Long-term Time Series Forecasting is crucial across numerous critical domains, yet its accuracy remains fundamentally constrained by the receptive field bottleneck in existing models. Mainstream Transformer- and Multi-layer Perceptron (MLP)-based methods mainly rely on finite look-back windows, limiting their ability to model long-term dependencies and hurting forecasting performance. Naively extending the look-back window proves ineffective, as it not only introduces prohibitive computational complexity, but also drowns vital long-term dependencies in historical noise. To address these challenges, we propose CometNet, a novel Contextual Motif-guided Long-term Time Series Forecasting framework. CometNet first introduces a Contextual Motif Extraction module that identifies recurrent, dominant contextual motifs from complex historical sequences, providing extensive temporal dependencies far exceeding limited look-back windows; Subsequently, a Motif-guided Forecasting module is proposed, which integrates the extracted dominant motifs into forecasting. By dynamically mapping the look-back window to its relevant motifs, CometNet effectively harnesses their contextual information to strengthen long-term forecasting capability. Extensive experimental results on eight real-world datasets have demonstrated that CometNet significantly outperforms current state-of-the-art (SOTA) methods, particularly on extended forecast horizons.

Underwater wireless optical communication (UWOC) enables high-rate data offloading for underwater sensing systems, but its strong directionality makes multi-node networking vulnerable to misalignment, occlusion, and dynamic link disruptions. Existing control-plane-driven recovery is often too slow for such transient failures. We present A-SCAN, a data-plane self-healing mechanism that maintains neighbor-angle mappings and performs lightweight angle-guided recovery without triggering global routing updates. Based on the locally recovered topology, Q-SHARP performs quality-aware multi-hop path selection and backup optimization in the control plane. Together, they separate fast local link recovery from slow global routing optimization, enabling more stable self-healing communication in directional UWOC networks.

Efficient task offloading is vital for latency-sensitive Industrial IoT (IIoT) systems. Existing deep learning-based approaches, however, face long training time, poor adaptability, and heavy reliance on labeled data. We propose SRCO, a Semi supervised Reservoir Computing-based Offloading framework that uses a fixed dynamic reservoir and trains only the readout layer, enabling fast model updates with minimal overhead. A semi-supervised strategy further exploits unlabeled data to reduce labeling cost. Experiments show that SRCO improves of floading accuracy by up to 15.6% and reduces training time by up to 59.6% compared with state-of-the-art methods, demonstrating strong efficiency and adaptivity for real-time IIoT applications.

Underwater networking is vital for enabling collaboration between divers, vehicles, and sensors in marine exploration, monitoring, and emergency response. Yet achieving reliable communication in such dynamic, bandwidth constrained environments remains challenging. Acoustic and radio frequency technologies suffer from attenuation, latency, and hardware overhead, while optical wireless systems typically require specialized transceivers or strict alignment, limiting practicality in mobile underwater networks. To address these limitations, we present AquaLink, a QR code–driven Optical Camera Communication (OCC) framework that enables robust underwater messaging using commodity smartphones and tablets. At its core, AquaQR employs blue–green 2-bit color encoding, Low-Density Parity-Check (LDPC) error correction, and geometric augmentations tailored for optical stability in turbid waters. An auto-configuration module adapts parameters before transmission, and a lightweight enhancement pipeline ensures real-time robustness under diverse conditions. Field trials in pool, lake, and coastal environments achieve over 90% decoding success at 5 m and up to 2× higher throughput than prior QR-based systems. By eliminating specialized hardware, AquaLink provides a scalable, low cost foundation for underwater visual networking, supporting message exchange, peer interaction, and localized link formation.

Wireless rechargeable sensor networks (WRSNs) have become an important research topic as they show merit in long-term monitoring operations. Existing techniques focus on improving system performance, while the issue of thermal effects is overlooked, leading to discrepancies between theoretical results and real-world applications. In this work, we explore and exploit the impact of the thermal effect on charging performance. At first, the thermal effect is modeled based on the Newton-Richman cooling law, followed by a new theoretical charging model based on such effect. We jointly consider the influences of charger’s self-generated heat and ambient temperature on charging utility. To address the uncertainty of temperature variation problem, we developed an online learning scheme called tHermalEffectAdapTive charging algorithm (HEAT) based on the combined multi-armed bandit method. The proposed algorithm can dynamically schedule charging tasks adaptive to temperature fluctuations while guaranteeing a logarithmic regret bound. Extensive test-bed experiments and simulations are conducted. The results demonstrate that our scheme outperforms state-of-the-art methods by at least 24.9% in charging utility across various ambient temperature conditions.

Image recognition plays a critical role in urban security, traffic management, and environmental monitoring, yet achieving high accuracy in obstructed scenes remains a challenge. To address this, we propose a Graph Convolutional Visual Prior-Transform Network (GCVPN), which significantly improves recognition accuracy and efficiency in complex environments. GCVPN introduces an image prior slicing and topology transformer to convert image data into graph-structured slice features, integrating domain overlap sampling and planar mapping to handle symmetry and enable precise, rapid anomaly detection. By combining a traditional VGG backbone with graph convolutional layers, GCVPN jointly captures topological relationships and feature semantics, while maintaining real-time efficiency with continuous recognition at 30 video frames per second. Extensive experiments demonstrate its effectiveness in photovoltaic panel anomaly detection and face occlusion recognition, highlighting strong potential for applications in intelligent surveillance and autonomous driving.

Underwater communication systems face challenges, including limited bandwidth, high latency, and void areas. This article introduces synergy optimization routing protocol (SORP) for Internet of Underwater (IoU) sensor networks, emphasizing link scheduling to address localization, energy consumption, latency, network longevity, and void regions. Leveraging belief-desire-intention (BDI) and fuzzy logic, SORP offers adaptive responses to varying network conditions. Theoretical modeling using NetLogo enhances the understanding of SORP’s behavior. In evaluation, SORP consistently outperforms others. Demonstrating superior energy efficiency (3.0–3.9) compared to PPWURC, state prediction-based data collection (SPDC), balanced routing protocol based on machine learning (BRP-ML) (40–120), and energy efficient clustering routing protocol based on arithmetic progression (5–6.5), SORP proves its efficacy. Latency analysis reveals SORP consistently displaying the lowest values (2.0–2.8), surpassing packet hierarchy and void processing, SPDC, and BRP-ML (8.3–30.0). With a perfect packet delivery ratio (PDR) of 98%, SORP showcases exceptional reliability. Network lifetime analysis positions SORP as a durable option, lasting from 3985 to 4010 rounds. Validation through an underwater communication system demonstrates speeds of 5 Mb/s and above. Simulation testing reveals a transmission speed of 80 bps with latency of less than 4 s and 98% PDR. Theoretical predictions indicate significant improvements in real-time transmission, reducing latency to less than 1 s with a speed of 5 Mb/s. This research presents an innovative and practical approach to address underwater communication challenges, highlighting the efficiency and reliability of SORP in routing protocols for underwater sensor networks. The combination of theoretical modeling and real-time testing offers a comprehensive understanding, emphasizing the potential real-world impact of SORP.

Anomaly detection in solar photovoltaic panels using Unmanned Aerial Vehicles (UAVs) faces challenges due to minimal texture variations from surface anomalies (e.g., shadows, eddy currents) in UAV-captured imagery, which constrain both detection precision and real-time performance. Existing approaches often lack quantitative anomaly analysis that integrates aerial imagery with operational data, thereby limiting their practical value and impeding sustainable industry advancement. To address these limitations, we propose Gate-convolution Support Vector Data Description (Gate-conv SVDD), a novel framework that enhances the efficiency and accuracy of anomaly detection through rapid parallel gated feature compression and hypersphere-based Support Vector Data Description (SVDD) clustering. This approach enables precise anomaly localization in high-resolution UAV imagery, as validated through simulations and controlled experimental datasets. Gate-conv SVDD further supports quantitative assessments of anomaly severity, thereby bridging the gap between image-based detection and actionable analysis. Designed for computational efficiency, the framework demonstrates strong potential for fast inference in support of real-time UAV imaging, subject to further hardware integration and field validation. Extensive experiments demonstrate that Gate-conv SVDD outperforms state-of-the-art methods, offering superior accuracy and robustness in controlled settings.

Identifying and detecting anomalies in industrial Internet of Things (IIoT) systems is vital for maintaining industrial safety. In IIoT scenarios, various industrial machines operate with differing periods that overlap temporally, resulting in complex multiperiodic temporal patterns. In addition, varying production tasks and environmental conditions alter sensor dependencies, complicating the modeling of intersensor dependency topologies. Existing methods, which rely on a fixed global topologies, struggle to adapt to these complex multiperiodic temporal patterns and evolving dependency topologies, leading to low anomaly detection accuracy. To tackle these problems, we propose MAGE, a multiperiodic adaptive graph evolution guided anomaly detection framework. MAGE first segments sensor data into distinct temporal periods, then employs a dynamic graph structure learning module to model evolving dependencies. Finally, a global-local association discrepancy module is employed to enhance the anomaly detection capability. Comprehensive experiments on five real-world datasets demonstrate MAGE's superior performance compared to state-of-the-art approaches.

In the Internet of Medical Things (IoMT), de novo peptide sequencing prediction is one of the most important techniques for the fields of disease prediction, diagnosis, and treatment. Recently, deep-learning-based peptide sequencing prediction has been a new trend. However, most popular deep learning models for peptide sequencing prediction suffer from poor interpretability and poor ability to capture long-range dependencies. To solve these issues, we propose a model named SeqNovo, which has the encoding-decoding structure of sequence to sequence (Seq2Seq), the highly nonlinear properties of multilayer perceptron (MLP), and the ability of the attention mechanism to capture long-range dependencies. SeqNovo use MLP to improve the feature extraction and utilize the attention mechanism to discover key information. A series of experiments have been conducted to show that the SeqNovo is superior to the Seq2Seq benchmark model, DeepNovo. SeqNovo improves both the accuracy and interpretability of the predictions, which will be expected to support more related research.

Benefiting from Wireless Power Transfer (WPT) technology, Wireless Rechargeable Sensor Networks (WRSNs) effectively address the lifetime bottleneck of sensor nodes, enabling them to work perpetually. Most state-of-the-art studies assume that all WRSNs’ information is known or precise in advance. However, sensor nodes may be deployed randomly in a large-scale area, and some critical information (such as node location) may be unavailable or difficult to obtain precisely. In this work, we eliminate the effect of uncertain or imprecise node location and formalize theMaximizingChargingEnergy utility for uncertain location nodesproblem (i.e., MCE problem). With magnetic resonance coupling and beamforming technologies, we propose a novel node localization method to determine precise node location information. In addition, we present a reinforcement learning framework and a charging path scheduling method to maximize charging energy. To validate the effectiveness of our proposed scheme in real-world scenarios, we conduct test-bed experiments. The results demonstrate that our approach significantly improves charging efficiency by an average of 20.9% in a large-scale network, even when the locations of sensors are entirely unknown.

Wireless Power Transfer (WPT) has revolutionized the field of Wireless Rechargeable Sensor Networks (WRSNs), enabling sustainable operation of sensor nodes. Traditional mobile charging methods often require sensors to be within line-of-sight or physically accessed by the mobile charger, which may potentially lead to user safety or privacy concerns. Addressing this concern, this work is the first to introduce and validate the feasibility ofThrough-Wallcharging. We formulate theWireless charging thrOughWalls (WOW) problem to simultaneously enhance user safety and maximize charging utility. Our approach leverages fundamental principles of electromagnetics to construct an accurate charging model for Magnetic Resonance Coupling-based WPT systems. Additionally, we thoroughly analyze the impact of wall obstruction and provide a generalized framework for through-wall charging. By employing discretization techniques and approximation algorithms, we derive a near-optimal solution to the WOW problem. Extensive simulations and test-bed experiments demonstrate that our proposed approach reduces the reliance on physical access to devices, simplifies deployment in complex environments, and thereby optimizes the travel paths of mobile chargers and enhances the overall performance and lifetime of WRSNs. Compared to conventional methods, our method benefits from more reasonable scheduling order and path construction, achieving an average energy efficiency improvement of 27.8%.

Neighbor discovery poses significant challenges in Underwater Optical Wireless Sensor Networks (UOWSNs) due to the unique characteristics of directional transceivers, line-of-sight communication, and mobility induced by water currents. Traditional methods typically rely on prerequisites and prior knowledge, such as centralized coordination, time synchronization, and information about the number of neighbors, which are often unavailable or impractical in underwater environments. In this paper, we make the first attempt to address the issue ofRobust andEfficientNeighborDiscovery (termed the REND problem) in UOWSNs with zero-knowledge. Here, zero-knowledge refers to the capability that enables sensors to identify neighbors in dynamic underwater optical channel conditions without prerequisites or prior knowledge. We design a zero-knowledge distributed directional neighbor discovery scheme inspired by gear meshing. We then propose a deterministic algorithm for the REND problem based on theoretical analysis. Additionally, to further reduce the discovery delay for the periodic REND problem, we develop a greedy-based approximation algorithm with a performance guarantee. Finally, extensive simulations demonstrate that the proposed scheme reduces the discovery delay by 34.9% on average and achieves an additional 54.4% reduction for periodic neighbor discovery. Furthermore, test-bed experiments are carried out to verify the applicability of our zero-knowledge scheme in real-world scenarios.

Wireless Rechargeable Sensor Networks (WRSNs) have become an important research issue as they can overcome the energy bottleneck problem of wireless sensor networks. However, existing 2D charging methods suffer from significant errors in 3D scenarios, which leads to a huge gap between theoretical results and practical applications, hindering the widespread adoption of WRSNs. In this paper, we address the chargIng utility maximizatioN problem In 3D environmenT (INIT) and provide a general solution suitable for any type of transceiver antenna. Specifically, we first establish an accurate 3D charging model to quantify the received power of sensors in 3D environments. Secondly, we design an angle-distance discretization scheme to determine appropriate charging spots for the Mobile Charger (MC). Then, we transform the mobile charging problem into a submodular function maximization problem and propose an approximation algorithm with guaranteed performance to solve it. Finally, our method has been extensively evaluated through experiments and simulations and has demonstrated considerable advantages over other comparison algorithms in real-world 3D environments. On average, it has achieved an impressive 34.8% improvement in charging utility and a remarkable 56.1% reduction in the number of dead sensors.

WiFi-based identity recognition differs from traditional identification technologies as it is not limited by lighting conditions and does not require dense, specialized sensors or wearable devices. This makes it valuable in modern human–machine interactions. However, the diversity of real-world environmental conditions substantially limits the application of existing WiFi-based identity recognition algorithms, particularly when applied across different environments. As a solution, we introduce the incremental wavelet capsule (IWC) model, which combines a newly designed wavelet convolution layer with a capsule network to accelerate precise feature extraction. We adopt a hybrid incremental learning strategy, solving the catastrophic forgetting1problem in cross-environment tasks and enabling the model to adapt to new environments in the data stream without forgetting the original environment. Furthermore, we developed a customized data augmentation method for WiFi signals, enhancing the model’s adaptability and stability across various environments. Experimental results show that the IWC model achieves an average recognition accuracy of 97.36% across five different environments and maintains an accuracy of 91.5% even when only 5% of the training data from a new environment is used. These findings demonstrate the model’s robust performance and practicality in cross-environment scenarios.

