturing time-varying characteristics to ensure accurate prediction of QoS values has become a key problem in the area. To solve this problem, in this article, we first apply probabilistic matrix factorization in QoS time series of sparse QoS matrices to extract time-varying feature series of users and services. Then we construct a gated feature extraction network (GFEN) to compensate for feature loss due to matrix factorization and enrich the limited information due to the sparsity of QoS matrices, where the heart of GFEN is an enhanced gated recurrent unit (EGRU) and a generative adversarial network is proposed to train GFEN. Extensive experimental results show that the proposed model outperforms state-of-the-art methods in terms of QoS prediction accuracy.

Quality of Service (QoS) prediction is a crucial issue in service recommendation, which has been widely studied in the past few years. It faces several challenges, including improving QoS prediction accuracy. Can one extract and use deep features of users and services to improve it? This work answers this question by proposing a deep-learning model for service QoS prediction. In this model, a feature mapping and inference network is first designed to obtain high-dimensional feature matrices of users and services, which can enhance data flow information and reflect the deep relationships among users and services. Then, feature compensation blocks are designed to compensate for the possible loss of feature information in feature mapping and inference. Finally, a QoS prediction network is constructed to fuse the obtained feature matrices to predict QoS values. Experimental results show that the proposed method can achieve higher prediction accuracy than ten typical and representative methods, thus advancing the state of the art in QoS prediction.

Mental fatigue is a prevalent issue in contemporary society and can negatively affect physical performance and concentration, increasing the likelihood of adverse consequences due to inattention during productive activities. Therefore, it becomes increasingly important to address and eliminate fatigue within a specific period of time. Aromatherapy, as a form of Complementary Alternative Medicine (CAM), is a non-invasive, cost-effective, and efficient method to combat fatigue. Previous studies have assessed the effects of specific aromatherapy oils using scales, but there is a lack of objective and reliable physiological indicators to prove the effectiveness of aromatherapy. Hence, this paper seeks to establish a model illustrating the effects of aromatic essential oil gases on the human body. A multimodal physiological fatigue signal acquisition system that integrates aromatherapy feedback was designed. In addition, an experimental paradigm was developed to explore the potential of aromatherapy in mitigating mental fatigue. Electroencephalogram (EEG) and Electrocardiogram (ECG) signals were collected, allowing for the analysis of time-frequency domain features in EEG and ECG signals, as well as Heart Rate Variability (HRV) features in ECG signals. Our findings indicate that specific aromatic gases demonstrate effectiveness in reducing mental fatigue. Furthermore, we employed the Support Vector Machine (SVM) algorithm to classify the state of human mental fatigue. Based on the classification results, the release of aromatic gas was controlled to provide targeted aromatic feedback. This innovative approach offers a promising avenue for objectively assessing and addressing mental fatigue through aromatherapy interventions.

Blockchain-enabled Internet of Things (IoT) has been receiving growing attention. However, IoT nodes are usually resources heterogeneous and subject to malicious attacks, such as the intentional delay of block verification and the transmission of invalid blocks. As a result, a random node-selection model for block transmission may lead to a low transmission rate and serious security risk. To solve the problem, this article proposes a rate-and-trust-based node selection model for block transmission. In our model, we calculate the block transmission rate of a node by the latency and connectivity among nodes and its trust value by its historical transmission and verification behaviors. On this basis, we propose a PageRank-based optimization algorithm for node selection that makes a tradeoff between the transmission rate and the security risk. Extensive experimental results show that the proposed model can achieve better performance than the state-of-the-art methods, including Bitcoin network, Ethereum network, and BlockP2P-EP protocol.

