Full fine-tuning of large vision-language models (VLMs) for cross-modal retrieval is computationally expensive and prone to overfitting. Adapter-based parameter-efficient transfer learning offers a practical alternative, but existing designs often suffer from structural redundancy and the loss of fine-grained visual details critical for accurate matching. These limitations are particularly detrimental for retrieval, which demands fine-grained perceptual discrimination beyond semantic alignment. To address these challenges, we propose B-HFA, a parameter-efficient framework for vision-language retrieval. B-HFA introduces a Block-wise Shared Adapter (B-Adapter) to reduce redundancy through structured parameter sharing, and a Hierarchical Feature Aggregation (HFA) module that dynamically integrates intermediate visual features guided by textual semantics. This design enables efficient adaptation while preserving visual fidelity essential for retrieval. Extensive experiments on multiple retrieval benchmarks demonstrate that B-HFA achieves competitive performance with only 0.13% of trainable parameters. Moreover, its competitive results on visual question answering suggest the generality of the proposed framework beyond retrieval tasks.

High-quality data are essential for machine learning and data-driven research, yet data scarcity and privacy concerns remain major obstacles in many domains. Generative models have recently emerged as a promising approach to synthesize data that follow the same statistical distribution as real datasets. However, generative models are vulnerable to membership inference attacks, which threaten data confidentiality by exploiting model outputs to infer whether specific samples were used in training. Existing defense strategies struggle to simultaneously preserve data utility and provide robust privacy protection. To address this challenge, we propose our PPGM-GAN, a Privacy-Preserving GAN for synthetic data against membership inference attack to balance both data utility and data privacy. PPGM-GAN balances privacy and utility through a privacy-utility tradeoff function that quantifies and optimizes both aspects under different adversarial knowledge. To enhance data utility, we incorporate conditional generation and key-attribute screening to ensure sufficient representation of infrequent attribute values. Additionally, differential privacy is employed during training to prevent overfitting and reduce privacy leakage. Experimental results demonstrate that PPGM-GAN outperforms state-of-the-art privacy-preserving generative models, producing high-utility synthetic data under the same privacy constraints.

Wireless rechargeable sensor networks (WRSNs) have overcome the energy limitation bottleneck through wireless power transfer (WPT) technology. Traditional research has primarily focused on enhancing charging efficiency, while the critical issue of location privacy security arising from wireless charging has received scant attention. Additionally, sensors are vulnerable to detection and harm by malicious attackers, posing a significant threat to network integrity. In this paper, we propose two attack schemes, termed Least Squares Method (LSM) attack model and Centroid Method (CM) attack model for compromising sensor location privacy by exploiting charging power information and mobile charger behaviors. To counter such threats, we develop a scheme aimed at maximizing the node location privacy protection capabilities of the network. We propose a theoretical analysis to exploit the features of the proposed scheme. Finally, extensive test-bed experiments and simulations have been conducted to validate the effectiveness of our algorithms. The results demonstrate that our algorithms can protect at least 78% of the nodes without significantly compromising their survival rate.

Artificial Intelligence Generated Content (AIGC) models typically have hundreds of billions of parameters, and developers experience prohibitively long pulling time from a central model registry to their local environments. Peer-to-peer (P2P)-enabled model distribution that pulls models from local peers within a cluster instead of the central model registry is emerging as a promising technique to reduce model pulling time. Nonetheless, such model distribution systems have to handle bursty concurrent pulling tasks. This may occupy the network bandwidth of some peers for a long time, thereby making the peers unable to respond to further pulling tasks. We thus aim to design a lightweight and scalable model distribution system to balance the network resource usage of peers, by proposing learning-driven algorithms to accurately predict network status between peers and implementing the design in real production environments. Specifically, we first propose a lightweight network measurement mechanism that combines active delay probing and passive bandwidth inference with low resource overhead. We also propose a learning-driven task scheduling algorithm based on a structural graph representation with a varied-multi-hop attention mechanism, to predict bursty patterns of concurrent pulling tasks. We then design an asynchronous model training and inference method to enable seamless incremental learning based on the dynamic network status data. We finally implement our system design and the learning-driven algorithm in a Cloud Native Computing Foundation (CNCF) projectDragonflythat has already been publicly released since its version$v2.1.0$. Real experiments in the Ant Group’s production environment show that our system reduces the total completion time by at least 10% and increases the average bandwidth utilization of peers by 20%, compared with mainstream systems and algorithms.

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.

The Internet of Things (IoT) offers vast potential to enhance the quality of life, but the excessive visual data generated during environmental monitoring presents significant challenges. Existing visual data minimization methods struggle with real-time data reduction, often applying uniform minimization ratios to compress already generated data, which leads to high computational overhead and distortion. To address these limitations, this paper introduces REDA, a real-time event-driven approach for minimizing visual data generation. REDA employs an event estimation method that integrates motion and multi-scale object detection to reduce false alarms, missed detections, and computational costs. Additionally, it introduces an Optimal-IoU loss function to handle gradient challenges and applies contextual optical flow and filtering techniques to minimize data loss and distortion. Theoretical analysis and experimental results demonstrate that REDA achieves superior real-time data minimization and efficiency compared to existing state-of-the-art solutions.

Current privacy-preservation data publishing technologies primarily focus on anonymizing datasets, often overlooking the inherent privacy degrees embedded within the data. This oversight makes it challenging to balance privacy and utility effectively. To address this issue, we introduce the Privacy Evaluation and Validation scheme to Measure the original Privacy Degree (PEVMPD), anchored in a novel$\eta$-inference model. PEVMPD operates in two phases: the identification of risk elements and the evaluation of privacy degrees. In the first phase, attributes are appraised using entropy and KL divergence to pinpoint sensitive attributes. Concurrently, the maximum entropy principle is employed to identify critical quasi-identifiers. The second phase involves applying these risk elements within our$\eta$-inference model to locate data vulnerable to privacy breaches and to quantify the corresponding privacy degree. This methodology enables data owners to make informed decisions about achieving an optimal privacy-utility tradeoff during data anonymization. Experimental results on real datasets validate the effectiveness of PEVMPD in enhancing privacy measures.

Underwater Optical Wireless Sensor Networks (UOWSNs) play important roles in resource exploration and maritime rescue. However, they face significant challenges in real-time data transmission due to the limited propagation range of optical signals (typically 10-100 m), frequent link disconnections caused by node mobility, and the extended distances to onshore servers. Traditional cloud computing solutions, designed for stable terrestrial networks with stationary edge servers and continuous connectivity, experience high latency (3-15 s) in UOWSNs, rendering them unsuitable for real-time applications in underwater environments. To address this issue, we propose a cloud-edge-end architecture tailored for UOWSNs, which can not only combat unique underwater environmental interference on link connection and topological changes but also guarantee robust and real-time communication. We develop a dynamic link-stability-based task offloading path selection (DLS-TOPS) algorithm for maximizing network resource profits. Afterward, we propose an online primal-dual task offloading (OPD-TO) algorithm for minimizing task completion time. Simulation results indicate that the proposed method significantly improves the real-time performance and resource profits of the network, reducing the total task completion time by more than 50% compared to baseline algorithms. We implemented a UOWSN with a cloud-edge-end architecture using commercial off-the-shelf and verified the applicability and effectiveness of the proposed scheme in emergency detection through testbed experiments.

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%.

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.

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.