When deploying software defined network (SDN), there is a need for optimized controller placement to ensure high network performance. Different solutions are proposed that focus on single optimization objectives, such as delay minimization or throughput maximization. This article proposes a hybrid artificial intelligence-based approach, the reliability–capacity aware joint controller placement and routing (ReCAP), which combines deep reinforcement learning and graph neural networks to identify the best placement for controllers, so there are optimal paths for communication between switches and controllers and improved network performance. Controller placement is optimized based on multiple objectives, including load balancing, path reliability and link capacity. ReCAP is validated using real network traces and evaluated using the Panopticon network. The experimental results show an improved performance for our ReCAP approach compared to existing solutions in terms of both efficiency and reliability.

Predicting RNAs family presents a significant challenge with broad implications in medicine and scientific inquiry. Leveraging the advancements in deep learning, recent studies have delved into utilizing these algorithms for RNAs family prediction. This article introduces an innovative algorithm integrating BiLSTM, transformer, and convolutional neural networks (CNN). Initially, RNA sequences undergo representation using k-mers, a strategy aimed at mitigating the impact of errors in the sequence modeling process. Following this, a word embedding technique is applied to represent the RNA sequences, thereby reducing computational complexity within the network. Experimental results demonstrate the superior performance of our model compared to other comparison algorithms in terms of recall, accuracy, and precision on the 10-fold test dataset. This demonstrates the excellent comprehensive performance of the proposed model in terms of robustness and efficiency.

Unmanned Underwater Vehicles (UUVs) have been increasingly used in underwater scenes, such as ocean exploration, marine rescue, and underwater pipeline. Underwater wireless optical communication (UWOC) technology has higher speed, higher bandwidth, and lower latency compared to traditional underwater communication technologies, making it a promising paradigm for the communication between UUVs. However, UWOC-enabled UUV networks face challenges in terms of the number of affordable network interfaces and the stringent energy limit due to the expensive cost of network interface for UUVs and the serious underwater working environment. In this paper, we propose an exact algorithm and an approximate algorithm to optimize the network topology to lower energy consumption for UUVs. The exact algorithm can effectively enumerate all the network topology to find the optimal one, which works for small-scale UUV networks with no more than 10 UUVs. The approximate algorithm uses local search algorithm to approach the exact answer in the time complexity of$O(kn^{2}\log n)$, which targets at large-scale UUV networks with 10 ~ 100 UUVs.$k$is the number of iterations, and$n$is the number of UUVs. Simulation results verify the accuracy and efficiency of the proposed two algorithms, and demonstrate when$k=2n^{2}$or$k=3n^{2}$, the relative error of the approximate algorithm is less than 0.5% when$n<10$and converges fast when$n=100$.

Machine learning models are increasingly recognized as valuable intellectual property (IP), prompting the development of a range of watermarking techniques aimed at safeguarding the IP of these models. However, in the context of federated learning (FL) models involving multiple owners, such as the participants in FL model training, conventional techniques designed for single-owner models prove ineffective due to limitations in their capacity and robustness. Few work has explored how to effectively embed watermarks to FL models for multiple-owners, which is non-trivial, especially when the number of owners is large. To fill this gap, we first analyze the capacity of existing watermarking methods. Second, we propose FedMark, a general large-capacity watermarking mechanism for FL, which leverages the Bloom Filter to achieve conflict-free watermarking of a large number of participants. Moreover, we propose a secret-sharing-based verification method to improve the watermarking robustness against false positives caused by Bloom Filter. Finally, comprehensive experiments show that our design can support over 150 participants to embed watermarks while the model accuracy varies within 1 %, and is robust to non-independent identical distributed data, different participant selection rates, model modifications, permutation attacks, scaling attacks and forging attacks.

Underwater Optical Wireless Communication (UOWC) is considered a promising approach, offering the potential for flexible and high-speed communication under the surface of the water. However, the interdependent relationship among three key performance elements, namely communication distance, bit error rate, and communication rate, has been largely overlooked. This oversight impedes the complete utilization of the system performance. In this work, we innovatively introduce a “UOWC Impossible Trinity” model and theorems to establish relationships among the three key performance elements, which clarify the inherent constraints within UOWC system optimization. Moreover, we formulate the Underwater Optical Communication Trade-offs (UOCT) Problem to maximize communication performance. Furthermore, we provide feasible non-dominated solution sets, considering the constraints of real environments and user demands of specific scenarios. Our model has been validated by extensive simulations, demonstrating that our approach not only clarifies fundamental limitations of UOWC systems, but also provides practical guidelines for designing and optimizing the systems. Our approach has been experimentally validated with an impressive accuracy of over 95%, surpassing conventional models, which not only enhances the understanding of UOWC system optimization but also validates the existence of inherent trade-offs. Furthermore, our approach demonstrates a significant increase in communication distance, outperforming traditional methods by more than 20%.

Underwater positioning plays a key role in many underwater operations. This paper presents the design, implementation, and evaluation of UWBeacon, a centimeter-level visible light-based underwater positioning system. UWBeacon consists of LED beacons as the light signal transmitter and a camera-based receiver as the target. To address unique challenges in underwater environment such as limited visibility and strong ambient interference, we exploit a novel design that utilizes polarized lights of different colors with different polarization angles for background subtraction. UWBeacon is implemented with commercial-off-the-shelf LEDs and cameras. Comprehensive experiments conducted in various real underwater environments show that UWBeacon can achieve a mean positioning error below 6 cm and an orientation error below 1.5° at a distance of 10 meters.

Contemporary individuals frequently face spine-related issues, which significantly impact their health and quality of life. However, traditional detection methods such as MRI and CT imaging entail high costs and radiation risks, limiting the screening and treatment of spine-related problems. This study leverages ubiquitous WiFi transceivers to collect WiFi Channel State Information (CSI) datasets representing three distinct spinal statuses. Employing a transformer-based neural network for data processing, we propose an efficient and cost-effective approach to assess spinal statuses, achieving a classification accuracy of 91%.

The convenience of QR codes has made them increasingly popular in the field of mobile payments, with many payment service providers (e.g., PayPal, Alipay, WeChat) offering QR code payment services. However, due to the openness of QR code scanning process, attackers can capture the code and perform fraudulent transactions before the original QR code is used, posing a significant security threat. In this paper, we design UniQR (Unique QR), which embeds device pose information as “physically unclonable” fingerprints into the QR code to prevent attackers from unauthorized use of it. We propose a pose matching method based on Perspective-n-Point (PnP) algorithm and the integrated sensors of the phone to bind the QR code to the payment device. This information binding effectively enhances the security of QR code payments. Additionally, we modify the QR code encoding using a segmented hybrid encoding method, allowing secure authentication information to be embedded by utilizing only a portion of the space originally designated for dummy data. We implemented UniQR on six different commercial phones and conducted experiments with 23 participants simulating both legitimate and illegitimate payment scenarios. The 97.46% success rate in legitimate user scans demonstrates the feasibility and robustness of UniQR.

Abstract The research addresses energy consumption, latency, and network reliability challenges in wireless sensor network communication, especially in military security applications. A multi-agent context-aware model employing the belief-desire-intention (BDI) reasoning mechanism is proposed. This model utilizes a semantic knowledge-based intelligent reasoning network to monitor suspicious activities within a prohibited zone, generating alerts. Additionally, a BDI intelligent multi-level data transmission routing algorithm is proposed to optimize energy consumption constraints and enhance energy-awareness among nodes. The energy optimization analysis involves the Energy Percent Dataset, showcasing the efficiency of four wireless sensor network techniques (E-FEERP, GTEB, HHO-UCRA, EEIMWSN) in maintaining high energy levels. E-FEERP consistently exhibits superior energy efficiency (93 to 98%), emphasizing its effectiveness. The Energy Consumption Dataset provides insights into the joule measurements of energy consumption for each technique, highlighting their diverse energy efficiency characteristics. Latency measurements are presented for four techniques within a fixed transmission range of 5000 m. E-FEERP demonstrates latency ranging from 3.0 to 4.0 s, while multi-hop latency values range from 2.7 to 2.9 s. These values provide valuable insights into the performance characteristics of each technique under specified conditions. The Packet Delivery Ratio (PDR) dataset reveals the consistent performance of the techniques in maintaining successful packet delivery within the specified transmission range. E-FEERP achieves PDR values between 89.5 and 92.3%, demonstrating its reliability. The Packet Received Data further illustrates the efficiency of each technique in receiving transmitted packets. Moreover the network lifetime results show E-FEERP consistently improving from 2550 s to round 925. GTEB and HHO-UCRA exhibit fluctuations around 3100 and 3600 s, indicating variable performance. In contrast, EEIMWSN consistently improves from round 1250 to 4500 s.

Machine learning (ML) has emerged as a promising avenue for enhancing the efficiency and intelligence of channel allocation processes. However, deploying ML algorithms on resource-constrained edge devices poses significant challenges due to their limited computational capabilities and storage capacities. In this study, we propose leveraging tiny ML (TinyML) techniques to address these challenges and optimize channel allocation within long range wide area network (LoRaWAN) deployments. Our key innovation lies in replacing traditional random channel allocation methods with TinyML-based approaches, wherein each edge device autonomously utilizes TinyML to select the most efficient channel prior to each uplink transmission. Furthermore, we conduct comprehensive comparisons between TinyML and conventional channel allocation techniques implemented on edge devices. Through extensive simulations, our results demonstrate that TinyML outperforms existing channel allocation mechanisms in terms of packet success ratio (PSR). Notably, when evaluating TinyML against conventional ML approaches in terms of model size and inference time, TinyML exhibits superior performance without compromising efficiency.

The use of Unmanned Aerial Vehicles (UAVs) as flying base stations to support various tasks, such as data collection, machine learning (ML) model training, and wireless communication in Internet of Things (IoT) networks, has garnered significant attention in recent years. Nonetheless, several challenges have arisen in this context, including data privacy concerns and limited onboard computational and communication resources. These challenges make the direct transmission of raw data to a central server for training impractical. Moreover, UAV-based networks are susceptible to fluctuating channel conditions and the heterogeneous computing capabilities of IoT devices. Therefore, enhancing the reliability and efficiency of such networks is imperative. In this paper, we introduce a novel framework known as the Asynchronous Federated Framework for IoT-enabled UAV (AF3N) networks. AF3N enables local model training with subsequent parameter transmission to the Mobile Edge Computing (MEC) server. To further enhance learning efficiency, we incorporate a device selection strategy into the AF3N framework. Additionally, we employ a multi-agent Asynchronous Advantage Actor-Critic (A3C)-based joint resource allocation algorithm aimed at reducing latency and energy utilization within the Internet of UAVs (IoUAV) network. Through extensive simulations we comprehensively examine the efficacy and performance of our proposed framework.

Handwriting recognition systems are a convenient and alternative way of writing in the air with fingers rather than typing on keyboards. However, existing recognition systems are limited by their low accuracy and the requirement to wear dedicated devices. To address these issues, we propose WiWrite, an accurate contactless handwriting recognition system that allows users to write in the air without wearing any wearable devices. Specifically, we employ a novelCSI division schemeto process the noisy raw WiFi channel state information (CSI), which stabilizes the CSI phase and reduces noise in CSI amplitude. To automatically retain low noise data for identification in the LOS scenario, we propose a self-paced dense convolutional network (SPDCN), which is a self-paced loss function based on a modified convolutional neural network coupled with a dense convolutional network. Furthermore, to achieve accurate handwriting recognition in the NLOS scenario, we combine ADOA and PCA algorithms to remove location-induced interference and extract action features. Comprehensive experiments show the merits of WiWrite, revealing that the recognition accuracy for the same-size input and different-size input are 93.6% and 89.0%, respectively. Moreover, WiWrite can achieve accurate recognition regardless of environment and target diversity in LOS and NLOS scenarios.

Benefitting from the discovery of wireless power transfer (WPT) technology, the wireless rechargeable sensor network (WRSN) has become a promising way for lifetime extension for wireless sensor networks. In practical WRSN scenarios, obstacles can be found almost everywhere. Most state-of-the-art researches believe that obstacles will always degrade signal strength, and omit the influence of obstacles for simplifying the computation process. However, overlooking the positive impacts of obstacles on signal propagation is inconsistent with the intrinsic features of electromagnetic waves. To address this issue, in this paper, we explore the wireless signal propagation process and provide a theoretical charging model to enhance the charging efficiency by leveraging obstacles. Through utilizing the concept of the Fresnel Zone model, we re-formalize the wireless charging model and discretize the charging area and charging time to determine the best charging locations as well as charging duration. We model the chargingEfficiencyMaximization withObstacles (EMO) problem as a submodular function maximization problem and propose a cost-efficient algorithm to solve it. Finally, test-bed experiments and extensive simulations are both conducted to verify that our schemes outperform baseline algorithms by$33.46\%$on average in charging efficiency improvement.

We propose the design, implementation, and evaluation of an instantaneous angular speed (IAS) measurement system, namely Wi-Rotate, using commercial-off-the-shelf (COTS) WiFi hardware. Wi-Rotate exploits the Channel State Information (CSI) of WiFi signals to extract the physical characteristics of the rotation object to achieve accurate contact-free IAS measurements. Wi-Rotate contains three main components: Wi-Fresnel model, Wi-Phase model, and a combination model. Wi-Fresnel model explores the signal amplitude variation features when the rotating object cuts the Fresnel zone boundary to track target rotation. Wi-Phase model leverages signal phase variation and formalizes the problem of determining IAS as a linear programming problem. The combination model combines the IAS values obtained by Wi-Fresnel and Wi-Phase and utilizes a clustering method to further improve measurement accuracy. Comprehensive experiments are conducted to demonstrate the advantages of Wi-Rotate in terms of accuracy, sensing range, and system latency. Wi-Rotate is able to achieve real-time rotation measurements at an accuracy higher than 99% when the target is within 2 meters. Even when the target is 3 meters away, Wi-Rotate can still achieve an accuracy of 94%, demonstrating the long-range tracking capability which is critical for industrial applications.

In the field of human-computer interaction (HCI), handwriting trajectory tracking and recognition have attracted significant attention due to their wide range of applications. However, many existing approaches rely on handheld devices and are highly susceptible to factors such as environmental conditions, location, and writing style. To overcome these limitations, we propose AirWrite, a novel contactless aerial system for handwriting trajectory tracking and recognition using mmWave technology. We introduce a signal clipping method based on the Doppler effect caused by user actions to accurately remove non-handwriting signals in the time domain. Additionally, we analyze power variations within the signal frequency interval to determine the handwriting frequency and employ a band-pass filter to eliminate dynamic environmental noise effectively. Through extensive experiments, we demonstrate that AirWrite can precisely track handwriting trajectories in noisy environments regardless of distance, angle, handwriting speed, character size, or in the presence of obstacles. Furthermore, we present an effective handwritten character recognition method for AirWrite that recognizes alphabets, numbers, and words. AirWrite can achieve an average accuracy of over 96% with only a 34 KB small dataset within 0.15 s for recognition.