Electroencephalogram (EEG) equipped with high time resolution that distracted by incoherent brain sources which including ocular artifacts (OAs) generating by blinks is of great significance. Therefore, these OAs got corrected before extracting information from EEG is indispensable. Improved Moment Matching (IMM) is a high-speed denoising algorithm suitable for removing OA in multi-channel EEG, which is an improvement of the moment matching method used to remove stripe noise in hyperspectral images. On foundation of this, this paper proposes an optimization algorithm for IMM based on Gaussian decay function (IMM_G). In the first place, the construction of the reference signal is optimized via utilizing a Gaussian decay function, thereby preventing the signal distortion caused by the filter. And then, a method for judging the to-be-processed interval is proposed to realizes the individualized processing of different channels. As a result, through quantitative comparison experiments with simulated data and real data from the UAIS laboratory, it was identified that IMM_G showed less time complexity, significantly enhanced arithmetic speed and denoising consequence while the detail retention ability of the original method for the non-blinking region of multi-channel EEG data is maintained. Hence, this method could be extensively used in High-density EEG (hdEEG) OAs removal scene.

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short Time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar.

Mental fatigue of the brain will cause the weakening of the psychological and physiological functions of the human body, which increases the risk of mistakes and accidents. This paper designs a mental fatigue detection and mitigation system based on human physiological signals and classical music. Through the fatigue assessment and mitigation experiment on 20 healthy subjects, it is verified that the system is able to judge the mental fatigue state of the subjects and play classical music to mitigate fatigue via acquiring and analyzing (alpha+theta)/beta of electroencephalogram (EEG) and heart rate variability (HRV) of ballistocardiogram (BCG). The designed system realizes signal acquisition and fatigue analysis only through using portable device, this makes it have certain application value in office building, hospital, aircraft driving and other scenes.

Drug addicts are characterized by difficulty neglecting monetary reward, but its underlying neural mechanisms remain unclear. The current study aimed to investigate the behavioral and electrophysiological signatures of abnormal attentional bias based on different amounts of reward in abstinent heroin addicts (AHAs). We used a modified attentional capture task while recording EEG in 18 AHAs and 18 age-, gander-, and education-matched healthy controls (HCs). We analyzed the attentional distribution of the relative positional changes in space of the target and reward-related stimulus. When targets integrated reward-related colors, participants were more responsive and deployed more attention to targets, especially those with high-value colors. When targets and reward-related distractors were spatially separated, high-value distractors captured the AHA's attention and slowed their responses. Moreover, AHAs had weaker attentional control than HCs, exhibiting an inability to suppress the attentional bias driven by high-value stimuli. Overall, these results demonstrated that AHAs was hypersensitive to task-irrelevant and previous reward-related stimuli, possibly due to damage to brain reward circuits caused by chronic heroin abuse. Our work provides novel behavioral and neurophysiological evidence that are closely associated with the maintenance and relapse of addiction.

Sleep inertia is a transitional state from sleep to wakefulness, accompanied by groggy feelings and cognitive impairment. Previous research on sleep inertia mainly used expensive and cumbersome equipment, and the analysis of physiological signals relied on computers. This work introduces a sleep inertia detection system that consists of a wearable low-power electroencephalogram (EEG) acquisition module based on STM32WB55 and ADS1299, and a data processing module based on the Xilinx®Zynq®-7000 XC7Z020. This work recorded the EEG signals of ten subjects in the alert and sleep inertia states to extract the delta power, alpha power, beta power, EEG vigilance, and sample entropy. A linear support vector machine (SVM) was then used to classify the two states based on all subjects’ EEG signals, with an accuracy of 72.5%, and the average accuracy based on a single participant was 8S.9%. Finally, the feature extraction algorithm and SVM parameters were entered into the Zynq®system-on-chip (SoC) development board to realize onboard processing of the algorithm. The system is capable of evaluating the severity of human sleep inertia, which has reference significance for the practical application of sleep inertia detection.

