Neural textures have emerged as pivotal assets in next-generation neural rendering pipelines. However, hardware limitations and programming interface constraints lead to suboptimal performance in multi-instance real-time rendering scenarios. This bottleneck becomes particularly acute for texture-intensive tasks such as font rendering. To address this, we propose Neural Outline Cache (NOC), a novel neural font texture supporting real-time anti-aliased rendering and procedural editing within modern neural graphics pipelines. NOC's lightweight network leverages multi-resolution hash encoding to cache spline-derived SDFs, delivering anti-aliased rendering via standard graphics pipelines. For massive-instance scalability, our cache buffer layout (CBL) and batch-fused inference (BFI), tailored for NOC, mitigate neural texture streaming bottlenecks. We constructed an evaluation dataset using five font styles. In offline rendering, our proposed method achieves overall average results of 57.35 dB PSNR, 0.998 SSIM, and 1.1584e-3 pixel RMSE, while maintaining approximately 0.5ms frame latency with 500 real-time instances. To demonstrate its versatility, we integrated a procedural editor for visual effects editing of NOC textures. These results all prove that NOC is a reliable, production-ready neural asset.

Understanding the nature of user-device interactions (e.g., who is using the device and what he/she is doing with it) is critical for many applications including time management, user profiles, and privacy protection. However, in scenarios where mobile devices are shared among family members or multiple employees in a company, conventional account-based statistics are not meaningful. This poses an even bigger problem when dealing with sensitive data. Moreover, fingerprint readers and front-facing cameras were not designed to continuously identify users. In this study, we developedMagPrint++, a novel approach to fingerprint users based on unique patterns in the electromagnetic (EM) signals associated with the specific use patterns of users. Initial experiments showed that time-varying EM patterns are unique to individual users. They are also temporally and spatially consistent, which makes them suitable for fingerprinting.MagPrint++has a number of advantages over existing schemes: i) Non-intrusive fingerprinting, ii) implementation both on COTS mobile phones and a small and easy-to-deploy device, and iii) high accuracy thanks to the proposed classification algorithm. In experiments involving 30 users,MagPrint++achieves$94.3\%$accuracy in classifying users from these traces, which represents a$10.9\%$improvement over the state-of-the-art classification method.

Mobile edge computing (MEC) has recently gained significant attention as a promising solution for processing delay sensitive and resource-intensive computational jobs. Existing system schedulers in MEC networks typically assume homogeneous service providers, uniformly distributed user equipment (UE), and identical service requirements, making them unsuitable for practical MEC scenarios where jobs are randomly generated with varying service and completion time requirements. Thus, in this work, we jointly optimize job scheduling and resource allocation in a heterogeneous multi-unmanned aerial vehicle (UAV) enabled MEC network, considering practical factors such as diverse service requirements of jobs, unknown distribution of UEs, and spatial-temporal job arrivals. We aim to reduce the overall job miss rate and the average energy consumption of both UAVs and UEs by jointly planning safe UAV trajectories and onboard resource allocation. To learn uncertain and dynamic UE-side states (e.g., job arrivals and mobility patterns) and ensure the UAV's safety during the flight, we propose a multi-agent safe reinforcement learning algorithm that combines a Shared Soft Actor-Critic architecture for extracting features of heterogeneous UAVs and a two-agent Markov Game of Intervention mechanism for collision avoidance, named SSAC-MGI. In particular, SSAC MGI further incorporates a fine-grained resource allocation scheme to improve onboard resource utilization and reduce job miss rate. Extensive real trace-driven simulations based on Alibaba cluster data validate the effectiveness and superiority of SSAC-MGI, compared with several state-of-the-art algorithms.

Mobile edge computing (MEC) is promising in handling delay-sensitive or resource-intensive tasks in mobile internet. Existing system schedulers in MEC networks usually schedule all service providers in a synchronous manner, which may not suit the practical scenario where tasks are randomly generated and require different computational resources and service times. In this work, we jointly optimize the task scheduling and resource allocation in an unmanned aerial vehicle (UAV)-enabled MEC network, where multiple UAVs asynchronously and cooperatively deliver task offloading and computing services to edge devices (EDs), for maximizing the average per-UAV energy utility and minimizing the overall task missing ratio. To enhance scheduling efficiency and jointly optimize task scheduling and resource allocation, we develop an asynchronous layered multi-agent proximal policy optimization (AL-MAPPO) algorithm, by incorporating the multi-UAV asynchronous action execution mechanism and a discrete-continuous layered action space into the general MAPPO framework. AL-MAPPO enables each UAV to perform flexible task scheduling and fine-grained resource allocation asynchronously. Extensive trace-driven simulations based on Alibaba Cluster Data V2017 validate the effectiveness of AL-MAPPO, compared with several baseline algorithms.

Mobile chargers (MCs) have been widely used in wireless rechargeable sensor networks (WRSNs) to deliver energy to sensor nodes. This paper concerns the fundamental problem of dispatching the minimum number of MCs to charge nodes within a large-scale WRSN, i.e., given a set of rechargeable nodes, we aim to minimize the total number of dispatched MCs by appropriately designing the charging path of each dispatched MC, such that the charging demand of each node is satisfied. Due to its complexity, we solve this problem by first dividing it into two subproblems, i.e., charging points selection and charging paths design, which are both NP-hard. Then, we propose a computational geometry-based two-step heuristic algorithm to solve the two subproblems respectively. In the first step, we develop a peeling-off searching algorithm (POSA) to determine the charging points where MCs can stop to charge nodes within their charging ranges, by jointly considering the charging efficiency and moving distance. In the second step, we gradually assign the determined charging points to each dispatched MC while constructing the corresponding charging path. During the path construction, we first generate a shortest closed tour connecting all the currently assigned charging points and then use a break-and-tie method to insert the depot to form the charging path. Extensive evaluations validate the superiority of our proposed algorithm, compared with some other algorithms.

With the rise of federated learning (FL) in the realm of machine learning for data privacy protection, its unique distributed data processing characteristics have garnered widespread attention. However, the implementation of FL faces many challenges, as achieving a balance between data privacy, model security, and system efficiency is difficult, often requiring the sacrifice of efficiency for privacy and security. Moreover, this process typically assumes the existence of a trusted server for coordination. Addressing these challenges, this article proposes a privacy-preserved and efficient FL framework with blockchain (PEFL). PEFL utilizes blockchain and differential privacy techniques to coordinate privacy protection among clients, and filters out anomalous model parameters through an aggregation-side detection algorithm to resist poisoning attacks. Under the assumption of an untrusted server, we design the model-validated fault-tolerant federation (MFF) consensus mechanism based on a committee, balancing efficiency expectations to regulate the server and ensure the reliability of the training process. Through experiments on the MNIST and CIFAR10 datasets, and comparison with typical FL schemes, PEFL demonstrates better defense against various attack models. Besides, it achieves higher training efficiency while ensuring privacy security.

Crowdsourced data refers to the information contributed by a large number of individuals, which may originate from various sources, including social media, online surveys, crowdsourcing tasks, etc. It is utilized for analysis and research across diverse scenarios. However, due to various subjective and objective factors, including the participants and sensing devices, the quality of the data collected for crowdsourcing tasks could be inconsistent. Therefore, how to filter out reliable information from the inconsistent data is crucial and difficult. Additionally, since participants consider their time and monetary costs, the crowdsourced datasets obtained are typically based on partial event observations, indicating a pronounced sparsity in the data. Current truth discovery methods struggle to adapt to datasets with varying levels of sparsity and lack effectiveness in evaluating and predicting sparse datasets that contain multiple judgments. In this article, we propose an adaptive hypergraph-based expectation-maximization (EM) truth discovery method for crowdsourced datasets with multiple judgments, named MHGEM (short for multidimensional-hypergraph EM). MHGEM leverages hypergraph topological metrics to model sparse datasets, enhancing the assessment of participant reliability and the prediction of truth for observed events. Experiments in both simulated and real-world scenarios demonstrate that MHGEM achieves higher predictive accuracy.

Federated edge learning (FEL) enables Industrial Internet of Things (IIoT) devices to collaboratively train machine learning models without exposing their local data. However, insecure communication environments pose significant threats to the security of model transmission in FEL. Signcryption, as a novel cryptographic primitive, can provide confidentiality, integrity, and other security guarantees for model transmission. Nevertheless, most existing signcryption schemes are designed for a single recipient and therefore cannot meet the multi-recipient requirements of FEL. Additionally, these schemes lack mechanisms for revoking signcryption privileges, which allows malicious edge nodes to continue participating in model transmission and disrupt the training of the global model. To address these challenges, this paper proposes the Lightweight and Efficient Signcryption for Federated Edge Learning in IIoT (LES), which achieves efficient one-to-many signcryption by leveraging bilinear pairings and Lagrange interpolation. LES balances computational and communication overhead while ensuring security. Moreover, the LES scheme incorporates blockchain technology and the Chinese Remainder Theorem to enable revocation of signcryption privileges, preventing compromised edge nodes from continuing to engage in model transmission. Formal security proofs of the LES scheme are provided. A comprehensive comparison between LES and eight representative signcryption schemes proposed in recent years highlights the feasibility of LES and its clear advantages in terms of computational and communication overhead. Under partial participation, LES achieves at least a 29.1% reduction in computational cost and an 81.8% reduction in communication cost compared with the most efficient single recipient scheme among the eight evaluated.

Although existing graph self-supervised learning approaches have paid attention to the directed nature of networks, they have often overlooked the ubiquitous scale-free attributes. This oversight has resulted in a theoretical gap in understanding graph self-supervised learning from the perspective of network structure. In this paper, we study the degree distribution characteristics of source and target nodes in directed scale-free networks, encompassing node and edge dimensions. Our theoretical analysis reveals the relationship between the average degree of nodes and their average in-degree and out-degree, which is instrumental in discerning positive and negative edges, as well as edge directionality . Here positive edges are the ones that exists in the original graph, and negative edges are the ones that not exists in the original graph. Furthermore, we uncover negative edges connecting to central nodes and positive edges to peripheral nodes to be less predictable. Based on these crucial theoretical insights, we propose DMGAE (Directed Masked Graph Autoencoder), a novel representation learning method for directed scale-free networks that offers interpretability . The DMGAE method employs a weighted graph based on edges to replace the original graph structure. It integrates a masking approach based on the weight of the edges. Additionally, it incorporates an adaptive negative sampling method, edge decoder and a degree decoder based on the difference between the in-degree and out-degree of the node. This enhances the model’s capability to learn edges and discern their directions. Empirical studies on extensive real-world network data show that, compared to the state-of-the-art methods, DMGAE not only generally has superior learning performance on directed networks, but also performs exceedingly well on undirected networks .

Multiple Object Tracking (MOT) is a critical task in computer vision with a wide range of practical applications. However, current methods often use a uniform approach for associating all targets, overlooking the varying conditions of each target. This can lead to performance degradation, especially in crowded scenes with dense targets. To address this issue, we propose a novel Condition-Aware Tracking method (CATrack) to differentiate the appearance feature flow for targets under different conditions. Specifically, we propose three designs for data association and feature update. First, we develop an Adaptive Appearance Association Module (AAAM) that selects suitable track templates based on detection conditions, reducing association errors in long-tail cases like occlusions or motion blur. Second, we design an ambiguous track filtering Selective Update strategy (SU) that filters out potential low-quality embeddings. Thus, the noise accumulation in the maintained track feature will also be reduced. Meanwhile, we propose a confidence-based Adaptive Exponential Moving Average (AEMA) method for the feature state transition. By adaptively adjusting the weights of track and detection embeddings, our AEMA better preserves high-quality target features. By integrating the above modules, CATrack enhances the discriminative capability of appearance features and improves the robustness of appearance-based associations. Extensive experiments on the MOT17 and MOT20 benchmarks validate the effectiveness of the proposed CATrack. Notably, the state-of-the-art results on MOT20 demonstrate the superiority of our method in highly crowded scenarios.

An effective auto-scaling framework is essential for microservices to ensure performance stability and resource efficiency under dynamic workloads. As revealed by many prior studies, the key to efficient auto-scaling lies in accurately learning performance patterns, i.e., the relationship between performance metrics and workloads in data-driven schemes. However, we notice that there are two significant challenges in characterizing performance patterns for large-scale microservices. Firstly, diverse microservices demonstrate varying sensitivities to heterogeneous machines, causing difficulty in quantifying the performance difference in a fixed manner. Secondly, frequent version upgrades of microservices result in uncertain changes in performance patterns, known as pattern drifts, leading to imprecise resource capacity estimation issues. To address these challenges, we propose Humas, a heterogeneity- and upgrade-aware auto-scaling framework for large-scale microservices. Firstly, Humas quantifies the difference in resource efficiency among heterogeneous machines for various microservices online and normalizes their resources in standard units. Additionally, Humas develops a least-squares density-difference (LSDD) based algorithm to identify pattern drifts caused by upgrades. Lastly, Humas generates capacity adjustment plans for microservices based on the latest performance patterns and predicted workloads. The experiment results conducted on 50 real microservices with over 11,000 containers demonstrate that Humas improves resource efficiency and performance stability by approximately 30.4% and 48.0%, respectively, compared to state-of-the-art approaches.

Although promising, federated edge learning (FEL) is being plagued by unreliable clients with low-quality parameters due to tight edge association and frequent edge aggregation. Existing efforts mainly focus on setting thresholds or identifying malicious behaviors to resist unreliable clients, which comes at the cost of losing their training samples and leads to unsustainable and collaborative inefficiencies. To tackle this issue, we propose the first sustainable contract, named FedSC, which allows for sustaining truthful contributions in more general conditions including clients’ multidimensional attributes and imperfect system monitoring. Specifically, by modeling the long-term strategic behaviors of self-interested clients as a Markov decision process, we quantify the impact of client behavior on their utilities and derive the critical conditions that make the rating-based contract sustainable, thereby promoting honest participation as the optimal choice for strategic clients. Since directly deriving the optimal design of FedSC under multiple constraints and nonlinear coupling of parameters is intractable, we characterize the impact of design parameters on objective function and analytically prove the existence of closed solution. Then, through a low-time-complexity greedy-based algorithm, the optimality of sustainable contracts under different system errors is guaranteed. Extensive experiments using both synthetic and real datasets demonstrate the effectiveness and superiority of FedSC compared to the state-of-the-art baselines. Excitingly, FedSC can reduce the number of free-riders up to 34.52% and improve the amount of contributed data and model performance up to 22.98% and 8.62%, respectively.

Recently, researchers have proposed many deep generative models, including generative adversarial networks (GANs) and denoising diffusion models. Although significant breakthroughs have been made and empirical success has been achieved with the GAN, its mathematical underpinnings remain relatively unknown. This paper focuses on a rigorous mathematical theoretical framework: the composite-functional-gradient GAN (CFG). Specifically, we reveal the theoretical connection between the CFG model and score-based models. We find that the CFG discriminator’s training objective is equivalent to finding an optimal$D(\mathrm {x})$. The optimal$D(\mathrm {x})$’s gradient differentiates the integral of the differences between the score functions of real and synthesized samples. Conversely, training the CFG generator involves finding an optimal$G(\mathrm {x})$that minimizes this difference. In this paper, we aim to derive an annealed weight preceding the CFG discriminator’s weight. This new explicit theoretical explanation model is called the annealed CFG method. To overcome the annealed CFG method’s limitation, as the method is not readily applicable to the state-of-the-art (SOTA) GAN model, we propose a nested annealed training scheme (NATS). This scheme keeps the annealed weight from the CFG method and can be seamlessly adapted to various GAN models, no matter their structural, loss, or regularization differences. We conduct thorough experimental evaluations on various benchmark datasets for image generation. The results show that our annealed CFG and NATS methods significantly improve the synthesized samples’ quality and diversity. This improvement is clear when comparing the CFG method and the SOTA GAN models.

Service function chain (SFC) enables network service providers to provide low-latency services to end devices, such as computation-intensive task offloading services through SFC in device-edge-cloud (DEC) computing. However, DDoS attacks can render SFC unavailable and impact task offloading in DEC computing. In this paper, we propose a trust-cooperative virtualized network function (VNF) model based on coalition formation for SFC provisioning. The proposed model records coalition formation information as transactions in the blockchain to protect the VNF information from being tampered with by attackers. Coalition formation for SFC provisioning consists of two steps: VNF node identity verification and the decision to join the coalition. To address the issue of unreliability in SFC deployment due to attacks, we propose a cooperative SFC provisioning algorithm based on security-aware coalition formation to identify trustworthy VNFs for SFC. Moreover, to handle the instability of SFC provisioning caused by DDoS attacks, we present an SFC reprovisioning algorithm based on the stochastic evolutionary coalition game with reward machines (SECGRM) under the constraint of VNF service times. Experimental results show that our proposed algorithms effectively combat malicious attacks and significantly reduce cooperative SFC provisioning latency compared with existing leading approaches.

Although Federated Learning (FL) offers significant potential for developing model marketplaces through collaborative training and privacy preservation, challenges such as insufficient training data and arbitrage issues severely impede the development of FL-based model marketplaces. Existing studies either lack satisfactory security guarantees or are too profit-driven to address potential arbitrage issues. In this paper, we propose a novel Personalized prIvacy-prEserving inCentive mEchanism named PIECE, with the aim of achieving social optimality while avoiding arbitrage. Specifically, we first formulate a dual-objective optimization problem to simultaneously maximize social utility and model performance while ensuring arbitrage-free conditions through differential privacy. Due to dynamic model training and heterogeneous privacy budgets that complicate the design of arbitrage-free properties, we model the transformation between local and global privacy requirements across scenarios as a privacy choice game. This game guarantees the identification of a constraint to generate desired model versions based on Nash equilibrium. Next, by generalizing the properties of different data-owner groups under equilibrium conditions, we prove that the dual-objective optimization problem is always conflict-free, thus allowing transformation into a social optimal problem without arbitrage. Furthermore, to tackle the significant difficulty in characterizing the model revenue and interpolating pricing, we propose a two-stage solution based on subadditivity relaxation. The first stage establishes a set of ideal prices as the target, while the second stage establishes polynomial-time solvability and provides rigorous arbitrage-free boundaries. Finally, comprehensive experiments on four real-world datasets validate the efficacy of PIECE. The results indicate a minimum 8% boost in model revenue within the specified marketplace scale, and a maximum 16.67% improvement in model performance compared to the state-of-the-art baselines.

The goal of multi-object tracking (MOT) is to estimate the location of objects and maintain their identities consistently to yield their individual trajectories. It has become a trend to fuse multi-sensor information to achieve 3D MOT, since it can leverage the advantages of different sensors to enhance tracking performance. However, it is a challenging work due to the necessity of fusing features with diverse attributes and wrong association caused by significant noise. In this paper, we propose adaptive weight parameter-based multi-feature fusion to create affinity function, alongside adaptive setting of data association searching range, aiming to augment camera-Lidar information fusion-based MOT framework. First, detected results from these two sensors are divided into three categories. Then, to fully utilize both motion and appearance information, adaptive weight parameter setting is proposed to embed appearance information into motion information, forming the basis for creating an affinity function for data association. Furthermore, to mitigate wrong association caused by object temporary occlusion or out-of-view, a method for adaptively adjusting the association searching area is introduced based on the number of frames in which tracking trajectories disappear. Finally, to prevent appearance information pollution caused by significant noise, a confidence score-based tracking trajectory appearance feature updating strategy is explored. The experiment results on KITTI and nuScenes MOT benchmark show remarkable performance improvement over other state-of-the-art MOT methods and demonstrate the effect of our designed modules.

In recent years, with increasing user demands for convenience, privacy, and personalized experiences, gait recognition has been widely studied across various domains, such as indoor intrusion detection and smart homes. Although computer vision solutions are extensively researched for their visual intuitiveness, Wi-Fi sensing is emerging as a new research focus due to its ability to preserve privacy. However, previous studies have primarily relied on abstract features with limited interpretability or required multiple Wi-Fi links. To address these issues, we propose Wi-GR, which utilizes a Wi-Fi link to extract robust and highly interpretable gait features for user recognition. First, we construct a multi-path gait signal model to establish a clear relationship between Channel State Information (CSI) and gait motion. Then, we design a gait signal separation and enhancement method to mitigate the effects of external non-target reflections and internal multi-part reflections, which significantly impact the extraction and interpretability of gait features. Finally, fine-grained gait features that visualize gait patterns are generated using MUSIC-based and GAN-based multi-part velocity profile generation algorithms, tailored for single-person and multi-person scenarios, respectively. Numerous experiments have demonstrated that Wi-GR achieves single-person recognition accuracies of 95.3%, 94.0%, and 93.2% for 30 persons in the meeting room, corridor, and lobby, respectively, and an average accuracy of 88.3% for two-person recognition.

Devices and servers in Federated Edge Learning (FEL) are self-interested and resource-constrained, making it critical to design incentives to improve model performance. However, dynamic network conditions raise energy consumption, while data heterogeneity undermines device cooperation. Current research overlooks the interplay between system efficiency and device clustering, resulting in suboptimal updates. To address these challenges, we develop BENCH, a bilateral pricing mechanism consisting of three core rules aimed at incentivizing participation from both devices and servers. Specifically, we first design a reward allocation rule, based on the Rubinstein bargaining model, which dynamically allocates rewards. Theoretically, we derive a closed-form solution for this rule, demonstrating BENCH achieves Nash equilibrium. Secondly, we design a device partitioning rule that leverages modularity to group similar devices, facilitating personalized edge aggregation to accelerate local data adaptation. Thirdly, we design an edge matching rule that employs the Kuhn-Munkres algorithm to balance the load at edge servers, thus minimizing the congestion. Together, these three rules enable hierarchical optimization of pricing and associations, effectively mitigating the impact of dynamic costs and device heterogeneity. Extensive experiments demonstrate BENCH's effectiveness in increasing device participation by 28.81% and improving model performance by 2.66% compared to state-of-the-art baselines.

Recent federated unlearning studies mainly focus on removing the target client's contributions from the global model permanently. However, the requirement for accommodating temporary user exits or additions in federated learning has been neglected. In this paper, we propose a novel recoverable federated unlearning scheme, named RFUL, which allows users to remove or add their local model to the global one at any time easily and quickly. It mainly consists of two main components,i.e.,knowledge unlearning and knowledge recovery. In knowledge unlearning, the target contributions can be eliminated by training with mislabeled target data, while preserving the non-target contributions through distillation using the original model. In knowledge recovery, the forgotten contributions can be restored by training the target data using classification loss, while the non-target contributions are maintained through feature distillation and parameter freezing on the classifier. Both knowledge unlearning and recovery processes only require the participation of target data, guaranteeing the algorithm's practicality in federated learning systems. Extensive experiments demonstrate the significant efficacy of RFUL. For knowledge unlearning, RFUL matches state-of-the-art methods using only target data, achieving a runtime speedup of 3.3 to 8.7 times compared to retraining across various datasets. For knowledge recovery, RFUL exceeds state-of-the-art incremental learning methods by 5.02% to 29.97% in accuracy and achieves a runtime speedup of 1.8 to 4.4 times compared to retraining on different datasets.