Benefitting from the recent breakthrough of wireless power transfer technology, Wireless Rechargeable Sensor Networks (WRSNs) have become an important research topic. Most prior arts focus on system performance enhancement in the ideal environment that ignores the impact of obstacles. This contradicts the practical applications in which obstacles can be found almost anywhere and have dramatic impacts on energy transmission. In this paper, we concentrate on the problem of charging a practical WRSN in the presence of obstacles to maximize the charging utility under specific energy constraints. First, we propose a new theoretical charging model with obstacles based on the Fresnel diffraction model and conduct experiments to verify its effectiveness. Then, we propose a spatial discretization scheme to obtain a finite feasible charging position set for mobile charger (MC), which largely reduces computation overhead. Afterwards, we re-formalize charging utility maximization with energy constraints as a submodular function maximization problem and propose a cost-efficient algorithm with an approximation guarantee to solve it. In addition, we present a theoretical analysis and a relevant mathematical proof of our algorithm. Finally, we demonstrate that our scheme outperforms other competing algorithms by 20.5% on average in terms of charging utility through test-bed experiments and extensive simulations.

Wireless Rechargeable Sensor Networks (WRSNs) have become an important research issue as they can overcome the energy bottleneck problem of wireless sensor networks. However, inaccurate discretization methods and imprecise charging models yield a huge gap between theoretical results and practical applications, making it difficult for wide adoptions. In this paper, we focus on designing a precise charging method for maximizing charging utility when line-of-sight (LOS) and none-line-of-sight (NLOS) charging cases exist in complicated environments. First, we design discretization methods for charging area and charging orientation for precisely constructing the charging model. Then, we develop a novel electromagnetic wave reflection model to describe the signal propagation model in the presence of obstacles. We formalize the mobile charging problem into a submodular function maximization problem which can be solved by a proposed algorithm with an approximation guarantee. Finally, extensive experiments and simulations demonstrate that our schemes outperform comparison algorithms by 32.5% on average in charging utility in complicated environments.

WiFi-based human activity recognition (HAR) plays an essential role in various applications such as security surveillance, health monitoring, and smart home. Existing HAR methods, though yielding promising performance in indoor scenarios, highly depend on a massive labeled dataset for training which is extremely difficult to acquire in practical applications. In this paper, we present an automatic data labeling and HAR system, termed AutoDLAR. Taking a semi-supervised cross-modal learning framework with a hybrid loss function as the core, AutoDLAR transfers rich visual information to automatically label WiFi signals for WiFi-based HAR. Specifically, we devise a lightweight and multi-view WiFi sensing model with a parallel feature embedding method to accurately identify activities and accelerate recognition speed. Then, we exploit the video data to fine-tune a well-established visual HAR model, generating effective pseudo-labels for guiding the WiFi model’s training. We also build a synchronized Video-WiFi dataset with seven types of human activities under different scenarios to enable training and validating the semi-supervised HAR system. Extensive experiments on our collected activity dataset and the emotion recognition benchmark demonstrate that AutoDLAR attains an average accuracy of over 95.89% without manual labeling and only spends the inference time of 3.35 ms, outperforming the state-of-the-art (SOTA) methods.

Gait is regarded as a unique feature for identifying people, and gait recognition is the basis of various customized services of the IoT. Unlike traditional techniques for identifying people, the Wi-Fi-based technique is unconstrained by illumination conditions and such that it eliminates the need for dense, specialized sensors and wearable devices. Although deep learning-based sensing models are conducive to the development of Wi-Fi-based identification, the latter technique relies on a large amount of data and requires a long training time, where this limits the scope of its use for identifying people. In this study, we propose a Wi-Fi sensing model called Wave-CapNet for human identification. We use data processing to eliminate errors in the raw data so that the model can extract the characteristics in channel state information (CSI). We also design a dedicated adaptive wavelet neural network to extract representative features from Wi-Fi signals with only a few epochs of training and a small number of parameters. Experiments show that it can identify human gait with an average accuracy of 99%. Moreover, it can achieve an average accuracy of 95% by using only 10% of the data and fewer than five epochs and outperforms state-of-the-art (SOTA) methods.

Meta-learning methods have shown to be effective in dealing with cold-start recommendation. However, most previous methods rely on an ideal assumption that there exists a similar data distribution between source and target tasks, which are unsuitable for the scenario that only extremely limited number of new user or item interactions are available. In this paper, we propose to boost meta-learning cold-start recommendation with graph neural network (MeGNN). First, it utilizes the global neighborhood translation learning to obtain consistent potential interactions for all new user and item nodes, which can refine their representations. Second, it employs the local neighborhood translation learning to predict specific potential interactions for each node, thus guaranteeing the personalized requirement. In experiments, we combine MeGNN with two representative meta-learning models MeLU and TaNP. Extensive results on two widely-used datasets show the superiority of MeGNN in four different scenarios.

Underwater Optical Wireless Sensor Network (UOWSN) is a promising technology as it can achieve high-speed communication in underwater environment. However, affected by the uncertainty of complex underwater environment, the network topology of UOWSN is highly dynamic, making it difficult to quantify flexibility or further optimize the topological structure. In this paper, we propose a flexibility-based network topology evaluation model (FEM) for UOWSNs. Then, a reinforcement learning model, termed FEM-DRL, for optimizing the network topology based on FEM is developed, which enables UOWSN to maintain an optimal topology when working in harsh underwater environments. Simulation results demonstrate that the proposed method can significantly improve the network flexibility and reduces the time cost for constructing network topology by 41.8% compared with baseline algorithms. Test-bed experiments verify the applicability and effectiveness in practical applications for detecting emergent events.

Underwater Optical Wireless Sensor Networks (UOWSNs) are gaining an increasing demand in industrial and commercial applications as they can achieve high-speed communication. However, prior arts concentrate on promoting the performance of UOWSNs, while the reliability issue has not been fully addressed. In this paper, we propose a novel reliable data delivery scheme based on a cluster structure. First, we determine the orientation of each sensor for directional optical communication, which aims to establish reliable next-hop links among sensors. We formalize such an orientation problem into a submodular function maximization problem and propose a greedy method with an approximation ratio guarantee to solve it. Then, a cluster head designation scheme is developed to improve the data delivery success rate while minimizing the number of cluster heads. Finally, extensive simulations are conducted to demonstrate the effectiveness of the proposed scheme. The results reveal that compared with other algorithms, the proposed scheme can ensure a data delivery success rate of over 98.5 % while only keeping 45.3% fewer cluster heads. Furthermore, test-bed experiments are carried out to verify the applicability of the proposed scheme in practical applications.

As a novel solution for IoT applications, wireless rechargeable sensor networks (WRSNs) have achieved widespread deployment in recent years. Existing WRSN scheduling methods have focused extensively on maximizing the network charging utility in the fixed node case. However, when sensor nodes are deployed in dynamic environments (e.g., maritime environments) where sensors move randomly over time, existing approaches are likely to incur significant performance loss or even fail to execute normally. In this work, we focus on serving dynamic nodes whose locations vary randomly and formalize the dynamic WRSN charging utility maximization problem (termed MATA problem). Through discretizing candidate charging locations and modeling the dynamic charging process, we propose a near-optimal algorithm for maximizing charging utility. Moreover, we point out the long-short-term conflict of dynamic sensors that their location distributions in the short-term usually deviate from the long-term expectations. To tackle this issue, we further design an online learning algorithm based on the combinatorial multi-armed bandit (CMAB) model. It iteratively adjusts the charging strategy and adapts well to nodes’ short-term location deviations. Extensive experiments and simulations demonstrate that the proposed scheme can effectively charge dynamic sensors and achieve a higher charging utility compared to baseline algorithms in both long-term and short-term.

WiFi-based Human Activity Recognition (HAR) faces challenges in achieving widespread deployment due to its reliance on massive data and limited scalability. However, the emergence of Few-Shot Learning (FSL) provides opportunities to address this issue. In this paper, we propose a cross-modal meta-learning approach based on Model-Agnostic Meta-Learning (MAML) to enable few-shot WiFi-based HAR. The hypothesis is that models can learn "learning methods" from thousands of diverse image classification tasks and apply them to WiFi-based HAR. By solely leveraging public image and WiFi signal datasets, the proposed approach trains a model capable of recognizing previously unseen activities with only 5 samples per class, achieving an average accuracy of 88.5% over thousands of tests.

Underwater Optical Wireless Communication (UOWC) enables high-speed data transmission among Autonomous Underwater Vehicles (AUVs). However, due to cost and weight constraints, AUVs can only carry a limited number of directional optical transceivers. This implies that each AUV can communicate with only 1 to 2 neighbors simultaneously, complicating the establishment of an Underwater Optical Wireless Communication Network (UOWN). To address the networking problem with the degree constraint, we propose a topology generation method based on Hamiltonian paths. The topology achieves improved global connectivity at the cost of local optimality while satisfying the communication device limitations of AUVs. Preliminary results show that the generated topology can reduce the average communication overhead.

The ability to detect which wireless devices are belonging to the same person from Wi-Fi access point (AP) enables many potential Internet-of-Things (IoT) applications, including continuous authentication and user-oriented devices isolation. The existing cryptographic-based solutions are not suitable for IoT devices with limited power and computing capabilities. The development of electronics and chip technology makes it possible to deploy machine learning (ML) algorithms on APs. In this article, we propose an on-body device clustering (OBDC) scheme. First, the OBDC extracts the trajectory and gait patterns from wireless signals when the user is moving. Second, it utilizes a hierarchical clustering algorithm to measure the similarity of wireless signal patterns between devices. Finally, if the devices are clustered into the same cluster, they are considered to be carried by the same person. Our real-world experimental results show that the devices from about 90% of users can be clustered correctly, while maintaining the devices from only 0.7% of users may be clustered into the same cluster with others’ devices incorrectly.

Internet of Things (IoT) devices are widely being used in various smart applications and being equipped with cognitive radio (CR) capabilities for dynamic spectrum allocation. Our objectives in this work are to achieve higher data rates and minimize end-to-end routing delays in CR-enabled IoT communication in order to maximize throughput. We propose a reinforcement learning (RL)-based routing approach in the cognitive radio network (CRN)-based IoT environment. The idea is to add the channel selection decision capability to the network layer in order to minimize packet collisions as well as end-to-end delay (EED). We perform a comprehensive performance evaluation of the proposed RL-IoT routing mechanism by simulating the cognitive radio-enabled Internet of Things (CR-IoT) communication environment in the cognitive radio cognitive network (CRCN) simulator and comparing the network performance achieved by our proposed mechanism with that of the recent AODV-based routing mechanism for IoT (AODV-IoT), ELD-CRN, and SpEED-IoT routing approaches. Our evaluation results show that the RL-IoT model performs better than existing approaches in terms of average data rate, throughput, packet collision, and EED.

Zero-shot learning (ZSL) typically suffers from the domain shift issue since the projected feature embedding of unseen samples mismatch with the corresponding class semantic prototypes, making it very challenging to fine-tune an optimal visual-semantic mapping for the unseen domain. Some existing transductive ZSL methods solve this problem by introducing unlabeled samples of the unseen domain, in which the projected features of unseen samples are still not discriminative and tend to be distributed around prototypes of seen classes. Therefore, how to effectively align the projection features of samples in unseen classes with corresponding predefined class prototypes is crucial for promoting the generalization of ZSL models. In this paper, we propose a novel Iterative Class Prototype Calibration (ICPC) framework for transductive ZSL which consists of a pseudo-labeling stage and a model retraining stage to address the above key issue. First, in the labeling stage, we devise a Class Prototype Calibration (CPC) module to calibrate the predefined class prototypes of the unseen domain by estimating the real center of projected feature distribution, which achieves better matching of sample points and class prototypes. Next, in the retraining stage, we devise a Certain Samples Screening (CSS) module to select relatively certain unseen samples with high confidence and align them with predefined class prototypes in the embedding space. A progressive training strategy is adopted to select more certain samples and update the proposed model with augmented training data. Extensive experiments on AwA2, CUB, and SUN datasets demonstrate that the proposed scheme achieves new state-of-the-art in the conventional setting under both standard split (SS) and proposed split (PS).

Since millions of smart vehicles in Internet-of-Vehicles (IoV) produce and relay data to analyze road conditions, creating social networks of vehicles in IoV is an important factor for the future Intelligent Transportation System (ITS). Likewise, the IoV architecture has seen vertical fragmentation of approaches used to meet the needs of different work domains. Therefore, IoV in combination with social networking, called Social IoV (SIoV), was created to address these alleged problems. However, one of the challenges in SIoV is that the social relations between vehicles grow and deplete very fast due to the extremely dynamic and unstable nature of the IoV. Therefore, a clustering-based scheme for SIoV, which is efficient in terms of stability can overcome this problem. We propose SeAC: an SDN-enabled adaptive clustering technique for SIoV. SeAC uses a 3D modeling approach to construct logical clusters that are based on factors such as physical location, social tie, and interest similarity among vehicles. Therefore, SeAC improves the stability of clusters and the efficiency of the underlying SIoV architecture. Additionally, by minimizing the trade-off between social and physical distances, SeAC lowers communication and computation costs. We evaluate SeAC, and the simulation results show that for two different topologies, the adaptive approach using SeAC can produce better results in terms of a stable cluster formation.

As an emerging multi-submersible system, Human Occupied Vehicle (HOV) under a convoy of a set of Autonomous Underwater Vehicles (AUVs) is regarded as the future framework for underwater exploration. In this work, to improve the interoperability and communication efficiency of the multi-submersible formations, we treat the multi-submersible system as a paradigm of the underwater Internet of Vehicle (IoV) and show how to utilize the Software-Defined Networking (SDN) technique to optimize the system architecture. With the assistance of SDN, we consider the ocean current factors and propose an artificial flow potential field algorithm that combines the artificial potential field algorithm and the gradient descent algorithm, to plan the path for the multi-submersible system. In particular, to improve the safety and efficiency of path planning, we propose a dual leader-follower algorithm-based escort formation obstacle avoidance mechanism for dealing with all categories of obstacle avoidance situations. Simulation tests show that the proposed scheme performs better in data delivery among the multi-submersible system, at a lower energy cost. And it shows high stability and strong practicability in multi-submersible formation control and path planning, respectively.

Wireless rechargeable sensor networks (WRSNs) have become a hot research issue owing to the breakthrough of wireless power transfer (WPT) technology. Previous theoretical schemes are mostly designed for 2-D networks, and few of them are tailored for 3-D scenarios, making them not suitable for wide adoptions in practical applications. In this paper, we address the issue of how to serve a 3-D WRSN with an unmanned aerial vehicle (UAV). Our main concern is to maximize the charged energy for sensors supplied by the UAV, which has energy constraints. We respectively develop a spatial discretization scheme to construct a finite feasible set of charging spots for the UAV in a 3-D environment and a temporal discretization scheme to determine the appropriate charging duration for each charging spot. Then, we reduce the problem into a submodular maximization problem with routing constraints and present a cost-efficient algorithm (CEA) with a provable approximation ratio to solve it. Finally, test-bed experiments are conducted to show the feasibility of our schemes in practical scenarios. Extensive simulations are taken to verify the superior performance of our algorithm in charged energy and robustness. The charged energy of our scheme outperforms other competing methods by at least$18.2\%$.

