As wearables continue to gain widespread popularity, ensuring secure and convenient authentication becomes imperative to safeguard the data stored within these devices. However, existing wearable-based authentication solutions often rely on specialized and costly hardware, have limited applicability to specific scenarios, and are vulnerable to permanent biometrics leakages. To address these limitations, this paper explores the uniqueness of hand motions and subtle vibrations associated with face tapping. Specifically, we propose TapPass as a secure and convenient authentication solution that leverages face tapping signals captured via the Inertial Measurement Unit (IMU) in wrist-worn wearables for user authentication. To address significant interference from other body motions, we utilize the energy ratio and duration analysis of IMU measurements, followed by deep learning-based extraction of clean face tapping signals. Additionally, we explore the uniqueness of face tapping signals and extract effective features encompassing motion, vibration, and integral aspects. Based on these features, we generate cancelable biometrics leveraging a linear convolution-based approach, bestowing re-registration capabilities upon traditionally invariant biometrics. This solution effectively eliminates concerns surrounding permanent biometrics leakage and enables accurate authentication in both single-user and multi-user scenarios. Extensive experiments with 24 volunteers over three months demonstrate that TapPass achieves accurate authentication while effectively tackling major attacks and motion interference, all while maintaining user-friendliness.

Video analytics (VA) has become essential in applications, yet it poses significant challenges related to privacy preservation, network bandwidth, and computational resources. With the increasing deployment of high-definition cameras, privacy concerns and resource constraints are becoming critical barriers to the widespread adoption of VA systems. Existing privacy-preserving techniques are often static, inefficient, and fail to adapt to dynamic, real-time scenarios. In this paper, we propose Parachute, a dynamic, resource-aware and privacy-preserving video analytics system that adaptively switches between a local mode and a collaborative mode in response to traffic conditions. The system uses local reinforcement learning to enable each individual camera to operate independently, and switches to multi-agent reinforcement learning for coordinated optimization when local resources become limited. Experiments on real-world datasets demonstrate that Parachute effectively balances detection accuracy and privacy protection, outperforming baseline methods under bandwidth constraints.

We study feature selection (FS) for flow-based intrusion detection and propose a deterministic hybrid-FS that fuses Mutual Information, Random-Forest, and XGBoost importances under a simplex search with a single threshold. Using CIC-IDS-2017, CSE-CIC-IDS2018, and NF-UNSW-NB15, we evaluate ten FS techniques paired with six ensembles under a leakage-safe protocol. The hybrid-FS consistently matches or exceeds the best single selectors while reducing feature count (e.g.,$78 \rightarrow 31$) and improving runtime. Throughput rises by$\sim$9–10% and per-flow latency drops from$0.44 \rightarrow 0.40$ms (p50) and$1.40 \rightarrow 1.20$ms (p99), with mean$\pm$95% CIs and paired tests. False-positive rate (FPR) decreases by 15–19% ($\approx$22 fewer false alarms per hour at 100k flows/h). Against representative PSO/GA hybrids, our fusion attains small but consistent macro-F1 gains and 15–25% FPR reductions at comparable latency. We clarify adversarial robustness with an explicit FGSM feature-space threat model and DeepPackGen configuration, and we diagnose cross-dataset shift with lightweight mitigations. A 24-hour SOC replay links FPR to analyst time savings (2.5–3.7 hours/day) without sacrificing macro-F1 or AUROC. The results position deterministic, compact FS as a practical choice for inline IDS where tail latency and alert volume matter.

Federated learning (FL) offers a decentralized, privacy-preserving framework for encrypted traffic classification (ETC), enabling network management and security. However, real-world deployment of federated ETC faces compound client specific feature, concept, and label drift, which degrades model performance. Existing ETC methods under FL settings typically address these drift types in isolation or partial combinations, overlooking their entanglement. Moreover, multiple-global model and personalized FL approaches are computational and communication expensive. To fill this gap, we propose DAIR FedMoE, a Drift-Adaptive, Imbalance-Aware, RL-Managed Federated Mixture-of-Experts framework to simultaneously handle the drift triad with single-global model while minimizing the computational and communication overhead. DAIR-FedMoE in tegrates a GShard Transformer with a hierarchical Mixture of-Experts (MoE) layer that routes encrypsted flows to either stable or drift-specialist experts based on per-client drift scores. Within each expert, entropy-guided loss reweighting empha sizes low-confidence classes to address dynamic label imbalance. Additionally, a reinforcement learning-based policy dynamically manages the expert pool by spawning, pruning, and merging experts, enabling efficient adaptation to evolving traffic patterns. Experiments on federated splits of ISCX-VPN, ISCX-Tor, VNAT, and USTC-TFC2016 show that DAIR-FedMoE achieves superior macro-F1, minority-class recall, and drift-recovery speed compared to state-of-the-art baselines, while preserving privacy and communication efficiency. The source code is available at https://github.com/dairfedmoe/DairFM.

Blood pressure (BP) is a critical indicator for life-threatening conditions. While invasive catheter-based methods offer high accuracy, non-invasive techniques typically require placement on specific body areas, introducing discomfort and rendering their accuracy sensitive to wearing conditions. To overcome these limitations, recent efforts have explored contactless BP monitoring using RF sensing. However, existing approaches often rely on deep learning models without grounding in physiological principles, resulting in poor generalization and limited clinical trustworthiness. In this paper, we proposehBP-Fi, a contactless BP measurement system driven byhemodynamicsacquired via RF sensing. In addition to its contactless convenience,hBP-Fi outperforms existing RF-based approaches by i) employing a physiologically grounded hemodynamic model of pulse generation that forms the basis for RF-based BP estimation, ii) enabling super-resolution arterial pulse tracking via beam-steerable RF scanning, iii) ensuring output trustworthiness through an interpretable (transparent-by-design) deep learning model, and iv) achieving robust generalizability to unseen users and scenarios via a CycleGAN-based training strategy. Extensive experiments with 35 subjects under practical scenarios demonstrate thathBP-Fi can achieve errors of -2.95$\pm$7.66 mmHg and 2.63$\pm$6.05 mmHg for systolic and diastolic blood pressures, respectively.

The unprecedented advancements in quantum technology have opened new prospects for the widespread adoption of quantum applications, placing new demands on the information transmission capabilities of large-scale quantum networks. Long-distance and stable entanglements are deemed as the lifeline in quantum network communication. However, some weaknesses, e.g., quantum decoherence, scarce quantum memory, and uneven-quality entanglement, of the quantum entanglement hinder the development. In this paper, we proposeSophon, an online transmission framework for quantum networks, which utilizes high-dimensional entanglements to concurrently transmit multi-qubit data to satisfy the transmission requirements of the real-time request set. We first model the quantum network with multi-qubit entanglement represented by$W$quantum state and then formulate the Entanglement Routing and Qubit Provisioning (ERQP) problem as a global-local optimization process. To solve theERQPproblem, we distributedly regard each network node as an RL agent for resource provisioning and extend the step-updating of the Markov Decision Process by introducing a centralized controller for entanglement route selection to optimize local and global objectives, respectively. Extensive simulations demonstrate, on the self-made simulation platform,Sophonachieves a$21.89\%-66.52\%$decrease in the communication cost, and is more robust on different scales of the network topology and the request set than the baselines.

In recent years, volumetric videos have gradually prospered as an intriguing video paradigm, offering users a fully immersive viewing experience with six Degrees of Freedom (DoF). However, most current live volumetric video streaming methods struggle to facilitate the real-time performance requirements due to the nature of frequent user interactions and the complexity of network environments during video playback. Inspired by the correlation between the human visual effects and adjacent frame motion features, we proposeCetus, a context-aware cross-layer coordination system for live volumetric videos. First, we present an application-layer Neural Radiance Fields (NeRF)-based codec framework that leverages spatio-temporal semantic information for optimizing the compression quality of each video frame. Second, we exploit a flexible cross-layer coordination framework that seamlessly integrates frame drop strategy with partially reliable transmission, orchestrating transport protocols and application-informed rates to enhance the Quality of Experience (QoE) for multiple users. Furthermore, we develop a lightweight branching decision tree algorithm that adaptively makes fine-grained frame drop decisions. Experimental evaluations of our implemented system prototype demonstrate that Cetus significantly outperforms existing baseline approaches. Compared to the state-of-the-art baselines, Cetus effectively improves video frame rate by at least 24.7% and video quality by an average of 32.6%.

Bandit with infinitely many arms (i.e.,X-armed bandit) is an important variant of multi-armed stochastic bandits, which is useful to model different networking problems under both wired and wireless settings, e.g., online caching, dynamic channel/power allocation, rate adaption. However, the problem becomes challenging when the characteristic of the networking setting is affected by the side information (i.e., context) and distributed behavior. In this paper, we identify and study a novel problem, decentralized contextualX-armed bandit, whereNagents collaboratively solve the problem within time spanT. The problem is nontrivial because the infinite arms challenge and statistical information consensus issue make our setting go beyond a simple combination ofX-armed bandits and multi-agent bandits. We develop a decentralized arm selection algorithm, called MACXUCB, by elaborating the contextual covering tree technique with a novelgossip communication protocol, which allows each agent to communicate efficiently with her neighbors. We prove that MACXUCB achieves a sublinear regret upper bound Õ(D1/2dX+2dY+4NdX+dY+1.5/dX+dY+2TdX+dY+1/dX+dY+2) given aggregation periodD, and the covering dimensionsdXanddYof arm and context spaces, which asymptomatically matches the lower bound Ω((DN)1/dX+dY+2TdX+dY+1/dX+dY+2) up to a time-dependent factor. Moreover, MACXUCB enjoys a sublinear communication complexity Õ(NDT0.5/(1–p)) when tuning parameterp∈ (0, 1/2). Finally, we carry out experiments to verify the performance of our MACXUCB. The results show the effectiveness and efficiency of our MACXUCB.

Serverless computing promises fine-grained resource elasticity and billing, making it an attractive way to build complex applications as multi-stage workflows. Nonetheless, existing workflow orchestration ignores the heterogeneous demands of the computation and communication parts within a stage, potentially resulting in resource inefficiency on either side. In this paper, we advocate forcomputation-communication-separated orchestrationto unleash hybrid resource (i.e., compute and network) elasticity. We present HyFaaS, a serverless workflow orchestrator that improves performance while ensuring cost efficiency. It seamlessly decouples computation and communication as a series of hybrid stages re-expressed within HyDAG, a novel workflow abstraction. HyFaaS uses a gray-box profiling model to identify their Pareto-optimal saturated configurations, and then deploys the saturated workflow to juggle communication and scaling overheads through two-level HyDAG partitioning. Along with event-driven runtime fine-tuning, HyFaaS further scales down the non-critical stages to reduce cost via branch-aware coordination. Experimental results show that HyFaaS surpasses existing solutions by 32.7%–50.4% on end-to-end latency, while lowering cost by up to 1.37×.

Electroencephalography (EEG) signals have emerged as a novel biometric feature in identity authentication. However, in highly sensitive scenarios such as remote access control and sensitive operation confirmation, identity authentication alone is insufficient to ensure system security. This paper proposes EEGAuth, an EEG-based secure and lightweight authentication system with cryptographic key generation, addressing the demand for integrated systems that enhance both security and user convenience by combining identity authentication and key generation into a unified solution. The proposed system employs a genetic algorithm for optimal channel selection, integrates a discrete wavelet transform with an autoencoder-based feature extraction framework, and implements a CNN-based architecture for robust identity authentication. In addition, the system discretizes feature vectors to generate unique and repeatable seeds, which are used as inputs to a secure hash function to produce keys. The evaluation results show that our model achieves a classification accuracy of 99.38% with only 15 channels, significantly outperforming state-of-the-art methods and baseline models. The generated cryptographic keys demonstrate robust security properties, as evidenced by their successful passage through NIST statistical test suite for randomness verification, scale index analysis for aperiodicity assessment, and autocorrelation testing for bit-sequence independence, collectively confirming their resistance to cryptographic attacks and compliance with security standards.

Industrial Internet of Things (IIoT) is an emerging technology that digitizes industrial production and realizes Industry 4.0. However, it shows that IIoT is difficult to enable sophisticated downstream applications without eliciting all devices to achieve collaborative intelligence. Existing works on IIoT either require the consolidation of various IIoT devices’ data into a single centralized server which has potential privacy breach, or coordinate devices to learn a global model in privacy-preserving federated learning (FL) but assume data across devices has the sample feature space and neglect the heterogeneity of IIoT devices. In this article, we propose Meta-computing-driven vertical FL (VFL) algorithms to achieve collaborative intelligence in IIoT where heterogeneous devices have imperfect data with incomplete features. Specifically, we first provide the modeling of N devices’ VFL to collectively train the submodels and the common model. We present the computing graph to clearly indicate the gradient evaluation. To enable a fast convergence performance, we design a variance-reduced gradient estimator that can be seamlessly integrated into the basic VFL. Finally, we evaluate our proposed VFL by conducting experiments on the MNIST dataset regarding image recognition and the DAWM dataset for detecting anomalies in wafer manufacturing. The experimental results show that our VFL for IIoT is both effective and efficient.

Biometrics has been increasingly integrated into wearables for enhanced data security in recent years. Meanwhile, wearable popularity offers a unique chance to capture novel biometrics via embedded sensors. In this article, we study new intracorporal biometrics combining the uniqueness of heart motion, bone conduction, and body asymmetry. Specifically, we introduce HeartPrint, a passive yet secure user authentication system exploiting the bone-conducted heart sounds captured by (widely available) dualin-ear microphones (IEMs). To eliminate interference, we devise a novel method combining modified non-negative matrix factorization and adaptive filtering. This extracts clean heart sounds while addressing interference of body sounds and audio produced by the earphones. We further explore the uniqueness of IEM-recorded heart sounds in three aspects to extract a novel biometric representation, based on which HeartPrint leverages a convolutional neural model equipped with a continual learning method to achieve accurate authentication under drifting body conditions. Furthermore, user-friendly registration and energy-effective authentication are facilitated by a data augmentation method using transformer-based GAN and an authentication interval control method. Extensive experiments with 45 participants confirm that HeartPrint can achieve 1.6% FAR and 1.8% FRR, while effectively coping with major attacks, complicated interference, and hardware diversity, while exhibiting robustness in real-world environments.

Free weight training (FWT) is of utmost importance for physical well-being. The success of FWT depends largely on choosing the suitable workload, as improper selections can lead to suboptimal outcomes or injury. Current workload estimation approaches rely on manual recording and specialized equipment with limited feedback. Therefore, we introducePPGSpotter, a wearable PPG-based FWT monitoring system in a convenient, low-cost, and fine-grained manner. By characterizing the arterial geometry compressions caused by the deformation of distinct muscle groups,PPGSpottercan infer essential FWT factors such as current workload, repetitions, and exercise type and provide recommendations for workload adjustment. To remove pulse-related interference, we develop an arterial interference elimination approach based on adaptive filtering, effectively extracting the pure motion-derived signal (MDS). Furthermore, we explore 2D representations of MDS within the phase space to extract spatiotemporal information, enablingPPGSpotterto address the challenge of resisting sensor shifts. Finally, we leverage a multi-task CNN-based network and workload adjustment guidance to achieve personalized FWT monitoring. Extensive experiments with 15 participants confirm thatPPGSpottercan achieve promising workload estimation (0.59 kg RMSE), repetitions estimation (0.96 reps RMSE), and exercise type recognition (91.57% F1-score) while providing valid workload adjustment recommendations (0.22 kg RMSE).

Federated learning (FL)has achieved state-of-the-art performance in distributed machine learning with privacy preservation, which promotes AIoT. However, FL is restricted by the expensive communication cost due to exchanging a large number of model parameters and model updates (e.g., gradients) between the aggregator and participants in multiple rounds. This could be challenging in resource-constrained networks where devices are often resource-constrained in terms of computation and communication. Existing works mainly focus on improving communication efficiency from local training and model/gradient compression; nevertheless, studying communication efficiency for FL from the perspective of gradient estimation remains unexplored. In this paper, we bridge this gap by conducting a systematic study on gradient estimation for the communication-efficient FL. We propose a bandit-based gradient estimation-aware FL ($\mathtt{BGEFL }$) framework that can directly estimate participants’ gradients with limited bandit feedback (i.e., their local function values). We prove that$\mathtt{BGEFL }$enjoys an$\mathcal {O}(1)$communication complexity, that is a constant-size uplink communication in which each client uploads only one point’s feedback in the uplink. Moreover, our bandit-based gradient estimator is communication-efficient, unbiased, and stable. We prove theconvergenceperformance of$\mathtt{BGEFL }$for training strongly convex, general convex, and non-convex models. Finally, we evaluate our$\mathtt{BGEFL }$over several datasets and the experimental results demonstrate the effectiveness of$\mathtt{BGEFL }$.