RF sensing has attracted a tremendous amount of attention and achieved promising progress in applications such as human gesture recognition and vital sign monitoring. This paper delves into sensing the moisture level of fabrics---an important metric for smart clothing, wound care, and textile manufacturing. We present TMSense, an innovative contact-free fabric moisture measurement system that leverages UWB signals for sensing. We introduce a set of signal processing methods to tackle the challenge of weak fabric reflections that can be easily overwhelmed by noise interference. Additionally, we adopt a model-driven approach to get rid of reliance on extensive datasets. By exploiting the changes in the dielectric properties induced by moisture in textile fabrics, we establish a theoretical model that bridges the characteristics of the RF signal with the moisture content. Based on this model, we successfully eliminate interfering factors such as target-device distance and target attributes through delicate signal processing and parameter calibration. Comprehensive experiments conducted under various conditions, including different materials, sample forms, and parameter settings, demonstrate an impressively low median error of 1.4% on textile moisture measurements, outperforming commodity moisture sensors on the market.

Relational data usually contain multiple Sensitive Attributes (SAs) and Quasi-Identifiers (QIs). Privacy leakage may occur if they are published directly. Therefore, many privacy models have been proposed. However, one of the most challenging issues is the association between attributes, which can cause both identity disclosure and attribute disclosure. Furthermore, these models always prioritize privacy over utility, so a rigorous (but often unnecessary) setting of privacy parameters could cause poor utility or even useless data. In this paper we propose a scheme called MSAAC that addresses both issues. To balance data privacy and utility, MSAAC adopts a utility-aware ($\alpha ,\beta$) privacy model. To guide data publishers to set$\alpha$and$\beta$reasonably, MSAAC has built-in measures on privacy gain and utility loss, and quantitatively trades privacy for utility and vice versa. Our second contribution is quantifying the association ofSA-SAusing lift degree and the association ofQI-SAusing a chi-square value. Based on them, MSAAC applies suppression and permutation techniques to properly anonymize them. Through both theoretical and experimental results, we show MSAAC can achieve better privacy while retaining higher utility than state-of-the-art solutions.

Vessel target recognition and localization are typically modeled using underwater acoustic signals, which contain a large amount of vessel operating characteristics and condition information. However, extracting operating characteristics from single signals faces heavy noise and non-stationarity challenges. Meanwhile, feature extraction using multimodal data faces the challenges of conflicting gradients between different modalities and ensuring the separability of vessel targets. To tackle these issues, we propose an audio-visual-textual features fusion method to recognize and localize vessel targets through Symbiotic Transformer (Symb-Trans) and Multi-View Regression (MVR) models. Specifically, the audio-visual samples are first preprocessed into paired time series and then projected into a unified optimization landscape via a Heterogeneous Batch Normalization (HetBN) layer to avoid gradient conflicts. Second, the Symb-Trans trains parallel encoders with cross-modal attention and embeds audio-visual representations for vessel target recognition. Finally, the MVR method learns neighboring target properties of a graph model from different perspectives, audio-visual-textual representations, to infer the collector-target distance. Since no off-the-shell multimodal dataset is available for vessel targets, we combine multiple public datasets, consisting of acoustic, and/or visual, and/or textural data, to obtain multimodal materials for model training and validation. Through experimental results and theoretical analysis, we show that Symb-Trans and MVR models outperform unimodal and generic multimodal state-of-the-art solutions for vessel target recognition and localization.

Data perturbation under differential privacy constraint is an important approach of protecting data privacy. However, as the data dimensions increase, the privacy budget allocated to each dimension decreases and thus the amount of noise added increases, which eventually leads to lower data utility in training tasks. To protect the privacy of training data while enhancing data utility, we propose a Utility-aware training data Privacy Perturbation scheme based on attribute Partition and budget Allocation (UPPPA). UPPPA includes three procedures: the quantification of attribute privacy and attribute importance, attribute partition, and budget allocation. The quantification of attribute privacy and attribute importance based on information entropy and attribute correlation provide an arithmetic basis for attribute partition and budget allocation. During the attribute partition, all attributes of training data are classified into high and low classes to achieve privacy amplification and utility enhancement. During the budget allocation, a γ-privacy model is proposed to balance data privacy and data utility so as to provide privacy constraint and guide budget allocation. Three comprehensive sets of real-world data are applied to evaluate the performance of UPPPA. Experiments and privacy analysis show that our scheme can achieve the tradeoff between privacy and utility.

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.

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.

Symbolic music generation relies on the contextual representation capabilities of the generative model, where the most prevalent approach is the Transformer-based model. Learning contextual representations are also related to the structural elements in music, i.e., intro, verse, and chorus, which have not received much attention of scientific publications. In this paper, we propose a hierarchical Transformer model to learn multiscale contexts in music. In the encoding phase, we first design a fragment scope localization module to separate the music parts into chords and sections. Then, we use a multiscale attention mechanism to learn note-, chord-, and section-level contexts. In the decoding phase, we propose a hierarchical Transformer model that uses fine decoders to generate sections in parallel and a coarse decoder to decode the combined music. We also designed a music style normalization layer to achieve a consistent music style between the generated sections. Our model is evaluated on two open MIDI datasets. Experiments show that our model outperforms other comparative models in 50% (6 out of 12 metrics) and 83.3% (10 out of 12 metrics) of the quantitative metrics for short- and long-term music generation, respectively. Preliminary visual analysis also suggests its potential in following compositional rules, such as reuse of rhythmic patterns and critical melodies, which are associated with improved music quality.

Content Centric Network (CCN) can be extended to efficiently and reliably support content delivery and solve the network performance degradation caused by dynamic topology and intermittent connectivity of Vehicle Ad hoc NETwork (VANET). However, the in-network caching mechanism of Vehicular Content Centric Network (VCCN) is vulnerable against Cache Pollution Attack (CPA), where attackers aim to fill the buffer space with non-popular contents by releasing fake requests. Unavoidably, the cache hit ratio of content requests from legal users is degraded and the content retrieval latency is increased under CPA. Hence, it is critical to detect and mitigate CPA. The current solutions for static CCN cannot be directly applied into dynamic VCCN. In this article, we propose a detection scheme based on hybrid heterogeneous multi-classifier ensemble learning, where CPA is determined by the cooperation of multiple vehicles. In our scheme, each vehicle can build or join a cluster whose head possesses more common moving attributes of position, speed and direction with other members. Besides, the cluster head as a base learner is responsible for training its own classifier by making some relevant statistics on requests and hit ratio. Specifically, the problem of ensemble classifier making from the individual classifiers is formulated as a linear optimization problem, with the goal of minimizing the false ratio of detecting CPA. The generalization ability of ensemble learning can make very accurate predictions on CPA. By comparison, our detection scheme outperforms the existing schemes in terms of detection ratio, hit ratio, retrieval delay. Besides, simulations have proved that the overfitting problem of adopting a singe base learning algorithm can be alleviated in our scheme.

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$18.2\%$.

Multi-access edge computing (MEC) enables extreme low-latency AI services, such as Augmented Reality (AR) and Virtual Reality (VR), by deploying cloudlets in locations close to users. Meanwhile, a 5G hierarchical service market is emerging with both large-scale and small-scale network service providers competing for both computing and network bandwidth resources of an infrastructure provider. In this paper, we investigate the problem of caching services originally deployed in remote clouds to cloudlets in an MEC network in a hierarchical service market. For the service caching problem, we first propose a novel approximation-restricted framework that guarantees the stability of the 5G service market. Under the proposed framework, we first propose an approximation algorithm with a provable approximation ratio for the problem with non-selfish network service providers. We then design an efficient Stackelberg congestion game with selfish network service providers, and analyze the Price of Anarchy (PoA) of the proposed Stackelberg congestion game to measure the efficiency loss of the game due to selfishness of network service providers. Considering that the request rate of each service may not be given in advance, we study the service caching problem with the uncertainlity of request rates, and propose an approximation algorithm and a Stackelberg game via leveraging the randomized rounding technique. We finally evaluate the performance of the proposed algorithms and mechanisms by both simulations and implementations in a real test-bed. Results show that the performance of our proposed mechanisms achieve around 9.2% less cost than those of existing approaches.

