Account migration in sharded blockchains presents a critical trade-off between optimization effectiveness and system availability. While dynamically reallocating accounts across shards can significantly reduce cross-shard transaction overhead, existing migration mechanisms cause service disruptions that intensify as state data volumes grow. To address this challenge, we propose BIND, a batch-wise account migration protocol that eliminates service interruptions by enabling continuous transaction processing throughout migration. BIND introduces a dual transaction pool architecture that isolates transactions involving migrating accounts while allowing non-migrating accounts to operate uninterrupted. To optimize migration efficiency, we design a reverse greedy heuristic algorithm that partitions accounts into batches based on community cohesion, maximizing intra-batch connectivity to front-load cross-shard communication reduction. We evaluate BIND using real Ethereum transactions, demonstrating superior performance over existing mechanisms. BIND achieves 12% higher overall throughput, reduces migration time to 23.6%-39.3% of the one-shot baseline (across 1-10Gbps bandwidth), and lowers cross-shard transaction rates by 24.1% compared to random batching. These results confirm BIND as a practical solution for large-scale, non-disruptive account migration in production sharded blockchains.

Near-field (NF) communications have attracted considerable interest, particularly with the implementation of extremely large-scale antenna arrays (ELAA). Additionally, the increase in communication frequencies and the expansion of reconfigurable intelligent surface (RIS) apertures contribute to this growing field. This paper investigates the synergy of simultaneously transmitting and reflecting (STAR)-RIS and non-orthogonal multiple access (NOMA) for secure transmission under hybrid far-field (FF) and NF scenarios. The secrecy sum rate (SSR) maximization problem is formulated by joint optimization of the power allocation, the beamforming at access point (AP), and the transmission/reflection coefficients (TRCs). Specifically, we consider the transmit power budget, unit-norm conditions, coupled phase shifts (CPS), quality of service requirements, and decoding order. To tackle this extremely challenging problem, we combine the successive convex approximation (SCA), Riemannian exact penalty method via smoothing, and penalty dual decomposition (PDD) and successfully develop an efficient iterative algorithm. Simulation results reveal that the proposed design exhibits superior effectiveness when compared to other traditional benchmarks.

Vehicular Edge Computing (VEC) is pivotal for latency-sensitive vehicular applications but confronts three critical challenges: traditional one-time identity authentication cannot adapt to high mobility, leaving privacy and data security vulnerabilities, offloading system security levels lack quantifiability, and security-performance optimization objectives are inherently conflicting. To address these limitations, we propose a lightweight continuous identity authentication-based secure offloading scheme for VEC. First, a three-entity collaborative architecture is designed, which leverages chameleon hash function (CHF) to reduce vehicle-side signature overhead, and Bloom filter (BF) to enable real-time verification during vehicle-roadside unit (RSU) handovers. Second, a dual-dimensional security framework that quantifies authentication and data signature levels is established, enabling on-demand security adjustment for diverse tasks. Third, to balance task latency minimization and security maximization, Unlike prior works that optimize security and offloading separately, this framework unifies both identity authentication and data transmission security into a holistic latency-oriented offloading design, filling critical research gaps in high-mobility VEC scenarios. To tackle this intricate problem, we decompose it into four sub-problems. These sub-problems are solved using Lagrangian duality, the Newton-Raphson method, and the branch-and-bound algorithm to obtain stable and highquality feasible solutions efficiently. Extensive simulations against four baseline schemes demonstrate that the proposed approach achieves fast convergence and priority performance.

Caching technology is widely used in multiple areas particularly in distributed computing, where its performance is highly dependent on the cache efficiency. The cache eviction algorithm serves as the core component of a cache, primarily aimed at improving cache efficiency by reducing the cache miss ratio. Numerous eviction algorithms are proposed in recent decades and state-of-the-art methods tend to adopt lazy promotion and quick demotion designs. Lazy promotion simplifies cache-hit operations for higher throughput, while quick demotion effectively filters the low-popularity objects. However, the two designs either fail to identify burst objects or suffer from unstable demotion precision. In order to address the above problems, we propose FIFO-MEP, an efficient FIFO cache with Multiple Eviction Points. The key design of FIFO-MEP is to introduce multiple fixed-position eviction points near the head of a FIFO queue. These eviction points enable repeated inspections of objects, leading to effective identification of burst objects. Meanwhile, by fixing positions of these eviction points, FIFO-MEP delivers stable demotion precision. We implement FIFO-MEP using libCacheSim and evaluated it on 5439 production traces for three typical cache sizes, and further verify its efficiency based on Memcached. The evaluation results show that FIFO-MEP reduces the miss ratio by an average of 15.8 % across all experimental configurations. Compared to the state-of-the-art S3-FIFO, FIFO-MEP achieves cache efficiency improvement by up to 21.8 % for large cache sizes. Furthermore, FIFO-MEP yields the best performance under 51 % of all tested conditions.