With the flourishing of global services, low-latency analytics on large-volume geo-distributed data has been a regular requirement for application decision-making. Serverless computing, with its rapid function start-up and lightweight deployment, provides a compelling way for geo-distributed analytics. However, existing research focuses on elastic resource scaling at the stage granularity, struggling to heterogeneous resource demands across component functions in wide-area settings. The neglect potentially results in the cost inefficiency and Service Level Objective (SLO) violations. In this paper, we advocate for fine-grained function orchestration to exploit wide-area resource elasticity. We thereby present Demeter, a fine-grained function orchestrator that saves job execution costs for geo-distributed serverless analytics while ensuring SLO compliance. By learning from volatile and bursty environments, Demeter jointly makes per-function placement and resource allocation decisions using a well-optimized multi-agent reinforcement learning algorithm with a pruning mechanism. It prevent the irreparable performance loss by function congestion control. Ultimately, we implement Demeter and evaluate it with the realistic workloads. Experimental results reveal that Demeter outperforms the baselines by up to 46.6% on cost, while reducing SLO violation by over 23.7% and bringing it to below 15%.

To meet the new requirements of Industry 4.0, the logistics field has introduced 3D reconstruction technology. Computer vision-based solutions face challenges like bad lighting conditions and line-of-sight constraints. Meanwhile, the widespread adoption of RFID tags in supply chains offers an opportunity to enhance current reconstruction methods. In this article, we propose TagRecon, a fine-grained multi-object 3D reconstruction scheme utilizing well-deployed RFIDs. Specifically, TagRecon transforms the task of reconstruction into a problem of estimating 3D bounding boxes for tagged packages. By placing dual anchor tags on each target package, TagRecon enables accurate inference of the package’s translation and rotation using RFID-based localization and orientation sensing. Our scheme introduces a novel method to estimate rotations and translations for tagged packages, utilizing the known geometric relationship of anchor tags. Besides, to achieve simultaneous reconstruction of multiple packages, we manage to match tags from various packages through the correlation between anchor tag pairs. As far as we know, this is the first RFID-based solution that can simultaneously realize 3D translation and rotation estimation of multiple objects to a fine granularity. Experiments validate TagRecon achieves a 28.0 cm translation error and 6.8°, 6.0°, and 7.5° rotation errors for roll, pitch, and yaw angles on average.

Acting as dynamic base stations, unmanned aerial vehicles have a significant advantage over conventional base stations for dynamic object localization and tracking. In practice, however, the localization and tracking performance highly depends on the observation sequence of time-variant received signal strength (RSS) on UAVs from the dynamic object, which can be severely distorted by complex land layouts. In this paper, we propose dynamic object tracking by UAV with radio map, which contains abundant time-variant RSS knowledge. We generate time-varying radio maps from complex real-world topographic data. Then, we derive the grid-based method for dynamic object position estimation with the non-memoryless observations from the dynamic object. Numerical results show that the proposed algorithm can considerably improve tracking precision and efficiency for real-world topographical data.

Blood pressure (BP) measurement is significant to the assessment of many dangerous health conditions. Apart from invasively inserting catheters into arteries, non-invasive approaches typically rely on wearing devices on specific skin areas with consistent pressure. However, this can be uncomfortable and unsuitable for certain individuals, and the accuracy of these methods may significantly decrease due to improper device placements and wearing states. Recently, contactless methods leveraging RF technology have emerged as a potential alternative. However, these methods suffer from the drawback of overfitting deep learning (DL) models without a sound physiological basis, resulting in a lack of clear explanations for their outputs. Consequently, such limitations lead to skepticism and distrust among medical experts. In this paper, we propose hBP-Fi, a contactless BP measurement system driven by hemodynamics acquired via RF sensing. In addition to its contactless convenience, hBP-Fi is superior to other RF sensing approaches in i) grounding on hemodynamics as the key physical process of heart-pulse activities, ii) exploiting beam-steerable RF devices to achieve a super-resolution scan on the fine-grained pulse activities along arm arteries, and iii) ensuring the trustworthiness of system outputs via an explainable (decision-understandable) DL model. Extensive experiments with 35 subjects demonstrate that hBP-Fi can achieve the error of -2.05±6.83 mmHg and 1.99 ± 6.30 mmHg for monitoring systolic and diastolic blood pressures, respectively.

Free weight training (FWT) is of utmost importance for physical well-being. However, the success of FWT depends on choosing the suitable workload, as improper selections can lead to suboptimal outcomes or injury. Current workload estimation approaches rely on manual recording and specialized equipment with limited feedback. Therefore, we introduce PPGSpotter, a novel PPG-based system for FWT monitoring in a convenient, low-cost, and fine-grained manner. By characterizing the arterial geometry compressions caused by the deformation of distinct muscle groups during various exercises and workloads in PPG signals, PPGSpotter can infer essential FWT factors such as workload, repetitions, and exercise type. To remove pulse-related interference that heavily contaminates PPG signals, we develop an arterial interference elimination approach based on adaptive filtering, effectively extracting the pure motion-derived signal (MDS). Furthermore, we explore 2D representations within the phase space of MDS to extract spatiotemporal information, enabling PPGSpotter to address the challenge of resisting sensor shifts. Finally, we leverage a multi-task CNN-based model with workload adjustment guidance to achieve personalized FWT monitoring. Extensive experiments with 15 participants confirm that PPGSpotter can achieve workload estimation (0.59 kg RMSE), repetitions estimation (0.96 reps RMSE), and exercise type recognition (91.57% F1-score) while providing valid workload adjustment recommendations.

There have been many studies leveraging bargaining to incentivize the sharing of network resources between resource owners and seekers. They predicted bargaining behavior and outcomes mainly by assuming that bargainers are fully rational and possess sufficient knowledge about their opponents. Our work addresses the prediction of bargaining behavior in network resource sharing scenarios where these assumptions do not hold, i.e., bargainers are bounded-rational and have heterogeneous knowledge. Our first key idea is using a multi-output Long Short-Term Memory (LSTM) neural network to learn bargainers’ behavior patterns and predict both their discrete and continuous decisions. Our second key idea is assigning a unique latent vector to each bargainer, characterizing the heterogeneity among bargainers. We propose a scheme to jointly learn the LSTM weights and latent vectors from real bargaining data, and utilize them to achieve a personalized behavior prediction. We prove that estimating our LSTM weights corresponds to a special design of LSTM training, and also theoretically characterize the performance of our scheme. To deal with large-scale datasets in practice, we further propose a variant of our scheme to accelerate the LSTM training. Experiments on a large real-world bargaining dataset demonstrate that our schemes achieve more accurate personalized predictions than baselines.

Continuous blood pressure (BP) monitoring using wearable devices has received increasing attention due to its importance in diagnosing diseases. However, existing methods mainly measure BP intermittently, involve some form of user effort, and suffer from insufficient accuracy due to sensor properties. In order to overcome these limitations, we study the BP measurement technology based on heart sounds, and find that the time interval between the first and second heart sounds (TIFS) of bone-conducted heart sounds collected in the binaural canal is closely related to BP. Motivated by this, we propose HearBP, a novel BP monitoring system that utilizes inear microphones to collect bone-conducted heart sounds in the binaural canal. We first design a noise removing method based on U-net autoencoder-decoder to separate clean heart sounds from background noises. Then, we design a feature extraction method based on shannon energy and energy-entropy ratio to further mine the time domain and frequency domain features of heart sounds. In addition, combined with the principal component analysis algorithm, we achieve feature dimension reduction to extract the main features related to BP. Finally, we propose a network model based on dendritic neural regression to construct a mapping between the extracted features and BP. Extensive experiments with 41 participants show the average estimation error of 0.97mmHg and 1.61mmHg and the standard deviation error of 3.13mmHg and 3.56mmHg for diastolic pressure and systolic pressure, respectively. These errors are within the acceptable range specified by the FDA’s AAMI protocol.

The rapid expansion of the Internet of Things (IoT) introduces critical challenges in scalability, mobility, and security, particularly in large-scale deployments. While information-centric networking (ICN) addresses these by enhancing content mobility, multipath support, and edge-embedded caching with inherent security features, it faces limitations in handling large heterogeneous environments due to its in-network caching and content-based forwarding strategies. Software-defined networking (SDN) complements ICN by employing a centralized controller to intelligently orchestrate content caching and forwarding, yet struggles with the efficient allocation and acquisition of content across expansive IoT systems. In response to these challenges, we propose CIC-SIoT, a novel information-centric SDN (IC-SDN) solution, designed to optimize the ICN-IoT framework. Our solution incorporates specialized algorithms for controllers, consumers, producers, and ICN nodes. These algorithms improve content forwarding decisions by moving beyond the traditional reliance on the forwarding information base (FIB) and instead utilizing the pending interest table (PIT) to efficiently manage and distribute content. Validated through ndnSIM and MATLAB simulations, CIC-SIoT achieves substantial performance enhancements, including an 80% increase in throughput, a 34% reduction in latency, and a 25% savings in bandwidth. Additionally, it reduces packet loss by 67% and communication overhead by 66%, compared to existing solutions. These results underscore the framework’s ability to significantly improve the efficiency and scalability of content distribution in IoT environments, highlighting its robustness and adaptability in addressing the complex dynamics of modern networked systems.

With the rapid development of mobile devices and the fast increase of sensitive data, secure and convenient mobile authentication technologies are desired. Except for traditional passwords, many mobile devices have biometric-based authentication methods (e.g., fingerprint, voiceprint, and face recognition), but they are vulnerable to spoofing attacks. To solve this problem, we study new biometric features which are based on the dental occlusion and find that the bone-conducted sound of dental occlusion collected in binaural canals contains unique features of individual bones and teeth. Motivated by this, we propose a novel authentication system, TeethPass$^+$, which uses earbuds to collect occlusal sounds in binaural canals to achieve authentication. Firstly, we design an event detection method based on spectrum variance to detect bone-conducted sounds. Then, we analyze the time-frequency domain of the sounds to filter out motion noises and extract unique features of users from four aspects: teeth structure, bone structure, occlusal location, and occlusal sound. Finally, we train a Triplet network to construct the user template, which is used to complete authentication. Through extensive experiments including 53 volunteers, the performance of TeethPass$^+$in different environments is verified. TeethPass$^+$achieves an accuracy of 98.6% and resists 99.7% of spoofing attacks.

Past tongue-jaw movement interaction systems typically require dedicated hardware and are uncomfortable to use, limiting their scalability and generalizability. This paper introducesCanalScan, the first system that recognizes tongue-jaw movements using commodity speakers and microphones mounted on ubiquitous off-the-shelf devices (e.g., smartphones). What inspires us is that tongue-jaw movements always cause ear canal deformations, and we find that for different tongue-jaw movements, dynamic features of ear canal deformations present unique patterns on acoustic reflections in the ear canal. Specifically,CanalScanfirst sends an acoustic signal to the ear canal, then parses the reflection signals for tongue-jaw movements recognition. To eliminate the impacts of body movements, we develop a body movement noise filtering method and a dynamic segmentation method to identify and separate the tongue-jaw movements-associated ear canal deformations from other types of body movements. We further propose a sensor position detection method and a data transformation mechanism to reduce the impacts of diversities in-ear canal shapes and relative positions between sensors and the ear canal.CanalScanexplores twelve unique and consistent features and applies a random forest classifier to distinguish tongue-jaw movements. Extensive experiments with twenty participants validate the generalizability, effectiveness, robustness, and high accuracy ofCanalScan.

Recent literature advances motion sensors mounted on smartphones and AR/VR headsets to speech eavesdropping due to their sensitivity to subtle vibrations. The popularity of motion sensors in earphones has fueled a rise in their sampling rate, which enables various enhanced features. This paper investigates a new threat of eavesdropping via motion sensors of earphones by developing EarSpy, which builds on our observation that the earphone's accelerometer can capture bone conduction vibrations (BCVs) and ear canal dynamic motions (ECDMs) associated with speaking; they enable EarSpy to derive unique information about the wearer's speech. Leveraging a study on the motion sensor measurements captured from earphones, EarSpy gains abilities to disentangle the wearer's live speech from interference caused by body motions and vibrations generated when the earphone's speaker plays audio. To enable user-independent attacks, EarSpy involves novel efforts, including a trajectory instability reduction method to calibrate the waveform of ECDMs and a data augmentation method to enrich the diversity of BCVs. Moreover, EarSpy explores effective representations from BCVs and ECDMs, and develops a neural network model with character-level and word-level speech recognition models to realize speech recognition. Extensive experiments involving 14 participants demonstrate that EarSpy reaches a promising recognition for the wearer's speech.

Federated Learning (FL)has achieved state-of-the-art performance in training a global model in a decentralized and privacy-preserving manner. Many recent works have demonstrated that incentive mechanism is of paramount importance for the success of FL. Existing incentives to FL either neglect communication efficiency, or consider communication efficiency but design the incentive mechanisms using non-cooperative games under complete information assumption, or study incentive mechanism under incomplete information but only apply to the sequential interaction setting. We shed light on this problem from the cooperative perspective and propose an incentive mechanism for communication-efficient FL based on the Nash bargaining theory. Specially, we formulate our incentive mechanism as a one-to-manyconcurrent bargaininggame among the aggregator and clients, and systematically analyze the Nash bargaining solution (NBS, game equilibrium) to design the incentive mechanism. It should be noted that the existingsequential bargainingis not suitable for incentivizing FL due to high (exponential) time complexity, which deteriorates the straggler problem in FL. Our formulated bargaining game is challenging due to the NP-hardness. We propose a probabilistic greedy-based client selection algorithm and derive an analytical payment solution as an approximate NBS. We prove the convergence guarantee of our incentive mechanism for communication-efficient FL. Finally, we conduct experiments over real-world datasets to evaluate the performance of our incentive mechanism.

With the ever-increasing demand for improving communication and independence for visually impaired people, automatic Braille recognition has gained increasing attention in facilitating Braille learning and reading. However, current approaches mainly require high-cost hardware, involve inconvenient operation, and disturb the normal touch function. In this paper, we proposeBrailleReaderas a low-cost and effortless Braille character recognition system without disturbing normal Braille touching. It exploits the wrist motion of Braille reading captured by the motion sensor available in the ubiquitous wrist-worn device to infer the encoded character information. To address the noise caused by other body and hand movements, we propose a novel noise cancellation method using the wavelet packet decomposition and reconstruction technique to separate clean wrist movement induced by the Braille dot. Moreover, we further explore the unique wrist movement pattern in three aspects to extract a novel and effective feature set. Based on this,BrailleReaderleverages a spiking neural network-based model to robustly recognize Braille characters across different people and different surface materials. Extensive experiments with 48 participants demonstrate thatBrailleReadercan perform accurate and robust recognition of 26 Braille characters.

Applying the serverless paradigm to edge computing improves edge resource utilization while bringing the benefits of flexible scaling and pay-as-you-go to latency-sensitive applications. This extends the boundaries of serverless computing and improves the quality of service for Function-as-a-Service users. However, as an emerging cloud computing paradigm, serverless edge computing faces pressing challenges, with one of the biggest obstacles being delay caused by excessively long container cold starts. Cold start delay is defined as the time between when a serverless function is triggered and when it begins to execute, and its existence seriously impacts resource utilization and Quality of Service (QoS). In this paper, we study how to minimize the total system cost by caching function containers and selecting routes for neighboring functions via edge or public clouds. We prove that the proposed problem is NP-hard even in the special case where the user request contains only one function, and that the unpredictability of user requests and the impact between adjacent time decisions require that the problem to be solved in an online fashion. We then design the Online Lazy Caching algorithm, an online algorithm with a worst-case competitive ratio using a randomized dependent rounding algorithm to solve the problem. Extensive simulation results show that the proposed online algorithm can achieve close-to-optimal performance in terms of both total cost and cold start cost compared to other existing algorithms, with average improvements of 31.6% and 51.7%.