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%.

Mobile edge computing (MEC) is an enabling technology for low-latency AI applications, by caching AI services originally deployed in remote data centers to 5G base stations in network edge. Due to limited computing resource of 5G base stations, not all services can be cached in base stations to meet the resource demands of user requests. Also, if the workload of a 5G base station reaches to its resource capacity, the energy consumption of the base station will be pushed up exponentially. To reduce the energy consumption and overcome resource limitations on base stations, an alternative is to allow the base stations to collaborate with each other to admit user requests. In this paper, we investigate the problem of collaborative service caching and request offloading between a 5G-enabled MEC and remote data centers, while meeting the quality of service (QoS) requirements of users, and resource capacities on base stations that are operated by multiple selfish network service providers. We aim to maximize the total payoff of all base stations. To this end, we first propose a two-stage optimization framework: In the first stage, we develop a mechanism that adopts a best-reply rule for dynamically distributed coalition formation. In the second stage, we propose a near-optimal payoff allocation method by devising a randomized algorithm with a provable approximation ratio. We then evaluate the performance of the proposed optimization framework by extensive experimental simulations. Simulation results show that the proposed framework outperforms its counterparts by achieving at least 30% higher payoff and 20% lower energy consumption of base stations.

Vehicular Content Centric Network (VCCN) has been proposed to address the issues of user mobility and sporadic connectivity in Vehicular Ad hoc Network (VANET). The in-network caching of VCCN can indeed improve the performance of content distribution in terms of cache hit and delivery latency by making each node store frequently accessed data. However, data caching unfortunately suffers from Cache Pollution Attack (CPA) that sends out fabricated requests to pollute the data cache. To prevent the degradation of network performance caused by such an attack, we propose a detection and defense scheme for CPA by adopting game theory. We model the attack scenario into a non-cooperative game model. First, we prove the non-existence of pure strategy Nash Equilibrium (NE) in the attacker and defender game, and propose a normal form game model with complete information and an extensive Bayesian form game model with incomplete information. Under the guidance of NE, we propose a punishment strategy to prevent the conspiracy attack and make the attackers behave normally. Moreover, we propose a cooperative detection scheme to give the Road Side Unit (RSU) the final decision on CPA based on the direct and indirect suspicious lists which are generated by each node according to the observed traffic pattern. Simulation evaluations demonstrate that our scheme outperforms state-of-the-art schemes in terms of cache hit, detecting ratio, and cache accuracy for detecting and defending CPA.

With the proliferation of location-aware devices, trajectory data have been used widely in real-life applications. However, trajectory data are often associated with sensitive labels, such as users’ purchase transactions and planned activities. As such, inappropriate sharing or publishing of these data could threaten users’ privacy, especially when an adversary has sufficient background knowledge about a trajectory through other data sources, such as social media (check-in tags). Though differential privacy has been used to address the privacy of trajectory data, no existing method can protect the privacy of both trajectory data and sensitive labels. In this article, we propose a comprehensive trajectory publishing algorithm with three effective procedures. First, we apply density-based clustering to determine hotspots and outliers and then blur their locations by generalization. Second, we propose a graph-based model to efficiently capture the relationship among sensitive labels and trajectory points in all records and leverage Laplace noise to achieve differential privacy. Finally, we generate and publish trajectories by traversing and updating this graph until we travel all vertexes. Our experiments on synthetic and real-life datasets demonstrate that our algorithm effectively protects the privacy of both sensitive labels and location data in trajectory publication. Compared with existing works on trajectory publishing, our algorithm can also achieve higher data utility.

In order to mitigate the performance degradation of intermittent vehicular network caused by traffic mobility and sporadic connectivity issues, Vehicular Content Centric Network (VCCN) has been proposed to apply many technologies in Content Centric Network (CCN) into vehicular ad hoc networks. The open in-network caching strategy of CCN enables sharing and coordination of the cached data among multiple nodes as an efficient data access without relying on remote fetching. Nonetheless, few studies have considered the effective use of overall cache capacity with these cooperative nodes, especially the issues of cache duplication. Furthermore, most content replacement polices have ignored the needs of cooperative contents when making cache decisions. In this paper, we design a novel Cooperative Caching scheme by using Mobility Prediction and Consistent Hash for VCCN (called CCMPCH). Specifically, based on the observation that vehicles with the same trajectory are more likely to maintain stable communication links, we adopt Prediction by Partial Matching (PPM) to forecast each vehicle’s path and cluster the vehicles with similar future path, moving direction, and moving speed into one group. In each cluster, the consistent hash algorithm is used to allocate contents among cooperative nodes, thereby reducing unnecessary cache duplication while maintaining strong content availability. A popularity-based cache replacement policy is also developed to prioritize cooperative contents. We evaluate CCMPCH via extensive simulations, which demonstrates its higher cache hit ratio, shorter content access delay, and lower hop count compared to other state-of-the-art schemes.

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.

More and more 5G and AI applications demand flexible and low-cost processing of their traffic through diverse virtualized network functions (VNFs) to meet their security and privacy requirements. As such, the Network Function Virtualization (NFV) market has been emerged as a major service market that allows network service providers to trade their network services among customers. Since each service market usually involves complex interplays among players with different roles, efficient mechanisms that guarantee stable and efficient operations of the NFV market are urgently needed. One fundamental problem in the NFV market is how to maximize the social welfare of all players so that all players have incentives to participate in the activities of the market. In this paper, we first formulate a novel social welfare maximization problem in an NFV market of a multi-tier edge cloud network, with the aim to maximize the total revenue collected from all players, and we implement VNF services on Virtual Machines (VMs) leased by service providers to fulfill customers with service requests, where the edge cloud network consists of both cloudlets in edge networks and remote data centers in the core network. We then design an efficient incentive-compatible mechanism for the problem, and analyze the existence of a Nash equilibrium of the mechanism. Also, we consider an online social welfare maximization problem with uncertain values of customers and without the knowledge of future request arrivals, for which we devise an online learning algorithm by adopting the Multi-Armed Bandits (MAB) method with a bounded regret. We finally evaluate the performance of the proposed mechanisms through simulations and a testbed. Results show that the proposed mechanisms deliver up to 27% higher social welfare than those of existing studies

By decoupling the control and data planes, Software-Defined Networking (SDN) can implement centralized manage-ment on the network. With the increasing scale of the network, the multi-controller SDN architecture is becoming more and more popular, because it can handle what SDN with a single controller is not able to address. However, the controller load imbalance may happen due to traffic dynamics in an SDN with multiple controllers, which results in congestion at a certain controller and seriously affects the scalability of the control plane. Though switch migration is an effective solution to this problem, how to migrate the traffic is an NP-hard problem. In this work, we propose a switch migration scheme based on deep Q-learning (DQN) by combining the powerful perception of deep learning with the decision-making ability of Q-learning. We first describe the SDN state formally. Then, the network state is represented by the two-dimensional array as the input of the Q network. The network features are extracted through the convolution layer, and the full connection layer is achieved. Finally, the output layer to predict the migration action in some states of a network is extracted. After the migration action is performed, we will get an instant reward or penalty. We implement our algorithm based on the keras deep learning framework and compare it with the classic Q-learning algorithm. The results show that our scheme is superior to the traditional method in terms of resource utilization and load balancing ability.