Wireless Rechargeable Sensor Networks (WRSNs) with perpetual network lifetime have been used in many Internet of Things (IoT) applications, like oceanic monitoring and precision agriculture. Rechargeable sensors, together with an Unmanned Aerial Vehicle (UAV), are collaboratively employed for fulfilling periodic coverage missions. However, traditional coverage solutions are normally based on static deployment of sensors and not suitable for such novel coverage requirements. In this paper, we propose the concept of Period-Area Coverage (PAC) problem, which requires the data of the overall area must be collected/monitored periodically. To solve the PAC problem, we employ a UAV that simultaneously acts as a mobile charger and sensor. It is responsible for charging nearly exhausted sensors and sensing vacant regions to realize complete event monitoring. To maximize the energy efficiency of the UAV, we propose a heuristic hexagon-based scheduling algorithm (HSA) which can also balance energy consumption. Furthermore, we develop an emergent node charging scheduling method to prevent node exhaustion, and introduce a grid-based boustrophedon scheduling algorithm (GBSA) to reduce the complexity. Finally, we present a charging re-allocation mechanism to further enhance energy efficiency. Extensive simulations demonstrate that the proposed schemes can solve the PAC problem and enhance energy efficiency by at least 18.2% compared to prior arts. Test-bed experiments conducted both in agriculture and oceanic monitoring applications validate the applicability of the proposed scheme in practical scenarios.

Wireless rechargeable sensor networks have become a hot research issue as it can overcome the limited energy bottleneck of wireless sensor networks owing to the recent breakthrough of wireless power transfer technology. Though network lifetime is prolonged and sensor nodes can sustain immortally, the issue of network robustness is overlooked, yielding most theoretical work unsuitable for practical applications when confronting with unpredictable packet loss. In this paper, we address the network robustness issue by maximizing the charging utility in a risk-averse view. First, we build a risk-averse model based on the concept of CVaR (Conditional Value at Risk), which trades-off charging utility and risk aversion for quantifying robustness. Then, we propose a spatial discretization scheme to construct a charging route for mobile charger, which can reduce computational overhead. Afterwards, a path optimization scheme is designed to further improve the charging utility. We convert the original problem into the submodular function maximization problem and propose a method with a performance guarantee while maximizing the system robustness. Finally, testbed experiments and simulations are conducted, and the results demonstrate that our schemes outperform comparison algorithms by at least 22.4% in effective energy in the presence of risks to guarantee system robustness.

The ubiquitous and fine-grained features of WiFi signals make it promising for realizing contactless authentication. Existing methods, though yielding reasonably good performance in certain cases, are suffering from two major drawbacks: sensitivity to environmental dynamics and over-dependence on certain activities. Thus, the challenge of solving such issues is how to validate human identities under different environments, even with different activities. Toward this goal, in this article, we develop WiTL, a transfer learning–based contactless authentication system, which works by simultaneously detecting unique human features and removing the environment dynamics contained in the signal data under different environments. To correctly detect human features (i.e., human heights used in this article), we design a Height EStimation (HES) algorithm based on Angle of Arrival (AoA). Furthermore, a transfer learning technology combined with the Residual Network (ResNet) and the adversarial network is devised to extract activity features and learn environmental independent representations. Finally, experiments through multi-activities and under multi-scenes are conducted to validate the performance of WiTL. Compared with the state-of-the-art contactless authentication systems, WiTL achieves a great accuracy over 93% and 97% in multi-scenes and multi-activities identity recognition, respectively.

Wireless rechargeable sensor networks (WRSNs), which benefit from recent breakthroughs in Wireless Power Transfer (WPT) technology, emerge as very promising for network lifetime extension. Traditional methods concentrate on system performance improvement while little attention has been paid to security, making them vulnerable to novel attacks. In this paper, we develop a novel Charging Spoofing Attack (CSA), in which a mobile charger (MC) is charging a node intuitively. Nevertheless, it is launching an attack based on the nonlinear superposition principle of electromagnetic waves, causing the target node to be unable to receive any energy and finally exhausted in vain. First, we explain and model the nonlinear superposition effect through experiments, which points out the potential of launching such a novel attack. Second, we formalize the attacking problem as a charging uTility optImization problem with key noDe timE window constraints (TIDE). Then, we propose an approximation algorithm termed CSA to solve the TIDE problem with a bounded performance guarantee. Theoretical analyses are presented to exploit the feature of CSA. Finally, to demonstrate the outperformed features of our scheme, extensive simulations and test-bed experiments are conducted, revealing that CSA can exhaust at least 80% of key nodes without being detected.

Wireless Rechargeable Sensor Networks (WRSNs) have become an important research issue as it can overcome the energy bottleneck problem of wireless sensor networks. However, inaccurate discretization methods and imprecise charging models yield a huge gap between theoretical results and practical applications, making it difficult for wide adoptions. In this paper, we focus on designing a precise charging method for maximizing charging utility when line-of-sight (LOS) and none-line-of-sight (NLOS) charging cases exist in complicated environments. First, we design discretization methods for charging area and charging orientation for precisely constructing the charging model. Then, we develop a novel electromagnetic wave reflection model to describe the signal propagation model in the presence of obstacles. We formalize the mobile charging problem into a submodular function maximization problem which can be solved by a proposed algorithm with an approximation guarantee. Finally, extensive experiments and simulations demonstrate that our schemes outperform comparison algorithms by 31.45% on average in charging utility in complicated environments.

The discovery of wireless power transfer technology enables power transferred between transceivers in a wireless manner, thus generating the concept of wireless rechargeable sensor networks (WRSNs). Previous arts paid little attention to network security issues, making them prone to novel attacks. In this work, we focus on developing a denial of charge attack for WRSNs, which aims at corrupting network functionalities by manipulating the malicious mobile charger. We formalize the maximization of destructiveness problem (MAD) and propose a denial of charge attacking method, termed MDoC, with a performance guarantee to solve it. MDoC is composed of two attacking rounds, which first triggers sensors to send requests to create a request explosion phenomenon and then figures out the longest charging route to yield nodes starving to death as much as possible. Finally, extensive testbed experiments and simulations are conducted to verify the performance of MDoC. The results reveal that MDoC attack is able to exhaust at least 20% additional nodes without being noticed.

Subset selection problem for task scheduling with general cost constraints exists widely in IoT applications. Its objective is to select several profitable tasks to execute under routing and cost constraints such that the total profit is maximized. Most prior arts only focus on either online tasks or offline tasks, which are usually inapplicable in practical applications where online tasks and offline tasks co-exist. In this paper, we study the subset selection problem for HybrId Task Scheduling with general cost constraints (HITS), in which both online and offline tasks are scheduled to maximize the overall profit. We first divide the HITS problem into online and offline subproblems and propose two algorithms to solve them with bounded approximation ratios. Furthermore, we propose an approximation algorithm for the hybrid scenario where both online and offline tasks are considered. Extensive simulations show that our proposed algorithm outperforms baseline algorithms by 21.5% averagely in profit and also performs well in pure online/offline scenarios. We further demonstrate the feasibility of our algorithm through test-bed experiments in a realistic scene.

Human authentication is critical to protect personal and property security. Existing contactless authentication methods face some drawbacks, such as requiring large data size and low accuracy in cross-domain recognition, which hinders widespread popularization in practical applications. In this paper, we design and implement WiLCA, a WiFi-based lightweight contactless authentication system. First, we devise a Channel State Information (CSI) stream selection scheme to extract human movement features and reduce the sample size in the recognition process. Then, an AGO model is proposed, in which a Siamese Neural Network (SNN) framework with a cross-entropy module is used to guarantee accurate human authentication with limited data, and a lightweight GhostNet accelerates authentication with cheap operations. At last, extensive experiments are conducted to demonstrate the advantages of WiLCA, revealing that compared with state-of-the-art methods, WiLCA can reduce the data size by at least 2.5 x and achieve accurate authentication with an accuracy of over 98%.

The social Internet of Things (IoT) (SIoT) helps to enable an autonomous interaction between the two architectures that have already been established: social networks and the IoT. SIoT also integrates the concepts of social networking and IoT into collaborative edge computing (CEC), the so-called CEC-based SIoT architecture. In closer proximity, IoT devices self-organize into a CEC-based SIoT computing cluster and provide social device-to-device (S-D2D) services, such as computation offloading, service discovery, and content delivery. In the CEC-based SIoT, however, cooperation based on social connections leads to a problem calledsocial and spatial physical trade-off. This problem is also referred to as themismatchproblem, which arises because the spatial neighbors in the social layer cannot always be related. The spatial distance thus calls for additional multi-hop transmissions. This work presents a novel solution called 3-D-social identifier structure(3-D-SIS)model. The 3-D-SIS model is based on 3-D social space (3-D-SS) and considers social ties and physical connections (i.e., intra-neighbor) of the SIoT devices and utilizes a 3-D structure to evaluate that relationship. Moreover, it minimizes the end-to-end delay and communication cost to address the mismatch problem. To validate the performance of the(3-D-SIS)model, we use the real traces of social networks(INFOCOM06). The results show that the 3-D-SIS selects the best neighbor in S-D2D communication and improves performance in terms of end-to-end delay and throughput.

The marine Internet of things (MIoT) is the application of the Internet of things technology in the marine field. Nowadays, with the arrival of the era of big data, the MIoT architecture has been transformed from cloud computing architecture to edge computing architecture. However, due to the lack of trust among edge computing participants, new solutions with higher security need to be proposed. In the current solutions, some use blockchain technology to solve data security problems while some use federated learning technology to solve privacy problems, but these methods neither combine with the special environment of the ocean nor consider the security of task publishers. In this article, we propose a secure sharing method of MIoT data under an edge computing framework based on federated learning and blockchain technology. Combining its special distributed architecture with the MIoT edge computing architecture, federated learning ensures the privacy of nodes. The blockchain serves as a decentralized way, which stores federated learning workers to achieve nontampering and security. We propose a concept of quality and reputation as the metrics of selection for federated learning workers. Meanwhile, we design a quality proof mechanism [proof of quality (PoQ)] and apply it to the blockchain, making the edge nodes recorded in the blockchain more high-quality. In addition, a marine environment model is built in this article, and the analysis based on this model makes the method proposed in this article more applicable to the marine environment. The numerical results obtained from the simulation experiments clearly show that the proposed scheme can significantly improve the learning accuracy under the premise of ensuring the safety and reliability of the marine environment.

Vehicular edge computing (VEC) is a promising paradigm based on the Internet of vehicles to provide computing resources for end users and relieve heavy traffic burden for cellular networks. In this paper, we consider a VEC network with dynamic topologies, unstable connections and unpredictable movements. Vehicles inside can offload computation tasks to available neighboring VEC clusters formed by onboard resources, with the purpose of both minimizing system energy consumption and satisfying task latency constraints. For online task scheduling, existing researches either design heuristic algorithms or leverage machine learning, e.g., deep reinforcement learning (DRL). However, these algorithms are not efficient enough because of their low searching efficiency and slow convergence speeds for large-scale networks. Instead, we propose an imitation learning enabled online task scheduling algorithm with near-optimal performance from the initial stage. Specially, an expert can obtain the optimal scheduling policy by solving the formulated optimization problem with a few samples offline. For online learning, we train agent policies by following the expert’s demonstration with an acceptable performance gap in theory. Performance results show that our solution has a significant advantage with more than 50 percent improvement compared with the benchmark.

Persistent packet loss in the cloud-scale overlay network severely compromises tenant experiences. Cloud providers are keen to diagnose such problems efficiently. However, existing work is either designed for the physical network or insufficient to present the concrete reason of packet loss. We propose to record and analyze the on-site forwarding condition of packets during packet-level tracing. The cloud-scale overlay network presents great challenges to achieve this goal with its high network complexity, multi-tenant nature, and diversity of root causes. To address these challenges, we present VTrace, an automatic diagnostic system for persistent packet loss over the cloud-scale overlay network. Utilizing the “fast path-slow path” structure of virtual forwarding devices (VFDs), e.g., vSwitches, VTrace installs several “coloring-matching-logging” rules in VFDs to selectively track the target packets and inspect them in depth. The detailed forwarding situation at each hop is logged and then assembled to perform analysis with an efficient path reconstruction scheme. Experiments are conducted to demonstrate VTrace’s low overhead and quick response. Besides, based on the idea “coloring-matching-counting”, VTrace can be easily extended toVTrace-statsto identify the culprit device for transient packet loss. We share experiences of how VTrace andVTrace-statsefficiently work after deploying them in Alibaba Cloud for years.

As an epoch-making technology, wireless power transfer incredibly achieves energy transmission wirelessly, enabling reliable energy supplement for Wireless Rechargeable Sensor Networks (WRSNs). Existing methods mainly concentrate on performance improvement theoretically, neglecting the fact that most Commercial Off-The-Shelf (COTS) rechargeable sensors (e.g., WISP and Powercast) are not allowed to conduct sensing and energy harvesting tasks simultaneously, termedcharging exclusivity. Therefore, their schemes are not feasible for practical applications. In this paper, we focus on the charging exclusivity issue in stochastic events monitoring while improving network performance. In specific, we pay close attention to trading off charging and sensing tasks and formulate a combinatorial optimization problem with routing constraints. We introduce novel discretization techniques and investigate the routing problem to reformulate the original problem into maximization of a submodular function. With a slightly relaxed budget, the output of our proposed algorithm is better than$(1-1/e)/2$of the optimal solution to the original problem with a smaller charging radius$(1-\xi)D_{c}$. Through extensive simulations, numerical results show that in terms of charging utility, our algorithm outperforms baseline algorithms by 21.3% on average. Moreover, we conduct test-bed experiments to demonstrate the feasibility of our scheme in real scenarios.

The rapid popularization of wireless power transfer (WPT) technology promotes the wide adoption of wireless rechargeable sensor networks (WRSNs). Traditional methods only focus on how to optimize network performance, and most of them overlook the energy waste issue induced by WPT. In this paper, we explore the potentials of recycling wasted energy when using WPT by means of freeloading. Specifically, with a slight modification on hardware, we expand the functionality of the mobile chargers (MCs), enabling them to harvest and recycle the WPT-induced wasted energy in the air to serve more sensors, which promotes energy efficiency. We model the problem, termed MEFree, as maximizing network energy efficiency by utilizing a limited number of freeloading MCs and scheduling their freeloading behaviors. Through jointly scheduling freeloading and charging tasks, the proposed scheme is able to solve the problem with a (1 − 1/e)/2 approximation ratio with a slightly relaxed budget. Extensive simulations are conducted and corresponding numerical results show that our proposed scheme significantly improves network energy efficiency by at least 18.8% and outperforms baseline algorithms by 19.1% on average in various aspects. Our test-bed experiments further demonstrate the practicability of our scheme in actual scenes.

WiFi-based human activity recognition has been widely used in many fields such as health diagnosis, intrusion detection and smart home. Most existing recognition methods can achieve a satisfying accuracy only in one domain, but low accuracy occurs when models are trained in source domain but are used in target domain. Meanwhile, considering finetuning network directly is impossible or easy to overfit with limited labeled target data, transfer learning based methods with domain adaptive layers are proposed to solve above problems but just aligning marginal distribution, which may lose massive fine-grained features. Based on this, we present an end-to-end deep subdomain adaptive network based activities recognition (DSANAR) using Channel State Information (CSI) that aligns marginal and matches conditional distribution simultaneously for more fine-grained features in each category of relevant subdomains based on a local maximum mean discrepancy (LMMD). Besides, by using a joint cross-entropy and an adaptive loss as training loss, DSANAR outperforms other state-of-art methods on an autonomous dataset with average 95.6% cross-domain accuracy.