In Internet of Things (IoT), physical-level cross-technology communication (CTC) enables IoT gateways to communicate with heterogeneous nodes economically. However, because of bandwidth asymmetry between heterogeneous technologies, many residual frequency resources are often not fully utilized. Without modification on hardware, in this article, we consider the coexistence of ultralow power (ULP) and WiFi nodes, and propose ERFR-CTC that enables an IoT gateway to fully exploit residual frequency resources without adding additional cost. With ERFR-CTC, the gateway can simultaneously communicate with ULP and WiFi nodes only via a single WiFi network interface card (NIC), which is not only economic but also very efficient. In particular, ERFR-CTC enables ULP nodes to correctly demodulate ULP signals without being interfered by WiFi signals. We then develop theoretical models to quantify available residual frequency resources and analyze the system throughput. Finally, extensive simulations verify that our model is very accurate and show that ERFR-CTC can increase the system throughput by up to 51.4%.

Dynamic frequency scaling (DFS) is a state-of-the-art power-saving technique. Various DFS-based WiFi schemes have been proposed for power saving. These schemes demonstrated the power-saving feasibility of DFS via hardware implementation or simulation. This paper is the first that proposes a general theoretical framework to evaluate the performance of these schemes, where we use Queuing theory to analyze the system throughput and use Semi-Markov theory to quantify the power consumption. In addition, we adopt the energy efficiency (i.e., the throughput per energy cost) to compare the gains of these schemes. This efficiency measure can be used to make a trade-off between system throughput and power consumption and therefore help us choose appropriate parameter settings. Extensive simulations verify that our theoretical model is very accurate and our theoretical results well match with universal software radio peripheral (USRP) experiment results. Our study shows that DFS can greatly improve the energy efficiency of WiFi networks even under low SNR conditions; for example, when SNR = 9.7 dB (i.e., the basic requirement for decoding packets in WiFi), the improvement is around 25 percent for 802.11b at rate 11 Mb/s and 16 percent for 802.11 ac at rate 1300 Mb/s. Our study also shows that DFS can be well integrated with other commonly used power-saving mechanisms to improve energy efficiency further.

In the attentional bias and cognitive processing relating to the abstinent heroin addicts (AHAs), there were considerable studies about event related potentials (ERP) and behavioral data. However, the large amount of data lead to longer data processing time, and few studies were done on single channel data about AHA. This study investigated whether single channel's data can be used to identify AHAs from healthy controls (HCs) accurately. Two groups of age-, education-, and gender-matched adults (22 AHAs, 21 HCs) performed on the gambling task. ERP features and behavior features were used to classify. For discriminating AHAs and HCs, ReliefF and SVM-RFE were applied for feature selection, and Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were used to search the optimal classification model of Support Vector Machine (SVM). We analyzed the statistical significance of all the features and obtained the classification result of different stimulation conditions. In statistics, we found that AHAs were significantly different from HCs in the amplitude of P300, ERP's mean value and ERP's variance under the monetary stimulation. For large money stimulation, P300 power in delta band and N100 power in delta band had significant difference between AHAs and HCs. Combining feature sorting algorithms and optimization algorithms, the results indicated that optimal performance was achieved by using ReliefF and GA. Use the above method, the best accuracy is 86.04% in four kind (+99, +9, -9, -99) of stimulation. This is the first study that used single channel's ERP data to identify AHAs with HCs, our study provided a new insight and objective method for the rapid diagnosis of AHAs.

Judging mental fatigue can be guided by acquiring and analyzing various physiological data. However, the existing equipment focuses on single physiological signals, such as electroencephalogram (EEG) and electrocardiogram (ECG), which ignores the preponderance of multimodal physiological signals in fatigue states. Alternatively, some equipment is difficult to operate and thus unsuitable for portable applications. To this end, this paper details the design of a miniaturized multi-physiological signal acquisition system. The system not only acquires EEG and ECG based on combining wavelet transform with Kalman filter, but also uses precise temperature sensors to synchronously acquire proximal skin temperature signals which are not easily interfered with by environmental noise or other physiological signals of human body. Through a fatigue assessment experiment on ten healthy subjects, it was verified that our equipment reliably detects mental fatigue states by monitoring and analyzing EEG, ECG, and proximal skin temperature data. In actual applications, this system appears to the traits of easy operation and good stability. It provides better hardware support for the pervasive applications and concrete implementation of mental health in multi-scene, and can popularize use.