Event matching is a key component in a large-scale content-based publish/subscribe system. The performance of most existing algorithms is easily affected by the subscription matching probability. In this paper, we propose a new data structure, named AWAW B+-Tree, which is based on the width of the predicates, to efficiently index the subscriptions. The most notable feature ofAW B+-Treeis its ability to combine the advantages of different matching methods, thus achieving high and robust performance in dynamic environments. First, we implement both a forward matching method (AFM) and a backward matching method (ABM) based onAW B+-Tree. Then, we introduce a hybrid matching method (AHM) that combines AFM and ABM. Moreover, we extendAW B+-Treein three aspects: approximate matching, string type matching, and fine-grained parallelization. We conducted extensive experiments to evaluate the performance of the proposed matching algorithms on synthetic and real-world datasets. The experiment results reveal that AHM achieves a reduction in matching time by up to 53.8% compared to the state-of-the-art method. Additionally, AHM exhibits improved performance robustness, with up to a 76.9% reduction in terms of the standard deviation of matching time. Particularly in dynamic scenarios, AHM is at least 2.3 times faster and 41.3% more stable than its counterparts. Furthermore, by implementing parallelization, the matching speed of 8 threads can be accelerated by 4.16 times compared to the single-thread matching speed.

Multi-hop cooperative task offloading (MCTO) allows resource-constrained edge clouds to collaborate and assist each other in completing computation-intensive tasks, such as training machine learning models through device-edge-cloud (DEC) computing. However, internal fake service attacks can pose a threat to the security and reliability of MCTO in DEC computing. In this paper, we propose a trust model based on a directed acyclic graph (DAG) and Proof-of-Work (PoW) to safeguard tasks against potential attacks. The edge node selection for task offloading involves two key steps: offloading confirmation and trust-based node selection. To mitigate the unreliability caused by internal fake service attacks during cooperative offloading, we propose a multi-hop offloading node selection algorithm based on the soft actor-critic (SAC) coalition. This algorithm helps identify trustworthy nodes for constructing secure offloading paths. Our experimental results demonstrate that the proposed algorithm effectively counters internal fake service attacks and significantly reduces cooperative offloading latency compared to existing leading approaches.

Consensus mechanism is the foundational protocol for achieving distributed consistency among replicas in a blockchain network. A well-designed consensus mechanism needs to balance performance such as complexity, consensus mechanism initiative and dynamic adaptability. Based on Hot-Stuff (a BFT-like consensus with O(n) complexity), we propose a practical and robust consensus mechanism, namely Practical and Robust HotStuff, denoted as PRO-HotStuff. Firstly, PRO-HotStuff redesigns a pacemaker that simultaneously supports replica synchronization and quorum certificate caching, and thus achieves practical view change with O(n) communication complexity while avoiding the additional phase introduced by HotStuff. Secondly, PRO-HotStuff gives the leader election basis by introducing the theory of planed behavior (TPB) and a reputation mechanism. It facilitates restricting the malicious replicas while encouraging the trustworthy replicas, thus to enhance the robustness of PRO-HotStuff. Thirdly, the implementation design of PRO-HotStuff as well as its reconfiguration mechanism for dynamic adaptability is presented. Proofs of correctness of PRO-HotStuff are also provided. Finally, experiments demonstrate that compared to existing HotStuff-like consensus mechanisms, PRO-HotStuff has significant advantages in terms of consensus performance, security, and dynamic adaptability.

In this work, we study the problem of dispatching an unmanned aerial vehicle (UAV) for data collection of Internet of Things (IoT) devices, where a UAV departs from a data center, then visits some IoT devices for data collection and finally returns to the data center. Different from most existing works on UAV-enabled data collection, we assume that the UAV’s collection point, i.e., the location where the UAV stays during the data collection process, can be deployed anywhere within the communication range of each IoT device, rather than being assumed to be in a fixed position. This new assumption is motivated by the fact that the collection point has a great impact on both time and energy consumption of the UAV during its data collection tour. Thus, in this work, we focus on minimizing the UAV’s task completion time and energy consumption during a data collection tour, by jointly optimizing the UAV’s collection point for each IoT device, flight trajectory and flight speed. We formulate this problem as a multiobjective optimization problem, which is solved by executing the following three successive steps: 1) we first employ the ant colony optimization (ACO) algorithm to decide the UAV’s visiting order of all IoT devices; 2) we then reduce the searching space of the collection point for each visited IoT device by using geometric theory and reformulate the original problem; and 3) we finally develop an enhanced multiobjective particle swarm optimization (EMOPSO) algorithm by incorporating a novel gbest selection strategy to identify the optimal collection point for each visited IoT device, based on which the corresponding flight trajectory as well as the flight speed is calculated. We refer to the above three-step hybrid algorithm as ACO-EMOPSO-G. Extensive evaluations validate the superiority of ACO-EMOPSO-G in terms of the tradeoff between the UAV’s task completion time and energy consumption, compared with some other data collection approaches.

The distributed nature of multitier swarm attacks renders it more difficult for a single-tier intrusion detection system (IDS) to secure cross-tier computation offloading in multitier sensor edge cloud (SEC). To perceive and prevent such attacks, we model IDSs in different layers as an IDS federation network (IDFN) and present a generic framework to prevent cooperative attacks and reconfigure the defense strategy of IDFN across the three-tier SEC. The framework provides single-tier, two-tier, and three-tier dynamic awareness models based on the susceptible-infected-susceptible (SIS) dynamical equations to characterize the update process of message states to obtain the equilibrium solution between alarm messages and normal messages captured by IDSs. For swarm attack events from multitier SEC, we model the cross-tier cooperative interactions between IDSs and swarm attackers as an event–condition–action (ECA) regret learning game (ERLG) to achieve a distributed IDS reconfiguration to reduce the overall SEC alarm messages while ensuring the equilibrium of message states with the cooperation of IDSs. Simulation results demonstrate that our proposed scheme is superior to other reconfiguration mechanisms under swarm attacks in three-tier SEC.

To realize carbon emission reduction, restricting regional carbon emissions while meeting electricity usage is a critical but not trivial problem. In this paper, we propose a game-based pricing scheme for joint carbon emission rights (CER) and electricity trading between the electricity prosumers within a microgrid. For modeling and theoretical analysis, we first introduce the utility functions of electricity producers and consumers, which are determined by CER and electricity prices in a coupled way. Then, the multi-leader multi-follower (MLMF) Stackelberg game and non-cooperative game are employed to formulate the electricity and CER pricing and trading, respectively. The game equilibriums convince that optimal prices for both electricity and CER exist to satisfy electricity usage while meeting the carbon emission restriction. For implementation, the blockchain with smart contracts is developed to undertake the CER and electricity trading in a transparent and credible way. A prototype system based on Fabric blockchain verifies the feasibility of the proposed scheme, which demonstrated a five-fold increase in the economics and electricity generation utility of the microgrid and achieved a 2% reduction in carbon emissions compared to the baseline model.

Linking medical data silos for medical model learning and sharing makes for better healthcare for humanity. Before that, two critical issues must be solved, i.e., patient privacy protection and data contributors’ rights and interests. This paper proposes an evolutionary medical data modeling and sharing (EMDMS) framework. Specifically, EMDMS adopts a federated learning scheme to coordinate the decentralized medical model learning and model aggregation without the leakage of raw data. A dual-loop federated learning mechanism with a tailored control strategy is developed for the realization of evolutionary model learning with the consideration of the ever-growing medical data. Then, a long-term pricing and revenue distribution strategy is designed for evolutionary model sharing, thus to make the medical model self-growth. It not only ensures fair benefits for data contributors but also enables low-cost sharing of models for public welfare. EMDMS runs on the sharded blockchain to support parallel tasks where dedicated smart contracts are implemented for EMDMS to guarantee security and trustworthiness. A prototype system with simulations on the Fed-ISIC2019 dataset demonstrates the effectiveness of EMDMS and its advantages over some existing typical solutions.

Nowadays, sign language is becoming increasingly important in people’s daily life. Existing solutions are often based on wireless signals (e.g., acoustic, visible, and WiFi) or wearable sensors to recognize gestures, but they suffer from vulnerability to environmental influences, poor security, and high energy consumption, which prevent them from accurately capturing finger micromovements. In this article, we propose RF-Sign, which uses passive radio-frequency identification (RFID) tags to capture multiple finger micromovements simultaneously to enable sign language support. In particular, two main issues are studied. One is the problem of positional differences when users make the same gesture, and the other is the problem of segmenting consecutive gestures using only empirical thresholding methods and ignoring the existence of differences in thresholds for different gestures. For position differences, we propose position models to normalize the hand’s horizontal rotation angle and radial distance. For segmenting consecutive gestures, we use the received signal strength (RSS) trend of the reference tag to represent the finger micromovements state. The experimental results show that the average accuracy reaches 92.81% under different angles, distances, and other conditions.

Feature pyramid network (FPN) can highly improve the performance of object detection by extracting multiscale features. However, current FPN-based methods suffer from intrinsic correlation of local information loss in each feature map, which brings about the semantic information effective transmission problem. In addition, 1 × 1 convolution in lateral connection of FPN may cause spatial information loss. In this article, we propose a novel semantic and spatial information enhancing feature pyramid network (Dual SIE-FPN), which mainly focuses on alleviating multiscale hierarchical feature transmission loss and enhancing the feature representation. Specifically, Dual SIE-FPN contains three modules: Lateral Feature Enhancement (LFE), Global Attention Upsampling (GAU), and Multiple Information Compensation (MIC). LFE is designed to capture deep semantic representation and enhance channel information. GAU is established to make up for spatial information loss caused by upsampling, and transmit the high-level features with the compensatory information to low-level features simultaneously. MIC is designed to work with LFE in parallel to further improve the information loss resulting from 1 × 1 convolution. Experimental results on MS COCO and UAVDT dataset demonstrate that Dual SIE-FPN achieves competitive performance compared to other state-of-the-art FPNs. In addition, our proposed Dual SIE-FPN can be embedded into any multiscale feature extraction-based computer vision tasks to improve the performance.

Acoustic target tracking has shown great advantages for device-free human-machine interaction over vision/RF-based mechanisms. However, existing approaches for portable devices solely track a single target, incapable of the ubiquitous and highly challenging multi-target situations such as double-hand multimedia controlling and multi-player gaming. In this paper, we proposeAMT$^+$, a pioneering smartphone MIMO system to achieve centimeter-level multi-target tracking. The challenge of multi-target occlusion is effectively addressed by employing multiple speaker-microphone pairs. However, the unique challenge raised by MIMO is the superposition of multi-source signals due to the cross-correlation among speakers. Initially, we tackle this challenge by designing a weak cross-correlation signal to reduce interference passively. InAMT$^+$, we’ve further integrated self-interference cancellation for active minimize interference. The most distinguishing advantage ofAMT$^+$lies in the elimination of the raised multipath effect, which is commonly ignored in previous work by hastily assuming targets as particles.AMT$^+$employs Doppler filtering over delay subtraction for echo suppression. Further, by non-particle target reflections modeling results, we introduce a distance-projection-based method for continuous target identification and tracking. Implemented on commercial smartphones,AMT$^+$achieves on average 0.54 cm, 1.37 cm, and 2.13 cm errors for single, double, and triple target tracking respectively, and on average 97.0% classification accuracy for 14 controlling gestures.

Wireless rechargeable sensor networks (WRSNs) are promising in maintaining sustainable large-area monitoring tasks. Mobile chargers (MCs) are commonly used in WRSNs to replenish energy to nodes due to its flexibility and easy maintenance. Most existing works on WRSNs focus on designing offline or model-based online charging methods, which need the exact system information to conduct the optimization. However, in practical WRSNs, the exact system information such as the nodes' locations and energy consumption rates may not be easily accessible to the optimizer due to their unpredictability and high dynamics. Thus, in this work, we jointly optimize the MC's trajectory design and charging scheduling in a general and practical WRSN with inaccessibility to the exact system information, such that the charging utility of the MC is maximized. To address this problem, we introduce the model-free reinforcement learning (RL) technique, which enables the MC to learn to jointly optimize its moving trajectory and charging scheduling by interacting with the environment and tracking feedback signals from nodes and obstacles in real time. Specifically, we develop a soft actor-critic based mobile security policy intervened algorithm (SAC-MSPI) based on a novel safe RL framework, which maximizes the MC's charging utility while maintaining the safe movement (not hitting obstacles) for the MC during the entire charging period. Extensive evaluation results show that the proposed SAC-MSPI algorithm outperforms existing main RL solutions and traditional algorithms with respect to the charging utility maximization as well as the collision avoidance.

In this work, we study the problem of dispatching multiple unmanned aerial vehicles (UAVs) for data collection in internet of things (IoT), where each UAV departs from its start point, visits some IoT devices for data collection and returns to its destination point. Considering the UAV’s limited onboard energy and the time required to collect data from all IoT devices, it is essential to appropriately assign the data collection task for each UAV, such that none of the dispatched UAVs consumes excessive energy and the maximum task completion time among all UAVs is minimized. To optimize those two conflicting objectives, we focus on minimizing the maximum task completion time and the maximum energy consumption among all UAVs, by jointly designing the flight trajectory, hovering positions for data collection and flight speed of each UAV. We formulate this problem as a multi-objective optimization problem with the aim of obtaining a set of Pareto-optimal solutions in terms of time or energy dominance. Due to the NP-hardness and complexity of the formulated problem, we propose a multi-strategy multi-objective ant colony optimization algorithm (MSMOACO), which is developed based on a constrained ant colony optimization algorithm with a fitnessguided mutation strategy and an adaptive hovering strategy being delicately incorporated, to solve the problem. To accommodate the practical scenario, we also design a novel geometry-based collision avoidance strategy to reduce the possibility of collisions among UAVs. Extensive evaluations validate the effectiveness and superiority of the proposed MSMOACO, compared with previous approaches.

For software-defined networking (SDN), multi-field packet classification plays a key role in the processing of flows, mainly involving fast packet classification and dynamic rule updates. Due to the increasing complexity and size of rulesets, it is becoming more difficult to design a packet classification algorithm which achieves fast lookup and update. In this paper, we propose a novel structure, PT-Tree, for packet classification with high overall performance. PT-Tree cascades the prefixes of multiple discriminatory bytes to achieve efficient partitioning of the ruleset, thereby reducing the search space and ensuring the performance of both lookup and update. Meanwhile, a multi-granularity priority-aware pruning mechanism (MPPM) based on PT-Tree filters out most of the candidate subsets, which further improves the lookup speed. In addition, we propose an auxiliary tree-based optimization method (ATOM) to cope with severely overlapping rules in the search space. Therefore, PT-Tree can better handle the case where the rules in certain fields are skewed. We conduct comprehensive experiments to evaluate the performance of PT-Tree. The results show that compared with the state-of-the-art, the lookup time of PT-Tree is reduced by at least 49.95% on average. Moreover, PT-Tree is also at least 7.13x and 33x faster than the baselines in terms of the update and construction speed on average, respectively. Meanwhile, the performance stability of PT-Tree on multiple rulesets improves by up to 13.68 times.

Packet classification, as a crucial function of networks, has been extensively investigated. In recent years, the rapid advancement of software-defined networking (SDN) has introduced new demands for packet classification, particularly in supporting dynamic rule updates and fast lookup. This paper presents a novel structure called DBTable for efficient packet classification to achieve high overall performance. DBTable integrates the strengths of conventional packet classification methods and neural network concepts. Within DBTable, a straightforward indexing scheme is proposed to eliminate rule replication, thereby ensuring high update performance. Additionally, we propose an iterative method for generating a discriminative bitset (DBS) to evenly partition rules. By utilizing the DBS, rules can be efficiently mapped in a hash table, thus achieving exceptional lookup performance. Moreover, DBTable incorporates a hybrid structure to further optimize the worst-case lookup performance, primarily caused by data skewness. The experiment results on 12 256k rulesets show that, compared to seven state-of-the-art schemes, DBTable achieves an overall lookup speed improvement ranging from 1.53x to 7.29x, while maintaining the fastest update speed.

Although federated learning (FL) enables collaborative training across multiple data silos in a privacy-protected manner, naively minimizing the aggregated loss to facilitate an efficient federation may compromise its fairness. Many efforts have been devoted to maintaining similar average accuracy across clients by reweighing the loss function while clients’ potential contributions are largely ignored. This, however, is often detrimental since treating all clients equally will harm the interests of those clients with more contribution. To tackle this issue, we introduce utopian fairness to expound the relationship between individual earning and collaborative productivity, and proposeFederated-UtoPia (FedUP), a novel FL framework that balances both efficient collaboration and fair aggregation. For the distributed collaboration, we model the training process among strategic clients as a supermodular game, which facilitates a rational incentive design through the optimal reward. As for the model aggregation, we design a weight attention mechanism to compute the fair aggregation weights by minimizing the performance bias among heterogeneous clients. Particularly, we utilize the alternating optimization theory to bridge the gap between collaboration efficiency and utopian fairness, and theoretically prove that FedUP has fair model performance with fast-rate training convergence. Extensive experiments using both synthetic and real datasets demonstrate the superiority of FedUP.

In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless rechargeable sensor network (WRSN), where a rotary-wing UAV travels along a fixed trajectory while providing wireless charging services for a set of sensor nodes deployed on the ground. Given the practical speed-related flight energy model, we focus on minimizing the UAV’s flight energy during a time-bounded charging tour by appropriately controlling the UAV’s travelling speed, such that the charging demand of each node is satisfied. We first investigate the optimal speed control with the minimized flight energy on arbitrarily-shaped trajectories in a 2D space. We adopt the spatial discretization to tackle the non-convexity of the formulated problem, which is then solved by interior-point method with the provable upper bound of the UAV’s flight energy. Next, we develop the optimal speed control for the UAV to travel along a 1D trajectory, i.e., a straight line, which is commonly seen in many UAV applications. Extensive evaluations validate the effectiveness of our speed control design in terms of the UAV’s flight energy minimization.

Energy harvesting technology enables wireless sensor networks (WSNs) to be self-sustainable, for maintaining long-term key performance indicators, such as the data throughput and sensing coverage. Due to the highly dynamic and complex environment, energy sources (ES) cannot provide stable energy supply, which needs the efficient learning algorithm to enable system adaptations. This article reports on the development of reinforcement learning (RL) methodology to long-term data collection in self-sustainable WSNs. Specifically, we consider the WSN as a 2-D rectangular region, where a mobile sensor (MS) can harvest energy from ambient environments while transmitting the collected data to a fixed sink. Due to the changing environment and the mobility of the MS, the harvested energy by the MS at each slot presents spatiotemporal dynamics within the network, which severely affects the performance of data throughput from the MS to the sink. The MS’s trajectory is investigated to maximize the long-term average MS-to-sink data throughput. Due to the unknown energy arrival information as well as the locations of ESs, we formulate the problem as a Markov decision process, which is then solved with model-free RL. In particular, the deep deterministic policy gradient (DDPG) is applied to tackle the continuous and deterministic movement space. Results show that the MS can learn and optimize the moving trajectory by intelligently tracking the aggregated received energy over slots. Finally, the MS can identify and move to the optimal location where the maximized long-term average MS-to-sink data throughput is achieved. Extensive numerical evaluations are conducted to investigate the impact of various system parameters on the network performance.

Industrial wireless sensor networks (IWSNs) have been considered as promising technology to enhance target tracking and state monitoring in process industries with harsh environments. In this article, the codesign of IWSNs and consensus-based sequential estimation (CSE) for typical long-belt process industries is proposed. Specifically, a group-based IWSNs deployment strategy is first designed to cover the transportation belt. Over the group-based IWSNs strategy, the CSE algorithm is then proposed to conduct target tracking and state estimation using the distributed Kalman filter. To reveal the interrelation of IWSNs and CSE, the upper bound on the error violation probability of state estimation is first deduced from the sequential estimation process. Then, the algorithm is developed for the determination of IWSNs parameters (such as the number of groups of the IWSNs and communication design) and the CSE algorithm parameters (such as iterations of sequential estimation and estimation accuracy prediction). A case study of slab temperature monitoring over the hot strip milling process demonstrates the effectiveness of the proposed codesign of IWSNs and CSE.

Mobile chargers (MCs) are usually dispatched to deliver energy to sensors in wireless rechargeable sensor networks (WRSNs) due to its flexibility and easy maintenance. This paper concerns the fundamental issue of charging path DEsign with the Minimized energy cOst (DEMO), i.e., given a set of rechargeable sensors, we appropriately design the MC’s charging path to minimize the energy cost which is due to the wireless charging and the MC’s movement, such that the different charging demand of each sensor is satisfied. Solving DEMO is NP-hard and involves handling the tradeoff between the charging efficiency and the moving cost. To address DEMO, we first investigate how to identify a single charging position where the MC could stay to charge a set of sensors distributed within a small area with the maximized charging efficiency. Then, based on the result obtained in the case of optimizing a single charging position, we develop a computational geometry-based algorithm to deploy multiple charging positions within the whole network, by considering the fixed and finite charging range of the MC. We prove that the designed algorithm has the approximation ratio of$O\!\left ({\ln \!N}\right)$, where$N$is the number of sensors. Then we construct the charging path by calculating the shortest Hamiltonian cycle passing through all the deployed charging positions within the network. In addition, we investigate the impact of the network topology as well as the distribution of charging demands among sensors on the MC’s energy cost during a charging tour. Extensive evaluations validate the superiority of our path design in terms of the MC’s energy cost minimization, compared with existing main algorithms.

The newly emerging “fail-slow” failures plague both software and hardware where the victim components are still functioning yet with degraded performance. To address this problem, this article presents Perseus , a practical fail-slow detection framework for storage devices. Perseus leverages a light regression-based model to quickly pinpoint and analyze fail-slow failures at the granularity of drives. Within a 10-month close monitoring on 248K drives, Perseus managed to find 304 fail-slow cases. Isolating them can reduce the (node-level) 99.99th tail latency by 48%. We assemble a large-scale fail-slow dataset (including 41K normal drives and 315 verified fail-slow drives) from our production traces, based on which we provide root cause analysis on fail-slow drives covering a variety of ill-implemented scheduling, hardware defects, and environmental factors. We have released the dataset to the public for fail-slow study.

The exponential growth of Massive Open Online Courses (MOOCs) surges the needs of advanced models for personalized Online Education Services (OES). Existing solutions successfully recommend MOOCs courses via deep learning models, they however generate weak “course embeddings” with original profiles, which contain noisy and few enrolled courses. On the other hand, existing algorithms provide recommendation orders according to the score of each course while ignoring personalized demands of users. To tackle the above challenges, we propose aMetaHierarchicalReinforcedRankingapproachMHRR, which consists of a meta hierarchical reinforcement learning pre-trained mechanism and an over-parameterized ranking regressor to enhance the representation learning of courses and learners while refining the ranking result of recommended courses. Specifically,MHRRcombines a user profile reviser and a meta embedding generator to provide course embedding representation enhancement for recommender services. Furthermore,MHRRtransforms learned representations generated from recommender services with Gaussian kernel approximation to over-parameterize the downstream learning to rank (LTR) models with representations in ultra-high dimensionality. We deployMHRRon a real-world MOOCs platform and evaluate it with a large number of baseline models. The results show thatMHRRoutperforms baseline algorithms on two major metrics, including Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG). Also, we conduct a 7-day online evaluation using the realistic traffic of a large-scale real-world MOOCs platform, where we can still observe significant improvement in real-world applications.MHRRperforms consistently both in the online and offline evaluation.