Wi-Fi-based indoor localization gained a lot of attention over recent years due to low cost and open access properties. However, existing schemes might not be applicable in the real environment if their robustness is low. This paper presents CLRS, a novel distributed Indoor Positioning System (IPS) with high robustness which uses Wi-Fi signals to divide the space twice based on Angle of Arrival (AoA) and Effective Channel State Information (ECSI). The proposed scheme trade the redundancy of Access Point (AP) quantity to improve the tolerance of data measurement error. We performed simulations as well as real-world experiments, in which simulation results proved that the theoretical average error is the least when the routers are placed vertically in our localization method while the real-world experiments proved the high accuracy and robustness of CLRS.

Millimeter-wave (mmWave) communication is a promising technology for future vehicular networks, where plenty of self-driving vehicles transmit a great amount of sensing data to the edge-cloud platform for real-time processing to ensure driving safety. While beam selection has been widely investigated in single mmWave base station (mmBS) scenario to maximize the throughput between the vehicle and the mmBS, it is still quite challenging to perform optimal beam selection in mmWave vehicular networks with multiple mmBSs. On the one hand, performing beam selection at a central controller with global information of the networks is infeasible due to the exponentially increased complexity. On the other hand, a distributed solution may suffer from the interference between overlapping beams among mmBSs which leads to severe throughput degradation. To fill this gap, in this paper, we propose a Multi-Agent Reinforcement Learning based cooperative Beam Selection (MARL-BS) algorithm for mmWave vehicular networks. Specifically, we model the beam selection problem as a multi-agent multi-armed bandit problem and then adopt Q-learning to learn how to coordinate the beam selection decisions. In the proposed approach, each mmBS acts as an agent and learns the Q-values of its own actions in conjunction with those of the other mmBSs. We further propose a modified combinatorial upper confidence bound (CUCB) algorithm to take advantage of exploring and exploiting all the candidate beams to avoid falling into local optimum. Finally, our simulations validate the proposed MARL-BS algorithm and confirm its higher performance compared with the other benchmark algorithms.

This paper presents the design, implementation, and evaluation of Shrimp, an underwater visible light communication (VLC) system. To address the unique issues in underwater environment such as water flow and scattered sunlight interference, we exploit the circularly polarized light (CPL) and double links for underwater VLC transmission. A coding scheme tailored for underwater communication based on double CPL design is developed. We prototype Shrimp on commercial-off-the-shelf (COTS) LEDs with fabricated printed circuit boards (PCBs). Extensive experiments conducted in an indoor water pool, a lake, and the sea demonstrate that Shrimp can combat against environmental interference and achieve robust communication in underwater environments. The communication distance can be up to 3 m in sea/lake water using a 3 W commodity LED, outperforming the VLC schemes designed for in-air communication.

The Internet-of-Things (IoT) devices, backed by resourceful fog computing, are capable of meeting the requirements of computationally-intensive tasks. However, many existing IoT applications are unable to perform well, due to different Quality-of-Service (QoS) requirements, while communicating with the fog server. Besides, constantly changing traffic demands of applications is another challenge. For example, the demand for real-time applications includes communicating over a path that is less prone to delay, and applications that offload computationally intensive tasks to the fog server need a reliable path that has a lower probability of link failure. This results in a tradeoff between conflicting objectives that are constantly evolving, i.e., minimizing end-to-end delay and maximizing the reliability of paths between IoT devices and the fog server. We propose a novel approach that takes advantage of machine learning (ML) and multiobjective optimization (MOO)-based techniques. The reliability of links is evaluated using an ML-based algorithm in an software-defined network (SDN)-enabled multihop scenario for the IoT-fog environment. By considering the two conflicting objectives, the MOO algorithm is used to find the Pareto-optimal paths. Our experimental evaluation considers two applications with different QoS requirements-a real-time application (App-1) using UDP sockets and a task offloading application (App-2) using TCP sockets. Our results show that: 1) the tradeoff between the two objectives can be optimized and 2) the SDN controller was able to make adaptive decision on-the-fly to choose the best path from the Pareto-optimal set. The App-1 communicating over the selected path finished its execution in 13% less time than communicating over the shortest path. The App-2 had 41% less packet loss using the selected path compared to using the shortest path.

The integration of software-defined networking (SDN) into legacy networks causes both operational and deployment issues. In this context, this article proposes a novel approach, called An Intelligent Solution for Improved Performance of Reliable and Time-sensitive Flows in hybrid SDN-based fog computing IoT systems (IHSF). The proposed IHSF approach has three solutions: 1) a novel algorithm to deploy SDN switches between legacy switches to improve network observability; 2) a ${K}$ -nearest neighbor regression algorithm to predict in real time the reliability of legacy links at the SDN controller based on historic data; this enables the SDN controller to make timely decisions, improving system performance; and 3) a reliable and time-sensitive deep deterministic policy gradient algorithm (RT-DDPG), which optimally computes forwarding paths in hybrid SDN-F for time-critical traffic flows generated by IoT applications. The simulation results show that our proposed IHSF solution has a better performance than the existing approach in terms of network observability time, number of disturbed flows, end-to-end delay, and packet delivery ratio.

As a more energy-efficient WPT technology, directional WPT is applied to supply energy for wireless rechargeable sensor networks (WRSNs). Conventional methods that ignore anisotropic energy receiving property of rechargeable sensors cause a waste of energy. To address this issue, in this paper, we focus on minimizing the charging delay with a directional charging scheme. At first, we introduce linear constraints to improve an energy transfer model, which is verified to be practical by experiments. Then, we concern a Minimal chArging Delay with Single charger (S-MAD) problem to promote efficiency, followed by an Optimal Direction Charge with Single charger (S-ODC) solution. Through discretizing charging power and angle, we bound the performance gap of the solution to the optimal one with$\text{a}^{^{^{^{}}}}\,\,\frac {1}{1-\epsilon ^{2}}$approximation ratio, where$\epsilon $is the error threshold of discretization. After that, we extend the original S-MAD problem into the large scale WRSN with multiple chargers (i.e., M-MAD) and solve it by proposing M-ODC (i.e., Optimal Direction Charge with Multiple chargers (M-ODC)). Theoretical analyses are presented to exploit the feature of the proposed schemes. Finally, we demonstrate that our methods outperform the baseline methods by an average of 34.2% through simulations and test-bed experiments.

By deploying edge servers on the network edge, mobile edge computing network strengthens the real‐time processing ability near the end devices and releases the huge load pressure of the core network. Considering the limited computing or storage resources on the edge server side, the workload allocation among edge servers for each Internet of Things (IoT) application affects the response time of the application’s requests. Hence, when the access devices of the edge server are deployed intensively, the workload allocation becomes a key factor affecting the quality of user experience (QoE). To solve this problem, this paper proposes an edge workload allocation scheme, which uses application prediction (AP) algorithm to minimize response delay. This problem has been proved to be a NP hard problem. First, in the application prediction model, long short‐term memory (LSTM) method is proposed to predict the tasks of future access devices. Second, based on the prediction results, the edge workload allocation is divided into two subproblems to solve, which are the task assignment subproblem and the resource allocation subproblem. Using historical execution data, we can solve the problem in linear time. The simulation results show that the proposed AP algorithm can effectively reduce the response delay of the device and the average completion time of the task sequence and approach the theoretical optimal allocation results.

Handwriting recognition system provides people a convenient and alternative way for writing in the air with fingers rather than typing keyboards. For people with blurred vision and patients with generalized hand neurological disease, writing in the air is particularly attracting due to the small input screen of smartphones and smartwatches. Existing recognition systems still face drawbacks such as requiring to wear dedicated devices, relatively low accuracy and infeasible for cross domain identification, which greatly limit the usability of these systems. To address these issues, we propose WiWrite, an accurate device-free handwriting recognition system which allows writing in the air without a need of attaching any device to the user. Specifically, we use Commercial Off-The-Shelf (COTS) WiFi hardware to achieve fine-grained finger tracking. We develop a CSI division scheme to process the noisy raw WiFi channel state information (CSI), which stabilizes the CSI phase and reduces the noise of the CSI amplitude. To automatically retain low noise data for identification, we propose a self-paced dense convolutional network (SPDCN), which consists of the self-paced loss function based on a modified convolutional neural network, together with a dense convolutional network. Comprehensive experiments are conducted to show the merits of WiWrite, revealing that, the recognition accuracies for the same-size input and different-size input are 93.6% and 89.0%, respectively. Moreover, WiWrite can achieve a one-fit-for-all recognition regardless of environment diversities.

As an epoch-making technology, wireless power transfer incredibly achieves energy transmission wirelessly, enabling reliable energy supplement for wireless rechargeable sensor networks (WRSNs). Existing methods mainly concentrate on performance improvement theoretically, neglecting the fact that most Commercial Off-The-Shelf (COTS) rechargeable sensors (e.g., WISP and Powercast) are not allowed to conduct sensing and energy harvesting tasks simultaneously, termed charging exclusivity. Therefore, their schemes are not feasible for practical applications. In this paper, we focus on the charging exclusivity issue in stochastic events monitoring while improving network performance. In specific, we pay close attention to trading off charging and sensing tasks and formulate a combinatorial optimization problem with routing constraints. We introduce novel discretization techniques and investigate the routing problem to reformulate the original problem into the maximization of a submodular function. With a slightly relaxed budget, the output of our proposed algorithm is better than (1 1/e)/2 of the optimal solution to the original problem with a -smaller charging radius (1 - ξ)Dc. Through extensive simulations, numerical results show that in terms of charging utility, our algorithm outperforms baseline algorithms by 21.3% on average. Moreover, we conduct test-bed experiments to demonstrate the feasibility of our scheme in real scenarios.

Recent breakthrough in wireless power transfer technology has enabled wireless sensor networks to operate virtually forever with the help of mobile chargers (MCs), thus generating the concept of wireless rechargeable sensor networks (WRSNs). However, existing studies mainly focus on developing charging tours with fixed network topology, most of which are not suitable for networks with dynamic topology, usually leading to massive packet/data loss. In this work, we explore the problem of charging scheduling for WRSNs with multiple MCs when confronting with dynamic topology. To minimize the energy cost to prolong the network lifetime, we convert the charging scheduling problem into a vehicle routing problem, which is proved to be NP-hard. Then we model the problem as a cooperative game taken among sensors and propose a cooperative game theoretical charging scheduling (CGTCS) algorithm to construct the optimal coalition structure. Then, we design an adaptive optimal coalition structure updating algorithm (AOCSU) to update the optimal coalition structure, which works well with network dynamics. We discuss the reasonability and feasibility to guarantee the cooperation among sensors through carefully designing the characteristic function and allocating cost based on Shapley value. Finally, test-bed experiments and simulations are conducted, revealing that CGTCS outperforms other related works in terms of expenditure ratio, total traveling cost, and charging time.

Benefitting from the recent breakthrough of wireless power transfer technology, Wireless Rechargeable Sensor Networks (WRSNs) have become an important research topic. Most prior arts focus on system performance enhancement in an ideal environment that ignores impacts of obstacles. This contradicts with practical applications in which obstacles can be found almost anywhere and have dramatic impacts on energy transmission. In this paper, we concentrate on the problem of charging a practical WRSN in the presence of obstacles to maximize the charging utility under specific energy constraints. First, we propose a new theoretical charging model with obstacles based on Fresnel diffraction model, and conduct experiments to verify its effectiveness. Then, we propose a spatial discretization scheme to obtain a finite feasible charging position set for MC, which largely reduces computation overhead. Afterwards, we reformalize charging utility maximization with energy constraints as a submodular function maximization problem and propose a cost-efficient algorithm with approximation ratio (e-1)/2e (1 - ε) to solve it. Lastly, we demonstrate that our scheme outperforms other algorithms by at least 14.8% in terms of charging utility through test-bed experiments and extensive simulations.

Wireless networks that can exchange any type of data are vulnerable to multiple intrusions and increase potential security risks, so the design of an Intrusion Detection and Prevention System (IDPS) that analyzes the packet features and detects different intruders (i.e., the types of attack) is necessary. Whereas, the existence of redundant and irrelevant features hinders the potential of IDPS. In this paper, we propose TL-IDPS, a Two-Level classification IDPS of wireless network based on optimized features. In the phase of intrusion detection, one-hot method, normalization and correlation estimation are used to mitigate the redundant features. Then, the fuzzy membership function with cuttlefish algorithm maps and consolidates the extracted features and selects optimal features. Based on the optimal features, Di-distance k-nearest neighbor (K-NN) as the first level classify the intruder or non-intruder. Further the type of intruder is identified by deep Q-network. From the result of detected intruders, the further arrival of those intruders is prevented. Experimental results conducted from multiple evaluation metrics using the UNSW-NB15 dataset prove that our proposed TL-IDPS is more effective than existing IDPS methods.

In the 21st century, the power quality and availability with customer demands to the society is the main challenging factor right now. Therefore, the gird monitoring system become a vital issue to monitor power grid system. The current smart grid system mainly focuses on smart metering system and improving the customer utility communication system. On customer management side, although those advancement provides an extra benefit, in spite of, the management of a grid system is one of the major dominating field in the era of Internet of Thing (IoT). From the field of industry and academia researches, Wireless sensor networks (WSNs) is getting to much popularity for monitoring power grid. Moreover, saving node energy enhance the lifespan of whole monitoring network. Due to lack of energy making policy, unnecessary activating all participate nodes consume node energy drastically, which is the main reason for shortening the lifetime of monitoring system. To solve this issue, maintenance technology provides the best opportunity to preserve node energy. This study investigates the issues that are associated with energy consumption using maintenance protocols in prospect of Rampal, Bangladesh power plant data. The modelling data has been collected through literature survey. Extensive simulation work has done for monitoring Rampal power using WSN. Finally, a comparative study of maintenance protocols were performed to maintain optimal network correction and thereafter extending the lifetime of monitoring network.

Persistent packet loss in the cloud-scale overlay network severely compromises tenant experiences. Cloud providers are keen to automatically and quickly determine the root cause of such problems. However, existing work is either designed for the physical network or insufficient to present the concrete reason of packet loss. In this paper, we propose to record and analyze the on-site forwarding condition of packets during packet-level tracing. The cloud-scale overlay network presents great challenges to achieve this goal with its high network complexity, multi-tenant nature, and diversity of root causes. To address these challenges, we present VTrace, an automatic diagnostic system for persistent packet loss over the cloud-scale overlay network. Utilizing the "fast path-slow path" structure of virtual forwarding devices (VFDs), e.g., vSwitches, VTrace installs several "coloring, matching and logging" rules in VFDs to selectively track the packets of interest and inspect them in depth. The detailed forwarding situation at each hop is logged and then assembled to perform analysis with an efficient path reconstruction scheme. Experiments are conducted to demonstrate VTrace's low overhead and quick responsiveness. We share experiences of how VTrace efficiently resolves persistent packet loss issues after deploying it in Alibaba Cloud for over 20 months.