In high-performance ultra-scale cloud computing, heterogeneous clusters consisting of x86 and ARM architecture platforms have become increasingly common to boost performance and energy efficiency. Ensuring high availability in these environments is crucial for meeting service-level agreements. However, DRAM failures, a primary cause of server downtimes, present significant challenges to reliability, availability, and serviceability. This paper provides an in-depth analysis of memory failure characteristics across cross-architecture platforms in large-scale heterogeneous clusters. We introduce MemSeer, an AIOps-integrated tool that utilizes a multi-grained memory failure prediction approach for x86/ARM heterogeneous clusters. MemSeer improves the F1-score by 17.3% and increases recall by an average of $27 \%$ across different lead times compared to state-of-the-art methods. These advancements show great promise in reducing memory failures in cluster environments, decreasing VM interruptions by up to 42.7% and averaging 24.2% in real-world implementations.

Compute eXpress Link (CXL) offers an effective interface for connecting CPUs with external computing and memory devices. CXL Memory eXpander Controller (CXL-MXC) is gaining attention for its ability to boost memory capacity and bandwidth more efficiently than traditional DDR DIMMs. Despite extensive research on MXC performance and adaptation, DRAM reliability in CXL architecture remains underexplored. Traditional fault tolerance mechanisms like replica or RAID-based systems would significantly increase bandwidth overhead in the CXL fabric, adversely affecting system performance. To address this, we propose the on-CXL-Memory-Expander-Controller ECC (CXL-ECC), by using Locally Recoverable Codes (LRC) as the Inter-Channel-ECC (IC-ECC) and offloading its process to the expander, we eliminate extra memory access requests in the CXL fabric. Consequently, we conduct several experiments to demonstrate that our approach enhances DRAM reliability by more than $10^{9}$, compared to state-of-the-art ECC methods. Relative to RAID-enabled CXL switch, it reduces additional bandwidth overhead from 63.5% to 3.4% and improves system performance by 12%.

Hardware prefetching is one of the most widely-used techniques for hiding long data access latency. To address the challenges faced by hardware prefetching, architects have proposed to detect and exploit the spatial locality at the granularity of spatial region. When a new region is activated, they try to find similar previously accessed regions for footprint prediction based on system-level environmental features such as the trigger instruction or data address. However, we find that such context-based prediction cannot capture the essential characteristics of access patterns, leading to limited flexibility, practicality and suboptimal prefetching performance. In this paper, inspired by the temporal property of memory accessing, we note that the temporal correlation exhibited within the spatial footprint is a key feature of spatial patterns. To this end, we propose Gaze, a simple and efficient hardware spatial prefetcher that skillfully utilizes footprint-internal temporal correlations to efficiently characterize spatial patterns. Meanwhile, we observe a unique unresolved challenge in utilizing spatial footprints generated by spatial streaming, which exhibit extremely high access density. Therefore, we further enhance Gaze with a dedicated two-stage approach that mitigates the over-prefetching problem commonly encountered in conventional schemes. Our comprehensive and diverse set of experiments show that Gaze can effectively enhance the performance across a wider range of scenarios. Specifically, Gaze improves performance by $\mathbf{5. 7 \%}$ and 5.4% at single-core, 11.4% and $\mathbf{8. 8 \%}$ at eight-core, compared to most recent low-cost solutions PMP and vBerti.

Blockchain technology, renowned for its decentralized architecture and cryptographic immutability, faces fundamental scalability limitations due to its global consensus requirement where all nodes validate every transaction. Although sharding offers potential scalability via parallel transaction processing across subnetworks, its practical implementation confronts dual limitations: chronic load imbalance leading to suboptimal resource utilization and prohibitive cross-shard communication impairing system efficiency. Facing these challenges, this paper proposes a dynamic sharding-optimized cluster-driven greedy algorithm(DSCGA) scheme that employs account partitioning through cluster analysis of transactional patterns for dynamic node allocation. This scheme also analyzes historical transaction records to construct an account transaction network, ensuring strong intra-shard transaction affinity and weak inter-shard correlations. To validate the effectiveness of this approach, experimental validation with Ethereum data confirms the effectiveness of the scheme in minimizing cross-shard transactions and alleviating load imbalance.