Mobile chargers (MCs) are usually dispatched to deliver energy to sensors in wireless rechargeable sensor networks (WRSNs) due to its flexibility and easy maintenance. This paper concerns the fundamental issue of charging path DEsign with the Minimized energy cOst (DEMO), i.e., given a set of rechargeable sensors, we appropriately design the MC’s charging path to minimize the energy cost which is due to the wireless charging and the MC’s movement, such that the different charging demand of each sensor is satisfied. Solving DEMO is NP-hard and involves handling the tradeoff between the charging efficiency and the moving cost. To address DEMO, we first develop a computational geometry-based algorithm to deploy multiple charging positions where the MC stays to charge nearby sensors. We prove that the designed algorithm has the approximation ratio of O(lnN), where N is the number of sensors. Then we construct the charging path by calculating the shortest Hamiltonian cycle passing through all the deployed charging positions within the network. Extensive evaluations validate the effectiveness of our path design in terms of the MC’s energy cost minimization.

To ensure an accurate power allocation against increasing intelligent jamming attacks on the offloading link of computation tasks, we investigate interactions between a cluster head node and an intelligent jammer using a Stackelberg game framework, under the constraint of the total power to use and the limited knowledge of its own channel gain for each player. In this game, the intelligent jammer gathers channel gain information and processes it using a deep neural network (DNN) to infer the accurate jamming power as an attack strategy. The cluster head node also exploits DNN to infer an accurate transmission power as a defense strategy according to the varying channel gain. We model the optimization of the attack and defense strategies using single channel jamming DNN (SJnet), multiple channel jamming DNN (MJnet), single channel sensor DNN (SSnet), and multiple channel sensor DNN (MSnet) for the single (multiple) channel jamming attacks. In addition, we extend the design to the scenario where the intelligent jammer can launch a hybrid mode jamming attack, and propose a DNN Stackelberg game-based defense scheme. Numerical simulation results demonstrate that our proposed mechanism is superior to other power allocation mechanisms under different scenarios in the sensor edge cloud.

Federated learning is an emerging paradigm to conduct the machine learning collaboratively but avoid the leakage of original data. Then, how to motivate the data owners to participate federated learning and contribute high-quality data is the crucial issue. In this article, a blockchain-based federated learning (BFL) with a reputation mechanism for high-quality model aggregation is proposed. Specifically, the blockchain transforms the federated learning into a decentralized and trustworthy manner. Over the blockchain, federated learning tasks, undertaken by smart contracts, can be conducted transparently and fairly. Besides, a reputation-constrained data contribution and reward allocation mechanism is designed to encourage data owners to participate in BFL and contribute high-quality data. The noncooperative game is adopted to analyze the behavior strategies of data owners. The existence of the unique equilibrium is proved and the equilibrium point indicates that the data owners can acquire highest reward with the contribution of the highest quality data. Thus, the model quality of BFL is guaranteed. Finally, simulations on the public data sets (MNIST and CIFAR10) demonstrate that BFL with a reputation mechanism can well promote the high-quality model aggregation of federated learning as well as can prevent malicious nodes from corrupting the training task.

Real-time fault diagnosis on vehicles can effectively avoid potential accidents, which, however, is difficult and challenging to be widely deployed due to the low computational capability and limited data storage of electric vehicles (EVs). To address this issue, we propose a vehicle-mounted fault diagnosis system with low computational complexity and small data storage, for achieving real-time monitoring of vehicle status. To facilitate the accurate and optimized feature selection, we had been collecting 6.52-GB real data from three EVs in 12 months. Motivated by those data, we first propose a multilabel feature selection algorithm to obtain the feature weights, based on which the optimal number of features is then calculated through the backpropagation neural network (BPNN), thus minimizing the computational cost of real-time fault diagnosis regarding sample dimensions. To further simplify the fault diagnosis system, i.e., reducing the minimum required capacity of data storage, we design a real-time diagnosis sliding window (RDSW) where the window moves forward as new samples arrive and the stale data outside the window are discarded. In particular, we calculate the optimal size of RDSW, which controls the minimum required number of samples to guarantee the accuracy of real-time fault diagnosis. Owing to the mechanism of RDSW, vehicles no longer need to store massive data to guarantee the accuracy of real-time fault diagnosis. In addition, the results of real-time fault diagnosis at each vehicle can be shared with other vehicles in cooperative intelligent transportation systems (C-ITS). Finally, comprehensive simulation is conducted to validate the effectiveness of the proposed diagnosis system in terms of accuracy, complexity and storage capacity.

Researchers have proposed many novel methods to detect abnormal taxi trajectories. However, most of the existing methods usually adopt a counting-based strategy, which may cause high false positives due to imprecisely identifying diverse trajectories as anomalies and therefore, they need the support of large-scale historical trajectories to work properly. To improve detection precision and efficiency, in this article, we propose STR, an online abnormal taxi trajectory detection method based on spatio-temporal relations. The basic principle behind STR is that given the displacement from the source point to a testing point, if the driving time and driving distance are not within the normal ranges, the point is identified as anomalous. To learn the two normal ranges for driving time and driving distance, STR defines two spatio-temporal models which characterize the relationship between displacement and driving distance/driving time. To improve detection efficiency, STR reduces the number of models that need to be learned by making full use of the similarity of transportation modes in different time periods and neighboring areas. The effectiveness and performance of STR are evaluated on real-world taxi trajectories. The experiment results show that compared with counting-based methods, STR achieves greater precision by reducing false positives. Furthermore, STR is more efficient than its counterparts and is suitable for online detection.

Visual tracking has achieved great progress due to numerous different algorithms. However, deep trackers based on classification or Siamese network still have their specific limitations. In this work, we show how to teach machines to track a generic object in videos like humans, who can use a few search steps to perform tracking. By constructing a Markov decision process in Deep Reinforcement Learning (DRL), our agents can learn to determine hierarchical decisions on tracking mode and motion estimation. To be specific, our Hierarchical DRL framework is composed of a Siamese-based observation network which models the motion information of an arbitrary target, a policy network for mode switch and an actor-critic network for box regression. This tracking strategy is more in line with human behavior paradigm, and is effective and efficient to cope with fast motion, background clutter and large deformations. Extensive experiments on the GOT-10k, OTB-100, UAV-123, VOT and LaSOT tracking benchmarks, demonstrate that the proposed tracker achieves state-of-the-art performance while running in real-time.

In the last few years, single image super-resolution (SISR) has benefited a lot from the rapid development of deep convolutional neural networks (CNNs), and the introduction of attention mechanisms further improves the performance of SISR. However, previous methods use one or more types of attention independently in multiple stages and ignore the correlations between different layers in the network. To address these issues, we propose a novel end-to-end architecture named global-context attention network (GCAN) for SISR, which consists of several residual global-context attention blocks (RGCABs) and an inter-group fusion module (IGFM). Specifically, the proposed RGCAB extracts representative features that capture non-local spatial interdependencies and multiple channel relations. Then the IGFM aggregates and fuses hierarchical features of multi-layers discriminatively by considering correlations among layers. Extensive experimental results demonstrate that our method achieves superior results against other state-of-the-art methods on publicly available datasets.

Abstract Human–computer interaction through touch screens plays an increasingly important role in our daily lives. Besides smartphones and tablets, laptops are the most prevalent mobile devices for both work and leisure. To satisfy the requirements of some applications, it is desirable to re-equip a typical laptop with both handwriting and drawing capability. In this paper, we design a virtual writing tablet system, VPad, for traditional laptops without touch screens. VPad leverages two speakers and one microphone, which are available in most commodity laptops, to accurately track hand movements and recognize writing characters in the air without additional hardware. Specifically, VPad emits inaudible acoustic signals from two speakers in a laptop and then analyzes energy features and Doppler shifts of acoustic signals received by the microphone to track the trajectory of hand movements. Furthermore, we propose a state machine-based trajectory optimization method to correct the unexpected trajectory and employ a stroke direction sequence model based on probability estimation to recognize characters users write in the air. Experimental results show that VPad achieves the average error of 1.55 cm for trajectory tracking and the accuracy over 90% of character recognition merely through built-in audio devices on a laptop.

Restaurant recommendation is one of the most recommendation problems because the result of recommendation varies in different environments. Many methods have been proposed to recommend restaurants in a mobile environment by considering user preference, restaurant attributes, and location. However, there are few restaurant recommender systems according to the internet of vehicles environment. This paper presents a recommender system based on the prediction of traffic conditions in the internet of vehicles environment. This recommender system uses a phased selection method to recommend restaurants. The first stage is to screen restaurants that are on the user’s driving route; the second stage is to recommend restaurants from the user attributes, restaurant attributes (with traffic conditions), and vehicle context, using a deep learning model. The experimental evaluation shows that the proposed recommender system is both efficient and effective.

Wireless power transfer (WPT) enables a reliable and convenient charging paradigm. This article concerns the fundamental issue of energy saving in wireless rechargeable sensor networks (WRSNs), i.e., given a fixed number of rechargeable sensors (RSs) with their locations and charging demands, we focus on a minimal charging expenditure (MAP) problem with directional WPT to decrease the energy expenditure of the charger, on condition that the charging demands of all sensors are satisfied. In particular, we consider the anisotropic energy receiving property of RSs, which is closely related to the distance and the angle between the sensor and the charger antenna's orientation in directional WPT. We transform the MAP problem into an optimal function placement (OFA) problem, which can be geometrically analyzed in a rectangular coordinate system and is NP-hard. First, we study the OFA problem in the case of uniformly distributed sensors with identical charging demands, we develop the uniform charging strategy (UCS) to bound the total charging expenditure as Θ(1) for any number of sensors N. Based on the acquired insights, we further studied the OFA problem when the distribution of charging demands is Gaussian. We bound the total charging expenditure as Θ(1) for any number of sensors N, by developing the layered charging strategy (LCS). Extensive simulation results confirmed the performance of our design compared with two baseline algorithms. Both of the theoretical and simulations results reveal that the total energy expenditure of the charger is strongly related to the sensors' charging demands, however, is less affected by the number of sensors in the network.

This article reports on the development of a deep Q-learning approach to long-term energy collection in self-sustainable sensor networks, which consists of two static chargers (SCs) and one rechargeable mobile sensor (MS). In particular, we assume that the SCs can harvest energy from the ambient environment and charge the MS via electromagnetic (EM) radiation. As the energy harvesting (EH) process is random and the radiated energy fades over distance, the achievable energy by the MS at each slot demonstrates spatiotemporal dynamics in a certain area. Thus, we focus on the problem of trajectory optimization for an autonomous MS to maximize the long-term average achievable energy per slot from both chargers. Due to the inaccessible charger-side information, such as the EH profile and locations of SCs as well as the transmit power, we introduce deep Q-learning, a model-free reinforcement learning approach, based on which the MS can learn and optimize the moving trajectory by intelligently tracking the aggregated received EM signal without any other explicit external information. Simulation results show that the MS can identify the best energy collecting location and finally moves there along the learned trajectory. We also investigate the impact of system parameters, such as initial position and moving cost per unit distance on the performance of the proposed training algorithm, such as convergence rate and stability via extensive numerical evaluations.

To enhance dependable resource allocation against increasing distributed denial-of-service (DDoS) attacks, in this article, we investigate interactions between a sensor device-edgeVM pair and a DDoS attacker using a game-theoretic framework, under the constraints of the task time, resource budget, and incomplete knowledge of the processing time of machine learning tasks. In this game, the sensor device expects an edgeVM to cooperate and choose its resource allocation strategy with the objective of satisfying the minimum resource required of machine learning tasks at the corresponding sensor device. Similarly, the attacker's objective is to strategically allocate resources so that the resource constraint of the machine learning tasks is not satisfied. Owing to a lack of complete information of the processing time of the machine learning tasks, this strategic resource allocation problem between the two players is modeled as a Bayesian Q-learning game, in which the optimal strategies of the sensor device-edgeVM pair and the attacker are analyzed. Furthermore, probability distributions are employed by the corresponding players to model the incomplete nature of the game and a greedy Q-learning algorithm is proposed to dependable resource allocation against DDoS attacks. Numerical simulation results demonstrate that the proposed mechanism is superior to other dependable resource allocation mechanisms under incomplete information for DDoS attacks in the sensor edge cloud.

With the development of multiaccess edge computing (MEC) technology at the network edge, efficient resources allocation and offloading between the resource-constrained edge clouds to maintain load balancing become a looming problem recently. However, most existing researches aimed at optimizing the allocation of computing resources are based on simulation and apply only for some typical Internet-of-Things (IoT) applications on mobile devices (i.e., they all lack of practicality and generality). In this article, we present a novel edge computing platform (CampEdge) with 36 edge nodes based on the wireless access point (AP) for adaptive resources allocation and computation offloading in a complicated dynamic campus environment. We first collected and sufficiently analyzed a large real-world WiFi data set, covering more than 8500 wireless APs and serves 44 000 active end users within an area of 3.1 km2over three months. A multiclass classification algorithm based on the random forest was then used with this data to accurately predict the state of resources usage at each edge node (i.e., in a busy state or normal state). To reasonably offload and transmit end-users' computational tasks to these edge nodes and optimize the total latency cost among the whole offloading process, we then illustrate a distributed computation offloading optimization strategy to formulate this complicated problem as a multiobjective latency optimization problem based on alternating direction method of multipliers (ADMM). Furthermore, we briefly discuss the convergence of our system. In experiments, CampEdge was shown to decrease user latency by up to 30%, compared to state-of-the-art methods. The proposed strategy was also shown to adaptively conform to a variety of compute-intensive and time-sensitive IoT applications for end users.

Connected vehicles have been considered as an effective solution to enhance driving safety as they can be well aware of nearby environments by exchanging safety beacons periodically. However, under dynamic traffic conditions, especially for dense-vehicle scenarios, the naive beaconing scheme where vehicles broadcast beacons at a fixed rate with a fixed transmission power can cause severe channel congestion and thus degrade the beaconing reliability. In this paper, by considering the kinematic status and beaconing rate together, we study the rear-end collision risk and define a danger coefficient ρ to capture the danger threat of each vehicle being in the rear-end collision. In specific, we propose a fully distributed adaptive beacon control scheme, called ABC, which makes each vehicle actively adopt a minimal but sufficient beaconing rate to avoid the rear-end collision in dense scenarios based on individually estimated ρ. With ABC, vehicles can broadcast at the maximum beaconing rate when the channel medium resource is enough and meanwhile keep identifying whether the channel is congested. Once a congestion event is detected, an NP-hard distributed beacon rate adaptation (DBRA) problem is solved with a greedy heuristic algorithm, in which a vehicle with a higher ρ is assigned with a higher beaconing rate while keeping the total required beaconing demand lower than the channel capacity. We prove the heuristic algorithm's close proximity to the optimal result and thoroughly analyze the communication overhead of ABC scheme. By using Simulation of Urban MObility (SUMO)-generated vehicular traces, we conduct extensive simulations to demonstrate the efficacy of our proposed ABC scheme. Simulation results show that vehicles can adapt beaconing rates according to the driving safety demand, and the beaconing reliability can be guaranteed even under high-dense vehicle scenarios.

Widespread and large-scale WiFi systems have been deployed in many corporate locations, while the backhual capacity becomes the bottleneck in providing high-rate data services to a tremendous number of WiFi users. Mobile edge caching is a promising solution to relieve backhaul pressure and deliver quality services by proactively pushing contents to access points (APs). However, how to deploy cache in large-scale WiFi system is not well studied yet quite challenging since numerous APs can have heterogeneous traffic characteristics, and future traffic conditions are unknown ahead. In this paper, given the cache storage budget, we explore the cache deployment in a large-scale WiFi system, which contains 8,000 APs and serves more than 40,000 active users, to maximize the long-term caching gain. Specifically, we first collect two-month user association records and conduct intensive spatio-temporal analytics on WiFi traffic consumption, gaining two major observations. First, per AP traffic consumption varies in a rather wide range and the proportion of AP distributes evenly within the range, indicating that the cache size should be heterogeneously allocated in accordance to the underlying traffic demands. Second, compared to a single AP, the traffic consumption of a group of APs (clustered by physical locations) is more stable, which means that the short-term traffic statistics can be used to infer the future long-term traffic conditions. We then propose our cache deployment strategy, named LeaD (i.e., Large-scale WiFi Edge cAche Deployment), in which we first cluster large-scale APs into well-sized edge nodes, then conduct the stationary testing on edge level traffic consumption and sample sufficient traffic statistics in order to precisely characterize long-term traffic conditions, and finally devise the TEG (Traffic-wEighted Greedy) algorithm to solve the long-term caching gain maximization problem. Extensive trace-driven experiments are carried out, and the results demonstrate that LeaD is able to achieve the near-optimal caching performance and can outperform other benchmark strategies significantly.

For large-scale spatiotemporal-aware publish/subscribe systems, it is critical to design an efficient forwarding engine to achieve fast matching and maintenance of events and subscriptions. For this goal, we propose a novel data structure called MO-Tree to index both subscriptions and events in a unified way. The design philosophy behind MO-Tree is to keep the data structure concise, which manifests in three aspects: limiting the height of MO-Tree, trading space for time, and avoiding node merging and splitting. The difficulty in designing MO-Tree is how to efficiently index width-variable intervals. We present a multi-level cell-overlapping partition scheme and build a theoretical model to optimize the cell width in each level. To evaluate the performance of MO-Tree, a series of experiments is conducted on real-world trace datasets. The experiment results show MO-Tree significantly outperforms the state-of-the-art in terms of matching speed and maintenance cost.

Multi-access edge computing (MEC) plays an important role in taking the cloud computing resources closer to the end-user at the network edge. The main challenge in such systems is to build models to efficiently predict and offload computational resources to maintain load balancing among edge clouds. In this paper, we present an Efficient Resources Allocation and Computation Offloading (ERACO) model with 36 edge nodes based on the large-scale wireless access point (AP) in a campus environment. We first collected and sufficiently analyzed this large WiFi dataset, covering more than 8,500 wireless APs and serves 44,000 active end-users within an area of 3.1 km2over three months. A multi-class classification algorithm was then used with this data to predict the usage of computing resources at each edge node. To reasonably offload these resources and decrease the latency between each edge node, we then propose the optimization strategy to formulate the complicated offloading problem as a multi-objective latency optimization problem using the alternating direction method of multipliers (ADMM). Experimental results show that the models decrease user latency by up to 30%, compared to state-of-the-art methods for a variety of applications.

Understanding the nature of user-device interactions (e.g., who is using the device and what he/she is doing with it) is critical to many applications including time management, user profiles, and privacy protection. However, in scenarios where mobile devices are shared among family members or multiple employees in a company, conventional account-based statistics are not meaningful. This poses an even bigger problem when dealing with sensitive data. Moreover, fingerprint readers and front-facing cameras were not designed to continuously identify users. In this study, we developed MagPrint, a novel approach to fingerprint users based on unique patterns in the electromagnetic (EM) signals associated with the specific use patterns of users. Initial experiments showed that time-varying EM patterns are unique to individual users. They are also temporally and spatially consistent, which makes them suitable for fingerprinting. MagPrint has a number of advantages over existing schemes: i) Non-intrusive fingerprinting, ii) implementation using a small and easy-to-deploy device, and iii) high accuracy thanks to the proposed classification algorithm. In experiments involving 30 users, MagPrint achieves 94.3% accuracy in classifying users from these traces, which represents an 10.9% improvement over the state-of-the-art classification method.

Although many matching algorithms have been proposed to improve the matching efficiency of the content-based publish/subscribe system, existing work seldom consider the real-time of event dissemination from the perspective of event matching. On the basis of two existing matching algorithms, in this paper, we propose a subscription-classifying and structure-layering (SCSL) optimization method for matching algorithms, aiming to improve real-time by shortening the determining time of matching subscriptions. The basic idea of SCSL is that subscriptions with high matching probabilities should be processed first in the process of event matching and their storage positions in the data structure should be adjusted in line with changing probabilities. One challenge of SCSL is the trade-off that needs to be made between the gains of improving real-time performance by identifying matching subscriptions earlier and the cost of increasing matching time due to subscription classification and adjustment. We design a concise scheme to classify subscriptions, establish a lightweight adjustment mechanism to deal with dynamics and propose an efficient greedy algorithm to compute the adjustment solution, which alleviates the impact of SCSL on matching performance. The experiment results show that the 95thpercentile of the determining time of matching subscriptions is improved by about 70%. Furthermore, we integrate SCSL into Apache Kafka to augment it as a content-based publish/subscribe system and test the effect of SCSL based on real-world stock trace data, which witnesses about 40% improvement on the average event transfer latency and confirms that SCSL can effectively improve the real-time performance of content-based publish/subscribe systems.

Recently, Siamese networks based tracking algorithms have shown favorable performance. Latest work focuses on better feature embedding and target state estimation, which greatly improves the accuracy. Nevertheless, the simple cross-correlation operation of the features between a fixed template and the search region limits their robustness and discrimination capability. In this paper, we pay more attention to learn an outstanding similarity measure for robust tracking. We propose a novel relation network that can be integrated on top of previous trackers without any need for further training of the siamese networks, which achieves a superior discriminative ability. During online inference, we utilize the feedback from high-confidence tracking results to obtain an additional template and update it, which improves the robustness and generalization. We implement two versions of the proposed approach with the SiamFC-based tracker and SiamRPN-based tracker to validate the strong compatibility of our algorithm. Extensive experimental results on several tracking benchmarks indicate that the proposed method can effectively improve the performance and robustness of the underlying trackers without reducing speed too much, and performs superiorly against the state-of-the-art trackers.

With increasing private and sensitive data stored in mobile devices, secure and effective mobile-based user authentication schemes are desired. As the most natural way to contact with mobile devices, finger touches have shown potentials for user authentication. Most existing approaches utilize finger touches as behavioral biometrics for identifying individuals, which are vulnerable to spoofer attacks. To resist attacks for on-touch user authentication on mobile devices, this paper exploits physical characters of touching fingers by investigating active vibration signal transmission through fingers, and we find that physical characters of touching fingers present unique patterns on active vibration signals for different individuals. Based on the observation, we propose a behavior-irrelevant on-touch user authentication system, TouchPass, which leverages active vibration signals on smartphones to extract only physical characters of touching fingers for user identification. TouchPass first extracts features that mix physical characters of touching fingers and behavior biometrics of touching behaviors from vibration signals generated and received by smartphones. Then, we design a Siamese network-based architecture with a specific training sample selection strategy to reconstruct the extracted signal features to behavior-irrelevant features and further build a behavior-irrelevant on-touch user authentication scheme leveraging knowledge distillation. Our extensive experiments validate that TouchPass can accurately authenticate users and defend various attacks.