Vehicular edge computing (VEC) offers a new paradigm to improve vehicular services and augment the capabilities of vehicles. In this article, we study the problem of task scheduling in VEC, where multiple computation-intensive vehicular applications can be offloaded to roadside units (RSUs) and each application can be further divided into multiple tasks with task dependency. The tasks can be scheduled to different mobile-edge computing servers on RSUs for execution to minimize the average completion time of multiple applications. Considering the completion time constraint of each application and the processing dependency of multiple tasks belonging to the same application, we formulate the multiple tasks scheduling problem as an optimization problem that is NP-hard. To solve the optimization problem, we develop an efficient task scheduling algorithm. The basic idea is to prioritize multiple applications and prioritize multiple tasks so as to guarantee the completion time constraints of applications and the processing dependency requirements of tasks. The numerical results demonstrate that our proposed algorithm can significantly reduce the average completion time of multiple applications compared with benchmark algorithms.

Motivated by practical scenarios in areas such as Mobile Edge Computing (MEC) and Content Delivery Networks (CDNs), we study online file caching on multiple caches, where a file request might be relayed to other caches or bypassed directly to the memory when a cache miss happens. We take the relaying, bypassing and fetching costs altogether into consideration. We first show the inherent difficulty of the problem even when the online requests are of uniform cost. We propose an O(log K)-competitive randomized algorithm Camul and an O(K)-competitive deterministic algorithm Camul-Det, where K is the total number of slots in all caches. Both online algorithms achieve asymptotically optimal competitive ratios, and can be implemented efficiently such that each request is processed in amortized constant time. We conduct extensive simulations on production data traces from Google and a benchmark workload from Yahoo. It shows that our algorithms dramatically outperform existing schemes, i.e., reducing the total cost by 85% and 43% respectively compared with important baselines and their strengthened versions with request relaying. More importantly, Camul achieves such a good total cost without sacrificing other performance measures, e.g., the hit ratio, and can perform consistently well on various settings of experiment parameters.

Full-duplex (FD) wireless is an attractive PHY technology with high potential to improve the throughput of WLAN due to bidirectional transmissions. However, existing FD MACs fail to fully take advantage of bidirectional transmissions because of neglecting a feature of FD wireless that whether to build bidirectional transmissions relies on the queuing state of peers. In this paper, we design PQ-MAC, the first FD MAC which exploits more bidirectional transmission opportunities by leveraging peers' queuing information. Since PQ-MAC seizes the neglected but important feature, PQ-MAC can improve the performance with a slight transmission overhead. Simulations show that, in a 1-cell FD WLAN, PQ-MAC can achieve higher throughput than existing MACs when the buffer of AP is relatively small (≤200 frames). When the buffer of AP is relatively large (>200 frames), PQ-MAC can reduce the queuing delay without the loss of throughput.

This paper addresses the energy efficiency issue for content sharing with collaborative mobile clouds (CMC). We start by maximizing the data rate of cellular transmissions under the maximum transmit power constraint of the cellular users, to obtain the optimal beamforming vectors. Using these vectors, we propose a water filling based data segmentation approach for content distribution. Furthermore, within the CMC, we design cost-effective resource allocation and power control mechanisms for device-to-device communications. Through performance comparisons, we disclose that our proposed scheme outperforms some previous study in terms of delay and energy consumption per mobile terminal, which further validates the effectiveness of our design.

Mobile edge computing (MEC) enables battery‐powered mobile nodes to acquire information technology services at the network edge. These nodes desire to enjoy their service under power saving. The sampling rate invariant detection (SRID) is the first downclocking WiFi technique that can achieve this objective. With SRID, a node detects one packet arrival at a downclocked rate. Upon a successful detection, the node reverts to a full‐clocked rate to receive the packet immediately. To ensure that a node acquires its service immediately, the detection performance (namely, the miss‐detection probability and the false‐alarm probability) of SRID is of importance. This paper is the first one to theoretically study the crucial impact of SRID attributes (e.g., tolerance threshold, correlation threshold, and energy ratio threshold) on the packet detection performance. Extensive Monte Carlo experiments show that our theoretical model is very accurate. This study can help system developers set reasonable system parameters for WiFi downclocking.