Nowadays, mobile smart devices are widely used in daily life. It is increasingly important to prevent malicious users from accessing private data, thus a secure and convenient authentication method is urgently needed. Compared with common one-off authentication (e.g., password, face recognition, and fingerprint), continuous authentication can provide constant privacy protection. However, most studies are based on behavioral features and vulnerable to spoofing attacks. To solve this problem, we study the unique influence of sliding fingers on active vibration signals, and further propose an authentication system, FingerSlid, which uses vibration motors and accelerometers in mobile devices to sense biometric features of sliding fingers to achieve behavior-independent continuous authentication. First, we design two kinds of active vibration signals and propose a novel signal generation mechanism to improve the anti-attack ability of FingerSlid. Then, we extract different biometric features from the received two kinds of signals, and eliminate the influence of behavioral features in biometric features using a carefully designed Triplet network. Last, user authentication is performed by using the generated behavior-independent biometric features. FingerSlid is evaluated through a large number of experiments under different scenarios, and it achieves an average accuracy of 95.4% and can resist 99.5% of attacks.

Off-screen handwriting recognitionenriches the handwriting interaction paradigm for mobile devices. However, the existing approaches are only applicable to the specific environment and equipment conditions. In this paper, we proposeAcouWrite, a general, scalable and real-time handwriting recognition system based on active acoustic sensing. In detail, AcouWrite relies onactive acoustic sensingusing only a pair of microphones and speakers on the smartphone to capture real-time handwriting input. Particularly, we extract theshort-time dCIR (st-dCIR)to monitor the changes in the acoustic transmission channel resulting from finger movement. Technically, we use aCNN-GRUclassifier to complete the recognition task in AcouWrite. Moreover, we use data augmentation and spelling error correction methods to improve AcouWrite's robustness. To improve the generalization of our AcouWrite for new characters, we incorporate the transfer learning module into our AcouWrite. In various real-world environments, experiments demonstrate that AcouWrite achieves a mean recognition accuracy of 97.62%, a word accuracy (WA) of 96.4% and a character error rate (CER) of 1.5% for 100 common words, and an average response time of 94 milliseconds.

Bowel sound (BS) is an important physiological signal of the human body, which is also an objective reflection of gastrointestinal motility. However, BS has characteristics of weak signal, strong noise, and randomicity, which bring great challenges to the daily detection of BS. In this paper, we propose BSMonitor, the first BS monitoring system with strong noise-resistant capability via earphones. BSMonitor uses one earphone attached to the abdomen to collect BS signals and the other earphone worn in the ear to collect external noises and internal noises. After eliminating the noises through the Kalman filter and band-pass filter, the signal containing BS is separated via the empirical mode decomposition. Then BSMonitor extracts MFCC features of BS signals and applies a carefully-designed LSTM network to perform highly-accurate BS detection. Finally, an alert mechanism calculates the frequency and duration of detected BS and compares with the normal values to alert users. Furthermore, to increase the amount and diversity of training data, we introduce a data augmentation method, which can further improve the accuracy and generalization of BSMonitor. Through extensive experiments with 18 volunteers, we find that BSMonitor not only achieves high accuracy of BS detection but also has strong generalization across different users and environments. Particularly, BSMonitor achieves accuracy up to 98.73% and 94.56% in thebenchmark experimentsand thecross experiments, respectively.

In mobile augmented reality applications, how to build a secure device connection between two previously unassociated devices without prior set-up is challenging, which also refers to the problem of device pairing. Most existing approaches to device pairing either have certain limits to be utilized in the environment of augmented reality or lack considerations of security issues. In this article, we design WVC, an intuitive, user-friendly, and secure device paring system for mobile augmented reality. It enables users to connect to a neighboring device by waving a finger to click towards it in the air. The system uses critical features from finger tracking trajectories to understand which device a user wants to interact with. Then, it designs key generation and correction algorithms to enhance security in device communication. In addition, we present solutions to defend against malicious attacks. We implement and test the system with 10 volunteers. The experimental results demonstrate the feasibility and effectiveness of the system under varied scenarios in which devices are close to each other at a small angle or located at different heights within 2 m .

Nowadays, the emerging short video streaming applications have gained substantial attention. With the rapidly burgeoning demand for short video streaming services, maximizing their Quality of Experience (QoE) is an onerous challenge. Current video preloading algorithms cannot determine video preloading sequence decisions appropriately due to the impact of users’ swipes and bandwidth fluctuations. As a result, it is still ambiguous how to improve the overall QoE while mitigating bandwidth wastage to optimize short video streaming services. In this article, we devise Gamora, a buffer-aware short video streaming system to provide a high QoE of users. In Gamora, we first propose an unordered preloading algorithm that utilizes a Deep Reinforcement Learning (DRL) algorithm to make video preloading decisions. Then, we further devise an Asymmetric Imitation Learning (AIL) algorithm to guide the DRL-based preloading algorithm, which enables the agent to learn from expert demonstrations for fast convergence. Finally, we implement our proposed short video streaming system prototype and evaluate the performance of Gamora on various real-world network datasets. Our results demonstrate that Gamora significantly achieves QoE improvement by 28.7%–51.4% compared to state-of-the-art algorithms, while mitigating bandwidth wastage by 40.7%–83.2% without sacrificing video quality.

The advent of 6G networks promises revolutionary advances in dynamism, intelligence, and decentralization. Realizing the full potential of 6G requires adaptable service level agreements (SLAs) that can optimize performance based on dynamic network conditions. In this paper, we suggested a method based on the Hyperledger Sawtooth blockchain’s smart contract with the Reptile meta-learning algorithm to solve the rigidity of static SLA and centralization problems. In order to sustain the quality of service in the radio access network and core network domain of 6G networks, this work focuses on SLA management for efficient resource allocation for the eMBB-plus slice. Our approach entails breaking down static SLAs into finer-grained components, transferring those components onto Hyperledger Sawtooth smart contracts, and using the Reptile meta-learning algorithm to forecast SLA metrics and resource requirements. A dynamic tariff model, also proposed within the smart contract, handles increased user demands. We evaluate the solution by analyzing Reptile performance, resource allocation, and SLA violations under dynamic demands. Results demonstrate the efficiency of this AI-driven, blockchain-based approach for automated, optimized 6G eMBB-plus resource management adhering to dynamic fine-grained SLAs. This work highlights the synergistic potential of AI and blockchain for trusted and intelligent 6G service delivery.

Video analytics play a pivotal role in enhancing the safety of intelligent surveillance and autonomous driving. However, the transmission of vast video data and the computational demands of video analytics present challenges within traditional cloud computing paradigms. To address latency concerns, dynamic video analytics often leverage edge deployments. Nevertheless, the efficient allocation of resources at the edge, balancing cost-effectiveness and accuracy, becomes crucial, especially when multiple video analytics services concurrently operate within the system. This paper introduces an edge-centric incentive mechanism designed to encourage greater participation from edge nodes in offloading tasks. The key focus is on addressing the dynamic nature of edge resources and optimizing system returns through a rational pricing mechanism. We propose a decentralized Soft Actor-Critic algorithm grounded in game theory (DSACG) to autonomously learn the optimal pricing strategy. A comprehensive theoretical analysis, supported by extensive simulations, substantiates the effectiveness of our proposed solution.

The 360-degree video streaming service provides a unique immersive viewing experience for users, who can freely change their Field-of-View (FoV) to view different portions of the videos. However, the demands for high throughput and low latency for 360-degree video pose substantial challenges to the current network infrastructure. Super Resolution (SR) is the procedure for reconstructing high-resolution images from low-resolution ones. Hence, caching video content on the network edge in advance, which is near end users, and applying the SR technique can significantly alleviate the transmission latency. In this article, we describeNOVA, an efficientNeural-OptimizedViewportAdaptive 360-degree video streaming system to improve the Quality of Experience (QoE) of users. In NOVA, we first design a foveated rendering SR approach to super-resolve video tiles utilizing computational resources at the edge. Subsequently, we present a meta-learning-based Multi-Agent Reinforcement Learning (MARL) algorithm to select SR depths and video tiles inside users’ viewports for agile video tile adaptation to optimize overall QoE under frequent network fluctuations. Finally, we implement the holistic prototype of NOVA and evaluate its performance on various real-world network datasets. Extensive experiments illustrate that compared to the state-of-the-art algorithms, NOVA improves average user-perceived QoE by up to 27%.

Recent literature advances motion sensors mounted on smartphones and AR/VR headsets to speech eavesdropping due to their sensitivity to subtle vibrations. The popularity of motion sensors in earphones has fueled a rise in their sampling rate, which enables various enhanced features. This paper investigates a new threat of eavesdropping via motion sensors of earphones by developing EarSpy, which builds on our observation that the earphone’s accelerometer can capture bone conduction vibrations (BCVs) and ear canal dynamic motions (ECDMs) associated with speaking; they enable EarSpy to derive unique information about the wearer’s speech. Leveraging a study on the motion sensor measurements captured from earphones, EarSpy gains abilities to disentangle the wearer’s live speech from interference caused by body motions and vibrations generated when the earphone’s speaker plays audio. To enable user-independent attacks, EarSpy involves novel efforts, including a trajectory instability reduction method to calibrate the waveform of ECDMs and a data augmentation method to enrich the diversity of BCVs. Moreover, EarSpy explores effective representations from BCVs and ECDMs, and develops a convolutional neural model with Connectionist Temporal Classification (CTC) to realize accurate speech recognition. Extensive experiments involving 14 participants demonstrate that EarSpy reaches a promising recognition for the wearer’s speech.

Location awareness in environments is one of the key parts for drones’ applications and have been explored through various visual sensors. However, standard cameras easily suffer from motion blur under high moving speeds and low-quality image under poor illumination, which brings challenges for drones to perform motion tracking. Recently, a kind of bio-inspired sensors called event cameras emerge, offering advantages like high temporal resolution, high dynamic range and low latency, which motivate us to explore their potential to perform motion tracking in limited scenarios. In this paper, we propose FlyTracker, aiming at developing visual sensing ability for drones of both individual and circumambient location-relevant contextual, by using a monocular event camera. In FlyTracker, background-subtraction-based method is proposed to distinguish moving objects from background and fusion-based photometric features are carefully designed to obtain motion information. Through multilevel fusion of events and images, which are heterogeneous visual data, FlyTracker can effectively and reliably track the 6-DoF pose of the drone as well as monitor relative positions of moving obstacles. We evaluate performance of FlyTracker in different environments and the results show that FlyTracker is more accurate than the state-of-the-art baselines.

With the development of society and the gradual increase of life pressure, the number of people engaged in mental work and working hours have increased significantly, resulting in more and more people in a state of fatigue. It not only reduces people’s work efficiency, but also causes health and safety related problems. The existing fatigue detection systems either have different shortcomings in diverse scenarios or are limited by proprietary equipment, which is difficult to be applied in real life. Motivated by this, we propose a multi-information fatigue detection system named WakeUp based on commercial smart speakers, which is the first to fuse physiological and behavioral information for fine-grained fatigue detection in a non-contact manner. We carefully design a method to simultaneously extract users’ physiological and behavioral information based on the MobileViT network and VMD decomposition algorithm respectively. Then, we design a multi-information fusion method based on the statistical features of these two kinds of information. In addition, we adopt an SVM classifier to achieve fine-grained fatigue level. Extensive experiments with 20 volunteers show that WakeUp can detect fatigue with an accuracy of 97.28%. Meanwhile, WakeUp can maintain stability and robustness under different experimental settings.

Sign language is expressed by movements of the hands and facial expressions, which is mainly used by the deaf community. Although some gesture recognition methods are put forward, they possess different defects and are not applicable to deal with the sign language recognition (SLR) problem. In this article, we propose an end-to-end American SLR system with built-in speakers and microphones in smartphones, which enables SLR at both word level and sentence level. The high-level idea is to use the inaudible acoustic signal to estimate channel information and capture the sign language in real time. We use channel impulse response to represent each sign language gesture, which can realize finger-level recognition. We also pay attention to conversion movements between two words and treat them as an additional label when training the sentence-level classification model. We implement a prototype system and run a series of experiments that demonstrate the promising performance of our system. Experimental results show that our approach can achieve an accuracy of 97.2% at word-level recognition and word error rate of 0.9% at sentence-level recognition, respectively.

Proper handwashing, having a crucial effect on reducing bacteria, serves as the cornerstone of hand hygiene. For elders with dementia, they suffer from a gradual loss of memory and difficulty coordinating handwashing steps. Proper assistance should be provided to them to ensure their hand hygiene adherence. Toward this end, we propose AWash, leveraging inertial measurement unit (IMU) readily available in most wrist-worn devices (e.g., smartwatches) to characterize handwashing actions and provide assistance. To monitor handwashing scenarios round-the-clock while achieving energy efficiency, we design methods that distinguish handwashing from other daily activities and dynamically adjust the sampling duty cycle. Upon detecting handwashing actions, we design several novel techniques to segment different handwashing actions and extract sensor-body inclination angles that handle particular interference of senile dementia patients. Moreover, a user-independent network model is built to recognize the handwashing actions of senile dementia patients without requiring their training data. Furthermore, we propose a transfer learning method that improves system performance. To meet users’ diverse needs, we use a state machine to make prompt decisions, supporting customized assistance. Extensive experiments on a prototype with eight older participants demonstrate that AWash can increase the user’s independence in the execution of handwashing.

Mobile devices are promising to apply two-factor authentication to improve system security. Existing solutions have certain limits of requiring extra user effort, which might seriously affect user experience and delay authentication time. In this paper, we propose PPGPass, a novel mobile two-factor authentication system, which leverages Photoplethysmography (PPG) sensors available in most wrist-worn wearables. PPGPass simultaneously performs a password/pattern/signature authentication and a physiological-based authentication. To realize both nonintrusive and secure, we design a two-stage algorithm to separate clean heartbeat signals from PPG signals contaminated by motion artifacts so that users do not have to deliberately keep their bodies still. In addition, to deal with noncancelable issues when biometrics are compromised, we design a repeatable and non-invertible method to generate cancelable feature templates as alternative credentials. We leverage the great power ofRandom ForestandSupport Vector Data Descriptionto detect adversaries and verify a user's identity. To the best of our knowledge, PPGPass is the first nonintrusive and secure mobile two-factor authentication based on PPG sensors. Extensive experiments demonstrate that PPGPass can achieve the false acceptance rate of 3.11% and the false recognition rate of 3.71%, which confirms its high effectiveness, security, and usability.

In the traditional video streaming service provisioning paradigm, viewers typically request video content through a central Content Delivery Network (CDN) server. However, because of the uncertain wide area network delays, the (remote) viewers usually suffer from long video streaming delay, which affects the quality of experience. Multi-Access Edge Computing (MEC) offers a way to shorten the video streaming delay by building small-scale cloud infrastructures at the network edge, which are in close proximity to the viewers. In this paper, we present novel centralized and distributed algorithms for the video content placement problem in MEC. In the proposed centralized video content placement algorithm, we leverage the Lyapunov optimization technique to formulate the video content placement problem as a series of one-time-slot optimization problems and apply an Alternating Direction Method of Multipliers (ADMM)-based method to solve each of them. We further devise a distributed Multi-Agent Reinforcement Learning (MARL)-based method with value decomposition mechanism and parallelization policy update method to solve the video content placement problem. The value Decomposition mechanism deals with the credit assignment among multiple agents, which promotes the cooperative optimization of the global target and reduces the frequency of information exchange. The parallelization of policy network can speed up the convergence process. Simulation results verify the effectiveness and superiority of our proposed centralized and distributed algorithms in terms of performance.