The abuse of personal identity information is one of the most serious problems worldwide. Most social services or businesses use the identity authorization to confirm their validity and legality and the copies of users' identity certification are usually recorded by the service providers. It is easy to leak the users' identity information due to the untrustworthy service provider or single-point security failure, and various social problems are then caused. To deal with such problems, this paper proposes a Blockchain-based Identity Authorization mechanism (BIA). First, an Identity Authorization Module (IAM) is devised, which reads the identity certificate and transform the identity plaintext to ciphertext under the authorization by the user's identity certificate entity and password. IAM guarantees the security of identity information by keeping its plaintext offline. Second, a Business Contract Module (BCM) is designed, which provides a general smart contract framework for identity authorization that can be adopted by most of social services or businesses. Third, a double-chain blockchain infrastructure is developed, whereby the encrypted identity information and service smart contracts are respectively recorded in the tamper-resistant, non-repudiable, and publicly verifiable way. Finally, a prototype system has been developed to verify the security, feasibility and effectiveness of the proposed BIA.

The IEEE 802.11p-based dedicated short range communication (DSRC) is essential to enhance driving safety and improve road efficiency by enabling rapid cooperative message exchanging. However, there is a lack of good understanding on the DSRC performance in urban environments for vehicle-to-vehicle (V2V) communications, which impedes its reliable and efficient application. In this paper, we first conduct intensive data analytics on V2V performance, based on a large amount of real-world DSRC communications trace collected in Shanghai city, and obtain several key insights as follows. First, among many context factors, the non-line-of-sight (NLoS) link condition is the major factor degrading V2V performance. Second, the durations of line-of-sight (LoS) and NLoS transmission conditions follow power law distributions, which indicate that the probability of experiencing long LoS/NLoS conditions both could be high. Third, the packet inter-reception (PIR) time distribution follows an exponential distribution in the LoS conditions but a power law in the NLoS conditions, which means that the consecutive packet reception failures rarely appear in the LoS conditions but can constantly appear in the NLoS conditions. Based on these findings, we propose a context-aware reliable beaconing scheme, called CoBe, to enhance the broadcast reliability for safety applications. The CoBe is a fully distributed scheme, in which a vehicle first detects the link condition with each of its neighbors by machine learning algorithms, then exchanges such link condition information with its neighbors, and finally selects the minimal number of helper vehicles to rebroadcast its beacons to those neighbors in bad link condition. To analyze and evaluate the CoBe performance, a two-state Markov chain is devised to model beaconing behaviors. The extensive trace-driven simulations are conducted to demonstrate the efficacy of CoBe.

Given the increasing popularity, mobile devices are exploited to enhance active driving safety nowadays. Among all safety services provided for vehicles, tracking the rotation angle of steering wheel in real time can monitor the vehicles' dynamics and drivers' behaviors at the same time. In this paper, we propose a steering tracking system, SteerTrack, which tracks the rotation angle of the steering wheel in real time leveraging audio devices on smartphones. SteerTrack seeks a device-free approach for steering tracking without requiring installation of specialized sensors on the steering wheels nor asking drivers to wear sensors on their wrists. Since the steering wheel is operated by a driver's hands, the rotation angle of the steering wheel can be tracked based on movements of the driver's hands. SteerTrack first builds an acoustic signal field inside of a vehicle and then analyzes the echoes reflected from the driver's hands with relative correlation coefficient (RCC) and reference frame to track the movement trajectory of hands under different steering maneuvers. Given the tracked movement trajectory, SteerTrackfurther develops a geometrical transformation-based method for estimating the rotation angle of the steering wheel in 3D driving environments by projecting the steering wheel to a 2D ellipse. Through extensive experiments in real driving environments with five volunteers for several weeks, SteerTrack can achieve an average steering wheel estimation error of 1.48 degree during driving, and 4.61 degree for turns.

Financial fraud detection is an important topic in business intelligence, and neural networks have been effectively applied to construct detection models for this problem. Nevertheless, the timeliness of fitted models degrades over time when they are deployed in online detection systems. To maintain model performance when the labels of new transactions are not available, we propose an incremental virtual learning (IVL) method to update neural networks continually. The basic idea of IVL is to make the output distribution of neural networks smoother in the adversarial direction by unlabeled data. IVL uses local distribution smoothness (LDS) as the loss function at the unsupervised incremental learning stage. When updating a neural network by IVL, unlabeled data can be fed into the neural network periodically, so IVL can be implemented in a real online system without violating time constraints. To evaluate the effectiveness of IVL, experiments are conducted on a real dataset, demonstrating that neural networks augmented by IVL (IVL-NN) perform better than unoptimized neural networks in term of area under the curve (AUC). Furthermore, the standard deviation of the daily AUCs improves up to 19.09%, which indicates that IVL improves the stability of neural networks.

Large-scale WiFi systems have been widely deployed in an increasing number of corporate places such as universities, big malls and companies, to provide fast Internet experience to users. However, user association patterns in such large-scale systems have not been well investigated, which is crucial for performance enhancement and intelligent system management. In this paper, we provide the analytics of a large-scale campus WiFi dataset, which includes more than 8,000 access points (APs) and 40,000 active users in the area of 3.0925 km2. By conducting extensive analysis on association patterns, we achieve several key insights as follows. First, user associations are highly dynamic as short association durations and frequent AP transitions prevail throughout the whole trace. Second, even though users may associate to many APs, they generally have a small preferable AP set in which they spend most of their WiFi connection time for data traffic; in addition, each user has distinct yet relatively fixed AP transition route, indicating that given its current associated AP, its next association AP is highly predictable. Third, diurnal association patterns are observed not only at single AP level, but also at the building and the system level, where the number of associated users and the data traffic vary periodically on a daily basis. These insights can provide valuable guidelines to numerous intelligent service provisions such as proactive service migration, edge content distribution, efficient network management.

How to deploy cache in large-scale WiFi system is not well studied yet quite challenging since numerous Aps turn to be heterogeneous in terms of traffic consumption, and future traffic conditions are unknown ahead. In this paper, given the cache storage budge, we explore the cache deployment in a large-scale WiFi system which contains 8,000 APs and serves more than 40,000 active users, to maximize the long-term caching gain, i.e., the total reduced backhaul traffic. Specifically, we first collect enormous user association records and conduct intensive statistical analysis on the collected data, gaining two major observations. First, per AP traffic consumption varies in a rather wide range and the AP proportion distributes evenly within the range, which indicates that the cache size should be heterogeneously allocated in accordance to the underlying traffic demands. Second, compared to a single AP, the traffic consumption of a group of APs (clustered by physical locations) is more stable, which means that the short-term traffic statistics can be used to infer the future long-term traffic conditions. We then propose our cache deployment strategy, named LEAD (i.e., Large-scale wifi Edge cAche Deployment), in which we first cluster large-scale APs into well-sized edge nodes, then conduct the stationary testing on edge level traffic consumption and sample sufficient traffic statistics in order to precisely characterize future traffic conditions, and finally devise the TEG (Traffic-wEighted Greedy) algorithm to solve the long-term caching gain maximization problem. Extensive trace-driven simulations are carried out and simulation results demonstrate the efficacy of LEAD.

Oceans are important for scientific research and also for global economic and military security. Usually, wireless ad hoc networks are chosen to transform real-time data collected by ocean monitoring sensors (nodes). Due to the random motion of waves or the random direction of the wind, nodes in the network might become detached from the coverage of the network. In this case, the detached nodes can either send the collected data directly to the base station at the cost of consuming more energy or wait for a period of time to rejoin the network with the price of sacrificing the real time of the collected data. In this paper, we model the optimal waiting time for detached nodes before directly sending the data in the dynamic environment of ocean monitoring. For this purpose, we need to address two problems. The first is how to calculate the rate of coverage with a different number and different broadcast radii of nodes. The second is when a node detaches from the coverage of the network, how much time will it need to wait before it rejoins the network. We first establish the motion model of nodes, which is the basis to deduce the probability distribution of a certain time when the detached node rejoins the network. Based on the probability distribution, the waiting time of the detached nodes can be optimally determined, aiming to achieve a good balance between energy consumption and data timeliness. Finally, a series of simulations is conducted to validate the effectiveness of our proposed method.

The matching algorithm is a critical component of the content-based publish/subscribe system, whose performance has direct effects on the QoS of the whole system. Aiming to improve and stabilize the matching performance, we propose a lightweight parallelization method called PhSIH on the basis of three existing algorithms. PhSIH fulfills Parallelization by horizontally Segmenting the Indexing Hierarchy of data structures to support multiple threads performing matching tasks in parallel on a common data structure. PhSIH can adaptively adjust the degree of parallelism according to the changing workloads in order to meet the performance requirement. The main work of PhSIH concerns dynamically adjusting the degree of parallelism and computing a task allocation solution for parallel threads. PhSIH is implemented in Apache Kafka to augment it as a content-based publish/subscribe system, which makes Kafka suitable for real-time fine-grained event dissemination scenarios, such as stock ticks. To evaluate the parallelization effect and adaptability of PhSIH, a series of experiments are conducted based on synthetic and real-world data. The experiment results demonstrate that PhSIH achieves a good parallelization effect on the three existing algorithms and possesses a desirable adaptability that stabilizes the performance of the matching algorithms.

Dredging is the removal of sediment from the bottom of lakes, rivers, harbors, and other water bodies. It is a routine necessity in waterways around the world because the natural process of sand and silt washing downstream results in the sediment gradually filling channels and harbors. However, during the dredging operation, some dredgers do not transport the sediment to the designated area as expected but throw it near the waterway meaning sediment may return to the waterway in a short period. This paper proposes an online dredging operation anomaly detection (DOAD) method to detect this kind of irregular behavior during the dredging operation based on automatic identification system (AIS) data. First, we establish a feature system to extract behavior features from AIS data. Furthermore, we jointly utilize t-distributed stochastic neighbor embedding (t-SNE) with neural networks and a Gaussian mixture model (GMM) to train a detection model in a semi-supervised way. Through the trained model, irregular behaviors can be efficiently detected in real time during the dredging operation. The effectiveness of DOAD is evaluated according to a series of experiments. To the best of our knowledge of the published literature, this work is the first to introduce the application of AIS data to detect irregular behaviors during dredging operations.

Deep learning is popular in many areas, but users must manually specify the resource configuration when submitting deep learning training jobs, usually over-provisioning resources. This kind of unreasonable resource configuration method results in slow training and low resource utilization. Therefore, it would be more convenient and efficient if users only need to specify the quality of service (QoS) for their jobs, and then the resources will be autoconfigured to meet the QoS. To satisfy this demand, we present a QoS-oriented scheduling and autoscaling framework that schedules and autoscales deep learning training jobs in the Kubernetes cluster. This paper focuses on the most important QoS requirement for deep learning training jobs: deadline. The goal of the framework is to guarantee that as many jobs as possible can be accomplished before their specified deadlines. To reach this goal, the framework schedules deep learning jobs by implementing a heuristic scheduling policy based on resource status and job deadline, and autoscales resource configuration by exploiting a characteristic of deep learning jobs: the predictability of training time. This predictability is used to predict whether a job can be accomplished before its deadline and estimate appropriate resource configuration if necessary. We implemented the framework by modifying the default scheduler of Kubernetes and conducted experiments to evaluate its performance. The experiment results show that our scheduling policy can improve the completion rate by 26% when the cluster resources are insufficient, and our autoscaling policy can improve the completion rate to 100% when the cluster resources are sufficient. We also show that the framework improves the utilization of allocated CPUs to 100%. Our proposed framework points to a new way of submitting and managing deep learning training jobs in the cluster.

This paper demonstrates the feasibility of a side-channel attack to infer keystrokes on touch screen leveraging an off-the-shelf smartphone. Although there exist some studies on keystroke eavesdropping attacks on touch screen, they are mainly direct eavesdropping attacks, i.e., require the device of victims compromised to provide side-channel information for the adversary, which are hardly launched in practical scenarios. In this work, we show the practicability of an indirect eavesdropping attack, KeyListener, which infers keystrokes on QWERTY keyboards of touch screen leveraging audio devices on a smartphone. We investigate the attenuation of acoustic signals, and find that a user's keystroke fingers can be localized through the attenuation of acoustic signals received by the microphones in the smartphone. We then utilize the attenuation of acoustic signals to localize each keystroke, and further analyze errors induced by ambient noises. To improve the accuracy of keystroke localization, KeyListener further tracks finger movements during inputs through phase change and Doppler effect to reduce errors of acoustic signal attenuation-based keystroke localization. In addition, a binary tree-based search approach is employed to infer keystrokes in a context-aware manner. The proposed keystroke eavesdropping attack is robust to various environments without the assistance of additional infrastructures. Extensive experiments demonstrate that the accuracy of keystroke inference in top-5 candidates can approach 90% with a top-5 error rate of around 6%, which is a strong indication of the possible user privacy leakage of inputs on QWERTY keyboard.

When facing fluctuating workloads, can the performance of matching algorithms in a content-based publish/subscribe system be adjusted to adapt to the workloads? In this paper, we explore the idea of endowing matching algorithms with adaptability. The prerequisite for adaptability is to enable the matching algorithm to possess the ability to dynamically and quantitatively adjust its performance. We propose PSAM, a Predicate-Skipping Adjustment Mechanism that realizes dynamic performance adjustment by smoothly switching between exact matching and approximate matching, following the strategy of trading off matching precision in favor of matching speed. The PSAM mechanism is integrated into an existing matching algorithm, resulting in a performance-adjustable matching algorithm called Ada-Rein. To collaborate with Ada-Rein, we design PADA, a Performance Adjustment Decision Algorithm that is able to make proper performance adjustment plans in the presence of fluctuating workloads. The effectiveness of Ada-Rein and PADA is evaluated through a series of experiments based on both synthetic data and real-world stock traces. Experiment results show that adjusting the performance of Ada-Rein at the price of a small false positive rate, less than 0.1%, can shorten event latency by almost 2.1 times, which well demonstrates the feasibility of our exploratory idea.

The development of smart homes has advanced the concept of user authentication to not only protecting user privacy but also facilitating personalized services to users. Along this direction, we propose to integrate user authentication with human-computer interactions between users and smart household appliances through widely-deployed WiFi infrastructures, which is non-intrusive and device-free. In this paper, we propose FingerPass which leverages channel state information (CSI) of surrounding WiFi signals to continuously authenticate users through finger gestures in smart homes. We investigate CSI of WiFi signals in depth and find CSI phase can be used to capture and distinguish the unique behavioral characteristics from different users. FingerPass separates the user authentication process into two stages, login and interaction, to achieve high authentication accuracy and low response latency simultaneously. In the login stage, we develop a deep learning-based approach to extract behavioral characteristics of finger gestures for highly accurate user identification. For the interaction stage, to provide continuous authentication in real time for satisfactory user experience, we design a verification mechanism with lightweight classifiers to continuously authenticate the user's identity during each interaction of finger gestures. Experiments in real environments show that FingerPass can achieve 91.4% authentication accuracy, and 186.6ms response time during interactions.

Given the increasing amount of time people spent on driving, the physical and mental health of drivers is essential to road safety. Breathing patterns are critical indicators of the well-being of drivers on the road. Existing studies on breathing monitoring require active user participation of wearing special sensors or relatively quiet environments during sleep, which are hardly applicable to noisy driving environments. In this work, we propose a fine-grained breathing monitoring system, BreathListener, which leverages audio devices on smartphones to estimate the fine-grained breathing waveform in driving environments. By investigating the data collected from real driving environments, we find that Energy Spectrum Density (ESD) of acoustic signals can be utilized to capture breathing procedures in driving environments. To extract breathing pattern in ESD signals, BreathListener eliminates interference from driving environments in ESD signals utilizing background subtraction and Ensemble Empirical Mode Decomposition (EEMD). After that, the extracted breathing pattern is transformed into Hilbert spectrum, and we further design a deep learning architecture based on Generative Adversarial Network (GAN) to generate fine-grained breathing waveform from the Hilbert spectrum of extracted breathing patterns in ESD signals. Experiments with 10 drivers in real driving environments show that BreathListener can accurately capture breathing patterns of drivers in driving environments.

NarrowBand-IoT (NB-IoT) is a radio access technology standardized by 3GPP to support a large set of use cases associated with the rapid deployment of massive machine-type communications. NB-IoT facilitates the connection of devices in inaccessible areas, extends battery life, and reduces device complexity. Unfortunately, the opacity of the underlying schema (i.e., the way that these benefits are achieved) makes it very difficult for most users and developers to manage deployment scenarios. In this study, we built an embedded system comprising a Raspberry Pi with an NB module, referred to as NBPilot, which interacts with NB networks to identify essential signalling messages transmitted by a Qualcomm NB modem. This system gives researchers and developers an unprecedented understanding of network behaviour as well as the ability to adjust them to their particular requirements. We employed the-state-of-art machine learning techniques for modeling and the analysis of NB performance. The efficacy of the proposed NBPilot system was established by applying it to a metropolitan NB-IoT network with over 2,000 NB sites for the collection and testing of data trace as well as the validation of a cellular station prior to going online.

Multipath profiling is to characterize multipath components of wireless channels, which can be done using Channel State Information (CSI) from WiFi devices. To do so with satisfactory accuracy, recent studies rely on either a large number of receiving antennas or large bandwidth. However, it is difficult for commodity WiFi devices to meet these requirements. In this paper, we propose a scheme, WiZoom, that can perform accurate multipath profiling using single-band CSI from commodity WiFi devices. In order to achieve accurate multipath profiling with limited bandwidth, WiZoom first incorporates the MUltiple SIgnal Classification (MUSIC) algorithm with CSI to estimate ToAs of multipath components, and then combines multiple antennas to improve the resolution of ToA estimation. WiZoom further estimates attenuations and phase shifts for multipath components using the ToAs. So far, multipath components are fully characterized, and all these estimated parameters form the multipath profile. We evaluate the performance of WiZoom using commodity WiFi devices in real environment, and results show that WiZoom achieves high accuracy in multipath profiling.

Full WiFi coverage becomes more and more prevalent in corporate places, such as university, big mall, airport, and so forth. To achieve full WiFi coverage in a wide area is costly due to the large-scale AP deployment spending and considerable operating expenditure. However, with limited literature available, how to deploy and manage those APs in an efficient and economical way, is still unknown for system providers. To bridge this gap, in this article, we first collect large-scale AP usage data in our campus WiFi system, which contains over 8000 APs and serves more than 40 000 active end-users in the area of 3.0925 km2. After mining large-scale spatio-temporal user associations, we obtain several key insights as follows. First, Idle Phenomenon prevails throughout the trace, in which large portion of APs are wasted without any user association. Second, AP usages in different buildings have very distinct characteristics in terms of user association and traffic consumption. Third, diurnal usage patterns are very obvious not only at singe AP level but also at the building and the whole system level. Many deployment and management strategies can benefit from these insights, e.g., heterogeneous AP deployment and intelligent AP management. Among them, we then propose an intelligent large-scale AP management scheme, called LAM, to dynamically control large-scale APs (ON or OFF) for energy saving and meanwhile without loss of WiFi coverage. In LAM, based on history association records, the user load of each AP is predicted by machine learning algorithms, and those APs whose idle durations are longer than the length of the predefined time window, will be switched off during the duration. We conduct extensive trace-driven experiments to demonstrate its efficacy; on average, more than 70% of power consumption can be markedly saved with over 92% of WiFi coverage guaranteed, which is able to save empirical $59 000 per year just for our system.

To prevent users privacy from leakage, more and more mobile devices employ biometric-based authentication approaches, such as fingerprint, face recognition, voiceprint authentications, and so on, to enhance the privacy protection. However, these approaches are vulnerable to replay attacks. Although the state-of-art solutions utilize liveness verification to combat the attacks, existing approaches are sensitive to ambient environments, such as ambient lights and surrounding audible noises. Toward this end, we explore liveness verification of user authentication leveraging users mouth movements, which are robust to noisy environments. In this paper, we propose a lip reading-based user authentication system, LipPass, which extracts unique behavioral characteristics of users speaking mouths through acoustic sensing on smartphones for user authentication. We first investigate Doppler profiles of acoustic signals caused by users' speaking mouths and find that there are unique mouth movement patterns for different individuals. To characterize the mouth movements, we propose a deep learning-based method to extract efficient features from Doppler profiles and employ softmax function, support vector machine and support vector domain description to construct multi-class identifier, binary classifiers and spoofer detectors for mouth state identification, user identification and spoofer detection, respectively. Afterward, we develop a balanced binary tree-based authentication approach to accurately identify each individual leveraging these binary classifiers and spoofer detectors with respect to registered users. Through extensive experiments involving 48 volunteers in four real environments, LipPass can achieve 90.2% accuracy in user identification and 93.1% accuracy in spoofer detection.

Full WiFi coverage is more and more prevalent in many places such as university, enterprise, big mall, etc. To achieve full WiFi coverage in a wide area is very costly. Not only extensive AP deployments are expensive, to operate and maintain such large-scale APs every day can also cost much, e.g., the huge power consumption. In this paper, we collect large-scale AP status data in our campus WiFi system, which contains over 8,000 APs and serves about 40,000 active end-users in the area of 3.0925 km2. After conducting empirical studies on AP loads, we find Idle Phenomenon prevails throughout the trace. A large portion of APs are running without any user association, which will inevitably lead to unnecessary energy consumption. Inspired by this, we propose an intelligent large-scale AP control scheme, named as ACE (i.e., AP Control with Energy saving), to dynamically control large-scale APs (On or Off for energy saving meanwhile without loss of WiFi coverage. In ACE, the load of each AP is predicted first by the random forest algorithm, and those APs whose idle durations last for more than the length of the pre-defined sliding window will be turned off. We conduct extensive trace-driven simulations to demonstrate the efficiency of the ACE scheme; specifically, more than 70% of power energy can be saved with over 92 % of user WiFi coverage guaranteed in average.

Classifying traffic flows into source applications is of great value for intelligent network management, which can help to detect malicious attacks, monitor the network, optimize network behaviors and then improve user experience, etc. However, to achieve high-accuracy traffic classification, especially in real time, is very challenging due to very complicated behaviors of traffic flows where network applications could often transmit traffics with encryption at randomized port numbers under highly dynamic network conditions. In this paper, by collecting extensive application traffic flows at the exit router of Shanghai Maritime University (the traffic rate can reach up to 7 GB/s at peak time), we identify that there is a very distinct characteristic in inner-connection of message (grouped by single or multiple consecutive TCP packets) sequence for different application flows. We then propose our traffic classification algorithm, which essentially adopts a Long Short-Term Memory (LSTM) neural network to output a classifier with message sequence vector (not necessarily covering all messages) of a traffic flow as the training input, to conduct online traffic flow classification. Extensive simulations are conduced considering varied training data size and diverse source applications, and an average about 97 % accuracy on per-flow classification can be achieved.