The foundation of urban computing and smart technology is edge computing. Edge computing provides a new solution for large-scale computing and saves more energy while bringing a small amount of latency compared to local computing on mobile devices. To investigate the relationship between the cost of computing tasks and the consumption of time and energy, we propose a computation offloading scheme that achieves lower execution costs by cooperatively allocating computing resources by mobile devices and the edge server. For the mixed-integer nonlinear optimization problem of computing resource allocation and offloading strategy, we segment the problem and propose an iterative optimization algorithm to find the approximate optimal solution. The numerical results of the simulation experiment show that the algorithm can obtain a lower total cost than the baseline algorithm in most cases.

Wireless Rechargeable Sensor Network (WRSN) becomes a hot research issue in recent years owing to the breakthrough of wireless power transfer technology. Most prior arts concentrate on developing scheduling schemes in 2-D networks where mobile chargers are placed on the ground. However, few of them are suitable for 3-D scenarios, making it difficult or even impossible to popularize in practical applications. In this paper, we focus on the problem of charging a 3-D WRSN with an Unmanned Aerial Vehicle (UAV) to maximize charged energy within energy constraints. To deal with the problem, we propose a spatial discretization scheme to obtain a finite feasible charging spot set for UAV in 3-D environment and a temporal discretization scheme to determine charging duration for each charging spot. Then, we transform the problem into a submodular maximization problem with routing constraints, and present a cost-efficient approximation algorithm with a provable approximation ratio of e-1/4e(1-ε) to solve it. Lastly, extensive simulations and test-bed experiments show the superior performance of our algorithm.

Wireless rechargeable sensor networks (WRSNs), benefiting from recent breakthrough in wireless power transfer (WPT) technology, emerge as very promising for network lifetime extension. Traditional methods focus on scheduling algorithms and system optimization, and the issue of charging security/threat is ignored, causing it vulnerable to attacks. In this paper, we develop a novel attack for WRSN through Denial of Charge (DoC) aiming at maximizing destructiveness. At first, we form a generalized on-demand charging model, which provides fundamental basis for designing charging attacks. Then a request prediction method (RPM) is introduced for predicting the emergences of charging requests. Afterwards, a Collaborative DoC attacking algorithm (CoDoC) is developed, which tempers/modifies and generates fake charging requests, yielding normal nodes exhausted. Finally, to demonstrate the outperformed features of CoDoC, extensive simulations and test-bed experiments are conducted. The results show that, CoDoC outperforms in making sensor exhausted as well as causing missing events.

The discovery of Wireless Power Transfer (WPT) technologies makes charging more convenient and reliable. Among all the existing WPT technologies, directional WPT is more efficient and has been successfully applied to supply energy for wireless rechargeable sensor networks (WRSNs). However, the state-of-the-art methods ignore the anisotropic energy receiving property of rechargeable sensors, resulting in energy wastage. In order to address this issue, in this paper, we point out that the received energy of a sensor is not only relative to the distance, but also relative to the angle between the sensor and the charger's orientation in directional WPT. Towards this end, we derive a pragmatic energy transfer model verified by experiments. In particular, we focus on a Minimal chArging Delay (MAD) problem to reduce charging delays. To obtain the optimal solution, we formulate the problem as a linear programming problem. Moreover, we introduce a method of charging power discretization, which significantly reduces the search space and bounds the performance gap to the optimal one with a 1/1-ϵ2approximation ratio. Besides, a merging method is introduced for a more practical application scenario. Finally, we demonstrate that our methods outperform the Set Cover baseline method by an average of 34.2% through simulations and experiments.

Wireless Rechargeable Sensor Networks (WRSNs) with perpetual network lifetime have been used in many Internet of Things (IoT) applications, like smart city and precision agriculture. Rechargeable sensors together with Unmanned Aerial Vehicles (UAVs) are collaboratively employed for fulfilling periodic coverage tasks. However, traditional coverage solutions are normally based on static deployment of sensors and not suitable for such coverage requirements. In this paper, we propose a new concept of coverage problem named Period-Area Coverage (PAC) which requires data of the overall area must be collected periodically. We focus on maximizing the energy efficiency of UAVs and propose two heuristic scheduling schemes to balance energy cost. Moreover, we adopt adjustable sensing range to further promote efficiency and develop a charging re-allocation mechanism for UAVs. Test-bed experiments and extensive simulations demonstrate that the proposed schemes can enhance energy efficiency by 18.2% compared to prior arts.

Benefitting from the discovery of wireless power transfer (WPT) technology, the wireless rechargeable sensor network (WRSN) becomes a promising way for lifetime extension for wireless sensor networks. However, in practical applications, obstacles can be found almost everywhere throughout the WRSN system. Most prior arts believe that obstacles will always degrade signal strength, they omit such influences for computation simplicity, which contradicts to the instincts of signal propagation, yielding their methods unsuitable for realistic adoptions. In this paper, we explore the wireless signal propagation process and provide a theoretical charging model to enhance charging efficiency by leveraging obstacles. Through utilizing the concept of the Fresnel Zones (FZs), we re-formalize the wireless charging model and discretize charging power to determine the best charging spots as well as charging durations. We model such charging efficiency maximization with obstacles (EMO) problem as a submodular function maximization problem and propose a cost-efficient algorithm with approximation ratio (e-1)/ε (1 - ε) to solve it. Finally, test-bed experiments and simulations are conducted to verify that our schemes outperform comparison algorithms by at least 10% in charging efficiency improvement.

Acoustic source localization in sensor network is a challenging task because of severe constraints on cost, energy, and effective range of sensor devices. To overcome these limitations in existing solutions, this paper formally describes, designs, implements, and evaluates a Half Plane Intersection method to Sequence-Based Localization, i.e., HPI-SBL, in distributed smartphone networks. The localization space can be divided into distinct regions, and each region can be uniquely identified by the node sequence that represents the ranking of distances from the reference nodes to the region. The key idea behind HPI-SBL is to turn the localization problem into half-plane intersection by processing the node sequence. The proposed design is evaluated through extensive simulations and physical experiments in an indoor test-bed with 30 smartphone nodes. Evaluation results show that HPI-SBL can effectively locate the acoustic source with good robustness.

Passive acoustic localization is an important technique in a wide variety of monitoring applications, ranging from health care over biological survey to structural health monitoring of buildings. However, the method obtains the location of an unknown sound source with low-cost and simple still is lacking. In this paper, we implement a passive sound source location system based on commercial off-the-shelf (COTS) mobile devices, typically a smartphone, are organized as Wireless Sensor Networks (WSNs). We use the Precise Time Protocol (PTP) in WLAN to achieve the time synchronization of mobile devices with a coarse grain, and then uses moving variance and linear interpolation to get the Time of Arrival (TOA) of the sound source signal. We also design and implement a robust Sequence-Based localization algorithm based on Linear Programming, i.e. LPSBL, which transforms the TOA information of the sound source signal arriving at these devices to a nodes sequence and estimate the location of the sound source by the nodes sequence. After plenty of experiments were carried out, it is verified that our system can provide sufficiently reasonable positioning accuracy and good robustness in an indoor environment.

Benefited from recent breakthrough in wireless power transfer technology, the lifetime of wireless sensor networks (WSNs) can be prolonged significantly, generating the concept of wireless rechargeable sensor networks (WRSNs). While most recent works have been focusing on WRSNs with a single wireless charging vehicle (WCV), we investigate the issue of multiple WCVs' on-line collaborative charging schedules in this work. In our design, termed mTS, the network area is divided into subdomains for designated WCVs. Each WCV schedules its charging scheduling path by responding to the interdependency of temporal and spatial correlations from different charging requests. Higher priorities are given to sensor requests with a mixture of closer charging deadlines and closer distances. We further analyze the system performance with an M/M/n/mTS queueing model. Our further study through simulations revealed that our scheme excels in successful charging rate, sensor survival rate, and other related performance metrics. Our field experiments further confirmed these results and showed some further interesting findings on different charging hardware and methods.

We consider the load-balancing design for forwarding incoming flows to access points (APs) in high-density wireless networks with both channel fading and flow-level dynamics, where each incoming flow has a certain amount of service demand and leaves the system once its service request is complete. The efficient load-balancing design is strongly needed for supporting high-quality wireless connections in high-density areas. In this work, we propose a Joint Load-Balancing and Scheduling (JLBS) Algorithm that always forwards the incoming flows to the AP with the smallest workload in the presence of flow-level dynamics and each AP always serves the flow with the best channel quality. Our analysis reveals that our proposed JLBS Algorithm not only achieves maximum system throughput, but also minimizes the total system workload in the heavy-traffic regime. Moreover, we observe from both our theoretical and simulation results that the mean total workload performance under the proposed JLBS Algorithm does not degrade as the number of APs increases, which is strongly desirable in high-density wireless networks.

Thunder localization provides an important solution to lightning location systems. This paper designs a smartphone- based thunder localization system, ThunderLoc. The key idea is to turn the localization problem into search problem in Hamming space by collecting the dual-microphone data of smartphones via crowdsensing mechanism. We utilized the TDOA of dual- microphone integrated in smartphone. After the quantization with a bit for the TDOA measurement from the smartphone nodes, thunder localization is performed by minimizing the Hamming distance between the measured binary sequence and the binary vectors in a database. Evaluation results demonstrate that ThunderLoc can effectively localize the virtual thunder with good robustness.

The ubiquitous and fine-grained features of WiFi signals make it promising for achieving device-free authentication. However, traditional methods suffer from drawbacks such as sensitivity to environmental dynamics, low accuracy, long delay, etc. In this paper, we introduce how to validate human identity using the ubiquitous WiFi signals. We develop WiAU, a device-free authentication system which only utilizes a Commodity Off-The-Shelf (COTS) router and a laptop. We describe the constitutions of WiAU and how it works in detail. Through collecting channel state information (CSI) profiles, WiAU automatically segments coherent activities and walking gait using an automatic segment algorithm (ASA). Then, a ResNet algorithm with two dedicated loss functions is designed to validate legal users and recognize illegal ones. Finally, experiments are conducted from different scenes to highlight the superiorities of WiAU in terms of high accuracy, short delay and robustness, revealing that WiAU has an accuracy of over 98% in recognizing human identity and human activities respectively.

Recent advances in Cross-Technology Communication (CTC) enable the coexistence and collaboration among heterogeneous wireless devices operating in the same ISM band (e.g., Wi-Fi, ZigBee, and Bluetooth in 2.4 GHz). However, state-of-the-art CTC schemes are vulnerable to spoofing attacks since there is no practice authentication mechanism yet. This paper proposes a scheme to enable the spoofing attack detection for CTC in heterogeneous wireless networks by using physical layer information. First, we propose a model to detect ZigBee packets and measure the corresponding Received Signal Strength (RSS) on Wi-Fi devices. Then, we design a collaborative mechanism between Wi-Fi and ZigBee devices to detect the spoofing attack. Finally, we implement and evaluate our methods through experiments on commercial off-the- shelf (COTS) Wi-Fi and ZigBee devices. Our results show that it is possible to measure the RSS of ZigBee packets on Wi-Fi device and detect spoofing attack with both a high detection rate and a low false positive rate in heterogeneous wireless networks.

As an emerging communication platform, opportunistic Cloud of Things (CoT) is promising for clients to exchange messages through opportunistic contacts in cloud computing-enabled Internet of Things. Recently, numerous socially aware schemes have been put forward, leveraging users’ social attributes and contact history to predict future contacts with the purpose of improving message forwarding efficiency and network throughput. However, individual privacy is generally overlooked in the prediction process and transmission stage of opportunistic CoT. In this paper, we construct a privacy-preserving message forwarding framework for opportunistic CoT to guarantee individual privacy and improve transmission efficiency. We first set up a two-layer architecture of a cloud server to improve communication efficiency for terminal clients. By integrating a security-based mobility prediction algorithm with a routing decision process, our scheme can effectively protect individual privacy. We integrate an attribute-based cryptographic algorithm with a message delivery process to enable our scheme to resist attacks, such as Sybil attack, drop for profit, and data tampered attack. Compared with some existing solutions, our scheme improves network security significantly at the cost of slightly increased communication overhead.

Fog computing has been merged with Internet of Vehicle (IoV) systems to provide computational resources for end users, by which low latency can be guaranteed. In this paper, we put forward a feasible solution that enables offloading for real-time traffic management in fog-based IoV systems, aiming to minimize the average response time for events reported by vehicles. First, we construct a distributed city-wide traffic management system, in which vehicles close to road side units can be utilized as fog nodes. Then, we model parked and moving vehicle-based fog nodes according to a queueing theory, and draw the conclusion that moving vehicle-based fog nodes can be modeled as an $M/M/1$ queue. An approximate approach is developed to solve the offloading optimization problem by decomposing it into two subproblems and scheduling traffic flows among different fog nodes. Performance analyses based on a real-world taxi-trajectory datasets are conducted to illustrate the superiority of our method.

The joint of WiFi‐based and vision‐based human activity recognition has attracted increasing attention in the human‐computer interaction, smart home, and security monitoring fields. We propose HuAc, the combination of WiFi‐based and Kinect‐based activity recognition system, to sense human activity in an indoor environment with occlusion, weak light, and different perspectives. We first construct a WiFi‐based activity recognition dataset named WiAR to provide a benchmark for WiFi‐based activity recognition. Then, we design a mechanism of subcarrier selection according to the sensitivity of subcarriers to human activities. Moreover, we optimize the spatial relationship of adjacent skeleton joints and draw out a corresponding relationship between CSI and skeleton‐based activity recognition. Finally, we explore the fusion information of CSI and crowdsourced skeleton joints to achieve the robustness of human activity recognition. We implemented HuAc using commercial WiFi devices and evaluated it in three kinds of scenarios. Our results show that HuAc achieves an average accuracy of greater than 93% using WiAR dataset.

Opportunistic Network (OppNet) is an emerging communication paradigm, by which nodes inside forward messages through personal contact opportunities. Recently, numerous studies have focused on predicting nodes meeting to promote routing efficiency and reduce transmission delay. However, individual privacy would likely be revealed to strangers or attackers during the execution of prediction. In this paper, we construct a privacy-reserved network framework for message forwarding to guarantee both efficient communication and individual privacy in OppNets, including a security-based prediction method and an attribute-based cryptosystem. Simulation results demonstrate that, our algorithm outperforms TRSS on average delivery ratio, generally by 15% for dropping probability and data tempered probability.

WiFi-based Human activity recognition has attracted attention in the human-computer interaction, smart homes, and security monitoring fields. We first construct a WiFi-based activity dataset, namely WiAR, to provide a benchmark for existing works. Then, we leverage the moving variance of CSI to detect the start and end of activity. Moreover, we present K-means-based subcarrier selection mechanism according to subcarrier's sensitivity on human activity to enhance the robustness of human activity recognition. Finally, we leverage several classification algorithms to evaluate the performance of WiAR. Our results show that WiAR satisfies primary demand and achieves an average accuracy of greater than 93% using SVM, 80% using kNN, Random forest, and Decision tree.

A method for detecting human motion in complex scenarios based on Channel State Information (CSI) is presented. First, the sensitivity of CSI phase information to human motion is explored, especially to the strenuous motion. Through a large number of experiments, the influence of human motion on CSI phase is found out, and the characteristics of signal changes are extracted. The One-class Support Vector Machine (OSVM) in machine learning is used to detect the multi-target strenuous human motion. Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) conditions are studied in the case of obstacles appearing in the wireless link when human motion occurred. LOS and NLOS are identified by the skewness of the channel impulse response (CIR) distribution. After identifying the LOS condition and NLOS condition in the current environment, the human motion is analyzed and detected, which further improves the accuracy of human motion detection from 70% to 91%.