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.

With the fast development of next-generation networking techniques, a Network Function Virtualization (NFV) market is emerging as a major market that allows network service providers to trade various network services among consumers. Therefore, efficient mechanisms that guarantee stable and efficient operations of the NFV market are urgently needed. One fundamental problem in the NFV market is how to maximize the social welfare of all players, so they have incentives to participate in activities of the market. In this paper, we first formulate the social welfare maximization problem, with an aim to maximize the total revenue of all players in the NFV market. For the social welfare maximization problem, we design an efficient incentive-compatible mechanism and analyze the existence of a Nash equilibrium of the mechanism. We also consider an online social welfare maximization problem without the knowledge of future request arrivals. We devise an online learning algorithm based on Multi-Armed Bandits (MAB) to allow both customers and network service providers to make decisions with uncertainty of customers' strategy. We evaluate the performance of the proposed mechanisms by both simulations and test-bed implementations, and the results show that the proposed mechanisms obtain at most 23% higher social welfare than existing studies.

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.

Mobile edge computing becomes a promising technology to mitigate the latency of various cloud services. In addition, network function virtualization (NFV) has been shown a great potential in reducing the operational cost of cloud services while enhancing the flexibility of virtual network function deployments, by implementing dedicated hardware network functions as pieces of software in generic servers. Recently, the GPU acceleration has been investigated to speed up flow processing in virtual network functions (VNFs), by leveraging the parallelism of GPUs. VNFs that need accelerations prefer to stay at cloudlets (locations) equipped with GPUs. However, little attention has been paid for the VNF placement that takes into account GPU-affinity in cloudlets of mobile edge clouds. In this paper, we consider the affinity-aware throughput maximization problem in a mobile edge cloud via leveraging the parallelism on GPUs for user requests with VNF requirements. We consider two types of affinities in the VNF placement: Thesoft-affinitythat allows VNFs to be executed by either CPUs or GPUs in cloudlets; and thehard-affinitythat only allows VNFs to be placed to the GPUs of a specified set of cloudlets. We formulate two corresponding VNF placement problems in a mobile edge cloud. Specifically, we first propose an exact solution to the soft-affinity throughput maximization problem by formulating an Integer Linear Program (ILP). We then propose an efficient algorithm for the problem, by proposing a randomized algorithm with a provable approximation ratio for the hard-affinity-aware throughput maximization problem and extending the proposed approximation algorithm to the soft-affinity throughput maximization problem. Furthermore, assuming that user requests arrive into the mobile edge cloud one by one without the knowledge of future arrivals, we devise an online algorithm with a good competitive ratio for this dynamic hard-affinity-aware throughput maximization problem. Finally, we evaluate the performance of the proposed algorithms, through simulations and implementations in a real test-bed. Experimental results show that the performance of the proposed algorithms outperform their existing counterparts and achieve higher throughput.

Data in its original form, however, typically contain sensitive information about individuals. Directly publishing raw data will violate the privacy of people involed. Consequently, it becomes increasingly important to preserve the privacy of published data. An attacker is apt to identify an individual from the published tables, with attacks through the record linkage, attribute linkage, table linkage or probabilistic attack. Although algorithms based on generalization and suppression have been proposed to protect the sensitive attributes and resist these multiple types of attacks, they often suffer from large information loss by replacing specific values with more general ones. Alternatively, anatomization and permutation operations can de-link the relation between attributes without modifying them. In this paper, we propose a scheme Sensitive Label Privacy Preservation with Anatomization (SLPPA) to protect the privacy of published data. SLPPA includes two procedures, table division and group division. During the table division, we adopt entropy and mean-square contingency coefficient to partition attributes into separate tables to inject uncertainty for reconstructing the original table. During the group division, all the individuals in the original table are partitioned into non-overlapping groups so that the published data satisfies the pre-defined privacy requirements of our (α; β; γ; δ) model. Two comprehensive sets of real-world relationship data are applied to evaluate the performance of our anonymization approach. Simulations and privacy analysis show our scheme possesses better privacy while ensuring higher utility.

Named Data Networking (NDN) is one of the most promising information-centric networking architectures that can improve the network performance by supporting the large scale content distribution. However, the use of in-network caching mechanism increases the opportunity of cache pollution attack, where the attackers intend to reduce the cache hit of legal users by releasing fake requests to fill the precious cache with non-popular contents. To prevent the degradation of network performance caused by such an attack, it is becoming particularly important to detect the attack and then throttle it. In this article, we propose a detection and defense scheme with the help of grey forecast, which can effectively exploit the regularity of past Interests and popularity by comprehensively considering three major factors to predict the future popularity of each cached content. If the predicted popularity of any content differs too much from the actually calculated one in several consecutive slices, the pollution attack will be determined. Once the attack is detected, the defense will be taken by suppressing the popularity increase of the suspicious content to mitigate the damage of the pollution attack. We also consider a special case, where there exists a sudden burst of traffic from legal users that cannot be simply dropped. The simulations in ndnSIM indicate that our proposed method is effective in detecting and defending the pollution attack with higher cache hit, higher detecting ratio, and lower hop count compared to other state-of-the-art schemes.

Conventional Internet of Things (IoT) applications involve data capture from various sensors in environments, and the captured data then is processed in remote clouds. However, some critical IoT applications (e.g., autonomous vehicles) require a much lower response latency and more secure guarantees than those offered by remote clouds today. Mobile edge clouds (MEC) supported by the network function virtualization (NFV) technique have been envisioned as an ideal platform for supporting such IoT applications. Specifically, MECs enable to handle IoT applications in edge networks to shorten network latency, and NFV enables agile and low-cost network functions to run in low-cost commodity servers as virtual machines (VMs). One fundamental problem for the provisioning of IoT applications in an NFV-enabled MEC is where to place virtualized network functions (VNFs) for IoT applications in the MEC, such that the operational cost of provisioning IoT applications is minimized. In this paper, we first address this fundamental problem, by considering a special case of the IoT application placement problem, where the IoT application and VNFs of each service request are consolidated into a single location (gateway or cloudlet), for which we propose an exact solution and an approximation algorithm with a provable approximation ratio. We then develop a heuristic algorithm that controls the resource violation ratios of edge clouds in the network. For the IoT application placement problem for IoT applications where their VNFs can be placed to multiple locations, we propose an efficient heuristic that jointly places the IoT application and its VNFs. We finally study the performance of the proposed algorithms by simulations and implementations in a real test-bed, Experimental results show that the performance of the proposed algorithms outperform their counterparts by at least 10 percent.

Communications in vehicular ad-hoc network (VANET) are subject to performance degradation as results of channel fading and intermittent network connectivity. The emerging Vehicular Content Centric Network (VCCN) is promising in supporting the needs of contents and alleviating the communication problems in VANET. Specifically, to improve the cache hit ratio and reduce the access delay of content retrieval, it helps to choose the appropriate vehicles to cache the frequently accessed data items. In this paper, we propose a Cooperative Caching scheme based on Social Attributes and Mobility Prediction (CCSAMP) for VCCN. CCSAMP is based on the observation that vehicles move around and are liable to contact each other according to drivers' common interests or social similarities. A caching node sharing more social attributes with the content requester is more likely to be interested in the same contents and distribute the contents to others with similar interests. Furthermore, a caching node that frequently meets other nodes is a better candidate to keep cache copies. To increase the network performance, CCSAMP also exploits the regularity of vehicle moving behaviors to predict the chance for a vehicle to reach hot zones based on Hidden Markov Model (HMM). We evaluate CCSAMP through the ONE simulator to demonstrate its higher cache hit ratio and lower content access delay compared to other state-of-the-art schemes.