Human-computer interaction based on touch screens plays an increasing role in our daily lives. Besides smartphones and tablets, laptops are the most popular mobile devices used in both work and leisure. To satisfy requirements of many emerging applications, it becomes desirable to equip both writing and drawing functions directly on laptop screens. In this paper, we design a virtual writing tablet system, VPad, for traditional laptops without touch screens. VPad leverages two speakers and one microphone, which are available in most commodity laptops, for trajectory tracking without additional hardware. It employs acoustic signals to accurately track hand movements and recognize characters user writes in the air. Specifically, VPad emits inaudible acoustic signals from two speakers in a laptop. Then VPad applies Sliding-window Overlap Fourier Transformation technique to find Doppler frequency shift with higher resolution and accuracy in real time. Furthermore, we analyze frequency shifts and energy features of acoustic signals received by the microphone to track the trajectory of hand movements. Finally, we employ a stroke direction sequence model based on possibility estimation to recognize characters users write in the air. Our experimental results show that VPad achieves the average trajectory tracking error of only 1.55cm and the character recognition accuracy of above 90% merely through two speakers and one microphone on a laptop.

A distributed cluster provides a common computation platform to efficiently run different types of jobs, such as real-time and batch jobs that have different QoS requirements. One challenge of a distributed cluster is to find a scheduling strategy that can reduce the overtime ratio of real-time jobs as much as possible while also improving the completion time of batch jobs, aiming to promote QoS even though the resources in the cluster are stretched. To address this issue, many efficient scheduling methods have been proposed. However, most existing approaches are naive and coarse-grained in means of either killing batch jobs to preempt resources for real-time jobs or reserving some resources in advance for real-time jobs to minimize the overtime ratio of real-time jobs, which prolongs the completion time of batch jobs and reduces the resource utilization of the cluster. In this paper, we propose UpPreempt, a fine-grained preemptive scheduling strategy for container-based clusters. When scheduling real-time jobs, UpPreempt considers the deadline of jobs and the resource usage of existing batch jobs. Performing resource preemption from multiple batch jobs is the basic idea of UpPreempt. The set of batch jobs to be preempted and the amount of resources taken from each job are finely determined. In this way, UpPreempt greatly alleviates the effect of preemption on the batch jobs in terms of completion time and avoids reserving resources for real-time jobs. We implement UpPreempt in YARN to evaluate its performance. The evaluation with various workloads shows that our proposed scheduling strategy can achieve a good trade-off between the overtime ratio of real-time jobs and the completion time of batch jobs without reducing the resource utilization of the cluster.

To prevent users' privacy from leakage, more and more mobile devices employ biometric-based authentication approaches, such as fingerprint, face recognition, voiceprint authentications, etc., to enhance the privacy protection. However, these approaches are vulnerable to replay attacks. Although state-of-art solutions utilize liveness verification to combat the attacks, existing approaches are sensitive to ambient environments, such as ambient lights and surrounding audible noises. Towards this end, we explore liveness verification of user authentication leveraging users' lip movements, which are robust to noisy environments. In this paper, we propose a lip reading-based user authentication system, LipPass, which extracts unique behavioral characteristics of users' speaking lips leveraging build-in audio devices on smartphones for user authentication. We first investigate Doppler profiles of acoustic signals caused by users' speaking lips, and find that there are unique lip movement patterns for different individuals. To characterize the lip movements, we propose a deep learning-based method to extract efficient features from Doppler profiles, and employ Support Vector Machine and Support Vector Domain Description to construct binary classifiers and spoofer detectors for user identification and spoofer detection, respectively. Afterwards, we develop a binary tree-based authentication approach to accurately identify each individual leveraging these binary classifiers and spoofer detectors with respect to registered users. Through extensive experiments involving 48 volunteers in four real environments, LipPass can achieve 90.21% accuracy in user identification and 93.1% accuracy in spoofer detection.

Vehicular ad hoc network (VANET) has been widely recognized as a promising solution to enhance driving safety, by keeping vehicles well aware of the nearby environment through frequent beacon message exchanging. Due to the dynamic of transportation traffic, especially for those scenarios where the density of vehicles is high, the naive beaconing scheme where vehicles send beacon messages at a fixed rate with a fixed transmission power can cause severe channel congestion. In this paper, we investigate the risk of rear-end collision model and define a danger coefficient ρ to characterize the danger threat of each vehicle being in a rear-end collision. We then propose a fully-distributed beacon congestion control scheme, referred to as ABC, which guarantees each vehicle to actively adapt a minimal but sufficient beacon rate to avoid a rear-end collision based on individual estimates of ρ. In essence, ABC adopts a TDMA-based MAC protocol and solves a NP-hard optimal distributed beacon rate adapting (DBRA) problem with a greedy heuristic algorithm, in which a vehicle with a higher ρ will be assigned with a higher beacon rate while keeping the total required beacon demand lower than the channel capacity. We conduct extensive simulations to demonstrate the efficiency of ABC design in different traffic density and a large variety of underlying road topologies.

Given the increasing popularity, mobile devices are exploited to enhance active driving safety nowadays. Among all safety services provided for vehicles, tracking the rotation angle of steering wheel in real time can monitor the vehicles' dynamics and drivers' behaviors at the same time. In this paper, we propose a steering tracking system, SteerTrack, which tracks the rotation angle of steering wheel in real time leveraging audio devices on smartphones. SteerTrack seeks a device-free approach for steering tracking without requiring installation of specialized sensors on steering wheels nor asking drivers to wear sensors on their wrists. Since the steering wheel is operated by a driver's hands, the rotation angle of steering wheel can be tracked based on movements of the driver's hands. SteerTrack first builds an acoustic signal field inside of a vehicle and then analyzes the echoes reflected from the driver's hands with relative correlation coefficient(RCC) and reference frame to track the movement trajectory of hands under different steering maneuvers. Given the tracked movement trajectory, SteerTrack further develops a geometrical transformation-based method for estimating the rotation angle of steering wheel in 3D driving environments by projecting the steering wheel to a 2D ellipse. Through extensive experiments in real driving environments with 5 volunteers for several weeks, SteerTrack can achieve an average error of 4.61 degree for estimating the rotation angle of steering wheel.

Under the IEEE 802.11p-based dedicated short range communication modules, vehicular safety applications rely on periodical broadcasts of safety beacons by each vehicle. However, the channel can be easily congested by high-frequency periodic beacons when the vehicle density becomes heavy. In this paper, through real-trace-based empirical study on vehicle-to-vehicle communication, we find that nonline-of-sight (NLoS) condition is the key factor on link performance degradation and blindly sending more packets in harsh NLoS conditions can hardly succeed but increase interferences to neighboring vehicles. Inspired by this, we propose a distributed beacon congestion control (DBCC) scheme to control beacon activities with considering link conditions, i.e., vehicles with more neighbors and better conditions of links with its neighbors, will be assigned with higher beacon rates. In DBCC, we first utilize two machine learning methods, i.e., naive Bayes and support vector machines, to train the features and output a classifier model which conducts online NLoS link condition prediction. With link status information, we then formulate a link-weighted safety benefit maximization (L-SBM) problem of the rate-adaptation under a TDMA broadcast MAC, which is proved to be NP-hard. A greedy heuristic algorithm for L-SBM is then proposed and the performance of the algorithm is evaluated. Extensive trace-driven simulations demonstrate the efficiency of DBCC design; particularly, the rate of beacon transmissions can be effectively controlled without exceeding the resource limit and the rate of transmission/reception collisions are greatly reduced.

Real-time driving behavior monitoring is a corner stone to improve driving safety. Most of the existing studies on driving behavior monitoring using smartphones only provide detection results after an abnormal driving behavior is finished, not sufficient for driver alerting and avoiding car accidents. In this paper, we leverage built-in audio devices on smartphones to realize early recognition of inattentive driving events including Fetching Forward, Picking up Drops, Turning Back, and Eating or Drinking. Through empirical studies of driving traces collected in real driving environments, we find that each type of inattentive driving event exhibits unique patterns on Doppler profiles of audio signals. This enables us to develop an Early Recognition system, ER, which can recognize inattentive driving events at an early stage and alert drivers timely. ER employs machine learning methods to first generate binary classifiers for every pair of inattentive driving events, and then develops a modified vote mechanism to form a multi-classifier for all four types of inattentive driving events, for atypical inattentive driving events along with other driving behaviors. It next turns the multi-classifier into a gradient model forestto achieve early recognition of inattentive driving. Through extensive experiments with eight volunteers driving for about two months, ER can achieve an average total accuracy of 94.80 percent for inattentive driving recognition and recognize over 80 percent inattentive driving events before the event is 50 percent finished.

When vehicle road-level localization cannot satisfy people's need for convenience and safety driving, lane-level localization becomes a corner stone in Intelligent Transportation System. Existing works of tracking vehicles on lane-level mostly depend on pre-deployed infrastructures and additional hardwares. In this paper, we utilize smartphones to sense driving conditions for vehicle lane-level localization on highways. By analyzing driving traces collected from real driving environments, we find that each type of lane-change has its unique pattern on the vehicle's lateral acceleration. Based on this observation, we propose a Lane-Level Localization (L3) system, which can perform real-time vehicle localization on lane-level only using smartphones when vehicles are driving on highways. Our system first uses embedded sensors in smartphones to capture the patterns of lane-change behaviors. Then, a Finite State Machine is employed to track vehicles on lane-level leveraging the patterns. Extensive experiments demonstrate that L3is accurate and robust in real driving environments. The experimental results show that, on average, L3achieves the accuracy of 91.49 percent on lane change detection and 90.31 percent on lane-level localization.

Double auction-based pricing model is an efficient pricing model to balance users' and providers' benefits. Existing double auction mechanisms usually require both users and providers to bid with the unit price and the number of VMs. However, in practice users seldom know the exact number of VMs that meets their task requirements, which leads to users' task requirements inconsistent with providers' resource. In this paper, we propose a truthful double auction mechanism, including a matching process as well as a pricing and VM allocation scheme, to bridge users' task requirements and providers' resources in two-sided cloud markets. In the matching process, we design a cost-aware resource algorithm based on Lyapunov optimization techniques to precisely obtain the number of VMs that meets users' task requirements. In the pricing and VM allocation scheme, we apply the idea of second-price auction to determine the final price and the number of provisioned VMs in the double auction. We theoretically prove our proposed mechanism is individual-rational, truthful and budget-balanced, and analyze the optimality of proposed algorithm. Through simulation experiments, the results show that the individual profits achieved by our algorithm are 12.35 and 11.02 percent larger than that of scaleout and greedy scale-up algorithms respectively for 90 percent of users, and the social welfare of our mechanism is only 7.01 percent smaller than that of the optimum mechanism in the worst case.

Benefiting from the pay-per-use pricing model of cloud computing, many companies migrate their services and applications from typical expensive infrastructures to the cloud. However, due to fluctuations in the workload of services and applications, making a cost-efficient VM configuration decision in the cloud remains a critical challenge. Even experienced administrators cannot accurately predict the workload in the future. Since the pricing model of cloud provider is convex other than linear that often assumed in past research, instead of typical scaling out strategy. In this paper, we adopt mix scale strategy. Based on this observation, we model an optimization problem aiming to minimize the VM configuration cost under the constraint of migration delay. Taking advantages of Lyapunov optimization techniques, we propose a mix scale online algorithm which achieves more cost-efficiency than that of scale out strategy. Experimental results shows that the mix scale algorithm saves 30.8% and 31.1% cost where controlling migration delay in a tolerable range under different workload respectively.

In the cloud computing architecture, virtual machine (VM) live migration is a fundamental research topic which has drawn extensive attention from communities of industry and academy. It is critical to transfer the VM memory pages that contain essential state information to resume the VM on another host during virtual machine (VM) live migration. There are many memory copy methods, such as pre-copy and post-copy. However, these methods have two limitations: application generality and performance imbalance. In this paper, we propose Ada-copy (adaptive copy), an adaptive memory copy strategy for VM live migration. The basic idea of Ada-copy is that the memory copy method of a VM should be determined by its workload characteristics. Specifically, based on the variation of current dirty page rate of memory, Ada-copy can adaptively select the most appropriate migration method to copy memory pages, thus addressing the two limitations of existing memory copy methods. To evaluate the effectiveness of our proposed strategy, we experiment with the Ada-copy on a variety of migration tasks with different dirty page rate and diverse memory usage workloads. Evaluation results show, compared with traditional methods, Ada-copy can significantly reduce the total migration time by 26%, the VM downtime by 42% and the amount of pages transferred by 35% in average.

Real-time driving behavior monitoring is a corner stone to improve driving safety. Most of the existing studies on driving behavior monitoring using smartphones only provide detection results after an abnormal driving behavior is finished, not sufficient for driver alert and avoiding car accidents. In this paper, we leverage existing audio devices on smartphones to realize early recognition of inattentive driving events including Fetching Forward, Picking up Drops, Turning Back and Eating or Drinking. Through empirical studies of driving traces collected in real driving environments, we find that each type of inattentive driving event exhibits unique patterns on Doppler profiles of audio signals. This enables us to develop an Early Recognition system, ER, which can recognize inattentive driving events at an early stage and alert drivers timely. ER employs machine learning methods to first generate binary classifiers for every pair of inattentive driving events, and then develops a modified vote mechanism to form a multi-classifier for all inattentive driving events along with other driving behaviors. It next turns the multi-classifier into a gradient model forest to achieve early recognition of inattentive driving. Through extensive experiments with 8 volunteers driving for about half a year, ER can achieve an average total accuracy of 94.80% for inattentive driving recognition and recognize over 80% inattentive driving events before the event is 50% finished.

Real-time abnormal driving behaviors monitoring is a corner stone to improving driving safety. Existing works on driving behaviors monitoring using smartphones only provide a coarse-grained result, i.e., distinguishing abnormal driving behaviors from normal ones. To improve drivers’ awareness of their driving habits so as to prevent potential car accidents, we need to consider a fine-grained monitoring approach, which not only detects abnormal driving behaviors but also identifies specific types of abnormal driving behaviors, i.e.,Weaving,Swerving,Sideslipping,Fast U-turn,Turning with a wide radius, andSudden braking. Through empirical studies of the 6-month driving traces collected from real driving environments, we find that all of the six types of driving behaviors have their unique patterns on acceleration and orientation. Recognizing this observation, we further propose a fine-grained abnormalDriving behaviorDetection and iDentification system,$D^{3}$, to perform real-time high-accurate abnormal driving behaviors monitoring using smartphone sensors. We extract effective features to capture the patterns of abnormal driving behaviors. After that, two machine learning methods,Support Vector Machine(SVM) andNeuron Networks(NN), are employed, respectively, to train the features and output a classifier model which conducts fine-grained abnormal driving behaviors detection and identification. From results of extensive experiments with 20 volunteers driving for another four months in real driving environments, we show that$D^{3}$achieves an average total accuracy of 95.36 percent with SVM classifier model, and 96.88 percent with NN classifier model.

To support mobile users in contributing sensing data for making urban management decisions, in ShanghaiGrid, unified data gathering operations are to be performed. For citywide coverage, public vehicles accept data from surrounding users and hand over to computing center through wireless base stations (BSs) deployed in the city. Meanwhile, several among the vehicles are hired as relays, which assist gathering from others with multicopy and multihop forwarding towards the BSs. However, the budget shared by deploying BSs and hiring relays is limited. We explore how to decide BS deployment and relay-based forwarding for efficient gathering under the budget. The challenge lies in the great uncertainty about collection opportunities of candidate locations and vehicles in future gathering processes. In this paper, we present an empirical approach for the problem. To tackle the challenges, we characterize collection performance as function of temporal data paths towards each candidate, and formulate the problem as a multiobjective optimization problem. To solve it, we reveal regular relations between the candidates and estimate expected importance of them with large set of real vehicular traces; and develop an algorithmic framework for BS deployment and corresponding forwarding strategy. Extensive trace-driven simulations demonstrate the efficacy of the approach.

When vehicle road-level localization cannot satisfy people's need for convenience and safety driving, lane-level localization becomes a corner stone in Intelligent Transportation System. Existing work on tracking vehicles on lane-level mostly depends on pre-deployed infrastructures and additional hardwares. In this paper, we utilize smartphone sensing of driving conditions for vehicle lane-level localization on highways. We analyze the driving traces collected from real driving environments, finding that each type of lane change has its unique pattern on the vehicle's lateral acceleration. Based on this observation, we propose a Lane-Level Localization (L3) system, which can perform real-time vehicle localization on lane-level only using smartphones when vehicles are driving on highways. Our system first uses embedded sensors in smartphones to capture the patterns of lane change behaviors. Then a Gaussian Distribution is employed to track vehicles on lane-level with tolerance of false detections. Extensive experiments demonstrate that L3 is accurate and robust in real driving environments. The experimental results show that, on average, L3 achieves accuracy of 91.49% on lane change detection and 86.94% on lane-level localization.

IEEE 802.11p based Dedicated Short Range Communication (DSRC) has been considered as a promising wireless technology for enhancing transportation safety and traffic efficiency. However, with limited literature available, there is lack of understanding about how IEEE 802.11p performs for vehicleto-vehicle (V2V) communications in urban environments. In this paper, we conduct intensive statistical analysis on V2V communication performance, based on the empirical measurement data collected from off-the-shelf IEEE 802.11p-compatible onboard units (OBUs). We have several key insights as follows. First, both line-of- sight (LoS) and non-line-of-sight (NLoS) durations follow power law distributions, which implies that the probability of having long LoS/NLoS conditions can be relatively high. Second, the packet inter-reception (PIR) time distribution follows an exponential distribution in LoS conditions but a power law in NLoS conditions. In contrast, the packet inter-loss (PIL) time distribution in LoS condition follows a power law but an exponential in NLoS condition. Third, the overall PIR time distribution is a mix of exponential distribution and power law distribution. The presented results provide solid ground to validate models, tune VANET simulators and improve communication strategies.

This paper discusses the time-constrained data delivery problem in vehicular ad hoc networks (VANETs). The unique characteristics of the network present great challenges to the issue. First, there are no always-connected forwarding routes between vehicles. Second, there is an intrinsic tradeoff between communication cost and delivery quality. Third, there is great uncertainty about vehicular mobilities. Exploiting vehicular trajectories, we present a constructive approach called TNC to tackle the challenges. In TNC, contact-based data forwarding and communication connections through the mobile network are incorporated. TNC first predicts the time-stamped inter-vehicle contacts and establishes expected contact graph, based on which it then computes the configuration for enhancing the connectivity of the network while introducing minimum number of mobile communication connections. Extensive simulations based on three real vehicular traces collected from 2,000 taxis and 1,400 buses in Shanghai, and 2,500 taxis in Shenzhen have been conducted and results demonstrate the efficacy of our approach.

Mobile advertising in vehicular networks is of great interest with which timely information can be fast spread into the network. Given a limited budget for hiring seed vehicles, how to achieve the maximum advertising coverage within a given period of time is NP-hard. In this paper, we propose an innovative scheme, POST, for mobile advertising in vehicular networks. The POST design is based on two key observations we have found by analyzing three large-scale vehicular traces. First, vehicles demonstrate dynamic sociality in the network; second, such vehicular sociality has strong temporal correlations. With the knowledge, POST uses Markov chains to infer future vehicular sociality and adopts two greedy heuristics to select the most “centric” vehicles as seeds for mobile advertising. Extensive simulations based on three real data sets of taxi and bus traces have been carried out. The results show that POSTcan greatly improve the coverage and the intensity of advertising. For all the three involved data sets, it achieves an average gain of 64 percent comparing with the state-of-art schemes.

Wireless trace data play an important role in wireless network researches. However, publishing the raw WLAN traces poses potential privacy risks of network users. Therefore, it is necessary to sanitize users' sensitive information before these traces are published, and provide high data utility for wireless network researches as well. Although some existing works based on various anonymization methods have started to address the problem of sanitizing WLAN traces, the anonymization techniques cannot provide strong and provable privacy guarantees. Differential Privacy is the only framework that can provide strong and provable privacy guarantees. However, we find that existing studies on differential privacy fail to provide effective data utility on multi-dimensional and large-scale datasets. Aim at WLAN trace datasets that have unique characteristics of multi-dimensional and large-scale, this paper proposes a privacy-preserving data publishing algorithm which not only satisfies differential privacy but also realizes high data utility. Furthermore, the theoretical analysis shows the noise variance of our sanitization algorithm is O(logo(1)n/ϵ2) which indicates the algorithm can achieve a higher data utility on large-scale datasets. Moreover, from the results of extensive experiments on an large-scale WLAN trace dataset, we also show that our sanitization algorithm can provide high data utility.

Recommending routes for a group of competing taxi drivers is almost untouched in most route recommender systems. For this kind of problem, recommendation fairness and driving efficiency are two fundamental aspects. In the paper, we propose SCRAM, a sharing considered route assignment mechanism for fair taxi route recommendations. SCRAM aims to provide recommendation fairness for a group of competing taxi drivers, without sacrificing driving efficiency. By designing a concise route assignment mechanism, SCRAM achieves better recommendation fairness for competing taxis. By considering the sharing of road sections to avoid unnecessary competition, SCRAM is more efficient in terms of driving cost per customer (DCC). We test SCRAM based on a large number of historical taxi trajectories and validate the recommendation fairness and driving efficiency of SCRAM with extensive evaluations. Experimental results show that SCRAM achieves better recommendation fairness and higher driving efficiency than three compared approaches.

Acquiring instant walking distance is desirable in indoor localization and map construction. However, due to the blackout of Global Positioning System (GPS) in indoor settings, to accurately estimate the indoor walking distance with minimum hardware requirement is very challenging. In this paper, we propose a lightweight scheme, called DiSen, to range the instant walking distance of smartphone users. After analysing the extensive walking trace data, we find that people have rather consistent walking behaviour even though they may change their walking speeds in different situations. Furthermore, the relationship between stride length and step frequency while walking can be well estimated using non-linear sigmoid model. Inspired by such insights, we first design a stride segmenting method to obtain reliable and accurate step frequency information from raw accelerometer readings. We then train a sigmoid model using acceleration and GPS information collected when a user walks in outdoor conditions and finally apply the model to indoor walking distance ranging. Real-world experiment results show that, in different walking speeds, DiSen can reach average distance estimation accuracy of 96%.

Real-time abnormal driving behaviors monitoring is a corner stone to improving driving safety. Existing works on driving behaviors monitoring using smartphones only provide a coarsegrained result, i.e. distinguishing abnormal driving behaviors from normal ones. To improve drivers' awareness of their driving habits so as to prevent potential car accidents, we need to consider a finegrained monitoring approach, which not only detects abnormal driving behaviors but also identifies specific types of abnormal driving behaviors, i.e. Weaving, Swerving, Sideslipping, Fast U-turn, Turning with a wide radius and Sudden braking. Through empirical studies of the 6-month driving traces collected from real driving environments, we find that all of the six types of driving behaviors have their unique patterns on acceleration and orientation. Recognizing this observation, we further propose a finegrained abnormal Driving behavior Detection and iDentification system, D3, to perform real-time high-accurate abnormal driving behaviors monitoring using smartphone sensors. By extracting unique features from readings of smartphones' accelerometer and orientation sensor, we first identify sixteen representative features to capture the patterns of driving behaviors. Then, a machine learning method, Support Vector Machine (SVM), is employed to train the features and output a classifier model which conducts fine-grained identification. From results of extensive experiments with 20 volunteers driving for another 4 months in real driving environments, we show that D3achieves an average total accuracy of 95.36%.