Electroencephalogram (EEG) is a noninvasive method to record electrical activity of brain and it has been used extensively in research of brain function due to its high time resolution. However raw EEG is a mixture of signals, which contains noises such as Ocular Artifact (OA) that is irrelevant to the cognitive function of brain. To remove OAs from EEG, many methods have been proposed, such as Independent Components Analysis (ICA), Discrete Wavelet Transform (DWT), Adaptive Noise Cancellation (ANC) and Wavelet Packet Transform (WPT). In this paper, we present a novel hybrid de-noising method which uses Discrete Wavelet Transform (DWT) and Kalman Filtering to remove OAs in EEG. Firstly, we used this method on simulated data. The Mean Squared Error (MSE) of DWT-Kalman method was 0.0017, significantly lower compared to results using WPT-ICA and DWT-ANC, which were 0.0468 and 0.0052, respectively. Meanwhile, the Mean Absolute Error (MAE) using DWT-Kalman achieved an average of 0.0052, which also performed better than WPT-ICA and DWT-ANC, which were 0.0218 and 0.0115, respectively. Then we applied the proposed approach to the raw data collected by our prototype three-channel EEG collector and 64-channel Braincap from BRAIN PRODUCTS. On both data, our method achieved satisfying results. This method does not rely on any particular electrode or the number of electrodes in certain system, so it is recommended for ubiquitous applications.

It has been reported that chronic heroin intake induces changes in central nervous system of human brain; however, few studies investigate the carry-over adverse effects on brain after heroin withdrawal. In this work we examined the alpha rhythms of resting-state Electroencephalogram (EEG) signals to measure the neuroelectrical differences between the heroin addicts after heroin withdrawal and normal control. Eyes-closed resting EEG signals from 20 heroin addicts with the abstinence length ranging from 4-16 months and 20 normal controls were recorded using 64 electrodes. Comparing the nonlinear characteristics of EEG signals, such as the correlation dimension, Kolmogorov entropy and Lempel-Ziv complexity, we found that the EEG signals from heroin addicts were significantly more irregular than those from normal controls. Furthermore, the topography of the each nonlinear feature was examined, and the abnormal changes were widely spread over the brain. These findings suggest that nonlinear methods may contribute to gain new insights into brain dysfunction in heroin addicts even after heroin abstinence.

In this paper, we investigate the energy-aware data- offloading of mobile user (MU) which schedules its traffic demand to a macro Base Station (BS) and a small-cell access point (AP) simultaneously. For saving the usage of licensed spectrum, we consider that the MU uses unlicensed spectrum to offload data. The open access of unlicensed spectrum, however, results in that the MU's data offloading suffer from uncontrollable interference, which comprises the benefit of data offloading. We propose an outage-probability to quantify such an adverse influence and formulate a joint rate-splitting and power allocation problem to minimize a system-wise cost accounting for both the MU's power consumption and the BS's licensed channel usage. Despite the non-convexity of the joint optimization problem, we transform it into three rate- allocation problems under different cases and derive the respective optimal solutions, which yield the globally optimal solution for the original problem. Numerical results are provided to validate the optimal offloading-solution.