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.

With increasing focus on Artificial Intelligence (AI) applications, Deep Neural Networks (DNNs) have been successfully used in a number of application areas. As the number of layers and neurons in DNNs increases rapidly, significant computational resources are needed to execute a learned DNN model. This ever-increasing resource demand of DNNs is currently met by large-scale data centers with state-of-the-art GPUs. However, increasing availability of mobile edge computing and 5G technologies provide new possibilities for DNN-driven AI applications, especially where these application make use of data sets that are distributed in different locations. One fundamental process of a DNN-driven application in mobile edge clouds is the adoption of “inferencing” - the process of executing a pre-trained DNN based on newly generated image and video data from mobile devices. We investigate offloading DNN inference requests in a 5G-enabled mobile edge cloud (MEC), with the aim to admit as many inference requests as possible. We propose exact and approximate solutions to the problem of inference offloading in MECs. We also consider dynamic task offloading for inference requests, and devise an online algorithm that can be adapted in real time. The proposed algorithms are evaluated through large-scale simulations and using a real world test-bed implementation. The experimental results demonstrate that the empirical performance of the proposed algorithms outperform their theoretical counterparts and other similar heuristics reported in literature.

The rise of big data brings extraordinary benefits and opportunities to businesses and governments. Enterprise users can analyze their consumers' data and infer the business value obtained, such as purchasing goods correlations, customer preferences, and hidden patterns. Meanwhile, with the emerge of big data processing frameworks, such as Hadoop and Tensor-flow, more and more mobile users are embracing big data analytics by issuing queries to analyze their data. In this paper, we investigate the problem of Quality-of-Service (QoS) aware query evaluation for big data analytics in a mobile edge cloud to maximize the system throughput while minimizing the query evaluation time of each admitted query, by exploring the materialization of intermediate query results. We consider dynamic big-data query evaluations where user queries arrive one by one without the knowledge of future arrivals, and the system needs to respond to each query by accepting or rejecting the query immediately. We propose an online algorithm for query admissions within a finite time horizon, the proposed algorithm can intelligently determine whether some immediate results during a query evaluation need to be materialized for later use of other queries, by making use of the Reinforcement Learning (RL) method with predictions. We finally investigate the performance of the proposed algorithm by simulations, and results show that the performance of the proposed algorithm is promising, by achieving a higher system throughput while reducing the average evaluation cost per query by from 20% to 52% compared to the comparison benchmarks.

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.

Mobile edge computing (MEC) is emerging as an enabling technology of low-latency network services, such as Augmented Reality (AR) and Virtual Reality (VR), by deploying cloudlets in locations close to users. In MEC networks, telcooperators can place their services to cloudlets, such that the service accessing delay of users is minimized. In this paper, we investigate a fundamental problem of caching services that are originally deployed in remote clouds to cloudlets in an MEC network within the proximity of users. Specifically, we focus on the service caching problem in a two-tiered MEC network with both remote clouds and cloudlets that are close to users, in which multiple network service providers competing computing and bandwidth resources. This setting is significantly different from existing studies that focused on offloading user tasks from mobile devices to cloudlets in MEC networks that typically do not consider a service market with multiple network service providers. For the service caching problem in a two-tiered MEC network, we propose a novel approximation-restricted framework that guarantees the stableness of the service market. Under the proposed framework, an approximation algorithm with an approximation ratio for the problem with non-selfish players and an efficient, stable Stackelberg congestion game with selfish players have been proposed. We also analyze the Price of Anarchy (PoA) of the proposed Stackelberg congestion game to measure the efficiency of the proposed game degrades due to selfish behavior of network service providers. We finally evaluate the performance of our mechanism on both simulated environments and a real test-bed. Results show that the performance of our proposed mechanism is promising.

Mobile edge computing (MEC) is envisioned as an enabling technology for extreme low-latency services in the next generation 5G access networks. In a 5G-enabled MEC, computing resources are attached to base stations. In this way, network service providers can cache their services from remote data centers to base stations in the MEC to serve user tasks in their close proximity, thereby reducing the service latency. However, mobile users usually have various dynamic hidden features, such as their locations, user group tags, and mobility patterns. Such hidden features normally lead to uncertainties of the 5G-enabled MEC, such as user demand and processing delay. This poses significant challenges for the service caching and task offloading in a 5G-enabled MEC. In this paper, we investigate the problem of dynamic service caching and task offloading in a 5G-enabled MEC with user demand and processing delay uncertainties. We first propose an online learning algorithm for the problem with given user demands by utilizing the technique of Multi-Armed Bandits (MAB), and theoretically analyze the regret bound of the algorithm. We also propose a novel architecture of Generative Adversarial Networks (GAN) to accurately predict the user demands based on small samples of hidden features of mobile users. Based on the proposed GAN model, we then devise an efficient heuristic for the problem with the uncertainties of both user demand and processing delay. We finally evaluate the performance of the proposed algorithms by simulations based on a realistic dataset of user data. Experiment results show that the performance of the proposed algorithms outperform existing algorithms by around 15%.

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.

Machine learning (ML) has been penetrating into our daily life by facilitating many daily applications, e.g., self-driving, cloud gaming, product fault detection and drones. Meanwhile, there is an emerging trend that adopts ML methods into network optimization problems, such as flow classification, traffic engineering, routing, and etc. Conventional ML methods need careful training for a specific application of a given network structure, and the trained model normally cannot be applied to other applications and network structures. In this paper, we aim to design adaptive ML methods for network optimization problems, with the trained models having the ability of being deployed to any similar problems. In particular, we consider the virtualized network function (VNF) provisioning problem as our target optimization problem. We first propose a deep Q-learning-based optimization framework for VNF provisioning in a mobile edge network with network capacity constraints, by devising an adaptive graph feature embedding method. We then propose a series of deep Q-learning based learning algorithms for the problems of service chaining and the throughput maximization, based on the proposed learning-based optimization framework. We also propose a novel design of master-slave dual neural network that enables the decisions on both cloudlet selections and routing path finding. To stabilize and accelerate the convergence of the proposed methods, we devise a novel environment generation and termination strategy and a new structure for the replay buffer. We also evaluate the performance of the proposed framework and algorithms by extensive simulations. Results show that the proposed algorithms outperform existing methods by around 12%, and the trained model in a network can be directly adapted to other network structures and settings.

Telecommunication networks are undergoing a disruptive transition toward distributed mobile edge networks with virtualized network functions (VNFs) [e.g., firewalls, intrusion detection systems (IDSs), and transcoders] within the proximity of users. This transition will enable network services, especially Internet-of-Things (IoT) applications, to be provisioned as network slices with sequences of VNFs, in order to guarantee the performance and security of their continuous data and control flows. In this article, we study the problems of delay-aware network slicing for multicasting traffic of IoT applications in edge networks. We first propose exact solutions by formulating the problems into integer linear programs (ILPs). We further devise an approximation algorithm with an approximation ratio for the problem of delay-aware network slicing for a single multicast slice, with the objective to minimize the implementation cost of the network slice subject to its delay requirement constraint. Given multiple multicast slicing requests, we also propose an efficient heuristic that admits as many user requests as possible, through exploring the impact of a nontrivial interplay of the total computing resource demand and delay requirements. We then investigate the problem of delay-oriented network slicing with given levels of delay guarantees, considering that different types of IoT applications have different levels of delay requirements, for which we propose an efficient heuristic based on reinforcement learning (RL). We finally evaluate the performance of the proposed algorithms through both simulations and implementations in a real testbed. The experimental results demonstrate that the proposed algorithms are promising.