Summary With the increasingly growing amount of service requests from the world‐wide customers, the cloud systems are capable of providing services while meeting the customers' satisfaction. Recently, to achieve the better reliability and performance, the cloud systems have been largely depending on the geographically distributed data centers. Nevertheless, the dollar cost of service placement by service providers (SP) differ from the multiple regions. Accordingly, it is crucial to design a request dispatching and resource allocation algorithm to maximize net profit. The existing algorithms are either built upon energy‐efficient schemes alone, or multi‐type requests and customer satisfaction oblivious. They cannot be applied to multi‐type requests and customer satisfaction‐aware algorithm design with the objective of maximizing net profit. This paper proposes an ant‐colony optimization‐based algorithm for maximizing SP's net profit (AMP) on geographically distributed data centers with the consideration of customer satisfaction. First, using model of customer satisfaction, we formulate the utility (or net profit) maximization issue as an optimization problem under the constraints of customer satisfaction and data centers. Second, we analyze the complexity of the optimal requests dispatchment problem and rigidly prove that it is an NP‐complete problem. Third, to evaluate the proposed algorithm, we have conducted the comprehensive simulation and compared with the other state‐of‐the‐art algorithms. Also, we extend our work to consider the data center's power usage effectiveness. It has been shown that AMP maximizes SP net profit by dispatching service requests to the proper data centers and generating the appropriate amount of virtual machines to meet customer satisfaction. Moreover, we also demonstrate the effectiveness of our approach when it accommodates the impacts of dynamically arrived heavy workload, various evaporation rate and consideration of power usage effectiveness. Copyright © 2014 John Wiley & Sons, Ltd.

Touch-screen technique has gained the large popularity in human-screen interaction with modern smartphones. Due to the limited size of equipped screens, scrolling operations are indispensable in order to display the content of interest on screen. While power consumption caused by hardware and software installed within smartphones is well studied, the energy cost made by human-screen interaction such as scrolling remains unknown. In this paper, we analyze the impact of scrolling operations to the power consumption of smartphones, finding that the state-of-art strategy of smartphones in responding a scrolling operation is to always use the highest frame rate which arouses huge computation burden and can contribute nearly 50 percent to the total power consumption of smartphones. In recognizing this significance, we further propose a novel system, energy-efficient engine (E3), which automatically tracks the scrolling speed and adaptively adjusts the frame rate according to user preference. The goal of E3is to guarantee the user experience and minimize the energy consumption caused by scrolling at the same time. Extensive experiment results demonstrate the efficiency of E3design. On average, E3can save up to 60 percent of the energy consumed by CPU and 35 percent of the overall energy consumption.

Content-based publish/subscribe systems have been employed to deal with complex distributed information flows in many applications. It is well recognized that event matching is a fundamental component of such large-scale systems. Event matching searches a space which is composed of all subscriptions. As the scale and complexity of a system grows, the efficiency of event matching becomes more critical to system performance. However, most existing methods suffer significant performance degradation when the system has large numbers of both subscriptions and their component constraints. In this paper, we present Hash Tree (H-Tree), a highly efficient index structure for event matching. H-Tree is a hash table in nature that is a combination of hash lists and hash chaining. A hash list is built up on an indexed attribute by realizing novel overlapping divisions of the attribute's value domain, providing more efficient space consumption. Multiple hash lists are then combined into a hash tree. The basic idea behind H-Tree is that matching efficiencies are improved when the search space is substantially reduced by pruning most of the subscriptions that are not matched. We have implemented H-Tree and conducted extensive experiments in different settings. Experimental results demonstrate that H-Tree has better performance than its counterparts by a large margin. In particular, the matching speed is faster by three orders of magnitude than its counterparts when the numbers of both subscriptions and their component constraints are huge.

Acquiring instant vehicle speed is desirable and a corner stone to many important vehicular applications. This paper utilizes smartphone sensors to estimate the vehicle speed, especially when GPS is unavailable or inaccurate in urban environments. In particular, we estimate the vehicle speed by integrating the accelerometer's readings over time and find the acceleration errors can lead to large deviations between the estimated speed and the real one. Further analysis shows that the changes of acceleration errors are very small over time which can be corrected at some points, called reference points, where the true vehicle speed is known. Recognizing this observation, we propose an accurate vehicle speed estimation system, SenSpeed, which senses natural driving conditions in urban environments including making turns, stopping and passing through uneven road surfaces, to derive reference points and further eliminates the speed estimation deviations caused by acceleration errors. Extensive experiments demonstrate that SenSpeed is accurate and robust in real driving environments. On average, the real-time speed estimation error on local road is 1.32mph, and the offline speed estimation error is as low as 0.75mph. Whereas the average error of GPS is 3.1mph and 2.8mph respectively.

Event matching is the process of checking high volumes of events against large numbers of subscriptions and is a fundamental issue for the overall performance of a large-scale distributed publish/subscribe system. Most existing algorithms are based on counting satisfied component constraints in each subscription. As the scale of a system grows, these algorithms inevitably suffer from performance degradation. We present REIN (REctangle INtersection), a fast event matching approach for large-scale content-based publish/subscribe systems. The idea behind REIN is to quickly filter out unlikely matched subscriptions. In REIN, the event matching problem is first transformed into the rectangle intersection problem. Then, an efficient index structure is designed to address the problem by using bit operations. Experimental results show that REIN has a better matching performance than its counterparts. In particular, the event matching speed is faster by an order of magnitude when the selectivity of subscriptions is high and the number of subscriptions is large.

Mobile advertising in vehicular networks is of great interest with which timely information can be fast spread into the network. Given a limited budget for hiring seed vehicles, how to achieve the maximum advertising coverage within a given period of time is NP-hard. In this paper, we propose an innovative scheme, POST, for mobile advertising in vehicular networks. The POST design is based on two key observations we have found by analyzing three large-scale vehicle traces. First, vehicles demonstrate dynamic sociality in the network; second, such vehicular sociality has strong temporal correlations. With the knowledge, POST uses Markov chains to infer future vehicular sociality and adopts one greedy heuristic to select the most “centric” vehicles as seeds for mobile advertising. Extensive trace-driven simulation results show that POST can greatly improve the coverage and the intensity of advertising.

SUMMARY It is well known that data centers are consuming a large amount of energy that incurs significant financial and environmental costs. Recently, there has been an increasing interest in utilizing green energy for data centers, where green energy sources include solar and wind. This paper studies the crucial problem of maximizing the utilization of green energy through scheduling complex jobs in data centers in order to reduce the use of traditional brown energy. However, it is highly challenging for data centers to make use of green energy. First, the availability of typical green energy is variable to dynamic changes of natural environments, for example, weather. Second, although predictions can be made for the future availability of green energy, it is inevitable that such predictions have errors. Third, jobs are associated with strict deadlines, and it is required that jobs are completed before their deadlines. Finally, because the reliability in a data center relies upon temperature, the awareness of temperature should be taken into account while maximizing the green energy. In this paper, we consider online scheduling of jobs whose arrivals to the data center system dynamically. In addition, we explicitly take the power consumption of switches into account when scheduling jobs onto computing nodes. Two solar energy‐aware algorithms called SEEDMin and SEEDMax have been proposed. Then, we extend SEED to RSEED with the awareness of reliability. To evaluate the effectiveness of the proposed algorithms, comprehensive simulations have been conducted, and the proposed algorithms are compared with other state‐of‐art algorithms. Experimental results demonstrate that both SEEDMin and SEEDMax can significantly increase the utilization of solar energy without violating job deadlines and overall energy budget. The amount of solar energy utilized by SEEDMin and SEEDMax is 33.4%and35.3% larger than that of two traditional scheduling algorithms, MinMin and MinMax, respectively. Also, it can be seen that RSEED greatly improves the reliability by decreasing the temperature. Copyright © 2013 John Wiley & Sons, Ltd.

SUMMARY Resource performance prediction is becoming more and more important in cloud environment, and CPU load prediction is helpful for system maintenance and application schedule. However, the best predictor often varies from one resource to another. At the same time, CPU load in cloud environment has a wide range of dynamics so that the best predictor for any particular CPU load time series may change over time. Ensemble method, which can use multiple models to obtain better performances, is investigated to support CPU load prediction in this paper. The novel ensemble model proposed consists of two layers. The predictor optimization layer can continuously incorporate new predictor instances and remove those ones with a poor performance. The ensemble layer is responsible for producing the final prediction based on the results of multiple predictor instances. In addition, the ensemble layer can also provide feedbacks to the predictor optimization layer, which helps it to adopt appropriate optimization strategies. Extensive experiments have been performed on CPU load traces collected from an in‐production private cloud environment, and it shows this ensemble model has a better performance than any individual model. Copyright © 2013 John Wiley & Sons, Ltd.

Data sharing in the cloud, fueled by favorable cloud technology trends, has emerging as a promising pattern in regard to enabling data more accessible to users in a convenient manner. To achieve data sharing, enterprises and customers in increasing numbers keep their data stored into cloud server. In this paper, we focus on seeking a solution that allows secure and effective access to the cloud data. We propose an effective and flexible privacy-preserving data policy, P2E, utilizing ciphertext policy attribute-based encryption (CP-ABE) and combining it with technique of identity-based encryption (IBE). In addition to ensuring strong data sharing security, the policy succeeds in preserving the privacy of cloud users. Security analysis indicates that the proposed policy is security and enforces fine-grained access control and full collusion resistance simultaneously. Furthermore, our performance analysis and experimental results show that P2E is as light as possible.

Traffic load is often unevenly distributed among the access points (APs) in enterprise WLANs. Such load imbalance results in sub-optimal network throughput and unfair bandwidth allocation among users. In this paper, we collect real traces from over twelve thousand WiFi users in Shanghai Jiao Tong University. Through intensive data analysis, we find that user behavior like leaving together may cause significant AP load imbalance problem. We also observe from the trace that users with similar application usage have the potential to leave together. Inspired by those observations, we propose an innovative scheme, Social-aware AP Selection Scheme(S3), which can actively learn the sociality information among users trained with their history application profiles and elegantly assign users based on the obtained knowledge. Both real prototype implementation and simulation results show that S3 is feasible and can achieve 41.2% balancing performance gain on average.

Mashup is a user-centric approach to create value-added new services by utilizing and recombining existing service components. However, as services become increasingly more spontaneous and prevalent on the Internet, finding suitable services from which to develop a mashup based on users' explicit and implicit requirements remains a daunting task. Several approaches already exist for recommending specific services for users but they are limited to proposing only services with similar functionality. In order to recommend a set of suitable services for a general mashup based on users' functional specifications, a novel social-aware service recommendation approach, where multi-dimensional social relationships among potential users, topics, mashups, and services are described by a coupled matrix model, is proposed in this paper. Accordingly, a factorization algorithm is designed to predict unobserved relationships, and as a result, a comprehensive service recommendation model can be readily constructed. Experimental results for a realistic mashup data set indicate that the proposed approach outperforms other state-of-the-art methods.

3G technology has stimulated a wide variety of high-bandwidth applications on smartphones, such as video streaming and content-rich web browsing. Although having those applications mobile is quite appealing, high data rate transmission also poses huge demand for power. It has been revealed that the tail effect in 3G radio operation results in significant energy drain on smartphones. Recent fast dormancy technique can be utilized to remove tails but, without care, can degrades user experience. In this paper, we propose a novel scheme SmartCut, which effectively mitigates the tail effect of radio usage in 3G networks with little side-effect on user experience. The core idea of SmartCut is to utilize the temporal correlation of packet arrivals to predict upcoming data, based on which unnecessary high-power-state tails of radio are cut out leveraging the Fast Dormancy mechanism. Extensive trace-driven simulation results demonstrate the efficacy of SmartCut design. On average, SmartCut can save up to 56.57% energy on average while having little side-effect to user experience.

Vehicular networks consist of highly mobile vehicles communications, where connectivity is intermittent. Due to the distributed and highly dynamic nature of vehicular network, to minimize the end-to-end delay and the network traffic at the same time in data forwarding is very hard. Heuristic algorithms utilizing either contact-level or social-level scale of vehicular mobility have only one-sided view of the network and therefore are not optimal. In this paper, by analyzing three large sets of Global Positioning System (GPS) trace of more than ten thousand public vehicles, we find that pairwise contacts have strong temporal correlation. Furthermore, the contact graph of vehicles presents complex structure when aggregating the underlying contacts. In understanding the impact of both levels of mobility to the data forwarding, we propose an innovative scheme, named ZOOM, for fast opportunistic forwarding in vehicular networks, which automatically choose the most appropriate mobility information when deciding next data-relays in order to minimize the end-to-end delay while reducing the network traffic. Extensive trace-driven simulations demonstrate the efficacy of ZOOM design. On average, ZOOM can improve 30% performance gain comparing to the state-of-art algorithms.

Cloud storage services enable users to remotely store their data and eliminate excessive local installation of software and hardware. One critical issue is how to enable a secure data collaboration service including data access and update in cloud computing. A data collaboration service is to support the availability and consistency of the shared data among multi-users. In this paper, we propose a secure and efficient data collaboration scheme SECO. In SECO, we employ a two-level hierarchical identity based encryption (HIBE) to guarantee data confidentiality against untrusted cloud. This paper is the first attempt to explore secure cloud data collaboration service that precludes information leakage and enables a one-to-many encryption paradigm, data writing operation and fine-grained access control simultaneously. Security analysis indicates that the SECO enforces fine-grained access control and collusion resistant. Extensive performance analysis and experiment results demonstrate that SECO is highly efficient and low overhead on computation and communication.

Touch-screen technique has gained the large popularity in human-screen interaction with modern smartphones. Due to the limited size of equipped screens, scrolling operations are indispensable in order to display the content of interest on screen. While power consumption caused by hardware and software installed within smartphones is well studied, the energy cost made by human-screen interaction such as scrolling remains unknown. In this paper, we analyze the impact of scrolling operations to the power consumption of smartphones, finding that the state-of-art strategy of smartphones in responding a scrolling operation is to always use the highest frame rate which arouses huge computation burden and can contribute nearly 50% to the total power consumption of smartphones. In recognizing this significance, we further propose a novel system, Energy-Efficient Engine(E3), which automatically tracks the scrolling speed and adaptively adjusts the frame rate according to individual user preference. The goal of E3 is to guarantee the user experience and minimize the energy consumption caused by scrolling at the same time. Extensive experiment results demonstrate the efficiency of E3 design. On average, E3 can save up to 58% of the energy consumed by CPU and 34% of the overall energy consumption.

ABSTRACT Designing and implementing an interoperable and flexible service process collaboration strategy is one of key issues for business to business integrations. To better support service process collaboration, an event view model is proposed, which is composed of a set of event types and their dependency relationships. It provides a general and flexible way to define a public view of a service process model and serves as the basis for defining service process collaboration protocols. In the paper, the basic concepts and a system framework for event‐based service process collaboration are first introduced. The definitions of event and the dependency relationships among event types are then presented. Especially, how to identify dependency relationships among composite event types is studied in detail. After discussing the definition of event view and its specifying approach, a procedure for transforming a BPEL process model into an event model and deriving dependencies among events is given. Finally, a case study is presented, and some implementation issues for defining and publishing an event view are discussed. Copyright © 2013 John Wiley & Sons, Ltd.

As an important cornerstone for clouds, virtualization plays a vital role in building this emerging infrastructure. Virtual machines (VMs) with a variety of workloads may run simultaneously on a physical machine in the cloud platform. The scheduling algorithm used in Xen schedules virtual CPUs (VCPUs) of a VM asynchronously and guarantees the proportion of the CPU time allocated to the VM. This proportional sharing (PS) method is beneficial as it simplifies the implementation of CPU scheduling in the virtual machine monitor (VMM), and can deliver near-native performance for some workloads. However, when workloads in VMs are concurrent applications such as multithreaded programs with the synchronization operation, it has been demonstrated that this method in the VMM can reduce the performance, due to the negative impact of virtualization on synchronization. To address this issue, we present a hybrid scheduling framework for CPU management in the VMM to adapt to the diversity of VMs running simultaneously on a physical machine. We implement a hybrid scheduler based on Xen, and experimental results indicate that the hybrid CPU management method is feasible to mitigate the negative influence of virtualization on synchronization, and improve the performance of concurrent applications in the virtualized system, while maintaining the performance of high-throughput applications.

Cloud computing has emerging as a promising pattern for data outsourcing and high-quality data services. However, concerns of sensitive information on cloud potentially causes privacy problems. Data encryption protects data security to some extent, but at the cost of compromised efficiency. Searchable symmetric encryption (SSE) allows retrieval of encrypted data over cloud. In this paper, we focus on addressing data privacy issues using SSE. For the first time, we formulate the privacy issue from the aspect of similarity relevance and scheme robustness. We observe that server-side ranking based on order-preserving encryption (OPE) inevitably leaks data privacy. To eliminate the leakage, we propose a two-round searchable encryption (TRSE) scheme that supports top-$(k)$ multikeyword retrieval. In TRSE, we employ a vector space model and homomorphic encryption. The vector space model helps to provide sufficient search accuracy, and the homomorphic encryption enables users to involve in the ranking while the majority of computing work is done on the server side by operations only on ciphertext. As a result, information leakage can be eliminated and data security is ensured. Thorough security and performance analysis show that the proposed scheme guarantees high security and practical efficiency.

Traffic estimation is crucial to a number of tasks such as traffic management and road engineering. We propose an approach for metropolitan-scale traffic estimation with probe vehicles that periodically send location and speed updates to a monitoring center. In our approach, we use the flow speed on a road link within a time slot to indicate the traffic condition of the road segment at the given time slot, which is approximated by the average value of probe speeds. By analyzing a large data set of two-year probe data collected from a fleet of around 4,000 taxis in Shanghai, China, we find that a set of probe data may contain a lot of spatiotemporal vacancies over both time and space. This raises a serious missing data problem for road traffic estimation, which results from the naturally uneven distribution of probe vehicles over both time and space. Through empirical study based on the data set of real probe data using principal component analysis (PCA), we have observed that there are hidden structures within the traffic conditions of a road network. Inspired by this observation, we propose a compressive sensing-based algorithm for solving the missing data problem, which exploits the hidden structures for computing estimates for road traffic conditions. Different from existing approaches, our algorithm does not rely on complicated traffic models, which usually require costly training with field study and large data sets. With extensive experiments based on the data set of real probe data, we demonstrate that our proposed algorithm performs significantly better than other completing algorithms, including KNN and MSSA. Surprisingly, our algorithm can achieve an estimate error of as low as 20 percent even when more than 80 percent of probe data are missing.

Traffic light sensing aims to detect the status of traffic lights which is valuable for many applications such as traffic management, traffic light optimization, and real-time vehicle navigation. In this work, we develop a system called POVA for traffic light sensing in large-scale urban areas. The system employs pervasive probe vehicles that just report real-time states of position and speed from time to time. POVA has advantages of wide coverage and low deployment cost. The important observation motivating the design of POVA is that a traffic light has a considerable impact on mobility of vehicles on the road attached to the traffic light. However, the system design faces three unique challenges: 1) Probe reports are by nature discrete while the goal of traffic light sensing is to determine the state of a traffic light at any time; 2) there may be a very limited number of probe reports in a given duration for traffic light state estimation; and 3) a traffic light may change its state with a variable interval. To tackle the challenges, we develop a new technique that makes the best use of limited probe reports as well as statistical features of light states. It first estimates the state of a traffic light at the time instant of a report by applying maximum a posterior estimation. Then, we formulate the state estimation of a light at any time into a joint optimization problem that is solved by an efficient heuristic algorithm. We have implemented the system and tested it with a fleet of around 4,000 probe taxis and 2,000 buses in Shanghai, China. Trace-driven experimentation and field study show that nearly 60 percent of traffic lights have an estimation error lower than 19 percent if 20,000 probe vehicles would be employed in the urban area of Shanghai. We further demonstrate that the estimation error rate is as low as 18 percent even when the number of available reports is merely 1 per minute.

In this study, we propose a module-level Evolvable Hardware (EHW) approach to design synchronous sequential circuits and minimize the circuit complexity (the number of logic gates and wires used). Firstly, we use Genetic Algorithm (GA) to implement state simplification and obtain near-optimal state assignment. Then, in the pre-evolution stage, EHW evolves a set of high performing circuits and uses data mining method to find frequently evolved blocks from these circuits. The frequently evolved block would be re-used as function or terminal for evolving better circuits in the re-evolution stage. EHW has a faster convergence so that the circuit with small complexity could be evolved. Auto starting ability of circuits would also be test by the fitness function of EHW. Finally, sequence detectors, modulon counters, and ISCAS'89 circuit are used as the proof for our evolutionary design approach. Simulation results are given, and our evolutionary algorithm is shown to be better than other methods in terms of convergence time, success rate, and maximum fitness across generations.

To many sensor networks, the robust operation depends on deploying relays to ensure wireless coverage. This paper considers the crucial problem of optimal relay placement for wireless sensor networks in indoor environments. The placement of relays is essential for ensuring communication quality and data collection. A number of existing algorithms have been proposed for ensuring full sensing coverage and network connectivity. These algorithms can hardly be applied to indoor environments because of the complexity of indoor environments, in which a radio signal can be dramatically degraded by obstacles like walls. We firstly theoretically prove that the indoor relay placement problem is NP-hard. We then predict radio coverage of a given relay deployment in indoor environments, and then propose a efficient greedy algorithm for computing the relay deployment locations for required coverage quality. This algorithm is proved to provide a Hnfactor approximation to the theoretical optimum, where Hn= 1 + 1/2 + ⋯ + 1/n = ln(n) + 1, and n is the number of all grid points. Experimental results demonstrate the proposed algorithm achieves better performance than two other algorithms. To our knowledge, our work is the very first that study the optimal relay placement problem for sensor networks in complex indoor environments.

In this paper, we focus on data privacy of searchable symmetric encryption (SSE) in cloud computing. For the first time, we formulate the privacy issue from the aspect of similarity relevance and scheme robustness and then prove server-side ranking based on order-preserving encryption (OPE) inevitably leaks data privacy. In order to solve this problem, we propose a two round searchable encryption (TRSE) scheme, supporting top-k multi-keyword search, in which novel technologies, i.e., homomorphic encryption and vector space model, are employed. Vector space model helps to provide sufficient search accuracy, and homomorphic encryption enables users involve in the ranking while majority of computing work is still done on server-side by operations only on ciphertext. In this way, information leakage can be eliminated and data security is ensured. Thorough security analysis and performance analysis show that the proposed scheme guarantees high security and practical efficiency.