Federated learning (FL) has reshaped the learning paradigm by overcoming privacy concerns and siloed data. In FL, an aggregator schedules a set of mobile users (MUs) to collectively train a global model with their local datasets and subsequently aggregates their model updates. However, the users have many uncertainties like unstable network connections and volatile availability, which leads to the straggler problem and deteriorates the efficiency of the FL system. Besides, the issue of non-IID datasets hinders the convergence performance of the global model. To hurdle the user uncertainties, we associate a deadline with the decision in each round and partially collect MUs' updates after the deadline, which can be achieved by considering surplus budget constraints. Moreover, we introduce fairness constraints for the non-IID issue. We propose a deadline-aware task replication for surplus client scheduling policy, called FEDDATE-CS. FEDDATE-CS is developed based on a novel contextual-combinatorial multi-armed bandit (CCMAB) learning framework with fairness guarantee. We extend the hypercube-based CCMAB framework by integrating the Lyapunov queuing technique and rigorously prove that FEDDATE-CS achieves a sublinear regret bound and provides an$[\mathcal{O}(1/V),\mathcal{O}(V)]$regret-fairness tradeoff for any fairness control factor$V>0$. We conduct extensive evaluations to verify the significant superiority of FEDDATE-CS over benchmarks.

Fitness can help to strengthen muscles, increase resistance to diseases, and improve body shape. Nowadays, a great number of people choose to exercise at home/office rather than at the gym due to lack of time. However, it is difficult for them to get good fitness effects without professional guidance. Motivated by this, we propose the first personalized fitness monitoring system, HearFit$^+$, using smart speakers at home/office. We explore the feasibility of using acoustic sensing to monitor fitness. We design a fitness detection method based on Doppler shift and adopt the short time energy to segment fitness actions. Based on deep learning, HearFit$^+$can perform fitness classification and user identification at the same time. Combined with incremental learning, users can easily add new actions. We design 4 evaluation metrics (i.e., duration, intensity, continuity, and smoothness) to help users to improve fitness effects. Through extensive experiments including over 9,000 actions of 10 types of fitness from 12 volunteers, HearFit$^+$can achieve an average accuracy of 96.13% on fitness classification and 91% accuracy for user identification. All volunteers confirm that HearFit$^+$can help improve the fitness effect in various environments.

Network Function Virtualization (NFV) provides the possibility to implement complex network functions from dedicated hardware to software instances called Virtual Network Functions (VNF) by leveraging the virtualization technology. Service Function Chaining (SFC) is therefore defined as a chain-ordered set of placed VNFs that handles the traffic of the delivery and control of a specific application. Due to the advantages of flexibility, efficiency, scalability, and short deployment cycles, NFV has been widely recognized as the next-generation network service provisioning paradigm. In this paper, we study the problem of online SFC control across geo-distributed datacenters, which is to dynamically place required VNFs on datacenter nodes and find routing paths between each adjacent VNF pair for each NFV service flow that varies over time. To that end, we first formulate this problem as an offline optimization problem whose goal is to minimize the average delay such that each datacenter's average cost does not exceed a given expense value. Considering that the offline optimization requires complete offline network information which is difficult to obtain or predict in practice, we present an online SFC control framework without requiring any future information about the traffic demands. More specifically, we leverage the Lyapunov optimization technique to formulate the problem as a series of one-time slot offline optimization problems and then apply a primal-decomposition method to solve each one-time slot problem. Simulation results reveal that our proposed online SFC control framework can efficiently reduce long-term average delay while keeping datacenter's long-term average cost consumption low.

Driver drowsiness is a significant factor in road crashes. However, existing solutions for driver drowsiness detection have major drawbacks of requiring special hardware, constrained recording conditions, and cannot handle the asynchronous and contradictory nature of multiple indicators. In view of this, we propose FDWatch, a novel drowsiness detection system that exploits the low-cost Photoplethysmogram (PPG) sensor and motion sensor integrated into wrist-worn devices. We design a set of novel algorithms to extract multiple drowsiness-related indicators covering major categories of human factors. In particular, we demonstrate that commodity PPG sensors can be utilized to detect yawning behavior; it contributes as an important indicator for drowsiness detection. The core of FDWatch is based on the Dempster-Shafer evidence theory. It considers different indicators as evidence describing the state of the driver from different angles. To make the extracted indicators applicable to Dempster-Shafer evidence theory, we employ backpropagation neural networks to obtain the basic probability assignment. Moreover, we propose a similarity-distance-based method to handle evidence conflicts. Extensive experiments with real-road driving data show that FDWatch can accurately detect driver drowsiness with a missing alarm rate of 3.57% and a false alarm rate of 3.68%.

Sound-related respiratory symptoms are commonly observed in our daily lives. They are closely related to illnesses, infections, or allergies but ignored by the majority. Existing detection methods either depend on specific devices, which are inconvenient to wear, or are sensitive to noises and only work for indoor environment. Considering the lack of monitoring method for in-car environment, where there is high risk of spreading infectious diseases, we propose a smartphone-based system, named SymListener, to detect respiratory symptoms in driving environment. By continuously recording acoustic data through a built-in microphone, SymListener can detect the sounds of cough, sneeze, and sniffle. We design a modified ABSE-based method to remove the strong and changeable driving noises while saving energy of the smartphone. An LSTM network is adopted to classify the three types of symptoms according to the carefully designed acoustic features. We implement SymListener on different Android devices and evaluate its performance in real driving environment. The evaluation results show that SymListener can reliably detect target respiratory symptoms with an average accuracy of 92.19% and an average precision of 90.91%.

The efficacy of Network Function Virtualization (NFV) depends critically on (1) where the virtual network functions (VNFs) are placed and (2) how the traffic is routed. Unfortunately, these aspects are not easily optimized, especially under time-varying network states with different QoS requirements. Given the importance of NFV, many approaches have been proposed to solve the VNF placement and Service Function Chaining (SFC) routing problem. However, those prior approaches mainly assume that the network state is static and known, disregarding dynamic network variations. To bridge that gap, we leverage Markov Decision Process (MDP) to model the dynamic network state transitions. To jointly minimize the delay and cost of NFV providers and maximize the revenue, we first devise a customized Deep Reinforcement Learning (DRL) algorithm for the VNF placement problem. The algorithm uses the attention mechanism to ascertain smooth network behavior within the general framework of network utility maximization (NUM). We then propose attention mechanism-based DRL algorithm for the SFC routing problem, which is to find the path to deliver traffic for the VNFs placed on different nodes. The simulation results show that our proposed algorithms outperform the state-of-the-art algorithms in terms of network utility, delay, cost, and acceptance ratio.

Handwritten signature authentication is an important service to defend against fraudulent activities. Current automated solutions rely heavily on dedicated devices and require certain user efforts. In this work, we explore the feasibility of a new type of signature authentication system, SAP - Signature Authentication with PPG Sensor, which leverages Photoplethysmography (PPG) sensors in wrist-worn wearable devices. To make SAP non-intrusive and secure, we design effective algorithms to separate the signature signals from the heartbeat signals in the raw PPG signals. We implement a low-cost hardware prototype of SAP. Our preliminary experimental results show that SAP can achieve an average F1 score of up to 98%.

With the rapid development of mobile devices and the fast increase of sensitive data, secure and convenient mobile authentication technologies are desired. Except for traditional passwords, many mobile devices have biometric-based authentication methods (e.g., fingerprint, voiceprint, and face recognition), but they are vulnerable to spoofing attacks. To solve this problem, we study new biometric features which are based on the dental occlusion and find that the bone-conducted sound of dental occlusion collected in binaural canals contains unique features of individual bones and teeth. Motivated by this, we propose a novel authentication system, TeethPass, which uses earbuds to collect occlusal sounds in binaural canals to achieve authentication. We design an event detection method based on spectrum variance and double thresholds to detect bone-conducted sounds. Then, we analyze the time-frequency domain of the sounds to filter out motion noises and extract unique features of users from three aspects: bone structure, occlusal location, and occlusal sound. Finally, we design an incremental learning-based Siamese network to construct the classifier. Through extensive experiments including 22 participants, the performance of TeethPass in different environments is verified. TeethPass achieves an accuracy of 96.8% and resists nearly 99% of spoofing attacks.

Handwritten signature authentication is a crucial service to defend against fraudulent activities. Existing automated solutions rely heavily on dedicated devices that are expensive and require different user efforts that affect the user experience. In this paper, we propose a new signature authentication system, PPGSign, which leverages Photoplethysmography (PPG) sensors in the existing wrist-worn wearable devices. The unique blood flow changes in the supplicant’s hand movement are exploited in this system to validate the signature. To make PPGSign nonintrusive and secure, we explore effective algorithms to separate the signature signals from the heartbeat signals in the raw PPG signals. We build a low-cost hardware prototype to verify our proposed method. Our experimental results show that PPGSign can achieve an average F1 score of up to 98%, which verifies the feasibility and efficiency of the proposed solution.

Mobile crowdsensing (MCS) enables a platform to recruit users to collectively perform sensing tasks from requesters. In order to maximize the completion qualities of tasks, an incentive mechanism should be well designed for the platform to incentivize high-quality users’ participation. The existing works largely adopt the Stackelberg game to model the strategic interactions in the incentive mechanism. However, there are practical issues that are less investigated in the context of the Stackelberg-based incentive mechanism. First, the platform has no knowledge about users’ sensing qualities beforehand due to their private information. Second, the platform needs users’ continuous participation in the long run, which results in fairness requirements. Third, it is also crucial to protect users’ privacy due to the potential privacy leakage concerns (e.g., sensing qualities) after completing tasks. In this article, we jointly address these issues and propose the three-stage Stackelberg-based incentive mechanism for the platform to recruit participants. In detail, we leverage combinatorial volatile multiarmed bandits (CVMABs) to elicit unknown users’ sensing qualities. We use the drift-plus-penalty (DPP) technique in Lyapunov optimization to handle the fairness requirements. We blur the quality feedback with tunable Laplacian noise such that the incentive mechanism protects locally differential privacy (LDP). Finally, we carry out experiments to evaluate our incentive mechanism. The numerical results show that our incentive mechanism achievessublinearregret performance to learn unknown quality with fairness and privacy guarantee.

The ever-growing security issues in various scenarios create an urgent demand for a reliable and convenient identification system. Traditional identification systems request users to provide passwords, fingerprints, or other easily stolen information. Existing works show that everyone’s gait and respiration have unique characteristics and are difficult to imitate. But these works only use gait or respiration information to achieve identification, which leads to low accuracy or long identification time. And they have no strong anti-interference ability, which leads to the limitation in practical application. Toward this end, we propose a new system which uses both gait and respiratory biometric characteristics to achieve user identification using Wi-Fi (GRi-Fi) in the presence of interferences. In our system, we design a segmentation algorithm to segment gait and respiration data. And we design a weighted subcarrier screening method to improve the anti-interference ability. In order to shorten the identification time, we propose a feature integration method based on the weighted average. Finally, we use a deep learning method to identify users accurately. Experimental results show that GRi-Fi can identify the users identity with an average accuracy of 98.3% in noninterference environments. Even in the presence of multiple interferences, the average identification accuracy also reaches 91.2%. In future applications, our system can be applied to many fields of Internet of Things, such as smart home systems and clocking in at companies.

With the rapid development of the Internet, more and more computing-intensive applications with high requirements on computing delay and energy consumption have emerged. Recently, the use of mobile-edge computing servers for auxiliary computing is considered as an effective way to reduce latency and energy consumption. In addition, applications such as autonomous driving will generate a large number of repetitive tasks. Using a cache to store the computational results of popular tasks can avoid the overhead caused by repetitive processing. In this article, we study the problem of computation offloading for users in multiple regions. The optimization goal is expressed as choosing offloading strategies and caching strategies to minimize the total delay and energy consumption of all regions. We first use the deep reinforcement learning (DRL) deep deterministic policy gradient (DDPG) framework to solve the problem of computational offloading in a single region. We also show the inefficiency of existing collaborative caching approaches in multiple regions, and propose a new collaborative caching algorithm (CCA) to improve the overall cache hit rate of the system. Finally, we integrate the DDPG and CCA algorithms to form a holistic efficient caching and offloading strategy for all regions. The simulation results show that the proposed algorithm can significantly improve the cache hit rate, and has an excellent performance in reducing the total system overhead.

The use of the Internet of Things and modern technologies has boosted the expansion of e-health solutions significantly and allowed access to better health services and remote monitoring of patients. Every service provider usually implements its information system to manage and access patient data for its unique purpose. Hence, the interoperability among independent e-health service providers is still a major challenge. From the structure of stored data to its large volume, the design of each such big data system varies, hence the cooperation among different e-health systems is almost impossible. In addition to this, the security and privacy of patient information is a challenging task. Building a unified solution for all creates significant business and economic issues. In this article, we present a solution to migrate existing e-health systems to a unified Blockchain-based model, where access to large scale medical data of patients can be achieved seamlessly by any service provider. A core blockchain network connects individual & independent e-health systems without requiring them to modify their internal processes. Access to patient data in the form of digital assets stored in off-chain storage is controlled through patient-centric channels and policy transactions. Through emulation, we show that the proposed solution can interconnect different e-health systems efficiently.

Bike Sharing Systems (BSSs) have emerged as an economical and eco-friendly solution to alleviate the last-mile problem in intelligent transport systems. As an exclusive route selection problem in BSSs, Bike Trip Planning (BTP) has a clear goal: to select the available routes with minimum time cost for bike users, while satisfying their basic needs, e.g., the bounded longest walking distance. In this paper, we propose a real-time Lyapunov-based Bike Trip Planning (LBTP) policy that considers the users’ waiting at stations, which has not been sufficiently studied in the literature. Both the total time cost and the service rate of all users are the core criteria of a BSS, so we make use of the Lyapunov optimization theory to make a tradeoff between them. Our policy can achieve self-organizing bike redistribution without extra redistribution budget required. The evaluation results show the superiority of our policy on the both system utility and user service rate compared with the existing BTP policies, and reveal the extra travel time fairness degree among different types of users under our policy.

Speeding is one of the biggest threatens to road safety. However, facilities like radar detector and speed camera are not deployed everywhere, as roads in some areas like campus and residential areas often lack these facilities. Several solutions either depend on pre-deployed infrastructures, or require additional devices, which motivate us to explore the practicability of using smartphones’ acoustic sensors to detect vehicle speed. In this paper, we propose a Hybrid Detection system for vehicle Speed (HDSpeed). We first investigate the relationship between acoustic pattern and vehicle speed. According to our findings on typical patterns of both electric vehicles (EVs) and gasoline vehicles (GVs), we separately extract different features from the acoustic signals of EVs and GVs. A CNN and an LSTMN are designed for training EV and GV models, respectively. Considering that applying neural networks obtains coarse-grained information like a speed section, we propose a detection method based on active acoustic sensing, in which method HDSpeed calculates the fine-grained speed by detecting the distance change between the smartphone and the passing vehicle. In addition, the previously detected speed section can eliminate interferences of surrounding moving objects. Through extensive experiments in real driving environments, HDSpeed achieves an average error of$2.17km/h$.

Driving safety has drawn much public attention in recent years due to the fast-growing number of cars. Smoking is one of the threats to driving safety but is often ignored by drivers. Existing works on smoking detection either work in contact manner or need additional devices. This motivates us to explore the practicability of using smartphones to detect smoking events in driving environment. In this paper, we propose a cigarette smoking detection system, named HearSmoking, which only uses acoustic sensors on smartphones to improve driving safety. After investigating typical smoking habits of drivers, including hand movement and chest fluctuation, we design an acoustic signal to be emitted by the speaker and received by the microphone. We calculate Relative Correlation Coefficient of received signals to obtain movement patterns of hands and chest. The processed data is sent into a trained Convolutional Neural Network for classification of hand movement. We also design a method to detect respiration at the same time. To improve system performance, we further analyse the periodicity of the composite smoking motion. Through extensive experiments in real driving environments, HearSmoking detects smoking events with an average total accuracy of 93.44 percent in real-time.

Network Function Virtualization (NFV) has been emerging as an appealing solution that transforms from dedicated hardware implementations to software instances running in a virtualized environment. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. These VNFs are pitched with a predefined order, and it is also known as the Service Function Chaining (SFC). Considering that the delay and resiliency are two important Service Level Agreements (SLA) in a NFV service, in this paper, we first investigate how to quantitatively model the traversing delay of a flow in both totally ordered and partially ordered SFCs. Subsequently, we study how to calculate the VNF placement availability mathematically for both unprotected and protected SFCs. After that, we study the delay-sensitive Virtual Network Function placement and routing problem with and without resiliency concerns. We prove that this problem is NP-hard under two cases. We subsequently propose an exact Integer Nonlinear Programming (INLP) formulation and an efficient heuristic for this problem in each case. Finally, we evaluate the proposed algorithms in terms of acceptance ratio, average number of used nodes and total running time via extensive simulations.