Named Data Network (NDN), as a promising information-centric networking architecture, is expected to support next-generation of large-scale content distribution with open in-network cachings. However, such open in-network caches are vulnerable against Cache Pollution Attacks (CPAs) with the goal of filling cache storage with non-popular contents. The detection and defense against such attacks are especially difficult because of CPA's similarities with normal fluctuations of content requests. In this work, we use a clustering technique to detect and defend against CPAs. By clustering the content interests, our scheme is able to distinguish whether they have followed the Zipf-like distribution or not for accurate detections. Once any attack is detected, an attack table will be updated to record the abnormal requests. While such requests are still forwarded, the corresponding content chunks are not cached. Extensive simulations in ndnSIM demonstrate that our scheme can resist CPA effectively with higher cache hit, higher detecting ratio, lower hop count, and lower algorithm complexity compared to other state-of-the-art schemes.

Mobile edge computing and network function virtualization (NFV) paradigms enable new flexibility and possibilities of the deployment of extreme low-latency services for Internet-of-Things (IoT) applications within the proximity of their users. However, this poses great challenges to find optimal placements of virtualized network functions (VNFs) for data processing requests of IoT applications in a multi-tier cloud network, which consists of many small- or medium-scale servers, clusters, or cloudlets deployed within the proximity of IoT nodes and a few large-scale remote data centers with abundant computing and storage resources. In particular, it is challenging to jointly consider VNF instance placement and routing traffic path planning for user requests, as they are not only delay sensitive but also resource hungry. In this article, we consider admissions of NFV-enabled requests of IoT applications in a multi-tier cloud network, where users request network services by issuing service requests with service chain requirements, and the service chain enforces the data traffic of the request to pass through the VNFs in the chain one by one until it reaches its destination. To this end, we first formulate the throughput maximization problem with the aim to maximize the system throughput. We then propose an integer linear program solution if the problem size is small; otherwise, we devise an efficient heuristic that jointly takes into account VNF placements to both cloudlets and data centers and routing path finding for each request. For a special case of the problem with a set of service chains, we propose an approximation algorithm with a provable approximation ratio. Next, we also devise efficient learning-based heuristics for VNF provisioning for IoT applications by incorporating the mobility and energy conservation features of IoT devices. We finally evaluate the performance of the proposed algorithms by simulations. The simulation results show that the performance of the proposed algorithms is promising.

Stringent delay requirements of many mobile applications have led to the development of mobile edge clouds, to offer low latency network services at the network edges. Most conventional network services are implemented via hardware-based network functions, including firewalls and load balancers, to guarantee service security and performance. However, implementing hardware-based network functions usually incurs both a high capital expenditure (CAPEX) and operating expenditure (OPEX). Network Function Virtualization (NFV) exhibits a potential to reduce CAPEX and OPEX significantly, by deploying software-based network functions in virtual machines (VMs) on edge-clouds. We consider a fundamental problem of NFV-enabled multicasting in a mobile edge cloud, where each multicast request has both service function chain and end-to-end delay requirements. Specifically, each multicast request requires chaining of a sequence of network functions (referred to as a service function chain) from a source to a set of destinations within specified end-to-end delay requirements. We devise an approximation algorithm with a provable approximation ratio for a single multicast request admission if its delay requirement is negligible; otherwise, we propose an efficient heuristic. Furthermore, we also consider admissions of a given set of the delay-aware NFV-enabled multicast requests, for which we devise an efficient heuristic such that the system throughput is maximized, while the implementation cost of admitted requests is minimized. We finally evaluate the performance of the proposed algorithms in a real test-bed, and experimental results show that our algorithms outperform other similar approaches reported in literature.

Multiprocessor platforms have been widely applied in safety-critical domains to accommodate the increasing computation requirement of modern real-time applications. In this paper, we present a workload-aware harmonic partitioned multiprocessor scheduling scheme for periodic real-time tasks with constrained deadlines under the fixed-priority preemptive scheduling policy. In particular, two grouping metrics effectively integrating both harmonicity and workload characteristic are designed to guide our task partition. With those metrics, our scheme can greatly improve system utilization by taking advantage of the combination of harmonic relationship exploration and workload awareness. Experiments show that our proposed scheme significantly outperforms existing approaches in terms of schedulability.

Named data networking(NDN) is a very promising architecture for future network, which can improve the network performance due to its in-network caching feature. However, the pervasive caching is vulnerable against False-Locality Attack (FLA), one kind of cache pollution attack, where attackers repeatedly request a specific set of non-popular contents to replace popular contents. Therefore, the cache hit of legal requests is reduced and the response delay is increased. To mitigate this attack and improve the network performance, we propose a detection scheme based on Convolutional Neural Network (CNN) by fully exploiting the regularity of past requests. The input data of CNN are related to the inherent characteristics of the cached contents including the request ratio, the standard deviation of repeated Interests, the variance of request interval and the change of cache hit ratio. The output of CNN indicates whether FLA has been launched. Simulations through multi-topologies are conducted to validate the performance of our scheme. Compared with other state-of-the-art schemes, it is more effective in detecting FLA with higher detecting ratio, higher cache hit and lower hop count.

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.

Driven by stringent delay requirements of mobile applications, the mobile edge cloud has emerged as a major platform to offer low latency network services from the edge of networks. Most conventional network services are implemented via hardware-based network functions, such as firewalls and load balancers, to guarantee service security and performance. However, implementing such hardware-based network functions incurs high purchase and maintenance costs. Network function virtualization (NFV) as a promising technology exhibits great potential to reduce the purchase and maintenance costs by implementing network functions as software in virtual machines (VMs). In this paper, we consider a fundamental problem of NFV-enabled multicasting in a mobile edge cloud, where each multicast request requires to process its traffic in a specified sequence of network functions (referred to as a service chain) before the traffic from a source to a set of destinations. We devise a provable approximation algorithm with an approximation ratio for the problem if requests do not have delay requirements; otherwise, we propose an efficient heuristic for it. We also evaluate the performance of the proposed algorithms against the state-of-the-art NFV-enabled multicasting algorithms, and results show that our algorithms outperform their counterparts.

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.

With the proliferation of location-aware devices, trajectory data have been widely collected, published, and analyzed in real-life applications. However, published trajectory data often contain sensitive attributes, so an attacker who can identify an individual from such data through record linkage, attribute linkage, or similarity attacks can gain sensitive information about this individual. To resist from these attacks, we propose a scheme called Data Privacy Preservation with Perturbation (DPPP). To protect the privacy of sensitive information, we first determine those critical location sequences that can identify specific individuals. Then we perturb these sequences by adding or deleting some moving points while ensuring the published data satisfy (l, α, β)-privacy, an enhanced privacy model from ldiversity. Our experiments on both synthetic and real-life datasets suggest that DPPP achieves better privacy while still ensuring high utility, compared with existing privacy preservation schemes on trajectory.

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.