This paper aims at improving the distance-based location management scheme for mobile communication networks. In location management, a mobile terminal (MT) is tracked based on its location-update area (LA). The improvement is brought about by joint optimization of LA center and LA size. For LA center optimization (LCO), we determine the optimal center position of the LA given the initial position of the MT upon each location update. The investigation of optimal LA center has eluded research to date. Based on the popular continuous-time random walk (CTRW) mobility model, we propose an analytical framework that uses a diffusion equation to determine the optimal LA center that minimizes the total cost of location management, consisting of the location update cost and terminal paging cost. This framework allows us to easily model the non-Markovian movement of the MT and evaluate the impact of various measurable physical parameters (such as length of road section, angle between road sections, and road section crossing time) and LA center. In particular, we show that proper LA center can significantly reduce the total cost. For example, for the circular LA and low Poisson call-arrival rate, optimizing the LA center alone has the potential of reducing the cost by up to 37 percent. Joint optimization of the LA center and terminal paging scheme can reduce the cost even further. Simulations results match the theoretical analysis to a gap within 3 percent, indicating that our theoretical model is very accurate.

This paper concerns with the local connectivity (i.e., the probability of node isolation) of wireless networks with directional antennas. We propose an analytical framework to study the local connectivity with the consideration of directional antenna models and various channel conditions. With the framework, we construct a novel directional antenna model called Iris. We show that Iris can better approximate realistic directional antennas and can be easily used to analyze the local connectivity compared with existing directional antenna models. Extensive simulations show that the theoretical results are in good agreement with the simulation results verifying the accuracy and the effectiveness of our analytical framework.

In this paper, we propose a novel multi-channel wireless network with infrastructure support, called an MC-IS network. To the best of our knowledge, we are the first to study the capacity and the delay of such an MC-IS network. In particular, we derive the upper bounds and the lower bounds on the network capacity of such MC-IS networks contributed by ad hoc communications, where the orders of the upper bounds are the same as the orders of the lower bounds, implying that the bounds are tight. We also found that the capacity of MC-IS networks contributed by ad hoc communications is mainly limited by connectivity requirement, interference requirement, destination-bottleneck requirement and interface-bottleneck requirement. In addition, we also derive the average delay of MC-IS networks contributed by ad hoc communications, which is bounded by the maximum number of hops.

This paper presents the order evaluation method for the multi-input and multi-output (MIMO) multidimensional (n-D) systems realization obtained by the new elementary operation approach (NEOA) proposed by the co-authors recently. It turns out that the proposed order evaluation method can obtain the exact order of the realization produced by the NEOA procedures for a given n-D transfer matrix without actually conducting the constructive realization procedures. Examples are given to illustrate the main ideas as well as the effectiveness of the proposed method.

Modeling and prediction of Electroencephalogram (EEG) signals is very important for Portable applications; EEG signals are however widely regarded as being chaotic in nature. An adaptive modeling technique that combines Discrete Wavelet Transformation (DWT) to predict contaminated EEG signals for removal of ocular artifacts (OAs) from EEG records is proposed as an effective a data processing tool for Interventions in Mental Illness Based on Bio-feedback. The proposed method is well suited for use in portable environments where constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices. Using simulated and measured data the accuracy of the proposed model is compared to the accuracy of other pre-existing methods based on Wavelet Packet Transform (WPT) and independent component analysis (ICA) using DWT and adaptive noise cancellation (ANC) for Portable applications. The results show that the our new model not only demonstrates an improved performance with respect to the recovery of true EEG signals, achieves improved computational speed, and demonstrates better tracking performance.

This paper concerns the impact of various antenna models on the network connectivity of wireless ad hoc networks. Existing antenna models have their pros and cons in the accuracy reflecting realistic antennas and the computational complexity. We therefore propose a new directional antenna model called Approx-real to balance the accuracy against the complexity. We then run extensive simulations to compare the existing models and the Approx-real model in terms of the network connectivity. The study results show that the Approx-real model can better approximate the best accurate existing antenna models than other simplified antenna models, while introducing no high computational overheads.