Hand hygiene has a significant impact on human health. Proper handwashing, having a crucial effect on reducing bacteria, serves as the cornerstone of hand hygiene. For the elder with dementia, they suffer from a gradual loss of memory and difficulty in coordinating steps in the execution of handwashing. Proper assistance should be provided to them to ensure their hand hygiene adherence. Toward this end, we propose AWash, leveraging only commodity IMU sensor mounted on most wrist-worn devices (e.g., smartwatches) to characterize hand motions and provide assistance accordingly. To handle particular interference of senile dementia patients in IMU sensor readings, we design a number of effective techniques to segment handwashing actions, transform sensory input to body coordinate system, and extract sensor-body inclination angles. A hybrid neural network model is used to enable AWash to generalize to new users without retraining or adaptation, avoiding the trouble of collecting behavior information of every user. To meet the diverse needs of users with various executive functioning, we use a state machine to make prompt decisions, which supports customized assistance. Extensive experiments on a prototype with eight older participants demonstrate that AWash can increase the user's independence in the execution of handwashing.

Human-machine interface based on tongue-jaw movements has recently become one of the major technological trends. However, existing schemes have several limitations, such as requiring dedicated hardware and are usually uncomfortable to wear. This paper presents CanalScan, a nonintrusive system for tongue-jaw movement recognition using only commodity speaker and microphone mounted on ubiquitous off-the-shelf devices (e.g., smartphones). The basic idea is to send an acoustic signal, then captures its reflections and derive unique patterns of ear canal deformation caused by tongue-jaw movements. A dynamic segmentation method with Support Vector Domain Description is used to segment tongue-jaw movements. To combat sensor position-sensitive deficiency and ear-canal-shape-sensitive deficiency in multi-path reflections, we first design algorithms to assist users in adjusting the acoustic sensors to the same valid zone. Then we propose a data transformation mechanism to reduce the impacts of diversities in ear canal shapes and relative positions between sensors and the ear canal. CanalScan explores twelve unique and consistent features and applies a Random Forest classifier to distinguish tongue-jaw movements. Extensive experiments with twenty participants demonstrate that CanalScan achieves promising recognition for six tongue-jaw movements, is robust against various usage scenarios, and can be generalized to new users without retraining and adaptation.

Fitness can help to strengthen muscles, increase resistance to diseases and improve body shape. Nowadays, more and more people tend to exercise at home/office, since they lack time to go to the dedicated gym. However, it is difficult for most of them to get good fitness effect due to the lack of professional guidance. Motivated by this, we propose HearFit, the first non-invasive fitness monitoring system based on commercial smart speakers for home/office environments. To achieve this, we turn smart speakers into active sonars. We design a fitness detection method based on Doppler shift and adopt the short time energy to segment fitness actions. We design a high-accuracy LSTM network to determine the type of fitness. Combined with incremental learning, users can easily add new actions. Finally, we evaluate the local (i.e., intensity and duration) and global (i.e., continuity and smoothness) fitness quality of users to help to improve fitness effect and prevent injury. Through extensive experiments including over 7,000 actions of 10 types of fitness with and without dumbbells from 12 participants, HearFit can detect fitness actions with an average accuracy of 96.13%, and give accurate statistics in various environments.

The efficacy of Network Function Virtualization (NFV) depends critically on (1) where the virtual network functions (VNFs) are placed and (2) how the traffic is routed. Unfortunately, these aspects are not easily optimized, especially under time-varying network states with different quality of service (QoS) requirements. Given the importance of NFV, many approaches have been proposed to solve the VNF placement and traffic routing problem. However, those prior approaches mainly assume that the state of the network is static and known, disregarding real-time network variations. To bridge that gap, in this paper, we formulate the VNF placement and traffic routing problem as a Markov Decision Process model to capture the dynamic network state transitions. In order to jointly minimize the delay and cost of NFV providers and maximize the revenue, we devise a customized Deep Reinforcement Learning (DRL) algorithm, called A-DDPG, for VNF placement and traffic routing in a real-time network. A-DDPG uses the attention mechanism to ascertain smooth network behavior within the general framework of network utility maximization (NUM). The simulation results show that A-DDPG outperforms the state-of-the-art in terms of network utility, delay, and cost.

Arterial blood pressure (ABP) monitoring using wearables has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control of hypertension. However, existing schemes mainly monitor ABP at discrete time intervals, involve some form of user effort, have insufficient accuracy, and require collecting sufficient training data for model development. To tackle these problems, we propose Crisp-BP, a novel ABP monitoring system leveraging the PPG sensor available in commercial wrist-worn devices (e.g., smartwatches or fitness trackers). It enables continuous, accurate, user-independent ABP monitoring and requires no behavior changes during collecting PPG data. The basic idea is to illuminate a skin/tissue, measure the light absorption, and characterize ABP-related blood volume change in the artery. To obtain accurate measurements and relieve the pain of training data collection, we use an arterial pulse extraction method that removes interference caused by capillary pulses. Moreover, we design a contact pressure estimation method to combat the deficiency of PPG waveform being sensitive to the contact pressure between the sensor and the skin. In addition, we leverage the great power of Bidirectional Long Short Term Memory and design a hybrid neural network model to enable user-independent ABP monitoring, so that users do not have to provide training data for model development. Furthermore, we propose a transfer learning method that first extracts general knowledge from online PPG data, then use it to improve the learning of a new model on our target problem. Extensive experiments with 35 participants demonstrate that Crisp-BP obtains the average estimation error of 0.86 mmHg and 1.67 mmHg and the standard deviation error of 6.55 mmHg and 7.31 mmHg for diastolic pressure and systolic pressure, respectively. These errors are within the acceptable range regulated by the FDA's AAMI protocol, which allows average errors of up to 5 mmHg and a standard deviation of up to 8 mmHg. Our results demonstrate that Crisp-BP is promising for improving the diagnosis and control of hypertension as it provides continuousness, comfort, convenience, and accuracy.

The safety of the elderly has attracted much attention nowadays. Among various daily activities, fall is one of the most dangerous events for the elderly, especially those who live alone. Most existing works on fall detection are based on wearable devices, which are inconvenient in using. Several solutions only use coarse-grained Wi-Fi signal information that contains many biases, and lack considerations on environmental changes. These situations motivate us to design a fine-grained and robust fall detection approach. In this article, we propose a fall detection system, called FallViewer, based on analyzing the channel state information (CSI) of Wi-Fi signals. To get fine-grained information, we propose phase and amplitude calibration methods for deviation correction. Then, an adjustment approach for antenna power is designed to eliminate the multipath interference. Furthermore, we apply a double sliding window to get a flexible threshold, which improves the robustness of FallViewer to various environments. Finally, FallViewer extracts features of the processed Wi-Fi signal and sends the features to a LibSVM for classification. Through experiments in different environments, FallViewer can detect fall events with an average accuracy of 95.8%, which indicates that FallViewer can work reliably and effectively.

Cloud radio access network (C-RAN) is a promising 5G network architecture by establishing baseband units (BBU) pools to perform baseband processing functionalities and deploying remote radio heads (RRHs) for wireless signal transmission and reception. Mobile-edge computing (MEC) offers a way to shorten the service delay by building small-scale cloud infrastructures at the network edge. By co-locating the BBU pool with edge cloud at the so-called BBU node, we can take full advantages of C-RAN and MEC for better spectrum utilization and delay-guaranteed services. In this article, we first study how to allocate each user's task to the BBU node and find the path from his/her accessing RRH node to the BBU node such that the maximum service delay among all the requests is minimized. We then consider this problem with survivability concerns, which is to use both primary and backup BBU nodes to issue the request such that the primary path and backup path are link disjoint. We analyze the complexities of these two problems and prove they are NP-hard in general. Subsequently, we devise a randomized approximation algorithm and an efficient heuristic to solve the considered problems, respectively. The simulation results show that the proposed algorithms outperform two benchmark heuristics in terms of acceptance ratio and maximum service delay.

Drowsy driving is one of the biggest threats to driving safety, which has drawn much public attention in recent years. Thus, a simple but robust system that can remind drivers of drowsiness levels with off-the-shelf devices (e.g., smartphones) is very necessary. With this motivation, we explore the feasibility of using acoustic sensors on smartphones to detect drowsy driving. Through analyzing real driving data to study characteristics of drowsy driving, we find some unique patterns of Doppler shift caused by three typical drowsy behaviours (i.e., nodding, yawning and operating steering wheel), among which operating steering wheels is also related to drowsiness levels. Then, a real-time Drowsy Driving Detection system named D3-Guard is proposed based on the acoustic sensing abilities of smartphones. We adopt several effective feature extraction methods, and carefully design a high-accuracy detector based on LSTM networks for the early detection of drowsy driving. Besides, measures to distinguish drowsiness levels are also introduced in the system by analyzing the data of operating steering wheel. Through extensive experiments with five drivers in real driving environments, D3-Guard detects drowsy driving actions with an average accuracy of 93.31%, as well as classifies drowsiness levels with an average accuracy of 86%.

Mobile Edge Computing (MEC) offers a way to shorten the cloud servicing delay by building the small-scale cloud infrastructures at the network edge, which are in close proximity to the end users. Moreover, Network Function Virtualization (NFV) has been an emerging technology that transforms from traditional dedicated hardware implementations to software instances running in a virtualized environment. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. Service Function Chaining (SFC) is defined as a chain-ordered set of placed VNFs that handles the traffic of the delivery and control of a specific application. NFV therefore allows to allocate network resources in a more scalable and elastic manner, offer a more efficient and agile management and operation mechanism for network functions and hence can largely reduce the overall costs in MEC. In this paper, we study the problem of how to place VNFs on edge and public clouds and route the traffic among adjacent VNF pairs, such that the maximum link load ratio is minimized and each user's requested delay is satisfied. We consider this problem for both totally ordered SFCs and partially ordered SFCs. We prove that this problem is NP-hard, even for the special case when only one VNF is requested. We subsequently propose an efficient randomized rounding approximation algorithm to solve this problem. Extensive simulation results show that the proposed approximation algorithm can achieve close-to-optimal performance in terms of acceptance ratio and maximum link load ratio.

Software-Defined Networking has become an integral technology for large scale networks that require dynamic flow management. It separates the control function from data plane devices and centralizes it in a domain controller. However, only a limited number of switches can be managed by a single and centralized controller which introduces challenges such as scalability, reliability, and availability. Distributed controller architecture resolves these issues but also introduces new challenges of uneven load and traffic management across domains. As real-world networks have redundant links, hence a significant challenge is to distribute traffic flows on multiple paths, within a domain, and across multiple independent domains. The selection of ingress and egress switches becomes even more problematic if the intermediate domain is non-cooperative. In this work, we propose a Dynamically Optimized and Load-balanced Path for Inter-domain (DOLPHIN) communication system, a customized solution for different SDN controllers. It provides control beyond the virtual switch elements in intra and inter-domain communication and extends the range of programmability to wireless devices, such as the Internet of Things or vehicular networks. Extensive simulation results show that the traffic load is distributed evenly on multiple links connecting different domains. We model data center communication and 5G vehicular network communication to show that, by load balancing the flow completion times of the different types of network traffic can be significantly improved.

Network Function Virtualization (NFV) has been emerging as an appealing solution that transforms complex network functions from dedicated hardware implementations to software instances running in a virtualized environment. Due to the numerous advantages such as flexibility, efficiency, scalability, short deployment cycles, and service upgrade, NFV has been widely recognized as the next-generation network service provisioning paradigm. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. These VNFs are pitched with a predefined order through which data flows traverse, and it is also known as the Service Function Chaining (SFC). In this article, we provide an overview of recent advances of resource allocation in NFV. We generalize and analyze four representative resource allocation problems, namely, (1) the VNF Placement and Traffic Routing problem, (2) VNF Placement problem, (3) Traffic Routing problem in NFV, and (4) the VNF Redeployment and Consolidation problem. After that, we study the delay calculation models and VNF protection (availability) models in NFV resource allocation, which are two important Quality of Service (QoS) parameters. Subsequently, we classify and summarize the representative work for solving the generalized problems by considering various QoS parameters (e.g., cost, delay, reliability, and energy) and different scenarios (e.g., edge cloud, online provisioning, and distributed provisioning). Finally, we conclude our article with a short discussion on the state-of-the-art and emerging topics in the related fields, and highlight areas where we expect high potential for future research.

Mobile Crowd Sensing (MCS) enables the platform to offer data-based service by incentivizing mobile users to perform sensing task and collecting sensing data from them. Most of the existing works on MCS only consider designing incentive mechanisms for a single MCS platform. In this paper, we study the incentive mechanism in MCS with multiple platforms under two scenarios: competitive platform and cooperative platform. We correspondingly propose new competitive and cooperative mechanisms for each scenario. In the competitive platform scenario, platforms decide their prices on rewards to attract more participants, while the users choose which platform to work for. We model such a competitive platform scenario as a two-stage Stackelberg game. In the cooperative platform scenario, platforms cooperate to share sensing data with each other. We model it as many-to-many bargaining. Moreover, we first prove the NP-hardness of exact bargaining and then propose heuristic bargaining. Finally, numerical results show that (1) platforms in the competitive platform scenario can guarantee their payoff by optimally pricing on rewards and participants can select the best platform to contribute; (2) platforms in the cooperative platform scenario can further improve their payoff by bargaining with other platforms for cooperatively sharing collected sensing data.

Micro finger gesture recognition is an emerging approach to realize more friendly interaction between human and smart devices, especially for small wearable devices, such as smartwatches and virtual reality glasses. This paper proposes airFinger, a novel solution utilizing NIR light sensing to realize both real-time gesture recognition and finger tracking aiming at micro finger gestures. Using a custom NIR-based sensor with novel algorithms to capture subtle finger movements, airFinger enables to detect a rich set of micro finger gestures and track finger movements in terms of scrolling direction, velocity, and displacement. Besides, airFinger is capable of effective noise mitigation, gesture segmentation, and reducing false recognition due to the unintentional actions of users. Extensive experimental results demonstrate that airFinger has robustness against individual diversity, gesture inconsistency, and many other impacts. The overall performance reaches an average accuracy as high as 98.72% over a set of 8 micro finger gestures among 10, 000 gesture samples collected from 10 volunteers.

Mobile devices are promising to apply two-factor authentication in order to improve system security and enhance user privacy-preserving. Existing solutions usually have certain limits of requiring some form of user effort, which might seriously affect user experience and delay authentication time. In this paper, we propose PPGPass, a novel mobile two-factor authentication system, which leverages Photoplethysmography (PPG) sensors in wrist-worn wearables to extract individual characteristics of PPG signals. In order to realize both nonintrusive and secure, we design a two-stage algorithm to separate clean heartbeat signals from PPG signals contaminated by motion artifacts, which allows verifying users without intentionally staying still during the process of authentication. In addition, to deal with non-cancelable issues when biometrics are compromised, we design a repeatable and non-invertible method to generate cancelable feature templates as alternative credentials, which enables to defense against man-in-the-middle attacks and replay attacks. To the best of our knowledge, PPGPass is the first nonintrusive and secure mobile two-factor authentication based on PPG sensors in wearables. We build a prototype of PPGPass and conduct the system with comprehensive experiments involving multiple participants. PPGPass can achieve an average F1 score of 95.3%, which confirms its high effectiveness, security, and usability.

Efficient and smart business processes are heavily dependent on the Internet of Things (IoT) networks, where end-to-end optimization is critical to the success of the whole ecosystem. These systems, including industrial, healthcare, and others, are large scale complex networks of heterogeneous devices. This introduces many security and access control challenges. Blockchain has emerged as an effective solution for addressing several such challenges. However, the basic algorithms used in the business blockchain are not feasible for large scale IoT systems. To make them scalable for IoT, the complex consensus-based security has to be downgraded. In this article, we propose a novel lightweight proof of block and trade (PoBT) consensus algorithm for IoT blockchain and its integration framework. This solution allows the validation of trades as well as blocks with reduced computation time. Also, we present a ledger distribution mechanism to decrease the memory requirements of IoT nodes. The analysis and evaluation of security aspects, computation time, memory, and bandwidth requirements show significant improvement in the performance of the overall system.