We develop a system called POVA for traffic light sensing in large-scale urban areas, where traffic light sensing aims to detect the status of traffic lights which is valuable for many applications such as traffic management, traffic light optimization and real-time vehicle navigation. The system employs pervasive probe vehicles that just report real-time states of position and speed from time to time. The important observation motivating the design of POVA is that a traffic light has a considerable impact on mobility of vehicles on the road attached to the traffic light. However, the system design faces three unique challenges, i.e., discrete probe reports, uneven distribution of reports over time and space, and variable interval of light states. To tackle the challenges, we develop a new technique that makes the best use of limited probe reports as well as statistical features of light states. It first estimates the state of a traffic light at the time instant of a report by applying maximum a posterior (MAP) estimation. Then, we formulate the state estimation of a light at any time into a joint optimization problem that is solved by an efficient heuristic algorithm. Trace-driven experimentation and field study show that the estimation error rate is as low as 21% even when the number of available reports is merely one per minute.

Recommending good driving paths is valuable to taxi drivers for reducing unnecessary waste in fuel and increasing revenue. Driving only according to personal experience may lead to poor performance. With the availability of large-scale GPS traces collected from urban taxis, we have the curiosity about whether we can discover the hidden knowledge in the trace data for smart driving recommendation. This paper focuses on developing a smart recommender system based on mining large-scale GPS trace datasets from a large number of urban taxis. However, such the trace datasets are in nature complex, large-scale, and dynamic, which makes mining the datasets particularly challenging. We first extract vehicular mobility pattern from the large-scale GPS trace datasets. Then, the optimal driving process is modeled as a Markov Decision Process (MDP). Solving the MDP problem results in the optimal driving strategy that gives smart recommendation for taxi drivers. In essence, the most rewarding driving paths can be derived in the long run. We have conducted extensive trace driven simulations and conclusive results show that our recommendation algorithm can successfully find good driving paths and outperforms other alternative algorithms.

In cloud computing environment, it is common for a multi-component application to be deployed into the IT infrastructure on demand. Although the Application Component Placement Problem or ACP has been studied both in the academy and industry for some years, so far these approaches all rely on a centralized node to collect the information about the network topology and hosts of the targeting environment so that it can make the optimized decision. In cloud environment, computing resources are shared by many applications and the IT infrastructure is dynamically changing, which makes collecting the dynamic information of the environment a more difficult task. In addition, when two or more clouds are involved in the deployment, there may exist privacy concern between different clouds so that a centralized approach cannot be applied. In this paper, we propose a distributed approach to solve the ACP problem. In our approach, we transform the ACP into a Distributed Constraint Optimization Problem or DCOP and solving the ACP becomes solving the DCOP. The detail of the algorithm and experiments are given.

Cloud computing provides an efficient way to expose computing capabilities as sophisticated services that can be accessed remotely. It eliminates the need for organizations to maintain extensive infrastructures. Parallel and distributed simulation over cloud will bring us not only the increased productivity and efficiency but also the lower barrier for usage. This paper introduces a private cloud system based on BOINC which supports parallel and distributed simulation. The design idea and implementation method of each component for our system is discussed in detail.

With multi-core processors becoming popular, exploiting their computational potential becomes an urgent matter. The functionality of multiple standalone computer systems can be aggregated into a single hardware computer by virtualization, giving efficient usage of the hardware and decreased cost for power. Some principles of operating systems can be applied directly to virtual machine systems, however virtualization disrupts the basis of spinlock synchronization in the guest operating system, which results in performance degradation of concurrent workloads such as parallel programs or multi-threaded programs in virtual machines.

Transaction management for mobile and ubiquitous computing aims at providing mobile users with reliable services in a transparent way anytime anywhere. To make such a vision a reality, transaction processing for the mobile and ubiquitous computing needs to adapt to the runtime environments dynamically. However, most existing mobile transaction models do not consider the context-based transaction management. In this paper, we propose a context-aware transaction model and context-driven coordination algorithms. They are built on an event-context-action mechanism, enabling the transaction processing to adapt well to dynamically changing transaction context. The simulation results have also demonstrated that our model and algorithms can significantly improve the successful commit ratio under unstable context conditions.

Traffic sensing is crucial to a number of tasks such as traffic management and city road network engineering. We build a traffic sensing system with probe vehicles for metropolitan scale traffic sensing. Each probe vehicle senses its instant speed and position periodically and sensory data of probe vehicles can be aggregated for traffic sensing. However, there is a critical issue that the sensory data contain spatiotemporal vacancies with no reports. This is a result of the naturally uneven distribution of probe vehicles in both spatial and temporal dimensions since they move at their own wills. This paper proposes a new approach based on compressive sensing to large-scale traffic sensing in urban areas. We mine the extensive real trace datasets of taxies in an urban environment with principal component analysis and reveal the existence of hidden structures with sensory traffic data that underpins the compressive sensing approach. By exploiting the hidden structures, an efficient algorithm is proposed for finding the best estimate traffic condition matrix by minimizing the rank of the estimate matrix. With extensive trace-driven experiments, we demonstrate that the proposed algorithm outperforms a number of alternative algorithms. Surprisingly, we show that our algorithm can achieve an estimation error of as low as 20% even when more than 80% of sensory data are not present.

Inter-contact times (ICTs) between moving vehicles are one of the key metrics in vehicular networks, and they are also central to forwarding algorithms and the end-to-end delay. Recent study on the tail distribution of ICTs based on theoretical mobility models and empirical trace data shows that the delay between two consecutive contact opportunities drops exponentially. While theoretical results facilitate problem analysis, how to design practical opportunistic forwarding protocols in vehicular networks, where messages are delivered in carry-and-forward fashion, is still unclear. In this paper, we study three large sets of Global Positioning System (GPS) traces of more than ten thousand public vehicles, collected from Shanghai and Shenzhen, two metropolises in China. By mining the temporal correlation and the evolution of ICTs between each pair of vehicles, we use higher order Markov chains to characterize urban vehicular mobility patterns, which adapt as ICTs between vehicles continuously get updated. Then, the next hop for message forwarding is determined based on the previous ICTs. With our message forwarding strategy, it can dramatically increase delivery ratio (up to 80%) and reduce end-to-end delay (up to 50%) while generating similar network traffic comparing to current strategies based on the delivery probability or the expected delay.

Agent-based service composition has provided a promising computing paradigm for the automatic web service composition. In this paper, a formal service agent model is proposed, which integrates the web service and software agent technologies into one cohesive entity. Based on the service agent model, a distributed planning algorithm for web service composition called DPAWSC is presented. DPAWSC formalizes web service composition into a graph search problem according to the dependence relations among service agents. The key to DPAWSC is that the alternative solution with smaller length has higher priority to be searched than one with larger length. DPAWSC is based on the distributed decision making of the autonomous service agents and addresses the distributed nature of web service composition. We evaluate the algorithm by simulation experiments and the results demonstrate that DPAWSC is effective for its ability to produce the high quality solution at a low cost of communications.

Intercontact time between moving vehicles is one of the key metrics in vehicular ad hoc networks (VANETs) and central to forwarding algorithms and the end-to-end delay. Due to prohibitive costs, little work has conducted experimental study on intercontact time in urban vehicular environments. In this paper, we carry out an extensive experiment involving thousands of operational taxies in Shanghai city. Studying the taxi trace data on the frequency and duration of transfer opportunities between taxies, we observe that the tail distribution of the intercontact time, that is, the time gap separating two contacts of the same pair of taxies, exhibits an exponential decay, over a large range of timescale. This observation is in sharp contrast to recent empirical data studies based on human mobility, in which the distribution of the intercontact time obeys a power law. By analyzing a simplified mobility model that captures the effect of hot areas in the city, we rigorously prove that common traffic influxes, where large volume of traffic converges, play a major role in generating the exponential tail of the intercontact time. Our results thus provide fundamental guidelines on design of new vehicular mobility models in urban scenarios, new data forwarding protocols and their performance analysis.

The high mobility of VANET makes information exchange across the network excessively difficult. Traditional approaches designed for stationary networks are not applicable due to the high dynamics among the nodes. Applying the routing techniques tailored for general mobile networks inevitably brings huge traffic burden to the crowded urban VANET and leads to low efficiency. To make the information exchange fluent and efficient, we explore the unique features of the urban VANET. By exploring the invariants in the mobile network topology, we are able to efficiently manage the information on top of the “intersection graph” transformed from the underlying network of road segments in the urban area. Our approach can thus achieve efficient query dissemination and data retrieval on this information organization. We intensively investigate and analyze a trace that records the movement of more than 4000 taxies in the urban area of Shanghai City over several months. We grasp the key impact of the fundamental factors that affect the VANET behaviors and accordingly develop tailored techniques to maximize the performance of this design. Experimental results validate the effectiveness and efficiency of our design.

Inter-contact time between moving vehicles is one of the key metrics in vehicular ad hoc networks (VANETs) and central to forwarding algorithms and the end-to-end delay. Due to prohibitive costs, little work has conducted experimental study on inter-contact time in urban vehicular environments. In this paper, we carry out an extensive experiment involving thousands of operational taxies in Shanghai city. Studying the taxi trace data on the frequency and duration of transfer opportunities between taxies, we observe that the tail distribution of the inter-contact time, that is the time gap separating two contacts of the same pair of taxies, exhibits a light tail such as one of an exponential distribution, over a large range of timescale. This observation is in sharp contrast to recent empirical data studies based on human mobility, in which the distribution of the inter-contact time obeys a power law. By performing a least squares fit, we establish an exponential model that can accurately depict the tail behavior of the inter-contact time in VANETs. Our results thus provide fundamental guidelines on design of new vehicular mobility models in urban scenarios, new data forwarding protocols and their performance analysis.

Identifying hot spots of moving vehicles in an urban area is essential to many smart city applications. The practical research on hot spots in smart city presents many unique features, such as highly mobile environments, supremely limited size of sample objects, and the non-uniform, biased samples. All these features have raised new challenges that make the traditional density-based clustering algorithms fail to capture the real clustering property of objects, making the results less meaningful. In this paper we propose a novel, non-density-based approach called mobility-based clustering. The key idea is that sample objects are employed as "sensors" to perceive the vehicle crowdedness in nearby areas using their instant mobility, rather than the "object representatives". As such the mobility of samples is naturally incorporated. Several key factors beyond the vehicle crowdedness have been identified and techniques to compensate these effects are proposed. We evaluate the performance of mobility-based clustering based on real traffic situations. Experimental results show that using 0.3% of vehicles as the samples, mobility-based clustering can accurately identify hot spots which can hardly be obtained by the latest representative algorithm UMicro.

We consider distributed top-k processing problem in a mobile scenario. Specially, we focus on a real application of a bus network (N nodes), where buses are equipped with cameras for real-time security surveillance. Due to the limited number of screens (k, k<;<;N) at the traffic management center, how to select k bus nodes with most passengers to upload image data by DSRC needs to be solved. We present a novel distributed scheme BSS to handle this so-called "top-k node selection" issue in bus network, in which challenges of low time cost and high accuracy is not trivial. BSS utilizes various strategies to speed up top-k node selection facing poor network condition and application requirements. Performance evaluation is carried out on a real-trace driven simulator, which utilizes about 700 buses in Shanghai. The testing results show that BSS has an excellent performance in terms of time cost and average degree of accuracy, which shows the effectiveness of BSS scheme for real-time security surveillance.

In this paper, we present our study of extracting a mobility model for vehicular ad hoc networks (VANETs) from a large amount of real taxi GPS trace data. In order to capture characteristics of the urban vehicle network from microscopic to macroscopic aspects, we design three parameters and extract their values from the GPS trace data. Using this mobility model, we can generate the synthetic trace to simulate the movement of taxis in the urban area of a metropolis. The validation is carried through extensive comparisons between the synthetic trace and the real trace. Validation results show that our mobility model has a good approximation to the real scenario.

Bus lane enforcement system aims to monitor illegal utilization of bus lane by non-permitted vehicles (violator). However, Road-side system leads to considerable infrastructure cost while bus mounted system has limited surveillance coverage. In this paper, we consider an interesting problem in bus mounted system: how to improve the surveillance coverage of bus mounted system without additional infrastructure cost? In other words, we attempt to identify not only the violator immediately in front of the bus, bus also the violators not close to the bus, whose number plates cannot be read directly because of sight blocking of bus mounted cameras. With utilization of communication between bus and existing cameras around intersections, we propose a novel cooperative violator identification scheme, DoubleChecking, with which violators can be sorted out from traffic flow with high accuracy. From theoretical analysis, DoubleChecking shows a good performance for violator identification, which demonstrates the effectiveness of the proposed scheme.

Localization is a fundamental problem in wireless sensor networks and its accuracy impacts the efficiency of location-aware protocols and applications, such as routing and storage. Most previous localization algorithms assume that sensors are distributed in regular areas without holes or obstacles, which often does not reflect real-world conditions, especially for outdoor deployment of wireless sensor networks. In this paper, we propose a novel scheme called reliable anchor-based localization (RAL), which can greatly reduce the localization error due to the irregular deployment areas. We first provide theoretical analysis of the minimum hop length for uniformly distributed networks and then show its close approximation to empirical results, which can assist in the construction of a reliable minimal hop-length table offline. Using this table, we are able to tell whether a path is severely detoured and compute a more accurate average hop length as the basis for distance estimation. At runtime, the RAL scheme 1) utilizes the reliable minimal hop length from the table as the threshold to differentiate between reliable anchors and unreliable ones, and 2) allows each sensor to determine its position utilizing only distance constraints obtained from reliable anchors. The simulation results show that RAL can effectively filter out unreliable anchors and therefore improve the localization accuracy.

Transaction management in Grids is responsible for ensuring the reliable execution of inherently distributed Grid applications. Grid transaction management is different from existing distributed transaction models because Grid resources are highly autonomous, dynamic and heterogeneous. This paper proposes a Grid transaction service (GridTS) and coordination algorithms that manage short-lived and long-lived Grid transactions respectively, providing reliability support for Grid applications. Unlike existing long-lived transaction models that require application programmers to develop compensating transactions, the GridTS can automatically generate compensating transactions during the execution of long-lived Grid transactions. The feasibility of GridTS and the effectiveness of proposed coordination algorithms are demonstrated through simulation studies.

System virtualization can aggregate the functionality of multiple standalone computer systems into a single hardware computer. It is significant to virtualize the computing nodes with multi-core processors in the cluster system, in order to promote the usage of the hardware while decrease the cost of the power. In the virtualized cluster system, multiple virtual machines are running on a computing node. However, it is a challenging issue to automatically balance the workload in virtual machines on each physical computing node, which is different from the traditional cluster system's load balance. In this paper, we propose a management framework for the virtualized cluster system, and present an automatic performance tuning strategy to balance the workload in the virtualized cluster system. We implement a working prototype of the management framework (VEMan) based on Xen, and test the performance of the tuning strategy on a virtualized heterogeneous cluster system. The experimental result indicates that the management framework and tuning strategy are feasible to improve the performance of the virtualized cluster system.

Virtualization is a new area for research in recent years, and virtualization technology can bring convenience to the management of computing resources. Together with the development of the network and the network computing, it gives the virtualization technology more scenarios. The cloud computing technology uses the virtualization technology as while. With the development of the technology, it meets some security problems, such as rootkit attacks and malignant tampers. Malicious programs can plug into the system, and be booted at the any time of the virtualized system. There is little theoretical research on booting a trusted virtualized system. We propose an active trusted model in order to give a theoretical model for not only analyzing the state of a virtualized system, but also helping to design trusted virtual machine application. TBoot is a project to boot a trusted virtual machine. We use our model to illustrate that TBoot can boot a trusted virtual machine theoretically.

Grid computing technology makes it possibly to use scientific workflow to be shared and reused among different users. However, a scientific workflow model usually needs to be tailored during reuse because of different problem contexts met by different users. These change logs provide additional information to help users customize scientific workflow. In this paper, we present a change sequence mining approach to automatically induce customized workflow based on different scientific experiment contexts. A docking case for drug design is described to validate the approach.

We consider a practical application of intelligent vehicles for intersection traffic control. Specially, we study the intersection traffic control problem using reservation-based intersection traffic control system, which utilizes the information exchange between intelligent vehicles and management agents around the intersections to direct traffic, instead of traffic lights. We focus on how to design an effective passing permission (PP) allocation strategy for this system. In this work, with an observation that will cause this system to be inefficient, we propose a novel look-ahead passing permission allocation strategy (LICP) for intersection traffic control. The large-scale testing results show that LICP can make nearly 25% performance improvement on average intersection delay than the previous first come, first serve method (FCFS).

Rapid advancement in wireless communication has made it possible to develop vehicular ad hoc networks, in which a vehicle can communicate with other vehicles via a wireless, multi-hope fashion. A variety of appealing real-world applications can be enabled by VANETs, such as driving safety and urban monitoring. Many location based routing algorithms have been proposed for data delivery in VANETs. Most of them assume that accurate location information is available when needed. In practice, however, such assumption is unrealistic. It incurs considerable cost to retrieve location information. In addition, a vehicle is on the fast move over time, and a location previously obtained may become invalid after certain time. This paper proposes a routing algorithm that is based on a practical location information model. To solve the problem of location inaccuracy and vehicle mobility, we devise a location predictor which estimates the possible location of a vehicle by using history information. Based on the greedy forwarding strategy, the proposed routing differentiates packets in terms of closeness to destination and jump distance. We evaluate the performance of the proposed algorithms with a large real trace of taxi motion in Shanghai. Trace-driven simulation results demonstrate that data delivery performance is improved.

The virtualization technology makes it feasible that multiple guest operating systems run on a single physical machine. It is the virtual machine monitor that dynamically maps the virtual CPU of virtual machines to physical CPUs according to the scheduling strategy. The scheduling strategy in Xen schedules virtual CPUs of a virtual machines asynchronously while guarantees the proportion of the CPU time corresponding to its weight, maximizing the throughput of the system. However, this scheduling strategy may deteriorate the performance when the virtual machine is used to execute the concurrent applications such as parallel programs or multithreaded programs. In this paper, we analyze the CPU scheduling problem in the virtual machine monitor theoretically, and the result is that the asynchronous CPU scheduling strategy will waste considerable physical CPU time when the system workload is the concurrent application. Then, we present a hybrid scheduling framework for the CPU scheduling in the virtual machine monitor. There are two types of virtual machines in the system: the high-throughput type and the concurrent type. The virtual machine can be set as the concurrent type when the majority of its workload is concurrent applications in order to reduce the cost of synchronization. Otherwise, it is set as the high-throughput type as the default. Moreover, we implement the hybrid scheduling framework based on Xen, and we will give a description of our implementation in details. At last, we test the performance of the presented scheduling framework and strategy based on the multi-core platform, and the experiment result indicates that the scheduling framework and strategy is feasible to improve the performance of the virtual machine system.

Currently, vehicles are equipped with forward facing cameras to assist the forensic investigations of events by proactive image capturing from streets and roads. With content redundancy and storage imbalance in this in-network distributed storage system, how to maximize its storage capacity is a challenge. In other words, how to maximize the average lifetime of sensory data (i.e. images generated by cameras) in network is a fundamental problem need to be solved. This paper presents, VStore, a cooperative storage solution for mobile surveillance in vehicular sensor networks (VSN). The mechanisms in VStore are designed for redundancy elimination by exchanging information between vehicles and storage balancing. Compared with previous work, we deal with new challenges in mobile scenario. Field testing was carried out on a real-trace driven simulator, which utilizes about 500 taxies in Shanghai city. The testing results show that VStore can largely prolong the average lifetime of sensory data by cooperative storage.

Unlike convectional omnidirectional sensors that always have an omni-angle of sensing range, directional sensors may have a limited angle of sensing range due to the technical constraints or cost considerations. A directional sensor network consists of a number of directional sensors, which can switch to several directions to extend their sensing ability to cover all the targets in a given area. Power conservation is still an important issue in such directional sensor networks. In this paper, we address the multiple directional cover sets (MDCS) problem of organizing the directions of sensors into a group of non-disjoint cover sets to extend the network lifetime. One cover set in which the directions cover all the targets is activated at one time. We prove the MDCS to be NP-complete and propose several algorithms for the MDCS. Simulation results are presented to demonstrate the performance of these algorithms.

Intelligent transportation systems have become increasingly important for the public transportation in Shanghai. In response, ShanghaiGrid (SG) project aims to provide abundant intelligent transportation services to improve the traffic condition. A challenging service in SG is to accurately locate the positions of moving vehicles in real time. In this paper, we present an innovative scheme, hierarchical exponential region organization (HERO), to tackle this problem. In SG, the location information of individual vehicles is actively logged in local nodes which are distributed throughout the city. For each vehicle, HERO dynamically maintains an advantageous hierarchy on the overlay network of local nodes to conservatively update the location information only in nearby nodes. By bounding the maximum number of hops the query is routed, HERO guarantees to meet the real-time constraint associated with each vehicle. A small-scale prototype system implementation and extensive simulations based on the real road network and trace data of vehicle movements from Shanghai demonstrate the efficacy of HERO.

Abstract The co‐channel interference problem in wireless mesh networks (WMN) is extremely serious due to the heavy aggregated traffic loads and limited available channels. It is preferable for mesh routers to dynamically switch channels according to the accurate estimation of co‐channel interference level in the neighborhood. Most developed interference estimation schemes, however, do not consider the impact of interface switching. Furthermore, the interference in wireless networks has been extensively considered as an all‐or‐nothing event. In this paper, we develop a weighted interference estimation scheme (WIES) for interface‐switching WMN. WIES takes a new version of multi‐interface conflict graph that considers the impacts of frequent interface switching as the interference relationship estimation scheme. Besides, WIES uses a weight to estimate the interference level between links. The weight utilizes two empirical functions to denote the impacts of the relative distance and characteristics of traffic loads in WMN. Extensive NS2 simulations show that WIES achieves significant performance improvements, especially when the interference level of the network is high. We also validate that the interference level of networks is affected by several system parameters such as the number of available channels and the ratio between interference range and transmission range. Copyright © 2008 John Wiley & Sons, Ltd.

In this paper, we present Passive Geographical Routing protocol (PGR), a routing mechanism particularly designed for Vehicle Ad hoc Networks (VANET). PGR has no route discovery phase which is needed by DSR or AODV. In PGR, each node periodically disseminates its location and velocity information along four orthogonal directions of its velocity to distribute routing state in the network. Data packets are first sent in a random direction, and then biased toward the direction according to the received routing state in intermediate nodes. With the nodes’ location and velocity information and assistance of the digital city road map, PSR can prolong the valid duration of routing state stored in the intermediate node. We evaluate PGR with large realistic traffic data in Shanghai. The experiments show that PGR offers a high packet delivery ratio comparing with the traditional routing protocols. The routing overhead is controlled in the network scales as O(n) for N-node networks. This routing scheme has good scalability properties in high dynamic topology of VANET.

Load balancing plays a key role in workload scheduling policies which count in the performance improvement of parallel applications. A critical problem of load balancing is to make a reasonable definition of load index. Unfortunately, few studies provided enough scientific justifications for the choice of load indices. In this paper, a reasonable approach for defining a load index based on factor analysis theory is introduced, which is helpful to reasonable designs of workload scheduling algorithms. An example testing on an accounting log of the CM-5 parallel machine is presented to show the usage of this method.