A new model to remove ocular artifacts (OA) from electroencephalograms (EEGs) is presented. The model is based on discrete wavelet transformation (DWT) and adaptive noise cancellation (ANC). Using simulated and measured data, the accuracy of the model is compared with the accuracy of other existing methods based on stationary wavelet transforms and our previous work based on wavelet packet transform and independent component analysis. A particularly novel feature of the new model is the use of DWTs to construct an OA reference signal, using the three lowest frequency wavelet coefficients of the EEGs. The results show that the new model demonstrates an improved performance with respect to the recovery of true EEG signals and also has a better tracking performance. Because the new model requires only single channel sources, it is well suited for use in portable environments where constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices. The model is also applied and evaluated against data recorded within the EUFP 7 Project--Online Predictive Tools for Intervention in Mental Illness (OPTIMI). The results show that the proposed model is effective in removing OAs and meets the requirements of portable systems used for patient monitoring as typified by the OPTIMI project.

IEEE 802.11e EDCA induces service differentiation by appropriate joint tuning of four adjustable contention parameters. Existing and emerging work has devoted considerable attention to the nonsaturated performance of EDCA networks due to the difficulty of predicting the joint influence of the four parameters. However, most existing nonsaturated EDCA models adopt complex extensions of a Markov-chain approach. In sharp contrast, this paper invokes an extension of a renewal-reward approach. Our extension has the following unparalleled advantages: good scalability, ease of understanding, fast computation speed, high accuracy, models joint differentiation of all four parameters, captures the impact of an arbitrary buffer size, and predicts a wide range of performance indicators including the buffer overflow probability and the MAC access delay distribution. Our nonsaturated EDCA model is a nontrivial augmentation of our previously proposed nonsaturated DCF model. Our results indicate that if we accurately model the nonsaturated collision probability, the same formulas used for the saturated performance descriptors can produce accurate results for nonsaturated operation, and therefore it is unnecessary to construct specific formulas for nonsaturated performance descriptors, as done in previous work. To illustrate the utility of our model, we also develop an admission control policy based on the proposed EDCA model for a CWmin-differentiation system. Simulations validate that this policy enables the system to run slightly below a critical point, beyond which the system performance deteriorates drastically.

With the booms of mobile communication, especially mobile smart phone, technologies to identify individuals for mobile security calls for some more strict requirements in user-friendly, real-time and ubiquitous aspects. In addition to traditional approaches (for example, password check), some advanced biometric methodologies have been applied in practice, such as fingerprint and iris based solutions, however, these solutions generally lack a true ubiquitous nature for mobile security. In this paper, we present a real time EEG based individual identification interface to support ubiquitous applications. The EEG signals are collected through a mono-polar single channel in real time via a mobile EEG device. An experiment involving about 20 subjects has been conducted to evaluate the interface. The experiment comprises three types of tests: accuracy test, time dimension test and capacity dimension test. The results of these experiments demonstrate that our approach is highly suitable to the demands of mobile security in ubiquitous environment. In addition, we integrate this interface into scenarios of ubiquitous application - Online Predictive Tools for Intervention in Mental Illness (OPTIMI).

We propose an approximate model for a nonsaturated IEEE 802.11 DCF network. This model captures the significant influence of an arbitrary node transmit buffer size on the network performance. We find that increasing the buffer size can improve the throughput slightly but can lead to a dramatic increase in the packet delay without necessarily a corresponding reduction in the packet loss rate. This result suggests that there may be little benefit in provisioning very large buffers, even for loss-sensitive applications. Our model outperforms prior models in terms of simplicity, computation speed, and accuracy. The simplicity stems from using a renewal theory approach for the collision probability instead of the usual multidimensional Markov chain, and it makes our model easier to understand, manipulate and extend; for instance, we are able to use our model to investigate the important problem of convergence of the collision probability calculation. The remarkable improvement in the computation speed is due to the use of an efficient numerical transform inversion algorithm to invert generating functions of key parameters of the model. The accuracy is due to a carefully constructed model for the service time distribution. We verify our model using ns-2 simulation and show that our analytical results based on an M/G/1/K queuing model are able to accurately predict a wide range of performance metrics, including the packet loss rate and the waiting time distribution. In contradiction to claims by other authors, we show that 1) a nonsaturated DCF model like ours that makes use of decoupling assumptions for the collision probability and queuing dynamics can produce accurate predictions of metrics other than just the throughput, and 2) the actual service time and waiting time distributions for DCF networks have truncated heavy-tailed shapes (i.e., appear initially straight on a log-log plot) rather than exponential shapes. Our work will help developers select appropriate buffer sizes for 802.11 devices, and will help system administrators predict the performance of applications.