Multiprocessor platforms, widely adopted to realize real-time systems nowadays, bring the probabilistic characteristic to such systems because of the performance variations of complex chips. In this paper, we present a harmonic partitioned scheduling scheme with workload awareness for periodic probabilistic realtime tasks on multiprocessors under the fixed-priority preemptive scheduling policy. The key idea of this research is to improve the overall schedulability by strategically arranging the workload among processors based on the exploration of the harmonic relationship among probabilistic real-time tasks. In particular, we define a harmonic index to quantify the harmonicity among probabilistic real-time tasks. This index can be obtained via the harmonic period transformation and probabilistic cumulative worst case utilization calculation of these tasks. The proposed scheduling scheme first sorts tasks with respect to the workload, then packs them to processors one by one aiming at minimizing the increase of harmonic index caused by the task assignment. Experiments with randomly generated task sets show significant performance improvement of our proposed approach over the existing harmonic partitioned scheduling algorithm for probabilistic real-time systems.

Software Defined Networking (SDN) is envisioned as a novel technology to enable reliable and scalable network management by decoupling the control plane and data plane. As the network scale increases, multiple controllers have been proposed to solve the problems of scalability and reliability caused by single controller. Although some controller placement schemes based on controller replication have been proposed to recover the controller failure in SDNs, few of them can solve the failure with the existing controllers. In this paper, we propose a QoS and cost aware fault tolerant scheme in multi-controller SDNs by exploring a fine-grained trade-off between controller cost and recovery time. With considering the controlle cost, load and communication delay in the failure recovery, we propose a heuristic algorithm to select backup controllers aiming to minimize the average recovery time, meanwhile avoiding the load oscillation in switch migration. Extensive simulations highlight that our scheme can improve the recovery efficiency compared with some other existing approaches.

To solve the bottleneck of a single controller in software-defined networks (SDNs), most of current works based on multiple controllers focus on decreasing propagation delay between the switch and the corresponding controller. However,this kind of methods ignores the synchronization between controllers, which may affect the network performance. Moreover, the controller deployment based on out-band has neglected the association between switches, which is quite costly. In this paper, we propose a community characteristic based strategy to achieve controller placement with optimal latency and balanced controller load. We first divide the network domain according to the relevance between switches, and then we design our controller deployment strategy by reconciling the propagation delay between controllers and the control delay between controllers and switches. We adopt in- band control mode instead of Euclidean distance to compute the distance among network entities. The simulation results show that our strategy can gain lower propagation delay latency and better load balance.

With the rise of wireless power transfer technology, charging scheduling issue is prevalent in wireless rechargeable sensor networks (WRSNs). Most prior arts focused on two-dimensional (2-D) networks with homogeneous mobile chargers. However, three-dimensional (3-D) networks with collaborations among heterogeneous mobile chargers are more practical. In this paper, we consider 3-D networks in which wireless charging vehicles (WCVs) are employed with unmanned aerial vehicles (UAVs). To prolong network lifetime, we focus on device sleep time and energy usage and propose a 3-D Dynamic Collaborative Scheduling scheme (3DCS). Theoretical values of energy threshold and partition number are determined to assign charging tasks to chargers. Then, scheduling algorithms that include target selection, infeasibility test, and target update, are developed. In addition, a collaborative algorithm is developed to re-assign charging tasks from busy chargers toward their neighboring chargers to further improve charging efficiency. Test-bed experiments and extensive simulations reveal that, compared with several distinguished scheduling schemes, our scheme has a superior performance in charging throughput, energy efficiency, and other characteristics.

Software defined network (SDN)can manage the whole network flexibly because of its programmability and logically centralized architecture. However, the centralized architecture of SDN makes it more vulnerable to Denial of Service (DoS)attack which is launched by sending a large number of malicious packet_in packets to consume the resources of the controller and data planes. In order to protect the normal operation of the network from DoS, we propose an effective DoS mitigation framework based on non-cooperative repeated game called PrioGuard. DoS can be detected based on the information entropy, packet_in rate and packet_in response rate. Furthermore, the penalty-incentive mechanism of repeated game is adopted to punish these attackers by lowering their priority in order to postpone their requests. The requests from attackers will be migrated to data plane cache, which can mitigate the interface cache of control plane and make the controller process the normal requests effectively. We have implemented a prototype system of PrioGuard. Simulation evaluations demonstrate that our scheme is very effective with less response time, less packet loss rate and lower controller load.

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.

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.

Recently, wireless power transfer is emerging as an enabling technology of wireless rechargeable sensor networks. Conventional methods that charge each sensor until its battery is full take unproportionally long time to finish, due to the limitation of charging efficiency and power transfer technologies. In this paper, we propose a mixed partial and full charge (MPF) scheme, including three specialized modules, i.e., evaluation module, adjustment module, and selection module. MPF allows nodes to be replenished "partially" by a mobile charging vehicle (MCV). When executing adjustment module, a concept of power and path adjustment window is proposed for determining a proper power allocation scheme as well as charging path. Then a scheduling strategy termed return mechanism is designed to further utilize the energy of the MCV and improve effective energy utilization. Finally, we build a high-accuracy charging test-bed and evaluate the applicability as well as performance of the proposed scheme. For large-scale networks, we also perform simulations to demonstrate the effectiveness of MPF in promoting survival rate and reducing traveling distance of the MCV.

The lack of schedulability evaluation of previous charging schemes in wireless rechargeable sensor networks (WRSNs) degrades the charging efficiency, leading to node exhaustion. We propose an Optimal Path Planning Charging scheme, namely OPPC, for the on-demand charging architecture. OPPC evaluates the schedulability of a charging mission, which makes charging scheduling predictable. It provides an optimal charging path which maximizes charging efficiency. When confronted with a non-schedulable charging mission, a node discarding algorithm is developed to enable the schedulability. Experimental simulations demonstrate that OPPC can achieve better performance in successful charging rate as well as charging efficiency.

The collaborative charging issue in Wireless Rechargeable Sensor Networks (WRSNs) is a popular research problem. With the help of wireless power transfer technology, electrical energy can be transferred from wireless charging vehicles (WCVs) to sensors, providing a new paradigm to prolong network lifetime. Existing techniques on collaborative charging usually take the periodical and deterministic approach, but neglect influences of non-deterministic factors such as topological changes and node failures, making them unsuitable for large-scale WRSNs. In this paper, we develop a temporal-spatial charging scheduling algorithm, namely TSCA, for the on-demand charging architecture. We aim to minimize the number of dead nodes while maximizing energy efficiency to prolong network lifetime. First, after gathering charging requests, a WCV will compute a feasible movement solution. A basic path planning algorithm is then introduced to adjust the charging order for better efficiency. Furthermore, optimizations are made in a global level. Then, a node deletion algorithm is developed to remove low efficient charging nodes. Lastly, a node insertion algorithm is executed to avoid the death of abandoned nodes. Extensive simulations show that, compared with state-of-the-art charging scheduling algorithms, our scheme can achieve promising performance in charging throughput, charging efficiency, and other performance metrics.

With the constant increase of social-network data published, the privacy preservation becomes more and more important. Although some literature algorithms apply K-anonymity to the relational data to prevent an adversary from significantly perpetrating privacy breaches, the inappropriate choice of K has a big impact on the quality of privacy protection and data utility. We propose a technique named Relationship Privacy Preservation based on Compressive Sensing (RPPCS) in this paper to anonymize the relationship data of social networks. The network links are randomized from the recovery of the random measurements of the sparse relationship matrix to both preserve the privacy and data utility. Two comprehensive sets of real-world relationship data on social networks are applied to evaluate the performance of our anonymization technique. Our performance evaluations based on Collaboration Network and Gnutella Network demonstrate that our scheme can better preserve the utility of the anonymized data compared to peer schemes. Privacy analysis shows that our scheme can resist the background knowledge attack.