Currently, vehicular sensor network (VSN) has been paid much attention for monitoring the physical world of urban areas. We have studied VSNs by utilizing about 4000 taxies and 1000 buses equipped with GPS-based mobile sensors in Shanghai to constitute a virtual vehicular sensor network. The communication-connection intermittence makes the routing issue nontrivial when delay-tolerant applications are deployed in VSNs. The existing DTN routing protocols can be categorized as "neighbor-oriented" and how to select a neighbor candidate was always neglected. In this paper, we present a new DTN routing protocol for Delay-Tolerant Vehicular Sensor Networks, Packet-Oriented Routing protocol (POR), which is designed to emphasize neighbor selection based on awareness of packets to be sent and in consideration of probability to complete transferring of these packets. Our results show that POR performs much better than the ordinary Epidemic routing, as well as other popular routing protocols applied in a similar setting.

The application of mobile communication technology to support road traffic constitutes a challenging, but at the same time very promising working area for research and development. Services reach from vehicular safety applications including collision and other safety warnings to non-safety applications like real-time traffic congestion and routing information, high-speed tolling, mobile infotainment, and many others. The creation of high-performance, highly reliable, highly scalable, and secure wireless vehicular ad hoc network (VANET) technologies, though, presents an extraordinary challenge to the wireless research community: a high degree of communication reliability is needed under unfavorable channel conditions. Clearly, the specificity of VANETs in terms of mobility behavior and applications scenarios and requirements makes VANET research an exciting and demanding application- and purpose- driven sub-discipline of wireless networking. In this talk, we discuss the challenges and opportunities of VANETs in metropolitan scales. A concrete case in Shanghai was also studied.

The capacity and flexibility of wireless mesh networks (WMNs) can be greatly improved by adopting dynamic channel assignment. However, dynamic channel assignment brings new challenges such as switching overheads and dependency problems. We propose a channel assignment protocol, called the hybrid channel assignment protocol (HCAP) for infrastructure WMNs (I-WMN). To find out a reasonable tradeoff between flexibility and switching overheads, HCAP adopts static interface assignment strategy for nodes that have the heaviest loads. For other nodes, it adopts a hybrid strategy. HCAP uses a slot-based coordination policy to implement communications between nodes that adopt hybrid strategy. NS2-based simulations show that HCAP not only improves capacity and scalability of I-WMNs, but also enhances per-flow fairness.

The existing vehicular sensors of taxi companies in most of cities can be used for traffic monitoring, however sensors are always set with a long sampling interval because of communication cost saving and network congestion avoidance. In this paper, we focus on the traffic data processing in vehicular sensor networks providing sparse and incomplete information. A performance evaluation study has been carried out in Shanghai by utilizing the sensors installed on 4000 taxis. Two types of traffic status estimation algorithms, the link-based and the vehicle-based, are introduced based on such data basis. The results from large-scale testing cases show that the traffic status can be fairly well estimated based on these imperfect data and we demonstrate the feasibility of such application in most of cities.

Due to critical resource restrictions, wireless sensor networks (WSNs) often face a trade-off between the cost of data transmission and the accuracy of event detection. By exploring the potential spatial and temporal correlations among sensory data, a WSN may intelligently select only a subset of nodes, whose data can still keep the major properties of those collected by the whole network, to transmit. Two important issues are examined in this study. First, which of those sensors should be selected? Second, how can the lifetime of the selected sensors be maximized? We propose a Singular Value Decomposition (SVD) based Sensory Data Suppression (SSS) Mechanism, which removes unnecessary data transmissions and prolong the lifetime of sensor networks. We also balance transmission duties among sensor nodes by leveraging the load balancing algorithms with both one-attribute and multi-attribute scenarios.

Service process usually needs to be changed during reuse because of the change of context. These change mapping relations provide additional information to help users customize service processes. In this paper, we present a mining approach to mining process change sequences based on different context and finding the best sequence to tailor the base process.

Intelligent transportation systems have become increasingly important for the public transportation in Shanghai. In response, ShanghaiGrid (SG) aims to provide abundant intelligent transportation services to improve the traffic condition. A challenging service in SG is to accurately locate the positions of moving vehicles in real time. In this paper we present an innovative scheme HERO to tackle this problem. In SG, the location information of individual vehicles is actively logged in local nodes which are distributed throughout the city. For each vehicle, HERO dynamically maintains an advantageous hierarchy on the overlay network of local nodes to conservatively update the location information only in nearby nodes. By bounding the maximum number of hops the query is routed, HERO guarantees to meet the real-time constraint associated with each vehicle. Extensive simulations based on the real road network and trace data of vehicle movements from Shanghai demonstrate the efficacy of HERO.

Wireless sensor networks (WSNs) are widely used for various monitoring applications. Users issue queries to sensors and collect sensing data. Due to the low quality sensing devices or random link failures, sensor data are often noisy. In order to increase the reliability of the query results, continuous queries are often employed. In this work we focus on continuous holistic queries like median. Existing approaches are mainly designed for non-holistic queries like average. However, it is not trivial to answer holistic ones due to their non-decomposable property. We propose two schemes for answering queries under different data changing conditions. While sensor data changes slowly, based on the data correlation between different rounds, we propose one algorithm for getting the exact answers. When the data changing speed is high, we propose another approach to derive the approximate results. We evaluate both designs through extensive simulations. The results demonstrate that our approach significantly reduces the traffic cost compared with previous works while maintaining the same accuracy.

Network diagnosis, an essential research topic for traditional networking systems, has not received much attention for wireless sensor networks. Existing sensor debugging tools like sympathy or EmStar rely heavily on an add-in protocol that generates and reports a large amount of status information from individual sen-sor nodes, introducing network overhead to a resource constrained and usually traffic sensitive sensor network. We report in this study our initial attempt at providing a light-weight network diag-nosis mechanism for sensor networks. We propose PAD, a prob-abilistic diagnosis approach for inferring the root causes of ab-normal phenomena. PAD employs a packet marking algorithm for efficiently constructing and dynamically maintaining the inference model. Our approach does not incur additional traffic overhead for collecting desired information. Instead, we introduce a prob-abilistic inference model which encodes internal dependencies among different network elements, for online diagnosis of an operational sensor network system. Such a model is capable of additively reasoning root causes based on passively observed symptoms. We implement the PAD design in our sea monitoring sensor network test-bed and validate its effectiveness. We further evaluate the efficiency and scalability of this design through ex-tensive trace-driven simulations.

We present a mobile sensor system for traffic monitoring (GSSTM), which is a typically Grid application in ShanghaiGrid to provide accurate realtime traffic status information. Several issues and challenges in GSSTM are discussed, such as Grid architecture design, data processing for traffic status estimation, storage strategy of massive data, etc. We implemented a prototype system of GSSTM and carried out a field testing in Shanghai city by using GPS-based sensors on 4000 taxis. The testing results show that by utilizing the Grid technology, the expected performance can be obtained, such as stability, scalability, etc. Meantime, traffic status can be fairly well estimated in GSSTM.

Using directional antennas to conserve bandwidth and energy consumption in ad hoc wireless networks has attracted much attention of the research community in recent years. However, limited research has focused on applying directional antennas to broadcasting. We devise a link reduction (LR) based broadcasting protocol for ad hoc wireless networks using directional antennas. LR outperforms most existing omnidirectional and directional broadcasting schemes in the sense that its normalized transmission cost and redundancy are significantly reduced, and consequently it is more bandwidth and energy- efficient. Based on 2-hop neighborhood information, LR relies on no location or angle-of-arrival (AOA) information. LR is a localized protocol and it achieves full delivery in ideal networks. Simulation is conducted in ideal networks where packet collision, channel contention, or node mobility is avoided.

In performance studies of vehicular ad hoc networks (VANETs), the underlying mobility model plays an important role. Since conventional mobile ad hoc network (MANET) routing protocols do not work efficiently in vehicular environments due to the rapid topology changes, the Delay-Tolerant Network (DTN) model is often applied. In this paper, we construct a new mobility model, the Shanghai Urban Vehicular Network (SUVnet) model by using the GPS data from more than 4,000 taxis we have collected, and then investigate the performance of two kinds of DTN routing, the non-geographic pure epidemic routing and our newly-proposed geographic DTN routing, the Distance-Aware Epidemic Routing (DAER). We use the popular random waypoint mobility model and a more complex microscopic traffic simulator generated model for performance comparison. With the two considered DTN routing protocols, conventional mobility models tend to give higher performance results than SUVnet model, the presumably more realistic mobility model.

A disruption-tolerant network (DTN) attempts to route packets between nodes that are temporarily connected. Difficulty in such networks is that nodes have no information about the network status and contact opportunities. The situation is different in public bus networks because the movement of buses exhibits some regularity so that routing in a deterministic way is possible. Many algorithms use a contacts oracle that provides the exact meeting times and durations between all nodes. However, in a real vehicular environment, an oracle is not always accurate, and deterministic routing gives poor results. In this paper, we present BLER, a routing algorithm that achieves effective routing in a buses environment. BLER, compared to other algorithms, performs routing at bus line level instead of bus level; it uses specific bus lines information to achieve good performances. We evaluate BLER on real traces of the bus network of Shanghai, and compare it to other routing algorithms. Performances provide good results for this kind of DTNs.

BitTorrent has emerged as a very popular peer-to-peer file sharing system, which uses an embedded set of incentive mechanisms to encourage contribution and prevent free-riding. However, BitTorrent's ability to prevent free-riding needs further study. In this paper, we present a fluid model with two different classes of peers to capture the effect of free-riding on BitTorrent-like systems. With the model, we find that BitTorrent's incentive mechanism is successful in preventing free-riding in a system without seeds but may not succeed in producing a disincentive for free-riding in a system with a high number of seeds. The reason for this is that BitTorrent does not employ any effective mechanisms for seeds to effectively guard against free-riding. Therefore, we propose a seed bandwidth allocation strategy for the BitTorrent system to reduce the effect of seeds on free-riding. Finally, simulation results are given that validate what we have found in our analysis and demonstrate the effectiveness of the proposed strategy.

This paper presents the FFT (flip-flop tone) DMAC protocol, a tone based MAC protocol using directional antennas to solve the deafness problem, hidden terminal and exposed terminal problems simultaneously. It uses two pairs of flip-flop tones. The first pair of tone is sent omni-directionally to reach every neighboring node to announce the start and the end of communication, and therefore to avoid the deafness problem. The second pair of tone is sent directionally towards the sender. It is used to solve the hidden terminal problem as well as the exposed terminal problem. Evaluation shows that FFT-DMAC can achieve better performance compared to the 802.11 and ToneDMAC protocol.

The number of available channels in a specific wireless network is bounded. Therefore, co-channel interference is inevitable in most wireless networks. The co-channel interference problem in wireless mesh networks is more serious than that in single-hop wireless networks. It is preferable for mesh nodes to dynamically adjust channels to elevate the interference level of networks. In this paper, we introduce the conception of communication constraint to denote the strategies that decide the mode of interface switching. We develop an interference estimation scheme. The proposed scheme uses a weight to represent the effects of loads distribution and relative distance between nodes on the interference level of network. We compare the performance of our solution with the model presented by Gupta and Kumar [14] through both graph-based and NS2 simulations. Extensive results show that our solution achieves better performance than the compared scheme when the number of available channels is small.

Intelligent transportation systems have become increasingly important for the public transportation in Shanghai. In response, ShanghaiGrid aims to provide abundant intelligent transportation services to improve the traffic condition. A fundamental service in ShanghaiGrid is to locate the nearest desirable vehicles for users. In this paper we propose an innovative protocol ANTS to locate a desirable vehicle close to the querying user. The protocol finely mimics the efficient searching strategy adopted by a lost desert ant in searching for its nest. Taking query locality into account, ANTS can retrieve the nearest vehicles satisfying the query with high probability but incurs small query latency and modest network traffic. ANTS is a fully distributed and robust protocol and therefore has good scalability. Extensive simulations based on the real road network and the trace data of vehicle movements in Shanghai demonstrate the efficacy of ANTS.

Unlike convectional omni-directional sensors that always have an omni-angle of sensing range, directional sensors may have a limited angle of sensing range due to technical constraints or cost considerations. A directional sensor network consists of a number of directional sensors, which can switch to several directions to extend their sensing ability to cover all the targets in a given area. Power conservation is still an important issue in such directional sensor networks. In this paper, we address the multiple directional cover sets problem (MDCS) of organizing the directions of sensors into a group of non-disjoint cover sets to extend the network lifetime. One cover set, in which the directions cover all the targets, is activated at one time. We prove the MDCS to be NP-complete and propose three heuristic algorithms for the MDCS. Simulation results are also presented to demonstrate the performance of these algorithms.

Exposed and hidden terminal problems decrease the performance of 802.11 MAC protocol in mobile ad hoc network (MANET). We figure out that the two problems are due to a single fundamental reason, that is, 802.11 uses an oversimplified interference model. We propose an interference-aware busy tone based MAC protocol (IABT) to improve the throughput of MANET. Encoding the interference information in the frequency, a busy tone will reserve the space necessary for the ongoing transmission. Our protocol can solve both the hidden and exposed terminal problem simultaneously. Examined by NS2 simulator, the protocol can reduce the MAC collisions and improve the performance substantially

It is crucial to design algorithms for energy-efficient query processing for a sensor network since sensor nodes are battery-powered, and thus their lifetimes are limited. We propose a history-based approach to optimizing query processing. We apply the approach to the top-k query problem and design new algorithms. Energy consumption can be reduced by pruning unnecessary sub-queries or guiding the query to right directions. Simulation results show that energy cost can be significantly reduced. This approach can be generalized for other query problems.

The efficiency of sensor networks depends on the coverage of the monitoring area. Although, in general, a sufficient number of sensors are used to ensure a certain degree of redundancy in coverage, a good sensor deployment is still necessary to balance the workload of sensors. In a sensor network with locomotion facilities, sensors can move around to self-deploy. The movement-assisted sensor deployment deals with moving sensors from an initial unbalanced state to a balanced state. Therefore, various optimization problems can be defined to minimize different parameters, including total moving distance, total number of moves, communication/computation cost, and convergence rate. In this paper, we first propose a Hungarian-algorithm-based optimal solution, which is centralized. Then, a localized scan-based movement-assisted sensor deployment method (SMART) and several variations of it that use scan and dimension exchange to achieve a balanced state are proposed. An extended SMART is developed to address a unique problem called communication holes in sensor networks. Extensive simulations have been done to verify the effectiveness of the proposed scheme.

Parallel application development for grids can benefit from tools that abstract the underlying grid middleware, hide the critical aspects of heterogeneous resources and provide utilities for parallelism. As a research on these issues, we have developed grid parallel programming interface (GridPPI), a Java library aiming at providing a framework for MPI-style development and execution of Java applications in service-oriented grids. GridPPI primarily comprises an open architecture that is adaptive to various grid platforms and an MPI-like parallel communication library that includes both basic and collective operations. We describe the design of GridPPI framework and its implementation on ChinaGrid Support Platform 2 (CGSP2) in detail, and present the performance evaluation

Optical grid will be an attractive proposition, as the bandwidth of electrical network becomes the bottleneck of grid application. However, few scheduling methods consider the communication contention on optical grid. This paper raises an optical grid model based on the characters of optical network. In this model, the network resource was granted the same level to be considered as the computation and storage resource. In order to reflect the reality in job scheduling, allocation of network resource for data transfer was taken into consideration. Then, this paper presents a communication contention-aware solution based on the list scheduling to minimize the total execution time for given tasks towards an optical grid, where the router algorithm was modified from Dijkstra route algorithm. Results proved the feasibility and efficiency of the solution proposed

The monitoring of resources is a crucial issue for the high-performance of Web computing, and the scheduling strategies, the management of resources, and the performance analysis are dependent on the monitoring data. The traditional monitoring solution does not provide a unified and consistent model to the devices, resources and services of heterogeneous environments. In this paper a novel monitoring approach is proposed based on the common information model CIM. CIM defines a basic meta-model, syntax for the description of managed object and hierarchical structure of generic object classes. The monitoring system RMCS is given that integrates and extends the existing monitoring system to gather and retrieve the monitoring information. The CIM-based method addresses the variety and heterogeneity of resources in object-oriented way that the monitoring resource is regarded as a class or object. The RMCS enables administrators and users define a wide variety of resources, customize the monitoring parameters and the display way. The experiments show that this monitoring approach provides the scalable and flexible capabilities, and satisfies the Web application requirements

ShanghaiGrid is the first metropolitan grid in China. The project aims to develop an environment to share distributed resources in Shanghai conveniently based on grid technology. Yet, resources are finite for ever, especially for some expensive and specific resources. Users must reserve these resources in advance before use them. Present solutions need users confirm the absolute start time of reservation before the reservation which is impossible in practice at most time. But they can often estimate the relative time to start reservation after a specific event, e.g. beginning or finishing a task. As a part of ShanghaiGrid, we develop an ECA rule-based workflow management system (EWMS) which can serve for arranging the relative start time of advance resource reservation as users' demand. We introduce the design architecture of EWMS in the paper, and give a modeling demo to show how to realize advance resource reservation through our system

With the improvement of the traffic complexity extent, the solution of traffic problems becomes more and more difficult in the reality, so traffic simulation is an effective method for analyzing the traffic states and problems. As the simulation area becomes larger and more complicate, which requires large computing and storage resources, it can't be resolved by the traditional computing technology. Due to grid technology's advantages on such aspects, a new simulation architecture named GHA, which implements the HLA 's component as grid service and combined with the agent technology is presented in this paper. Thus the simulation architecture can offer high-capable computing resources to solve the complex traffic issues with great expansibility and flexibility; moreover, it also makes solid foundation to the authenticity of traffic simulation by adopting agent model the character of traffic entity. At last, a traffic simulation platform is implemented based on GHA and the performance tests are given

BitTorrent is a very popular P2P file sharing system. It has been successful at distributing large files quickly and efficiently. Embedded in BitTorrent is a set of incentive mechanisms to encourage sharing and contribute, and prevent systematic free-riding. In this paper, a fluid model with two classes of peers is used to capture the effect of free-riding in a BitTorrent system. With the model, we explore how does free-riding influenced the BitTorrent system. From results, it is shown that BitTorrent mechanism is successful to guard against free-riding. Finally, we discuss the dying process of a BitTorrent system and the probability of system dead that is induced by free-riding

The variance of user interest is a significant factor for designing peer-to-peer content-based retrieval system which leads to the birth of this paper. This paper gives the preliminary scheme to solve the problem of Web search of Internet scale. All the personalized peers form an interest-oriented Web server union (IOWSU) by employing structured P2P routing mechanism. After a query request from a peer is posted to the union, an optimized and deterministic result based on the search peer's interest is returned from the union. The result of our simulation demonstrates the method proposed in this paper is practicable and efficient

Abstract The goal of the ShanghaiGrid is to provide information services to the people. It aims to construct a metropolitan‐area information service infrastructure and establish an open standard for widespread upper‐layer applications from both communities and the government. The Information Service Grid Toolkit and a typical application called the Traffic Information Grid are discussed in detail. Copyright © 2005 John Wiley & Sons, Ltd.

Abstract The research of transaction processing in Web Services and Grid Services is active in academic and engineering areas. However, the formal method of transaction processing has not been fully investigated in the literature. This paper proposes a preliminary theoretical model called Membrane Calculus based on Membrane Computing and Petri nets to formalize Grid transactions. Five kinds of transition rules in Membrane Calculus (including object rules and membrane rules) are introduced and the operational semantics of transition rules are defined. Then, a typical long‐running transaction example is presented to demonstrate the use of Membrane Calculus. Finally, the rewriting logic tool Maude is adopted to specify and execute the specification of this example. Copyright © 2006 John Wiley & Sons, Ltd.

The economic mechanism is very suitable for solving the problem of resource management in the grid computing environment, and it can guarantee the interest of participators in the grid with fairness and efficiency. In this paper, we propose an economics-based resource management framework for grid computing, and then we focus on how to determine the price of resources with the economic mechanism. We present a general equilibrium method for general resources and a double auction method for special resources in the grid environment respectively. Simulations are performed and the experimental results indicate that the two methods are effective for corresponding application scenarios.

The response time of SOAP invocation over Http is an important designing factor and evaluating metric for QoS and Web services related computing. This paper presents a novel model to compute response time of SOAP over Http/1.1. It takes several influencing parameters into account, including compression methods, maximum segment size, round trip time, initial value of time-out sequence, the number of packets per ACK, maximum congestion control window size and packet loss rate. How to set these parameters to compute the response time of SOAP over Http/1.1 is illustrated and the model is validated with TPC-H benchmark based simulation data.

Integration and share of spatial information is a fundamental technology for Web based Geographic Information System (GIS), but it is difficult to implement due to the heterogeneity of spatial data. Therefore, we propose a web service based solution to facilitate the integration and share of spatial data. The solution utilizes Geography Markup Language (GML) to encapsulate spatial information, uses Web Service Description Language (WSDL) to describe XML Web Map Service (WMS) and the method for service publication and registry is introduced. Based on this solution, we develop a system with practical value in logistics information industry for its good performance in heterogeneous spatial data sharing and service integration.

The variance of job size is a significant factor for designing peer-to-peer system which leads to the born of JSChord(Job Size based Chord). JSChord is a peerto- peer overlay constructed on Chord aimed to exploit the variance of job size. The fundamental idea of JSChord is to group several Chord rings together to distribute the jobs according to their size. JSChord has achieved better performance than Chord with its special routing algorithm as shown in the simulation.

The paper presents secure protocols and new secure challenges based on looser radio propagation assumptions. According to these, a new secure enhancement mechanism for secure protocols is proposed. Then we exemplify the DoS attacks and protections as the illustration. The research indicates it will increase the usability of secure protocols in common applications.

Wireless sensor networks will be used in a variety of adaptive applications. Appropriate middleware is needed to support the development of adaptive WSNs applications. In this paper, we introduce a middleware architecture that facilitates the development of adaptive WSNs applications. The whole architecture is event-driven and a kind of adapted ECA rules model lies at the core of the architecture. We discuss the structure of this kind of adapted ECA rules and how to implement the ECA rules in the architecture. The programming model that centers on constructing ECA rules is also discussed.

We study two key problems arising from mobile peer-to-peer media streaming: the stability of interconnection between supplying peers and requesting peers in the mobile peer-to-peer streaming system; and fast capacity amplification of the entire mobile peer-to-peer streaming system. Many algorithms for the fast capacity amplification of the peer-to-peer streaming system have been proposed. However, these algorithms are inefficient in the mobile ad-hoc networks. Topological dynamics of mobile peer-to-peer are exacerbated by the changes of mobile user's location and interest. We observe that the mobile users exhibit correlated mobility patterns in practical ad-hoc networks. We use the stable group algorithm to characterize user mobility in mobile ad-hoc networks. Based on the stable group, we then propose a distributed stable-group differentiated admission control algorithm (SGDAC/sub p2p/), which leads to fast amplifying of the system's total streaming capacity using self-growing. Finally, extensive simulation results are presented to compare between the SGDAC/sub p2p/ and traditional methods to prove the superiority of the algorithm.