This paper proposes a simple and practical call admission control (CAC) scheme for one-hop IEEE 802.11 distributed coordination function (DCF) networks in heterogeneous environments. The proposed scheme is the first CAC scheme derived from an asymptotic analysis of the critical traffic load, where the critical traffic load represents the threshold for queue stability. The salient feature of our CAC scheme is that it can be performed quickly and easily without the need for network performance measurements and complex calculations. Using the proposed scheme, we specifically investigate the voice capacity of 802.11 DCF networks with unbalanced traffic. Extensive simulations covering both ad hoc and infrastructure-based networks, and a variety of nonsaturated traffic types, show that the proposed CAC scheme is very effective.

This paper proposes a new call admission control (CAC) scheme for one-hop homogeneous 802.11 DCF networks. Using the proposed scheme, we can perform admission control quickly and easily without the need for network performance measurements and complex calculations. The CAC rule is derived under asymptotic conditions, but our extensive numerical examples show that it works well for practical-sized networks with a finite retransmission limit and realistic nonsaturated traffic.

We propose an approximate model for a nonsaturated IEEE 802.11 DCF network that is simpler than others that have appeared in the literature. Our key simplification is that the attempt rate in the nonsaturated setting can be approximated by scaling the attempt rate of the saturated setting with an appropriate factor. Use of different scaling factors leads to variants of the model for a small buffer and an infinite buffer. We develop a general fixed-point analysis that we demonstrate can have nonunique solutions for the infinite buffer model variant under moderate traffic. Nevertheless, in an asymptotic regime that applies to light traffic, we are able to prove uniqueness of the fixed point and predict the offered load at which the maximum throughput is achieved. We verify our model using ns-2 simulation and show that our MAC access delay results are the most accurate among related work, while our collision probability and throughput results achieve comparable accuracy to (D. Malone et al., 2007), (K. Duffy et al., 2007).

IEEE 802.11e standard develops a reference design for a sample scheduler and admission control unit to support the contention-free access. However, the reference design can not efficiently utilize the bandwidth. This paper proposes an equalspacing- based (equal-SP) design to address the problem. In the equal-SP design, which generalizes the reference design, each stream is scheduled with equal-spacing and different streams are scheduled with different spacings. The equal-SP design not only keeps all advantages of the reference design (i.e., it is simple, easy to implement, and can guarantee the delay requirement), but it is compatible with the standard and can also utilize the bandwidth efficiently.

IEEE 802.11e standard develops a reference design to support the contention-free access. In the reference design, the packet transmission opportunity (TXOP) duration is calculated based on the mean data rate and the mean packet size, whereas the scheduled service interval (SI) is calculated based on the most stringent delay requirement. Such design can not effectively utilize the bandwidth and support the guaranteed packet loss requirement. This paper proposes a packet-loss-based and bandwidth-utilization-based (PB-based) design to address the two problems. In the PB-based design, the TXOP is calculated based on the data rate and packet size fluctuation, whereas the SI is calculated based on different delay requirements. In addition, when packet loss requirement is not taken into account, we propose an equal-spacing-based (ES-based) design to improve the bandwidth utilization. The proposed designs are helpful to provide more comprehensive QoS support.

IEEE 802.11e standard develops a reference design for a sample scheduler and admission control unit to support the contention-free access. However, the reference design can not effectively utilize the bandwidth. This paper proposes an equal-spacing-based (equal-SP) design to address the problem. In the equal-SP design, which generalizes the reference design, each stream is scheduled with equal-spacing and different streams are scheduled with different equal-spacings. The equal-SP design not only keeps all advantages of the reference design, but it can also utilize the bandwidth effectively.