In this article, we propose points-of-interest (POI)-tagging App-assisted incentive mechanism (PTASIM), an incentive mechanism that explores the cooperation with POI-tagging App for mobile edge crowdsensing (MEC). PTASIM requests App to tag some edges to be POI, which further guides App users to perform tasks at that location. We further model the interactions of users, platform, and App by a three-stage decision process. App first determines the POI-tagging price to maximize its payoff. Platform and users subsequently decide how to determine tasks reward and select edges to be tagged, and how to select the best task to perform, respectively. We analyze the optimal solution in those stages. Specifically, we prove that greedy algorithm could provide the optimal solution for platform's payoff maximization in polynomial time. The numerical results show that: 1) the cooperation with App brings long-term and sufficient participation; and 2) the optimal strategies reduce platform's tasks cost as well as improve App's revenues.

In contrast to the current host-centric architecture, Information-Centric Networking (ICN) adopts content naming instead of host address and in-network caching to enhance the content delivery, improve the data distribution, and satisfy users' requirements. As ICN is being incrementally deployed in different real-world scenarios, it will exist with IP-based services in a hybrid network setting. Full deployment of ICN and total replacement of IP protocol is not feasible at the current stage since IP is dominating the Internet. On the other hand, re-designing TCP/IP applications from ICN perspective is a time-consuming task and requires a careful investigation from both business and technical point of view. Thus, the coexistence of ICN and IP is one of the suitable solutions. Towards this end, we propose a simple yet efficient coexistence solution based on Network Function Virtualization (NFV) technology. We define a set of communication regions and control virtual functions. A gateway node is used as an intermediate entity to fetch and deliver content over regions. The simulation results show that the proposed approach is valid and allow content fetching and delivering from different ICN and/to IP regions in an efficient manner.

Since the number of cars has grown rapidly in recent years, driving safety draws more and more public attention. Drowsy driving is one of the biggest threatens to driving safety. Therefore, a simple but robust system that can detect drowsy driving with commercial off-the-shelf devices (such as smart-phones) is very necessary. With this motivation, we explore the feasibility of purely using acoustic sensors embedded in smart-phones to detect drowsy driving. We first study characteristics of drowsy driving, and find some unique patterns of Doppler shift caused by three typical drowsy behaviors, i.e., nodding, yawning and operating steering wheel. We then validate our important findings through empirical analysis of the driving data collected from real driving environments. We further propose a real-time Drowsy Driving Detection system (D3-Guard) based on audio devices embedded in smartphones. In order to improve the performance of our system, we adopt an effective feature extraction method based on undersampling technique and FFT, and carefully design a high-accuracy detector based on LSTM networks for the early detection of drowsy driving. Through extensive experiments with 5 volunteer drivers in real driving environments, our system can distinguish drowsy driving actions with an average total accuracy of 93.31% in real-time. Over 80% drowsy driving actions can be detected within first 70% of action duration.

Internet of Things (IoT) and blockchain (BC) technologies have been dominating their respective research domains for some time. IoT offers automation at the finest level in different fields, while BC provides secure transaction processing for asset exchanges. The capability of IoT devices to generate transactions prompts their integration with BC as the next logical step. The biggest challenges in this integration are the scalability of ledger and rate of transaction execution in BC. On one hand, due to their large numbers, IoT devices will generate transactions at a rate which current block chain solutions cannot handle. On the other hand, implementing BC peers onto IoT devices is impossible due to resource constraints. This prohibits direct integration of both technologies in their current state. In this paper, we propose a solution to address these challenges by using a local peer network to bridge the gap. It restricts the number of transactions which enters the global BC by implementing a scalable local ledger, without compromising on the peer validation of transactions at local and global level. The testbed evaluations show significant reduction in the block weight and ledger size on global peers. The solution also indirectly improves the transaction processing rate of all peers due to load distribution.

Smart city is a key component in Internet of Things, so it has attracted much attention. The emergence of mobile crowd sensing (MCS) systems enables many smart city applications. In an MCS system, sensing tasks are allocated to a number of mobile users. As a result, the sensing related context of each mobile user plays a significant role on service quality. However, some important sensing context is ignored in the literature. This motivates us to propose a context-aware multiarmed bandit (C-MAB) incentive mechanism to facilitate quality-based worker selection in an MCS system. We evaluate a worker's service quality by its context (i.e., extrinsic ability and intrinsic ability) and cost. Based on our proposed C-MAB incentive mechanism and quality evaluation design, we develop a modified Thompson sampling worker selection (MTS-WS) algorithm to select workers in a reinforcement learning manner. MTS-WS is able to choose effective workers because it can maintain accurate worker quality information by updating evaluation parameters according to the status of task accomplishment. We theoretically prove that our C-MAB incentive mechanism is selection efficient, computationally efficient, individually rational, and truthful. Finally, we evaluate our MTS-WS algorithm on simulated and real-world datasets in comparison with some other classic algorithms. Our evaluation results demonstrate that MTS-WS achieves the highest cumulative utility of the requester and social welfare.

Mobile edge computing (MEC) offers a way to shorten the cloud servicing delay by building the small-scale cloud infrastructures, such as cloudlets at the network edge, which are in close proximity to end users. On one hand, it is energy consuming and costly to place each cloudlet on each access point (AP) to process the requested tasks. On the other hand, the service provider should provide delay-guaranteed service to end users, otherwise they may get revenue loss. In this paper, we first model how to calculate the task completion delay in MEC and mathematically analyze the energy consumption of different equipments in MEC. Subsequently, we study how to place cloudlets on the network and allocate each requested task to cloudlets and public cloud with the minimum total energy consumption without violating each task's delay requirement. We prove that this problem is NP-hard and propose a Benders decomposition-based algorithm to solve it. We also present a software-defined network (SDN)-based framework to deploy the proposed algorithm. Extensive simulations reveal that the proposed algorithm can achieve an (close-to-)optimal performance in terms of energy consumption and acceptance ratio compared with two benchmark heuristics.

The usage of hybrid energy supplied sensors in the Internet of Things has enabled longer lifetime of sensors and expanded scope of applications. These sensors can combine advantages of environmental energy harvesting techniques and wireless energy harvesting techniques. However, how to coordinate them is still a challenge and has not been studied extensively. In this article, we present a system based on mobile crowd wireless charging to manage energy of hybrid energy supplied sensors. When environmental energy is insufficient, the system will utilize smart devices carried by mobile users as chargers to provide wireless energy. We construct and study a W3W problem in the system: when to leverage mobile crowd wireless charging to support rechargeable sensors, where to perform wireless energy transfer, and whom to allocate and incentivize as chargers to maximize useful energy value over all sensors subject to a budget. In order to control the actual quality of wireless energy charging, we propose a design principle named task completion trustfulness. We consider offline and online conditions and design corresponding algorithms with incentive allocations. Extensive simulations are conducted to demonstrate the effectiveness of our algorithms, which also validates our theoretical results.

Information-Centric Networking is considered as one of the most promising architecture for IoT. The use of content-centric approach may improve the content access & dissemination, reduce the content retrieval latency, and enhance the network performance. The use of in-network caching in ICN enhances the data availability in the network, overcomes the issue of single-point failure, and improves IoT devices power efficiency. In this paper, we present a Near-ICN Cache Placement (NCP) scheme for IoT taking traffic class into consideration. NCP is designed to select the optimal replica cache by minimizing: the cost of moving the data from content producer to replica nodes, the cost of caching the content in the replica and the cost of delivery the content to consumers. Hence, we presented a multi-objective optimization problem, with a heuristic caching selection algorithm. We evaluated NCP with various performance metrics against different caching schemes. The obtained results show improvement in the cache utilization, with fast data retrieval, and enhancement in the network cache distribution & diversity.

With the rapid increasing of the number of mobile devices and their embedded sensing technologies, mobile crowd sensing (MCS) has become an emerging modern sensing paradigm for performing large-scale urban sensing. One of the key challenges of large-scale mobile crowd sensing systems is how to effectively select the minimum set of appropriate participants from the huge user pool to perform the sensing tasks. The capability of a particular user for certain task depends on many factors, such as her moving pattern/behavior, device capability, sensor quality, or even uploading bandwidth. Many of these information of participants are unknown by the selection mechanism. Therefore, self-learning based approaches have been proposed to learn the users' capability for certain tasks via multiple trials and their online performances. In this paper, we first model the cumulative participant selection problem as a combinational multi-armed bandit problem and present an online selection algorithm which leverages the historical performing records of participants to learn the different capabilities (both sensing probability and time delay) of participants. Further, to consider the cost of switching participant for particular tasks, we then introduce the cumulative participant selection problem with switch costs and propose a corresponding online learning method. For both proposed learning algorithms, we provide regret analysis. In addition, extensive simulations with real-world mobile datasets are conducted for the evaluations of the proposed methods. Our simulation results confirm the effeteness of them.

In mobile crowdsensing (MCS), to select the optimal set of participants for a particular sensing task, the cloud-based MCS platform requires mobile users to submit their bids and their sensing quality data. This can cause privacy breaches. One possible solution is to leverage secure sharing or bidding schemes to protect participants' personal information during selection. However, these schemes suffer from high overheads, poor scalability and more importantly, the group formation has never been studied. To address this issue and to enhance the protection of user privacy, we propose a set of novel privacy-preserving grouping methods, which place participants into small groups over hierarchical edge clouds. By doing this, not only can the participants be hidden in groups, but also the overall privacy-preserving participant selection becomes more scalable. The design goal to minimize the communication cost during secure sharing/bidding within groups, while satisfying each participant's requirement for privacy preservation. For different scenarios and optimization functions, we propose a set of grouping schemes to fulfill this goal. Extensive simulations over both synthetic and real-life datasets illustrate the efficiency of proposed mechanisms.

With the rapid increasing of smart phones and their embedded sensing technologies, mobile crowd sensing (MCS) becomes an emerging sensing paradigm for performing large-scale sensing tasks. One of the key challenges of large-scale mobile crowd sensing systems is how to effectively select the minimum set of appropriate participants from the huge user pool to perform the tasks. However, the capabilities of individual participants are usually unknown by the selection mechanism, which leads to the most challenging issue of participant selection. While online learning techniques can be used to learn the participant's capability, the diverse expertise of each individual makes a single capability metric is not sufficient. To address the multi-expertise of participants, in this paper we introduce a new self-learning architecture which leverages the historical performing records of participants to learn the different capabilities (both sensing probability and time delay) of participants. Formulating the participant selection problem as a combinational multi-armed bandit problem, we present an online participant selection algorithm with both performance guarantee and bounded regret. Extensive simulations with a real-world mobile dataset demonstrate the efficiency of the proposed solution.

Pedestrian dead reckoning enables pervasive indoor localization without a site survey on fingerprints or an intensive deployment of infrastructures. But accumulated errors in dead reckoning limit the spread of pervasive indoor location-based services. Existing landmark-based approaches mostly rely on resetting the user’s position with the landmark position only when the user is detected while on arrival at a landmark. However, such methods are still restricted by the specific movement patterns and sparse landmark distributions so that the opportunity for position calibration is limited. In this paper, an accurate peer-assisted localization system (calledSoundMark) on a smartphone with no prior infrastructure or fingerprinting is proposed. It calibrates mobile user’s dead reckoning position by leveraging the location constraints between another stationary user who arrives at a landmark. To detect whether a user arrives at a landmark, motion pattern is extracted by fusing the multiple sensors. Then, user activity in the landmark is decomposed to determine whether the user is stationary for performing audio ranging. Besides, SoundMark also applies a mobility-induced time-difference-of-arrival-based audio ranging to extract the location constraints between the peers for localization. SoundMark is implemented on the Android platform for evaluations. The results show that the accuracy of proposed peer-assisted localization is within 2.1 m at the percentage of 80%.

Wireless energy harvesting is promising to be a new opportunity to prolong the lifetime of rechargeable sensors in the Internet of Things. However, how to recharge the sensors and manage charging energy is still a problem. Most existing methods are based on robots or vehicles carrying battery packs to charge sensors. They are vulnerable to the sensors distributed in complex terrain and have high hardware maintaining cost. To address this issue, we present crowd-charging (CC), a novel crowdsourcing-based wireless energy charging model for rechargeable sensors. It leverages smart devices carried by users as mobile crowd energy resources (chargers) to provide wireless energy to sensors. The challenge is how to incentivize and allocate mobile users to optimize the total charging quality of all the sensors. Besides monetary energy revenues, we design a bonus game to entertain participated users. We also propose three user allocation algorithms, CC algorithm (CCA), and two improved versions of CCA, i.e., CC and CC . The improved algorithms give great improvement for our model and they have different advantages suitable for different situations. We conduct extensive simulations and demonstrate the effectiveness of our algorithms with numerical results.

The distributed Information-Centric Networking architecture has shown enormous potential to replace the host centric Internet architecture. A number of solutions such as Named Data Networking have become available. Building application services and integrating other technological design on top of ICNs is a challenging task, and has many open issues, hence an efficient distributed architecture needs to be developed. In this paper, we address the case of using IoT architecture targeted for ambient assisted living applications, on top of named data networking. We have proposed a complete architecture and implementation details for device & service networking, communication model, management, and naming. Within each model we have proposed mechanisms which support node mobility, hand-off, packet design, and push & pull data services without changing NDN data exchange model. This architecture is flexible, scalable, and can be adapted to other application specific IoT networks. We also have implemented the proposal on NDN simulator, and evaluated different services. The communication overhead and mobility implications have been studied to show effectiveness of new services with negligible cost to the network.

Mobile crowd sensing has become a promising paradigm for mobile users to collect information. Considering that the task information push is not free and there are many users who are not interested in the current task or provide noisy sensing data, one of the imminent problems is how to recommend high-quality and interested users in real time and steer participators to collect data with adequate budgets. However, it is difficult to predict the data quality and users' interest without the validity of real data. In this paper, we propose a user recommender system where the users' data qualities for sensing tasks are derived from historical statistical data to filter out the non-interested and malicious users in current task. The aim is to recruit a sub-group of participators for efficient crowd sensing, in order to maximize the platform utility. We show that our problem is NP-hard, and model the recruitment process as a sub-modular problem. Finally, an approximation algorithm is designed to guarantee the platform utility and participators' profits. We evaluate our algorithm on simulated data set and the results indicate that the platform utility and data quality improves significantly.

This paper explores the limits of acoustic ranging on smart phone in the scenario of device-free hand tracking. Tracking the hand is challenging since it requires continuously locating the moving hand in the air with fine resolution. Existing work on hand tracking relies on special hardware or requires users hold the mobile device. This paper presents EchoTrack, which continuously locates the hand by leveraging mobile audio hardware advances without special infrastructure supported. EchoTrack measures the distance from the hand to the speaker array embedded in smart phone via the chirp's Time of Flight (TOF). The speaker array and hand yield a unique triangle. The hand can be located with this triangular geometry. The trajectory accuracy can be improved with the method of Doppler shift compensation and trajectory correction (i.e., roughness penalty smoothing method). We implement a prototype on smart phone and the evaluation shows that EchoTrack can achieve tracking accuracy within about three centimeters of 76% and two centimeters of 48%.

Summary This paper investigates the selfish load balancing problem in mobile distributed crowdsourcing networks. Conventional methods heavily relied on cooperation among users to achieve balanced resource utilization in a platform‐centric view. In achieving fairly low communication and computational overhead, this work leverages the d‐choice method based on Ball and Bin theory for effective balancing under limited information and the Proportional Allocation scheme for selfish load balancing, maintaining good load balancing property among selfish users. Even with limited information, the balancing performance could be improved significantly. Moreover, theoretical analysis has been presented in convergence property. Extensive evaluations have been made to show that Chance‐Choice outperforms several existing algorithms. Typically, comparing with Proportional Allocation scheme, it could decrease the load gap between the maximum and the minimal in system by 50% to 80% and reduce the overhead complexity from O(n) to O(1) comparing with the Max‐weight Best Response algorithm, where n denotes the number of mobile users in a crowdsourcing system. Copyright © 2017 John Wiley & Sons, Ltd.