Abstract Because of the stochastic key pre‐distribution and complicated network topology, designing an energy‐efficient node capture attack algorithm is of great challenge. Although many algorithms have been proposed for node capture attack, previous methods lack of concerning minimizing resource expenditure in modeling attacking behavior. In this paper, we propose a novel way of modeling the node capture attack. First, we transform the problem into a set covering problem with a shortest Hamiltonian cycle problem, which has been shown to be NP‐hard. Consequently, we also develop a heuristic called minimum resource expenditure node capture attack (MREA) to maximize destructiveness while minimizing resource expenditure. Moreover, extensive simulations are conducted to show the performance of MREA. Simulation results show that MREA outperforms other algorithms in reducing the attack rounds and saving resource expenditure. Copyright © 2016 John Wiley & Sons, Ltd.

Due to the disconnected and store-and-forward architecture in multimedia social networks (MSNs), routing becomes a great challenge with the frequent path disruptions. Moreover, some nodes in MSNs tend to be selfish or malicious, e.g. they sometimes will not forward packets for other nodes or will launch passive and active attacks in order to save their limited resources such as bandwidth, battery or storage. In order to address this issue, we propose a fuzzy-based trust management technique for context-based routing in MSNs. We incorporate social trust metrics and quality of service metrics into our trust model. By adopting fuzzy sets, every node can evaluate the credibility of other nodes based on the direct and indirect relationship. By ranking all its neighbors according to the trust values, each node can purge untrustworthy nodes. Since only trusted nodes’ packets will be forwarded, the selfish or malicious nodes have the incentive to behave well again in order to be able to send packets. Additionally, we perform extensive security and performance evaluation with the opportunistic network environment simulator. The simulation results show that our trust model can dynamically update the trust value in real time, effectively measure the trust relationship and correctly identify malicious or selfish nodes. Furthermore, the proposed trust routing is a lightweight protocol balancing the message overhead and delivery ratio.

Summary In this paper, a combined clustering algorithm namelyenhanced clustering cloak(ECC), for protecting location privacy and query privacy is proposed. An iterative K‐means clustering method is developed to group the user requests into clusters for providing location safety. Meanwhile, a hierarchical clustering method for preserving the query privacy is used when creating clusters.ECCprovides users with desirable spatial and temporal tolerances. It can defend sampling attacks, homogeneity attacks, and query association attacks simultaneously. Simulation results present that theECCalgorithm not only has merits in smaller number of clusters, shorter cloaking time, higher entropy and QoS level but also preserves location privacy and query privacy in continuous location based services. Copyright © 2014 John Wiley & Sons, Ltd.

Summary Wireless sensor networks (WSNs) are expected to be widely deployed to collect data in military and civilian applications. Because of the open nature of WSNs, it is easy for an adversary to eavesdrop sensor communication and to trace packets, causing privacy concern for the sensor devices. The privacy issue, especially location privacy, can be critical for monitoring applications in WSNs. A unique case of location privacy is that of the sources, which are vulnerable of being captured and target attacks. In this paper, we propose a scheme to protect the source–location privacy based on a novel use of multiring topology. To achieve a uniformly distributed traffic pattern throughout the network, the source node selects two random rings each from its external rings and internal rings and a set of two random angles with a sum of 180 degrees for each packet. The packet is sent at one of the angles in each ring. Fake packets are also injected to provide path diversity and to increase attack time, which is defined as the time that the adversary takes to locate the source successfully. These techniques protect the source node from packet tracing attacks as well as traffic analysis attacks. Our analysis and simulations, performed in the NS2 simulator and MATLAB, demonstrate that our proposed scheme can provide better spatial traffic evenness and longer attack time, along with a modest increase of hop count and energy consumption.Copyright © 2013 John Wiley & Sons, Ltd.

Virtualization is an important enabling technology for many large data centers and cloud computing environments, and virtual machine (VM) migration plays a key role in the load balancing among the hosts of the data center. However, the existing load balancing schemes based on VM migration have serious influence on the fault-tolerant level of the services in the data center, and thus the reliability of the services cannot be guaranteed. In this paper, a novel guaranteeing fault-tolerant requirement load balancing scheme (GFTLBS) is proposed. GFTLBS migrates the VMs to balance the load without violating the fault-tolerant requirement of all services. The simulation results show that the scheme can guarantee the fault-tolerant requirements of all services while keeping the load balance.

ABSTRACT Wireless sensor networks are often deployed in hostile and unattended environment that are very prone to node capture attack. In node capture attack, information such as key, data on captured nodes, can all be extracted by the adversary. Node capture attack in wireless sensor networks suffers from low efficiency and high resource expenditure. To enhance the efficiency of node capture attack, we propose here a high efficiency node capture attack algorithm that is based on route minimum key set, namely greedy node captured based on route minimum key set (GNRMK). To obtain the route minimum key set, the sensor network is mapped as a flow network. The route minimum key set can be calculated by the maximum flow of the flow network. Then, an overlapping value is assigned to each node on the basis of route minimum key set. The node with maximum overlapping value will be captured in every round of attack. Simulation results indicate that, compared with other node capture attack schemes, GNRMK can compromise the network by capturing fewer nodes. Moreover, the fraction of traffic compromised is much higher. Copyright © 2012 John Wiley & Sons, Ltd.

The large-scale deployment of location-based services (LBSs) brings about the potential abuse of their clients' personal information. Therefore, location privacy in LBSs is significant. Ensuring location privacy for mobile users is an effort to prevent semi-honest or dishonest service providers from abusing the location information. Though there exist several techniques to preserve location privacy of mobile users, these techniques cannot effectively protect the location privacy in continuous location-based services. In this paper, we propose a location anonymity scheme based on the fake queries in continuous location-based services. To prevent attackers from tracing a mobile user by his continuous queries, some fake continuous queries will be injected by some neighbors. These fake queries can produce equivalent fake paths similar to the user's mobile path, because they are generated according to the user's speed and mobile direction. It is so difficult for the attackers to distinguish the real continuous queries from other fake queries. Security analysis shows that our scheme can resist the continuous queries attack, maximum speed attack and abnormal points attack. Experimental results manifest that our scheme can provide stringent privacy guarantees and is beyond the limitation of existing algorithms based on K-anonymity technique.

Privacy protection in pervasive environments has attracted great interests in recent years. Two kinds of privacy issues, location privacy and query privacy, are threatening the security of the users. In this paper, a novel combined clustering algorithm for protecting location privacy and query privacy, namely ECC, is proposed. ECC applies a iterative K-means clustering method to group the user requests into clusters for providing location safety while utilizing a hierarchical clustering method for preserving the query privacy. ECC provides the mobile users with their desired anonymity levels and spatial tolerances. Experimental results manifest that the ECC algorithm shows merits in shorter cloaking time and is able to preserve location privacy and query privacy in continuous location based services.

As an important part of the smart space, body sensor networks (BSNs) provide continuous health monitoring and automation assistance for smart environment residents. A high degree of security and reliability for BSN is extremely required. An adaptive and flexible intrusion-tolerant scheme for BSN, namely ITFBS, is proposed. ITFBS dynamically detects intrusions according to the collected intrusion-related information, and it can provide an adaptive intrusion-tolerant strategy with passive replication by utilising two-step threshold-based intrusion detection and replicas classification. The correctness and effectiveness of ITFBS is theoretically proved, and the experimental results show that ITFBS can effectively tolerate intrusions with low power consumption and high adaptability.