Recently, the low-power, low-cost and reliable ZigBee technology have received significant research attention with the increasing popularity of applications such as smart home system, patient monitor in hospitals. Coexisiting with the WiFi devices on the crowded unlicensed ISM band, such as hotspots and mobile phones, ZigBee will receive significant performance influence. The throughput and Packet Reception Rate (PRR) of ZigBee will decrease with the increasing number of WiFi devices. Because of the incompatible PHY/MAC layer, ZigBee devices will suffer near 50% packet loss when coexisting with WiFi devices. The existing mechanism such as CSMA/CA is surprisingly inadequate for solving this problem. As the WiFi traffic typically appears bursty, the channel will be free for more than 60% of the time. In this paper, we propose a new metric called channel idle indicator (CII) which can quantify the channel quality. Based on the CII and logistic regression, we build a channel idle state prediction model which can help ZigBee devices to use the white space of WiFi channel efficiently. Particularly, our approach is light-weight, which can be easily implemented on the ability-limited commercial off-the-shelf (COTS) ZigBee devices. Extensive experiments show that our scheme can achieve over 91% of the PRR, which is near 40% higher than the B-MAC protocol. When the WiFi throughput is 3Mbps, our scheme achieves near 1.5x throughput over B-MAC. Carrying on further, our scheme consumes less energy via degrading packet loss rate in the energy consumption part.

Currently, various Medium Access Control (MAC) protocols have been proposed for underwater Wireless sensor networks. Unlike terrestrial networks, underwater networks utilize acoustic waves, which have comparatively lower loss and longer range in underwater environments. However, the use of acoustic waves incurs long propagation delays that typically lead to low throughput especially in protocols that require receiver feedback such as multimedia stream delivery and the energy cost of transmission is much higher than receptions. Thus, collision and retransmission should be reduced in practice in order to reduce energy cost and improve throughput. Based on these motivations, we propose a novel MAC protocol called NCDC-MAC. NCDC-MAC leverages network coding and duty cycle, the combination of which is seldom explored, to solve these challenges. Heterogeneous wireless networks and node roles are considered while designing our algorithms. Meanwhile, fairness including schedule and service time assignment is supported in our approach. Extensive simulations show that our approach can achieve significantly better performance.

This paper presents DiVA, a novel hybrid range-free and range-based acoustic source localization scheme that uses an ad-hoc network of microphone sensor nodes to produce an accurate estimate of the source's location in the presence of various real-world challenges. DiVA uses range-free pairwise comparisons of sound detection timestamps between local Voronoi neighbors to identify the node closest to the acoustic source, which then estimates the source's location using a constrained range-based method. Through simulation and experimental evaluations, DiVA is shown to be accurate and highly robust, making it practical for real-world applications.

WiFi signals based applications, such as indoor localization, human activity recognition and trace tracking, have been increasing attention in the past decade. WiFi signals are sensitive to the change of indoor environment, so the above mentioned applications encounter challenges which contain dynamic indoor environment, device diversity and motion influence. We mainly survey techniques of motion detecting and estimate changes of WiFi signals reflected by motion behaviors. Moreover, we analyze the impact of motion behaviors on applications based on WiFi signals, and deeply research developmental trend of WiFi signals. Finally, we pay closed attention to opportunities and future research directions in this new and large open area.

Many small businesses and public areas offer free Wi-Fi access, but may wish to restrict network access only to their customers or patrons inside the physical property. Unfortunately, due to the nature of wireless networks, this is difficult to accomplish. We develop and implement CLAC, a Crowdsourced Location aware Access Control scheme using physical layer information to address this challenge. It crowdsources both channel state information (CSI) and received signal strength (RSS) of already validated users to classify future users. We propose and use two CSI metrics in CLAC: CSI Cross-Antenna Stability Metric and CSI Cross-Frame Stability Metric, which summarize well the spatial and temporal CSI characteristics respectively. CLAC is evaluated in an office and a classroom. Evaluation results show that CLAC performs well in both environments, allowing most valid users inside the area to access the network, while the chance that invalid users outside the boundary may access the network is small.

For a given set of communication links whose senders transmit at a fixed power level, it is a hot problem to select a maximum set of links that can be transmitted simultaneously, which is known to be NP-hard. The existing algorithm only apply to the condition of omnidirectional transmission. This paper addresses the problem in a plane wireless network where the nodes use directional antennas under physical interference model. We develop a directional interference model applicable to such networks, and first propose the approximation algorithm to solve scheduling problem under this model. We proved the correctness of the algorithm by mathematical analysis. We have also proved the great advantages of using directional antenna by extensive simulations.

Acoustic source localization in sensor network is a challenging task because of severe constraints on cost, energy, and effective range of sensor devices. To overcome limitations in existing solutions, this paper formally describes, designs, implements, and evaluates a Hamming Distance-based Method for Acoustic Source Localization, i.e., HammingLoc, in distributed smartphone networks. The key idea behind HammingLoc is to turn the localization problem into search problem in Hamming space. Time Differences of Arrival (TDOAs) of signals pertaining the same smartphone are estimated through the simple Generalized Cross-Correlation method. After the quantization with a bit for the TDOA measurement from the smartphone nodes, source localization is performed by minimizing the Hamming distance between the measured binary sequence and the binary vectors in a database. The proposed design is evaluated through theoretical analysis, extensive simulations, and physical experiments (an indoor test-bed with 30 smartphone nodes). Evaluation results demonstrate that HammingLoc can effectively localize the acoustic source with good robustness

Restricted by finite battery energy, traditional wireless sensor networks (WSNs) can only maintain for a limited period of time, resulting in serious performance bottleneck in long-term deployment of WSN. Fortunately, the advancement in the wireless energy transfer technology provides a potential to free WSNs from limited energy supply and remain perpetual operational. A mobile charger called wireless charging vehicle (WCV) is employed to periodically charge each sensor node and keep its energy level above the minimum threshold. Aiming at maximizing the ratio of the WCV's vocation time over the cycle time as well as guaranteeing the perpetual operation of networks, we proposes a feasible and optimal solution to this issue within the context of a real-time large-scale deployed WSN.

With the popularity of network utility, network pricing is becoming an emerging research hotspot. This paper studies the revenue maximization problem based on network utilization optimizing approach. In order to reduce the implemental complexity, we present a SG (Super Group) method whose time complexity is O(1) to regroup users. A precision control variable ε is introduced to control the group size. We then design a distribution related network resource reschedule scheme called RR (Resource Reschedule scheme) to optimize the network utilization. Two important factors, resource threshold and monitoring timeslot, which will affect the dynamic reschedule process were proposed and tested in the simulation experiment. After combining SG method and RR scheme, we make the pricing process faster and more practical. We also prove that our new approach can achieve the same or even more revenue gaining than original usage-based pricing scheme.

This paper proposes a new acoustic source localization scheme, called Probabilistic Cutting Method (PCM), with randomly deployed smartphones which equipped with known location and direction dual-microphones. Instead of using the value of TDOA (Time Difference Of Arrival), we just use binary information (0/1) and probability to convert the localization problem into plane cutting issues. We can easily come up with the Basic Cutting Method (BCM), but it may appear empty set when error (location error, angle error or error anchors) occurs. PCM can effectively avoid the problem along with lower positioning error. When comparing PCM with TDOA and BCM in different aspects, simulation evaluation results indicate that PCM algorithm achieves highly robustness and accuracy.

In recent years, Wi-Fi has seen extraordinary growth; however, due to the cost, performance and security issues, many Wi-Fi hotspot owners would like to restrict the network access only to individuals inside the physical property. Unfortunately, due to the nature of wireless, this is difficult to accomplish, especially with the off-the-shelf omni-antenna devices. In this work, we develop and implement CLaWa, a Crowdsourced Location Aware Wi-Fi Access Control scheme to address this challenge. Our system is based on observations of differing characteristics of physical layer information across physical boundaries such as walls and corners. CLaWa crowdsources both channel state information (CSI) and received signal strength (RSS) of already validated users to classify future users. We have also selected an appropriate machine learning algorithm for CLaWa. Evaluation results show that CLaWa can identify the boundary around a given area precisely, thus granting network access only to users inside the area while not validating users outside the boundary. Compared to indoor localization schemes, CLaWa is a lightweight solution which does not require expensive localization operations.

This paper presents DiVA, a new acoustic source localization scheme that uses an ad-hoc network of microphone sensor nodes to produce an accurate estimate of the source's location. DiVA uses pairwise comparisons of sound detection timestamps between local Voronoi neighbors to identify the node closest to the acoustic source and then estimates the source's location. The scheme improves on the state of the art by effectively dealing with anchor nodes' position error, time stamp measurement error and time synchronization error in real world conditions. Through simulation and experimental evaluations, DiVA is shown to be more robust than existing solutions under different error conditions.

In recent years, the problem of spectrum mobility in Cognitive Radio (CR) Networks has been widely investigated. In order to fully utilize spectrum resources, many spectrum handoff techniques based on game theory have been proposed, but most studies only concern how to achieve better payoffs for users, without paying much attention to the Quality of Service (QoS). Thus, we propose a new channel switching model based on game theory, using a prioritized approach to meet the diverse needs of users, such as bandwidth, delay, and jitter. Once the Nash equilibrium is achieved, our model will provide different QoSes by setting different priorities to different users. We also propose two acceleration methods to reach the Nash equilibrium more quickly. Finally, we evaluate the performance of the proposed schemes using real channel availability measurements. Experiments results show that our model can provide differentiated services and our algorithm is guaranteed to reach an approximate Nash equilibrium within polynomial time.

Indoor localization based on WiFi signal strength fingerprinting techniques have been attracting many research efforts in past decades. Many localization algorithms have been proposed in order to achieve higher localization accuracy. In this paper, we investigate Bayes learning algorithms and some common-used machine learning algorithms. We identify a general problem of Zero Probability (ZP) which may cause significant decrease of accuracy. In order to solve this problem, we propose an Improved Naive Bayes Simple learning algorithm, namely INBS, based on our data set characteristic. INBS is applicable even though Zero Probability problem occurs. We design experiments based on off-the-shelf WiFi devices, mobile phones and well-known machine learning tool Weka. Our experiments are conducted on a floor covering 560m2in a campus building and a laboratory covering 78m2. Experiment results show that INBS outperforms traditional Naive Bayes and k-Nearest Neighbors (k-NN) algorithms and two common-used machine learning algorithms in terms of accuracy.

Unlike terrestrial networks that mainly rely on radio waves for communications, underwater networks utilize acoustic waves, which have comparatively lower loss and longer range in underwater environments. However, acoustic waves incurs long propagation delays that typically lead to low throughput especially in protocols that require receiver feedback such as multimedia stream delivery. In addition, energy cost of transmission underwater is much higher than reception (almost 125:1 [1]). Thus, collision and retransmission should be reduced in order to reduce energy cost and improve throughput Receiver-based protocols, like RIPT and COS-TS, can significantly reduce collision and retransmission. But they are time and energy consuming because nodes are controlled to turn into receiver mode by control packets or a timer regardless of load. In this paper, we propose an underwater practical MAC protocol, called UPMAC. The main objective of UPMAC is to adapt to the network load conditions by providing two modes (high and low load modes) and switching between them based on different offered load. Turn-around time overhead is reduced and it is less vulnerable to control packet corruption, since we reduce the use of control packets by the technique of piggyback. UPMAC provides a low data collision rate in both one-hop and multi-hop situation because we use Receiver-based approach in high load mode. Extensive simulations show that our approach can achieve significantly better performance in both general and Sea Swarm (tree) topologies.

ABSTRACT Wireless sensor networks (WSNs) which is proposed in the late 1990s have received unprecedented attention, because of their exciting potential applications in military, industrial, and civilian areas (e.g., environmental and habitat monitoring). Although WSNs have become more and more prospective in human life with the development of hardware and communication technologies, there are some natural limitations of WSNs (e.g., network connectivity, network lifetime) due to the static network style in WSNs. Moreover, more and more application scenarios require the sensors in WSNs to be mobile rather than static so as to make traditional applications in WSNs become smarter and enable some new applications. All this induce the mobile wireless sensor networks (MWSNs) which can greatly promote the development and application of WSNs. However, to the best of our knowledge, there is not a comprehensive survey about the communication and data management issues in MWSNs. In this paper,focusing on researching the communication issues and data management issues in MWSNs, we discuss different research methods regarding communication and data management in MWSNs and propose some further open research areas in MWSNs.Copyright © 2011 John Wiley & Sons, Ltd.

This paper focuses on studying the sensor nodes deployment towards accurate data fusion for ship movement controlling. Furthermore, this study provides a node deployment layout with better measurement accuracy, which is surprisedly different from the layout that we originally predicted.

With the development of Internet of Things (IoT), bridging wireless sensor networks (WSNs) and other networks has become important. We divide bridging solutions into two categories: hardware solutions and middleware solutions. Hardware solutions have both low power short distance wireless interfaces and other types of transmission interfaces, e.g. GPRS, 3G/4G, via hardware implementations. This kind of solutions is more stable and more applicable for deployed sensor networks. In middleware solutions, the whole system processes appropriate protocol conversion. This kind of solutions is more independent of hardware, making it easier to be reused in different networks. This paper briefly presents key points of each solution and evaluates advantages and disadvantages of them in terms of different criteria. Eventually, we derive the most appropriate situation for each solution from our comparisons and discussions.

The design and analysis of routing algorithm is an important issue in Wireless Sensor Networks (WSNs). Most traditional geographical routing algorithms cannot achieve good performance in duty-cycled networks. In this paper, we propose a k-connected overlapping clustering approach with energy awareness, namely k-OCHE, for routing in WSNs. The basic idea of this approach is to select a cluster head by energy availability (EA) status. The k-OCHE scheme adopts a sleep scheduling strategy of CKN, where neighbors will remain awake to keep it k-connected, so that it can balance energy distributions well. Compared with traditional routing algorithms, the proposed k-OCHE approach obtains a balanced load distribution and consequently a longer network lifetime.

SUMMARY Wireless converge‐cast networks (WCNs), such as data collection‐based wireless sensor networks, exhibit certain phenomena called funneling effect, where the region close to the sink node is heavily overloaded. In this paper, we identify that the funneling effect occurs not only close to the sink but also within the network region where nodes have collision and induce heavy traffic to relay; we name it hot‐spot funneling effect. This paper aims to improve the throughput and fairness of WCNs by mitigating the micro funneling effect. We propose a new mechanism, the backoff differentiation for contention resolution (BDCR), which is targeted to a system‐wide high throughput on the basis of the contention resolution mechanism. To achieve high spatial reuses, BDCR divides the network into several regions and does backoff differentiation within each region. Within each backoff differentiation region, the backoff window range is adjusted according to the traffic rate, and at the same time, the backoff values are set with the awareness of the traffic intensity level. All regions share the same algorithm, which uses Kelly's rate control theory and method to allow each sensor to locally adjust its backoff value. One of the key advantages of BDCR is that it is extremely easy to implement. With extensive simulations and testbed experiments, BDCR is proved to achieve much higher throughput over the traditional carrier sense multiple access and some recent media access control protocols in literature, particularly when the network suffers intensive congestions. Copyright © 2012 John Wiley & Sons, Ltd.