Software Defined Networks (SDNs) decouple control plane from data plane and enable fine-grained traffic management by a logically centralized controller. Reducing the flow latency is of great importance in traffic management, which benefits both service providers and end users. Routing design and flow scheduling are typical ways to improve the flow transmission efficiency. However, existing studies usually consider them separately, due to the complexity of joint consideration. In this paper, we combine the routing and scheduling together and propose a latency-aware routing scheme with bandwidth assignment, which can efficiently reduce the flow latency with a moderate complexity. In the routing design, we utilize the global flow information to reduce both the latency of the newly arrived flow and its interference with existing flows in the network. Given flow forwarding paths determined by routing, the flow scheduling dynamically reallocates the bandwidth to all flows so as to further reduce the total flow latency. Experimental results show that our scheme outperforms the scheme currently available in OpenFlow, with an improvement of up to 60% on flow efficiency and a higher percentage of flows that meet their deadlines.

Hand tracking systems are becoming increasingly popular as a fundamental HCI approach. The trajectory of moving hand can be estimated through smoothing the position coordinates collected from continuous localization. Therefore, hand localization is a key component of any hand tracking systems. This paper presents EchoLoc, which locates the human hand by leveraging the speaker array in Commercial Off-The-Shelf (COTS) devices (i.e., a smart phone plugged with a stereo speaker). EchoLoc measures the distance from the hand to the speaker array via the Time Of Flight (TOF) of the chirp. The speaker array and hand yield a unique triangle, therefore, the hand can be localized with triangular geometry. We prototype EchoLoc on iOS as an application, and find it is capable of localization with the average resolution within five centimeters of 73% and three centimeters of 48%.

Sleep monitoring system helps to diagnose various health problems. Traditional solutions for sleep monitoring are usually invasive or limited to medical facilities. Radio Frequency (RF) based methods require specialized devices or dedicated wireless sensors. Recently, Wi-Fi based methods without any wearable or dedicated devices obtain more attention, however, they all assume that all the users are in a relatively quiet environment without moving targets. In this paper, we develop a system called Mo-Sleep, which adopts off-the-shelf Wi-Fi devices to continuously collect fine-grained wireless Channel State Information (CSI) in a room. We introduce a motion detection module in our system to identify whether the CSI information has been interfered by a moving target. We then use Principal Component Analysis (PCA) to obtain accurate breath signal. Our prototypic system demonstrates that the proposed scheme can not only remove interfered CSI, but also obtain real time breath rate every five seconds.

With the rapid increasing of smart phones and their embedded sensing technologies, mobile crowd sensing (MCS) becomes an emerging sensing paradigm for performing large-scale sensing tasks. One of the key challenges of large-scale mobile crowd sensing systems is how to effectively select the minimum set of participants from the huge user pool to perform the tasks and achieve certain level of coverage. In this paper, we introduce a new MCS architecture which leverages the cached sensing data to fulfill partial sensing tasks in order to reduce the size of selected participant set. We present a newly designed participant selection algorithm with caching and evaluate it via extensive simulations with a real-world mobile dataset.

In this work, we propose a time-sensitive incentive-aware mechanism for mobile video offloading by using the idea of crowdsourcing, where video packet holder cooperates with mobile users to deliver video packets to destination. The objective is to maximize video provider and mobile relay users' payoffs. We formulate the interaction among video packet provider and mobile relay users as a two-person cooperative game, where the video packets are treated as commodities. We apply the Nash bargain solution to obtain the optimal cooperation decision and payment. We carry out extensive simulation based on the real-world traces to validate the superiority of our proposed scheme.

Device-to-device (D2D) is a new paradigm that enhances network performance by offering a wide variety of advantages over traditional cellular networks, e.g., efficient spectral usage and extended network coverage. Efficient data dissemination is indispensable for supporting many D2D applications such as content distribution and location-aware advertisement. In this work, we study the problem of multi-copy data dissemination with probabilistic delay constraint in mobile opportunistic D2D networks. We first formally formulate the problem and introduce a centralized heuristic algorithm which aims to discover a graph for multicasting, in order to meet delay constraint and achieve low communication cost. While the centralized solution can be adapted to a distributed implementation, it is inefficient in a mobile opportunistic D2D network, since it intends to apply a deterministic transmission strategy in a nondeterministic network by delivering all data packets via a predetermined route. Based on such observation, we develop a distributed online algorithm based on the optimal stopping strategy that makes an efficient decision on every transmission opportunity. Extensive simulations under real-world traces and random walk mobility model are carried out to learn the performance trend of the proposed schemes under various network settings.

One of the solutions to improve mobile Delay Tolerant Network (DTN) performance is to place additional stationary nodes, called throwboxes, to create a greater number of contact opportunities. In this paper, we study a key optimization problem in a time-evolving throwbox-assisted DTN: throwbox selection, to answer the questions such as “how many active throwboxes do I need?” and “which throwboxes should be activated?” We formally define two throwbox optimization problems: min-throwbox problem and k-throwbox problem for time-evolving DTNs modeled by weighted space-time graphs. We show that min-throwbox problem is NP-hard and propose a set of greedy algorithms which can efficiently provide quality solutions for both challenging problems.

The open shortest path first (OSPF) protocol has been widely applied to intra-domain routing in today's Internet. Since a router running OSPF distributes traffic uniformly over equal-cost multi-path (ECMP), the OSPF-based optimal traffic engineering (TE) problem (i.e., deriving optimal link weights for a given traffic demand) is computationally intractable for large-scale networks. Therefore, many studies resort to multi-protocol label switching (MPLS) based approaches to solve the optimal TE problem. In this paper we present a generalized routing framework to realize the optimal TE, which can be potentially implemented via OSPFor MPLS-based approaches. We start with viewing the conventional optimal TE problem in a fresh way, i.e., optimally allocating the residual capacity to every link. Then we make a generalization of network utility maximization (NUM) to close this problem, where the network operator is associated with a utility function of the residual capacity to be maximized. We demonstrate that under this framework, the optimal routes resulting from the optimal TE are also the shortest paths in terms of a set of non-negative link weights that are explicitly determined by the optimal residual capacity and the objective function. The network entropy maximization theory is employed to enable routers to exponentially, instead of uniformly, split traffic over ECMP. The shortest-path penalizing exponential flow-splitting (SPEF) is designed as a link-state protocol with hop-by-hop forwarding to implement our theoretical findings. An alternative MPLS-based implementation is also discussed here. Numerical simulation results have demonstrated the effectiveness of the proposed framework as well as SPEF.

Recent advances in ambient energy-harvesting wireless sensor networks (WSNs) technologies have made it possible to power the network by energy generated from the environment and thereby increase its lifetime. Various energy sources including light, vibration and heat can be harvested by sensor nodes. However, time-varying energy harvesting also bring new design challenging for WSNs. In this paper, we study a fault-tolerant topology design problem for an energy-harvesting heterogeneous WSN, where multiple supernodes with rich resources are used to improve the performance. We first model the network as a directed and weighted space-time graph in which both spacial and temporal information are preserved. We then define the fault-tolerant topology problem which aims to build a sparser time-varying structure from the original space-time graph while maintaining k-connectivity for the fault-tolerant purpose. Six different algorithms are proposed to solve the problem. Simulation results demonstrate that our proposed methods can save up to around 80% costs.

Mobile opportunistic networks (MONs) are intermittently connected networks, in which a multitude of mobile devices are carried by people and packets are delivered among devices via opportunistic communications. Routing in MONs is very challenging as it must handle network partitioning, long delays, and dynamic topology. Recently, new possibilities of social-based approaches which use social characteristics of mobile nodes to make forwarding decisions become a new trend in MONs. In this paper, we consider the location history with access patterns of a mobile user as its social features as well and propose several new geo-social metrics which reflect the location and social relationships among users. Several new routing algorithms are designed based on these new geo-social metrics to achieve efficient and stable routing in MONs. We evaluate them with a large-scale real-life mobile tracing dateset. Simulation results confirm the effectiveness of proposed geo-social methods.

The multi-tier architecture is prevalently adopted by cloud applications, such as the three-tier web application. It is highly desirable for both tenants and providers to provide virtual networks in an efficient and elastic way, where tenant applications can automatically scale in or out with varying workloads and providers can accommodate as many requests as possible in the underlying network. However, due to potential conflicts between efficiency and elasticity, it is challenging to achieve these two goals simultaneously in abstracting tenant requirements and designing corresponding provisioning algorithms. In this paper, we propose an efficient and elastic virtual network provisioning solution called Easy Alloc, which is comprised of an elasticity-aware abstraction model and a virtual network provisioning algorithm. To accurately capture the tenant requirement and maintain the provisioning simplicity for providers, the elasticity-aware model enables two types of decoupling, i.e., Always-on VMs for normal load and on-demand VMs for dynamic scaling, and the bandwidth requirement of each VM for intra- and inter-tier communications. Then we formulate the virtual network provisioning as an overhead minimization problem, where the objective simultaneously considers the bandwidth and elasticity overhead. Due to the NP-completeness of this problem, we leverage two heuristics, slot reservation and tier iteration, to obtain an efficient algorithm. Extensive simulation results show that compared with a typical elasticity-agnostic method under a heavy load, Easy Alloc enables a 9% increase of request acceptance rate and a 16.8% improvement of the successful extension rate. To the best of our knowledge, this is the first work targeting at the elastic virtual network provisioning.

Communication privacy and latency perceived by users have become great concerns for delay-sensitive Internet services. Existing anonymous communication systems either provide high anonymity at an expense of prolonged latency (e.g., mix-net), or offer better real-time performance by sacrificing the ability against traffic analysis attacks (e.g., Onion Routing). The emerging Software-Defined Networking (SDN) introduces additional challenges to communication anonymity, due to the existence of a centralized controller that has a global view of the entire network traffic. In this paper, we propose a new anonymous communication scheme for delay-sensitive services under SDN scenarios, which can simultaneously protect communication privacy and reduce the end-to-end latency. A self-adaptive method based on the mix-net framework is designed to dynamically modify the waiting threshold of mix nodes, which helps to reduce the communication latency. In order to preserve the degree of anonymity, the self-adaptive method is incorporated with a random walking strategy for packets forwarding. Both theoretical analysis and experimental results prove that our scheme provides a moderate degree of anonymity and effectively reduces the latency derived from mix-net by up to 50%.

Reducing the flow latency is of great importance in traffic management, which benefits both service providers and end users. Routing design and flow scheduling are typical ways to improve the flow transmission efficiency. However, existing studies usually consider them separately, due to the complexity of joint consideration. Here, we propose a latency-aware routing scheme with bandwidth assignment in the Software Defined Networks, which can efficiently reduce the flow latency with a moderate complexity, to combine the routing and scheduling together.

Delay tolerant networks (DTNs) recently have drawn much attention from researchers due to their wide applications in various challenging environments. Previous DTN research mainly concentrates on information propagation and packet delivery. However, with possible participation of a large number of mobile devices, how to maintain efficient and dynamic topology becomes crucial. In this paper, we study the topology design problem in a predictable DTN where the time-evolving topology is known a priori or can be predicted. We model such a time-evolving network as a weighted directed space-time graph which includes both spacial and temporal information. Links inside the space-time graph are unreliable due to either the dynamic nature of wireless communications or the rough prediction of underlying human/device mobility. The purpose of our reliable topology design problem is to build a sparse structure from the original space-time graph such that (1) for any pair of devices, there is a space-time path connecting them with a reliability higher than the required threshold; (2) the total cost of the structure is minimized. Such an optimization problem is NP-hard, thus we propose several heuristics which can significantly reduce the total cost of the topology while maintain the “reliable” connectivity overtime. In this paper, we consider both unicast and broadcast reliability of a topology. Finally, extensive simulations are conducted on random DTNs, a synthetic space DTN, and a real-world DTN tracing data. Results demonstrate the efficiency of the proposed methods.

Vehicular ad hoc network(VANET) is increasingly studied recently due to its promising benefits to the urban life. The roadside unit(RSU) is one of the most important components for VANET. An effective deployment of RSUs will enhance the efficiency of the data delivery in VANET. In this paper, we propose to address the RSU deployment problem by formulating it as a mobility clustering problem. We adopt affinity propagation(AP) algorithm to capture the spatial temporal mobility influence in different time period. The union set of the obtained clustering centers which have the maximum influence to its cluster members is then the solution to RSU deployment. To validate our proposed deployment scheme, we evaluate the performance of the network with the deployed RSUs in terms of delivery ratio, average delay and hop count by adapting the conventional GPSR to VANET as the routing protocol. The simulation results show our proposed approach achieves near optimal solutions with much lower complexity compared to the exhaustive method.

In mobile social delay tolerant networks, many human-carried mobile devices are moving around in a restricted physical space and occasional contact opportunities among these devices are used to deliver data. Routing design in mobile social delay tolerant networks has been a challenging task due to lack of instantaneous end-to-end paths, large transmission delays, and time-varying network topology. In this paper, aiming to precisely measure the social relationships between mobile nodes, we introduce two kinds of time-varying closeness, direct and indirect closeness, where the temporal constraint is considered. Then we propose both single-copy and multi-copy closeness-based routing schemes, which consider direct and indirect social relationships under temporal constraint. Extensive simulations on real-life data traces show the proposed schemes can achieve better performances than existing DTN routing methods.

Recent advances in Delay Tolerant Networks (DTNs) have overcome limitations in connectivity by relying on intermittent contacts between mobile nodes to deliver packets. However, lack of rich contact opportunities still causes poor delivery ratio and long delay of DTN routing. One of the solutions to improve mobile DTN performance is to place additional stationary nodes, called throwboxes, to create a greater number of contact opportunities. In this paper, we study a key optimization problem in a time-evolving throwbox-assisted DTN: k-throwbox placement problem, to answer "where should I put my k throwboxes to optimize the performance?". We model a time-evolving DTN as a weighted space-time graph which includes both spacial and temporal information. We prove that k-throwbox placement problem is NP-hard and propose a set of greedy algorithms which can efficiently provide quality solutions. One of the proposed algorithms can guarantee an (1 - 1/e) approximation for the k-throwbox placement problem. Simulation results based on random time-evolving DTNs and real life DTN traces demonstrate the efficiency of the proposed methods.

We consider the problem of online dynamic channel accessing in multi-hop cognitive radio networks. Previous works on online dynamic channel accessing mainly focus on single-hop networks that assume complete conflicts among all secondary users. In the multi-hop multi-channel network settings studied here, there is more general competition among different communication pairs. A simple application of models for single-hop case to multi-hop case with N nodes and M channels leads to exponential time/space complexity O (MN), and poor theoretical guarantee on throughput performance. We thus novelly formulate the problem as a linearly combinatorial multi-armed bandits (MAB) problem that involves a maximum weighted independent set (MWIS) problem with unknown weights. To efficiently address the problem, we propose a distributed channel access algorithm that can achieve 1/ρ of the optimum averaged throughput where each node has communication complexity O (r2+D) and space complexity O (m) in the learning process, and time complexity O (D mρr) in strategy decision process for an arbitrary wireless network. Here ρ = 1 + ε is the approximation ratio to MWIS for a local r-hop network with m <; N nodes, and D is the number of mini-rounds inside each round of strategy decision.

Vehicular networks have been increasingly used for applications like road infrastructure monitoring and traffic jam detection, etc. Data forwarding is a well-known challenging problem in vehicular networks, which suffers from delay and error due to the frequent network disruption and fast topological change. The minimizations of the delivery delay and network cost are both central to data forwarding in vehicular networks. However, previous works usually focus on only one of the two objectives and most of them do not make good use of vehicle trajectory information. In this paper, we formulate the V2V (vehicle to vehicle) data forwarding problem as a novel multi-objective Markov Decision Process (MDP). We exploit the vehicle trajectory information and traffic statistics to estimate the parameters of the MDP (i.e., transition probabilities, rewards). The optimal routing policy is then developed by solving the multi-objective MDP. We conduct extensive simulations on a taxi network in a mega-city, the experimental results validate the effectiveness of our proposed mechanism.

In Vehicular Ad Hoc Networks (VANETs), moving vehicles are considered as mobile nodes in the network and they are connected to each other via wireless links when they are within the communication radius of each other. Efficient message delivery in VANETs is still a very challenging research issue. In this paper, a Q-learning based routing protocol (i.e., QGrid) is introduced to help to improve the message delivery from mobile vehicles to a specific location. QGrid considers both macroscopic and microscopic aspects when making the routing decision, while the traditional routing methods focus on computing meeting information between different vehicles. QGrid divides the region into different grids. The macroscopic aspect determines the optimal next-hop grid and the microscopic aspect determines the specific vehicle in the optimal next-hop grid to be selected as next-hop vehicle. QGrid computes the Q-values of different movements between neighboring grids for a given destination via Q-learning. Each vehicle stores Q-value table learned offline, then selects optimal next-hop grid by querying Q-value table. Inside the selected next-hop grid, we either greedily select the nearest neighboring vehicle to the destination or select the neighboring vehicle with highest probability of moving to the optimal next-hop grid predicted by the two-order Markov chain. The performance of QGrid is evaluated by using real life trajectory GPS data of Shanghai taxies. Simulation comparison among QGrid and other existing position-based routing protocols confirms the advantages of proposed QGrid routing protocol for VANETs.

In this paper, we model the data dissemination in online social networks (OSNs) and study the scaling laws of traffic load. We propose a three-layered system model to formulate data dissemination sessions for social applications in OSNs. The layered model consists of the physical network layer, social relationship layer, and application session layer. By analyzing mutual relevances among these three layers, we investigate the geographical distribution feature of dissemination sessions in OSNs. Based on this, we derive the traffic load of OSNs under a realistic assumption that every source sustains a data generating rate of constant order. To the best of our knowledge, this is the first work to address the issue of traffic load scaling for OSNs by modeling the social data dissemination from a layered perspective.

Delay Tolerant Networks (DTNs) are intermittently connected networks, such as mobile social networks formed by human-carried mobile devices. Routing in such mobile social DTNs is very challenging as it must handle network partitioning, long delays, and dynamic topology. Recently, social-based approaches, which attempt to exploit social behaviors of DTN nodes to make better routing decision, have drawn tremendous interests in DTN routing design. In this paper, we propose a novel social-based routing approach for mobile social DTNs, where a new metric social energy is introduced to quantify the ability of a node to forward packets to others, inspired by general laws in particle physics. Social energy is generated via node encounters and shared by the communities of encountering nodes. Similar to the radiation of energy in physics, the social energy of any node decays over time. Our proposed Social Energy Based Routing (SEBAR) protocol considers social energy of encountering nodes and is in favor of the Dode with a higher social energy in its or the destination's social community. Our simulations with real-life wireless traces demonstrate the efficiency and effectiveness of SEBAR method by comparing It with several existing DTN routing schemes.

This paper presents the magnetic field modeling-based energy efficient routing for WSN which models the routing problem as a current-carrying solenoid and a free to turn magnet put into a uniform magnetic field respectively. The optimal routing is then performed through the path with the maximum generated magnetic field intensity for the solenoid technique and with minimum torque for the magnetic torque technique. The proposed approaches have been incorporated into AODV. The case studies and the simulation show that the load distribution of our proposed methodology is more balanced than that of the conventional AODV.

Recent advances in ambient energy-harvesting technologies have made it possible to power wireless sensor networks (WSNs) from the environment for long durations. However, the energy availability in an energy-harvesting WSN varies with time and thus may cause the network topology to evolve over time. In this paper, we study the topology design problem in a time-evolving and energy-harvesting WSN where the time-evolving topology and dynamic energy cost are known a priori or can be predicted. We model such a network as a node-weighted space-time graph which includes both spacial and temporal information. To reduce the cost of supporting time-evolving networks with limited harvesting energy sources, we propose a new efficient topology design problem which aims to put more sensors into sleep while still maintaining the network connectivity over time. We prove that the optimization problem of finding the optimal awake sensor set with the minimum total cost is NP-hard. Thus, we propose several topology design algorithms which can significantly reduce the total cost of topology while maintaining the connectivity over time. Simulation results from random time-evolving and energy-harvesting WSNs demonstrate the efficiency of the proposed methods.

In this work, we address a critical task of detecting the user behavior of driving and texting simultaneously using smartphones. We propose, design, and implement TEXIVE which achieves the goal of distinguishing drivers and passengers, and detecting texting operations during driving utilizing irregularities and rich micro-movements of users. Without relying on any external infrastructures and additional devices, and no need to bring any modification to vehicles, TEXIVE is able to successfully detect dangerous operations with good sensitivity, specificity and accuracy. We conduct experimental study of TEXIVE with the help of a number of volunteers using various vehicles and smartphones. Our results indicate that TEXIVE has a classification accuracy of 87.18%, and precision of 96.67%.

Shortest path routing protocol intends to minimize the total delay between every pair of destination node and source node. However, it is also well-known that shortest path routing suffers from uneven distribution of traffic load, especially in dense wireless networks. Recently, several new routing protocols are proposed in order to balance traffic load among nodes in a network. One of them is circular sailing routing(CSR) [1], [2] which maps nodes on the surface of a sphere and select routes based on surface distances. CSR has been demonstrated with better load balance than shortest path routing via simulations. However, it is still open that what load distribution CSR can achieve. Therefore, in this paper, we theoretically analyze the traffic load distribution of CSR in a dense circular wireless network. Using the techniques developed by Hyytia and Virtamo [3], we are able to derive the traffic load of any point inside the network. We then conduct extensive simulations to verify our theoretical results with grid and random networks.

With possible participation of a large number of wireless devices in delay tolerant networks (DTNs), how to maintain efficient and dynamic topology becomes crucial. In this paper, we study the topology design problem in a predictable DTN where the time-evolving network topology is known a priori or can be predicted. We model such network as a weighted space-time graph with both spacial and temporal information. Links inside the space-time graph are unreliable due to either the dynamic nature of wireless communications or the rough prediction of underlying human/device mobility. The aim of our topology design problem is to build a sparse space-time structure such that (1) for any pair of devices, there is a space-time path connecting them with the reliability larger than a required threshold; (2) the total cost of the structure is minimized. We first show that this problem is NP-hard, and then propose several heuristics which can significantly reduce the total cost of the topology while maintain the “reliable” connectivity over time.

Mobile Opportunistic Networks (MONs) are intermittently connected networks, such as pocket switched networks formed by human-carried mobile devices. Routing in MONs is very challenging as it must handle network partitioning, long delays, and dynamic topology. Flooding is a possible solution but with high costs. Most existing routing methods for MONs avoid the costly flooding by selecting one or multiple relays to deliver data during each encounter. How to pick the “good” relay from all encounters is a non-trivial task. To achieve efficient delivery of messages at low costs, in this paper, we propose a new group-based routing protocol in which the relay node is selected based on social group information obtained from historical encounters. We apply a simple formation method to build multi-level social groups, which summarizes the wide range of social relationships among all mobile participants. Our simulations demonstrate the efficiency and effectiveness of the proposed method by comparing it with several existing MON routing schemes.

Location privacy has been a serious concern for mobile users who use location-based services provided by the third-party provider via mobile networks. Recently, there have been tremendous efforts on developing new anonymity or obfuscation techniques to protect location privacy of mobile users. Though effective in certain scenarios, these existing techniques usually assume that a user has a constant privacy requirement along spatial and/or temporal dimensions, which may not be true in real-life scenarios. In this paper, we introduce a new location privacy problem: Location-aware Location Privacy Protection (L2P2) problem, where users can define dynamic and diverse privacy requirements for different locations. The goal of the L2P2 problem is to find the smallest cloaking area for each location request so that diverse privacy requirements over spatial and/or temporal dimensions are satisfied for each user. In this paper, we formalize two versions of the L2P2 problem, and propose several efficient heuristics to provide such location-aware location privacy protection for mobile users. Through multiple simulations on a large data set of trajectories for one thousand mobile users, we confirm the effectiveness and efficiency of the proposed L2P2 algorithms.

Mobile devices in delay tolerant networks (DTNs) are used and carried by people, whose behaviors could be described by social models. Understanding social behaviors and characteristics of mobile users can greatly help the routing decision in DTN routing protocols. However, to obtain the stable and accurate social characteristics in dynamic DTNs is very challenging. To achieve efficient delivery of messages at low costs, in this paper, we propose a novel enhanced social group-based routing protocol in which the relay node is selected based on multi-level cross-community social group information. We apply a simple group formation method with both historical encounters (social relationships in physical world)and social features of mobile users (social relationships in social world) and build multi-level cross-community social groups, which summarize the wide range of social relationships among all mobile participants. Our simulations over a real-life data set demonstrate the efficiency and effectiveness of the proposed method by comparing it with several existing DTN routing schemes.

Well known "hole" problem is hardly avoided in wireless sensor networks because of various actual geographical environments. Existing geographic routing protocols (such as GFG [1] and GPSR [2]) use perimeter routing strategies to find a detour path around the boundary of holes when they encounter the "local minimum" during greedy forwarding. However, this solution may lead to uneven energy consumption around the holes since it consumes more energy of the boundary sensors. It becomes more serious when holes appear in most of routing paths in a large scale sensor network. In this paper, we propose a novel distributed strategy to balance the traffic load on the boundary of holes by virtually changing the sizes of these holes. The proposed mechanism dynamically controls holes to expand and shrink circularly without changing the underlying forwarding strategy. Therefore, it can be applied to most of the existing geographic routing protocols which detour around holes. Simulation results show that our new strategy can effectively balance the load around holes thus prolong the network life of sensor networks when using with GPSR.

Efficient data delivery in vehicular ad hoc networks (VANETs) is still a challenging research issue. Position-based routing protocols have been proven to be more suitable for dynamic VANETs than traditional ad hoc routing protocols. However, position-based routing assumes that intermediate nodes can always be found to setup an end-to-end connection between the source and the destination, otherwise, it suffers from network partitions which are very common in VANETs and leads to poor performances. This paper addresses data delivery challenge in the possible disconnected VANETs by combining position-based forwarding strategy with store-carry-forward routing scheme from delay tolerant networks. The proposed routing method makes use of vehicle driving direction to determine whether holding or forwarding the packet. Experimental results show that the proposed mechanism outperforms existing position-based solutions in terms of packet delivery ratio.

Three dimensional (3D) wireless sensor networks have attracted a lot of attention due to its great potential usages in both commercial and civilian applications. Topology control in 3D sensor networks has been studied recently. Different 3D geometric topologies were proposed to be the underlying network topologies to achieve the sparseness of the communication networks. However, most of the proposed 3D topologies cannot bound the node degree, i.e., some nodes may need to maintain large number of neighbors in the constructed topologies, which is not energy efficient and may lead to large interference. In this paper, we extend several existing 3D topologies to a set of new 3D topologies with bounded node degree. We provide theoretical analysis on their power efficiency and node degree and also simulation evaluations over random 3D sensor networks. The simulation results confirm nice performance of these proposed 3D topologies.

Topology control protocol aims to efficiently adjust the network topology of wireless networks in a self-adaptive fashion to improve the performance and scalability of networks. This is especially essential to large-scale multihop wireless networks (e.g., wireless sensor networks). Fault-tolerant topology control has been studied recently. In order to achieve both sparseness (i.e., the number of links is linear with the number of nodes) and fault tolerance (i.e., can survive certain level of node/link failures), different geometric topologies were proposed and used as the underlying network topologies for wireless networks. However, most of the existing topology control algorithms can only be applied to two-dimensional (2D) networks where all nodes are distributed in a 2D plane. In practice, wireless networks may be deployed in three-dimensional (3D) space, such as under water wireless sensor networks in ocean or mobile ad hoc networks among space shuttles in space. This article seeks to investigate self-organizing fault-tolerant topology control protocols for large-scale 3D wireless networks. Our new protocols not only guarantee k -connectivity of the network, but also ensure the bounded node degree and constant power stretch factor even under k −1 node failures. All of our proposed protocols are localized algorithms, which only use one-hop neighbor information and constant messages with small time complexity. Thus, it is easy to update the topology efficiently and self-adaptively for large-scale dynamic networks. Our simulation confirms our theoretical proofs for all proposed 3D topologies.

Although most existing wireless systems and protocols are based on two-dimensional design, in reality, a variety of networks operate in three-dimensions. The design of protocols for 3D networks is surprisingly more difficult than the design of those for 2D networks. In this paper, we investigate how to design load balancing routing for 3D networks. Most current wireless routing protocols are based on Shortest Path Routing (SPR), where packets are delivered along the shortest route from a source to a destination. However, under uniform communication, shortest path routing suffers from uneven load distribution in the network, such as crowed center effect where the center nodes have more load than the nodes in the periphery. Aim to balance the load, we propose a novel 3D routing method, called 3D Circular Sailing Routing (CSR), which maps the 3D network onto a sphere and routes the packets based on the spherical distance on the sphere. We describe two mapping methods for CSR and then provide theoretical proofs of their competitiveness compared to SPR. For both proposed methods, we conduct simulations to study their performance in grid and random networks.

Routing in wireless networks has been heavily studied in the last decade and numerous routing protocols were proposed in literature. Most of the existing routing protocols are based on shortest path routing. Shortest path routing enjoys minimizing the total delay, but may lead uneven distribution of traffic load in a network. For example, wireless nodes in the center of a network usually have heavier traffic load since most of the shortest routes go through the center. To solve this problem, Popa et al. (2007) recently proposed a novel routing method, called curveball routing (CBR), which can balance the traffic load and vanish the crowded center effect. In CBR, nodes are mapped on a sphere and packets are routed on those virtual coordinates on the sphere. While CBR achieves better load balancing for the network, it also uses longer routes than the shortest paths. This can be treated as the cost of load balancing. In this paper, we focus on studying this cost of load balancing for curveball routing. Specifically, we theoretically prove that for any network, the distance traveled by the packets using CBR is no more than a small constant factor of the minimum (the distance of the shortest path). The constant factor, we called stretch factor, is only depended on the ratio between the size of the network and the radius of the sphere used in CBR. We then conduct extensive simulations to evaluate the stretch factor and load distribution of CBR and compare them with the shortest path routing in both grid and random networks. We also study the trade-off between stretch factor and load balancing.

Routing in wireless networks has been heavily studied in the last decade and numerous routing protocols were proposed in literature. The packets usually follow the shortest paths between sources and destinations in routing protocols to achieve smallest traveled distance. However, this leads to the uneven distribution of traffic load in a network. For example, wireless nodes in the center of the network will have heavier traffic since most of the shortest routes go through them. In this paper, we first describe a novel routing method, called circular sailing routing (CSR), which can distribute the traffic more evenly in the network. The proposed method first maps the network onto a sphere via a simple stereographic projection, and then the route decision is made by the distance on the sphere instead of the Euclidean distance in the plane. We theoretically prove that for a network the distance traveled by the packets using CSR is no more than a small constant factor of the minimum (the distance of the shortest path). We then extend CSR to a localized version, Localized CSR, by modifying the greedy routing without any additional communication overhead. Finally, we further propose CSR protocols for 3D networks where nodes are distributed in a 3D space instead of a 2D plane. For all proposed methods, we conduct simulations to study their performances and compare them with global shortest path routing or greedy routing.

We address the problem of gateway placement for throughput optimization in multi-hop wireless mesh networks. Assume that each mesh node in the mesh network has a traffic demand. Given the number of gateways need to be deployed (denoted by k) and the interference model in the network, we study where to place exactly k gateways in the mesh network such that the total throughput is maximized while it also ensures a certain fairness among all mesh nodes. We propose a novel grid-based gateway deployment method using a cross-layer throughput optimization. Our proposed method can also be extended to work with multi-channel and multi-radio mesh networks. Simulation result demonstrates that our method can effectively exploit the resources available and perform much better than random and fixed deployment methods.

Topology control in wireless ad hoc and sensor networks has been heavily studied recently. Different geometric topologies were proposed to be the underlying network topologies to achieve the sparseness of the communication networks or to guarantee the package delivery of specific routing methods. However, most of the proposed topology control algorithms were only applied to 2D networks where all nodes are distributed in a 2D plane. In practice, the ad hoc and sensor networks are often deployed in 3D space, such as notebooks in a multi-floor building and sensor nodes in a forest. This paper seeks to investigate power efficient topology control protocols for 3D ad hoc and sensor networks. In our new protocols, we extend several 2D geometric topologies to 3D case, and propose some new 3D Yao-based topologies. We also prove several properties (e.g., bounded degree and constant power stretch factor) for them in 3D space. The simulation confirms our theoretical proofs for these proposed 3D topologies.