Searching for available parking spots in congested areas has arouse widespread and close concerns from academia and industry in recent decades. Plenty of related work has been done to design multiple parking systems, but few of them has taken fairness into account. In this paper, we propose a smart parking scheme towards fairness. The scheme is characterized by allowing parking lots to select prospective vehicles, in order to avoid the phenomenon called “multiple cars chasing single spot”. Moreover, the proposed scheme manages reservation period of each parking spot, therefore, time wasted on cruising for vacant parking space greatly reduces, and the smart parking system is guaranteed to be more equitable. Performance analysis via extensive simulations demonstrates its efficiency and practicality.

In this paper1, we present the architecture, design, and simulation of an intelligent system for Temperature Monitoring used in metropolitan heating. The system consists of several TelosB-compatible motes, a Nokia uSDCard, and a smart phone. We use TelosB Motes to collect temperature data, and to transport the data to the smart phone. The uSDCard, as the middle layer, connects the smart phone with the ZigBee compatible devices. The smart phone, as the terminal, processes data and manages the TelosB Motes. We use the smart phone as the final terminal because it has a rich set of user interfaces and has access to various kinds of networks, allowing our system to be extended more easily and more user-friendly. In real scenes, our system can reduce the temperature reading fluctuation, and ultimately save the energy consumption for heating companies, providing a better living environment for indoor users.

Location information of nodes is the basis for many applications in wireless sensor networks (WSNs). However, most previous localization methods make the unrealistic assumptions: (i) all nodes in WSN are always awake and (ii) the radio range of nodes is an ideal circle. This overlooks the common scenario that sensor nodes are duty-cycled in order to save energy and the radio range of nodes is irregular. In this paper we revisit the Distance-Vector-based (DV-based) positioning algorithms, particularly, Hop-Count-Ratio based Localization (HCRL) algorithm and investigate the following problems: (i) how is the relationship between the number of sleeping neighbor sensor nodes and the localization accuracy and (ii) how is the relationship between the degree of irregularity (DOI, which is a parameter of radio range irregularity) and the localization accuracy. We conduct a large number of experiments in WSNs' simulator NetTopo, and find that the parameters: the number of waking nodes, DOI, anchor node density and localization error, are interactional, i.e., for a given deployed static WSN, there is an optimal number of waking nodes and an optimal anchor node density, which can minimize network energy consumption without losing much of the localization accuracy. Furthermore, waking up more sensor nodes cannot always help to increase the localization accuracy, which actually is different from our intuitive thinking: more waking nodes can help to increase the localization accuracy of DV-based localization algorithms at all time.

Network lifetime is one of the most critical issues for wireless sensor networks (WSNs) since most sensors are equipped with non-rechargeable batteries with limited energy. To prolong the lifetime of a WSN, one common approach is to dynamically schedule sensors' active/sleep cycles (i.e., duty cycles) with sleep scheduling algorithm. In this paper, we propose a new sleep scheduling algorithm, named EC-CKN (Energy Consumed uniformly-Connected K-Neighborhood) algorithm, to prolong the network lifetime. The algorithm EC-CKN, which takes the nodes' residual energy information as the parameter to decide whether a node to be active or sleep, not only can achieve the k-connected neighborhoods problem, but also can assure the k awake neighbor nodes have more residual energy than other neighbor nodes at the current epoch. Based on the algorithm EC-CKN, we can obtain the state transition probability at the n'th epoch, and upper bound and lower bound of the network lifetime by Markov chain and Markov decision chain.

In this poster, we present the architecture, design, and the preliminary simulation results of a system for Metropolitan Heating, via intelligent temperature monitoring. The system consists of several Crossbow TelosB-compatible motes, a Nokia uSDCard, and a smart phone. The motes are used to collect indoor temperature data, which is further relayed to the smart phone's uSD interface. The uSDCard connects the phone with IEEE 802.15.4/ZigBee compatible devices. We use the smart phone as the sink terminal because it has a rich set of user interfaces and can access to different kinds of networks, such as GPRS and 3G. In real scenes, our system can reduce the reading fluctuation, and ultimately save the energy consumed for heating company, achieving a better living condition in the houses.

In Wireless Sensor Networks (WSNs) a multitude of location-dependent applications have been proposed recently, which is very intriguing for researchers to discover and design more accurate and cost-effective localization algorithms. In an isotropic networks, the Euclidean distance between a pair of nodes may not correlate closely with the hop count between them because the corresponding shortest path may have to curve around intermediate holes, resulting in poor distance estimation. And without the help of a large number of uniformly deployed seed nodes, those schemes fail in an isotropic WSNs. To address this issue and improve the accuracy of localization, we propose the Removing Heavily Curved Path (RHCP) scheme in this paper. RHCP takes advantage of selecting the paths which are not heavily affected by the holes to recalculate the location of each unknown node. Through simulation, the results reveal that RHCP performs better than original DV-Hop in an isotropic networks with different shape of holes. In addition, through iterations of RHCP, the results get improved for different anchor node densities.

User profiles backup and migration among social networking providers have become more urgent after the conflict between Tencent, the largest Instant Messaging(IM) service provider in China, and Qihu360, the largest antivirus company in China. So far, exchanging contact list among emails has been wildly used. Mainstream email service providers commonly make use of Comma Separated Value (CSV) files to export/import contact lists. Meanwhile, XML format is popular to transfer information among different platforms due to its rich hierarchical data structures. By using CSV files and XML files, we propose a new method to do relationship migration among social networking providers, which consists of three stages: 1) information retrieval from original social networking provider, 2) information storage and re-processing, and 3) relationship migration and recovery in the target provider. In order to evaluate our solution, we have designed and implemented a real experiment to test the migration from RenRen (the largest social network in China) to my Elgg (our test bed server).

Range-Free algorithms, appealing to people for their cost-efficiency, suffer from the precision problem. Some methods try to combine received signal strength indication (RSSI) with range-free localization algorithms to improve the accuracy, but RSSI is sensitive to the radio irregularity. Based on the well known RIM model, we present a new method of RSSI calibration, namely MRIRC, to mitigate the impact of radio irregularity. MRIRC divides nodes within a continuous angle into groups with the same level of RSSI deviation. By doing this, given an irregular deviation input, MRIRC can get a maximum angle (worst case), which guarantees that the nodes in the same group are in the same level of radio irregularity, thereby improving the accuracy of the distance estimations. We conduct simulations for large-scale sensor networks, and the results show that MRIRC achieves superior performance over the other two typical Range-Free algorithms.

In this paper, we focus on achieving better energy conservation for geographic routing algorithms in duty-cycled WSNs when there is a mobile sink. We simplify the problem as a topology coverage one, and propose a multi-metric geographic algorithm (MMGR) which uses multi-metric candidates (MMCs) for geographic routing. The analysis and extensive simulation results show that MMGR can achieve better energy conservations than McTPGF, while retaining good performance of end-to-end delay and hop counts.

With the development of real-time business, the original traditional WMN has been difficult to satisfy the needs of the real-time business. This article is in support of this delay sensitive type under the background of real-time application business in WMN. This improved mechanism is proposing an pre-scanning algorithm focusing on the higher delay in WMN link layer scanning which based on traditional WMN link layer switching mechanism.

Based on a fundamental understanding of a high-quality routing protocol called Better Approach To Mobile Ad-hoc Networking or BATMAN, we provide an experimental study of its performance considering a representative set of meaningful measures with a real Wireless Mesh Network (WMN) test bed deploying in our campus. Analysis and experiments results show that BATMAN is not only an efficient and stable routing protocol, but also can satisfy the requirements of multimedia communication in mesh networks.

To suit the needs of data collection, routing protocols in WSN are normally required to form a collection tree where data flows from the source nodes to the sink nodes. These protocols, such as CTP and Multihop LQI, generally target at reducing the packet delivery cost without considering load balancing issues. We argue that load balancing is crucial for WSNs because load imbalance may cause certain nodes, which we call hot spots, to run out of energy faster than expected. The load imbalance may lead to holes and prominently degrade the performance of the network. In this paper, we propose BCTP (Balanced Collection Tree Protocol), which enhances CTP by enabling the network to migrate the load of the node under heavy traffic. BCTP uses the average transmission rate as the metric to measure a node's long term traffic load. Once a node is found heavily loaded, BCTP adopts a stochastic routing strategy to balance the load. BCTP is evaluated by test bed experiments with 9 Telosb motes. Experiment result shows that BCTP can reduce the load of the hot spot by up to 61.9% in a densely deployed network.

Focusing on achieving better geographic routing performance of the two-phase geographic greedy forwarding (TPGF) in duty-cycled wireless sensor networks (WSNs) when there is a mobile sink, this paper proposes a geographic distance based connected-k neighborhood (GCKN) algorithm. The algorithm analysis and simulation results show that GCKN can obtain shorter length of the transmission paths explored by TPGF in duty-cycled mobile sink WSNs, compared with the original connected-k neighborhood (CKN).

One of the most frequently concerned metrics for evaluating the performance of MAC protocol is throughput. In this paper, we propose a novel Markov Chain based TDMA MAC protocol which is called Predictive-TDMA (P-TDMA) to achieve high performance of throughput for Mesh Networks. In order to give a more effective time slot assignment strategy rather than even assignment, we use Markov Chain to predict the possibility of an end-user device's future working condition, whether it's more likely to be idle or busy. Then based on the ratio of all end-user devices' possibilities of working condition, we assign a proper number of time slots to each end-user device to maximize throughput. Through simulation, the results show that under the scenario that the amount of data is randomly requested by each end-user device, throughput of our design is around 10% better than even assignment strategy. And under the scenario that some end-user devices request much more amount of data than others, which happens more frequently in realistic situations, throughput of our design can be 200% better than even assignment strategy.

Visualization tools make it easier for users to observe the status of the wireless sensor networks (WSNs). So far, developers of WSNs have created various visualization tools under certain project/research backgrounds. These tools, however, are limited in certain application scenarios, since the underlying packet formats are hard coded into the programs. For this purpose, we present NetViewer, a universal visualization tool for all the applications, and we provide many useful data services to fulfill different needs of various fields, such as Replay lets researchers and developers debug their WSNs easily, Server gives user a method to disseminate data through Internet. To achieve better compatibility, NetViewer allows the user to set the application-related packet format through an XML file. Based on the packet format defined, users can also extract the desired fields from the packets. Furthermore, NetViewer provides a rich set of interfaces as well as a well defined API for future improvements.

Wireless sensor networks (WSNs) have been widely used in many applications. The highly correct location information of sensor nodes is crucial for these applications. Nowadays there are mainly two types of localization algorithms: Range-based localization algorithms and Range-free localization algorithms. The drawback of former type is strict requirements on the hardware configuration. The latter type is cost-effective alternative approach. However, this approach can achieve high accuracy only in some ideal scenarios, and some methods even require a lot of complex calculations. In this paper, we put forward a novel PAV-based Calibration localization method (PAVC) locating the unknown nodes. The Pool Adjacent Violators (PAV) algorithm can be used to calibrate the average distance per hop of hop-based positioning algorithm. PAVC improves the localization accuracy compared with previous hop-based algorithms, which is demonstrated by the simulation and testbed results.

This paper aims at mitigating the so-called Funneling Effect for S-MAC, particularly by improving the throughput and fairness of S-MAC. Wireless sensor networks (WSNs) exhibit some phenomenon named Funneling Effect resulting from the accumulation of disproportionate large number of packets in the regions close to the sink. The collision and congestion due to the Funneling Effect strongly weaken the vitality and robustness of WSNs. As for S-MAC which achieves great energy efficiency, the mitigation of funneling effect seems more significant and urgent. In this paper, targeted to alleviate the funneling effect for S-MAC, we propose a SMAC-based proportional fairness backoff scheme (SPFB). Based on the schedule and contention scheme in S-MAC, SPFB employs Kelly's shadow price theory to achieve the proportional fairness as well as optimizes the back off mechanism to improve the throughput. The contention window range is dynamically adjusted according to the load of individual node. With extensive simulations, we can show that SPFB can achieve much higher throughput than traditional S-MAC, especially when the network is heavy loaded. SPFB can also gain good energy efficiency.

This paper focuses on studying the impacts of a duty-cycle based CKN sleep scheduling algorithm for our previous designed TPGF geographical multipath routing algorithm in wireless sensor networks (WSNs). It reveals the fact that waking up more sensor nodes cannot always help to improve the exploration results of TPGF in a duty-cycle based WSN. Furthermore, this study provides the meaningful direction for improving the application-requirement based QoS of stream data transmission in duty-cycle based wireless multimedia sensor networks.

Localization problem in wireless sensor networks (WSNs) has been widely studied recently. However, most previous work simply assume that all the nodes stay awake during the localization phase. This assumption clearly overlooks the common scenario that sensor nodes are usually duty-cycled in order to save energy. In this paper we propose a kind of novel DV (distance vector)-based localization algorithm which performs pretty good in duty-cycled network. In order to get a good localization accuracy, the DV-based positioning algorithms need to keep a critical minimum average neighborhood size (CMANS) for every sensor node. However, in the time-varying connectivity (TVC) (this phenomenon results from duty-cycling) network, it is difficult to keep CMANS for every node all the time. We can use CKN sleep scheduling algorithm to tackle this problem. CKN sleep scheduling algorithm can save energy while keeping certain CMANS. We further propose a novel localization algorithm: Spline Function Localization (SFL) algorithm which guarantees high accuracy even under small neighborhood size. Finally, we estimate the performance of our algorithm and compare with several classical DV-based localization algorithms (DVHOP and HCRL (Hop-Count-Ratio based Localization)) in simulation. Experimental results confirm that our algorithm has much higher accuracy under duty-cycled network.

This paper introduces an intelligent remote monitor system based on ZigBee sensor networks for IDC (Internet Data Center) room. The system consists of two parts: three types of ZigBee modules and the server module. The three types of ZigBee modules contain first-cluster node, second-cluster node and sensor end-node. The system can control remote IDC room based on the connection of the ZigBee protocols and traditional Internet networks. The data collected by the sensor end-nodes in the IDC room are transmitted through ZigBee networks to the server that can communicate with the remote monitor PC with Internet connection. The functions the system implements include data collection, analysis, management, storage, automatic alarm, display and control. By monitoring the IDC room unmanned, the proposed system reduces the energy consumption and the number of management staff, at the same time, provides reliable and robust monitoring, then control the equipments in real-time. Consequently, it ensures the safety and stable operation for the IDC room.

This paper aims at improving the throughput of the wireless sensor networks (WSNs), particularly to overcome the so-called funneling effect for WSNs with converge-cast patterns. Due to the disproportionate larger number of packets accumulated in the sensors that are closer to the sink, there is a need to decrease the collisions and increase the throughput around the sink area as well as the nodes that experience a heavy pass-through traffic. In this paper, we proposed a new scheme, namely PFB (Proportional Fairness Backoff), which provides additional scheduling opportunities to nodes closer to the sink. The new scheme employs Kelly's shadow price theory to achieve the proportional fairness, which takes advantage of the tree topology that is the de facto standard in today's WSNs. In PFB, the size of backoff window is dynamically adjusted with respect to the height of nodes belong in the tree. With close-form analysis and extensive simulations, we show that PFB can achieve up to 100% throughput increase over the widely used CSMA when the network is highly loaded.