With the rapid advancement of intelligent transportation systems and the continuous improvement of supporting infrastructure, the development of the Internet of Vehicles (IoV) has garnered increasing attention from researchers. The decentralized and immutable characteristics of blockchain can significantly enhance the security, data integrity, and transparency within the IoV, thereby driving rapid progress in blockchain-based solutions in the IoV ecosystem. However, low-latency decision-making applications face limitations due to performance bottlenecks and other challenges in blockchain technology. To address this issue, this paper proposes a blockchain grouping-based model that segments blockchain nodes into three interconnected sub-modules, each responsible for distinct functions. This model enables efficient decision-making during continuous system operations via asynchronous processes, ensuring decision accuracy through multi-layer verification. Furthermore, this paper introduces a Kademlia-based blockchain communication protocol, leveraging physical addresses in the IoV to enhance the speed of routing table construction between vehicle nodes. The performance of the proposed model was evaluated through experiments with the MCTS-Greedy algorithm. The results indicate that the blockchain-based decision model significantly improves decision-making efficiency and demonstrates robustness and stability under various conditions.

The use of denoising diffusion models is becoming increasingly popular in the field of image editing. However, current approaches often rely on either image-guided methods, which provide a visual reference but lack control over semantic consistency, or text-guided methods, which ensure alignment with the text guidance but compromise visual quality. To resolve this issue, we propose a framework that integrates a fusion of generated visual references and text guidance into the semantic latent space of a frozen pre-trained diffusion model. Using only a tiny neural network, our framework provides control over diverse content and attributes, driven intuitively by the simple prompt. Compared to state-of-the-art methods, the framework generates images of higher quality while providing realistic editing effects across various benchmark datasets. The code is available at https://github.com/SadAngelF/Editing-via-Step-Wise-Alignment.

In recent years, the proliferation of renewable energy sources has heightened the focus on networked energy systems. These systems face significant challenges due to the unpredictable nature of energy generation and consumption, as well as the complexity of managing numerous components and parameters. To address the challenges associated with the time-consuming nature of optimization problems and the expansive solution space, we propose an innovative energy management method based on a generative diffusion model applicable to general networked energy systems. This approach aims to balance energy supply and demand while minimizing transmission costs. The efficacy of this method is validated through evaluations on real-world datasets and simulations, demonstrating a 26.6% cost reduction compared to the state-of-the-art model and a 62.8% decrease in execution time compared to existing optimizers. This research highlights the potential of generative diffusion techniques in networked energy management. Code: https://github.com/gale13/GEM.

Personalized Federated Learning (pFL) mitigates the impact of statistical heterogeneity on FL architecture to some extent by allowing participants to use personalized models based on local data distributions. The existing pFL methods optimize from the perspective of model structure, attempting to adopt strategies that maintain model processing or quickly adapt to local data distribution capabilities. Our proposed method draws inspiration from the concept of variational inference, guiding model updates by comparing prior and posterior data distributions, and innovatively applying model variational perturbations to improve robustness. Finally, we conducted multidimensional experiments and the results show that our method outperforms the current baseline. Code: https://github.com/RezinChow/VPFL.

The widespread deployment of cameras has led to an exponential increase in video data, creating vast opportunities for applications such as traffic management and crime surveillance. However, querying specific objects from large-scale video datasets presents challenges, including (1) processing massive and continuously growing data volumes, (2) supporting complex query requirements, and (3) ensuring low-latency execution. Existing video analysis methods struggle with either limited adaptability to unseen object classes or suffer from high query latency. In this paper, we present LOVO, a novel system designed to efficiently handle compLex Object queries in large-scale VideO datasets. Agnostic to user queries, LOVO performs one-time feature extraction using pre-trained visual encoders, generating compact visual embeddings for key frames to build an efficient index. These visual embeddings, along with associated bounding boxes, are organized in an inverted multi-index structure within a vector database, which supports queries for any objects. During the query phase, LOVO transforms object queries to query embeddings and conducts fast approximate nearest-neighbor searches on the visual embeddings. Finally, a cross-modal rerank is performed to refine the results by fusing visual features with detailed textual features. Evaluation on real-world video datasets demonstrates that LOVO outperforms existing methods in handling complex queries, with near-optimal query accuracy and up to 85x lower search latency, while significantly reducing index construction costs. This system redefines the state-of-theart object query approaches in video analysis, setting a new benchmark for complex object queries with a novel, scalable, and efficient approach that excels in dynamic environments.

In large-scale data-parallel analytics, shuffle operations often become performance bottlenecks due to network overhead from all-to-all data movement and disk I/O overhead from write/read of persistent intermediate data. Push-based shuffle is widely adopted to mitigate this overhead by enabling sequential I/O through early transmission and pre-merge. However, existing push-based-shuffle scheduling strategies based on single-shuffle-based workload prediction and task scheduling fails to account for hierarchical data dependencies in practical scenarios involving complex DAG workflows, leading to load imbalance and poor data locality.

Graph Neural Networks (GNNs) excel in many applications but struggle when trained with noisy labels, especially as noise can propagate through the graph structure. Despite recent progress in developing robust GNNs, few methods exploit the intrinsic properties of graph data to filter out noise. In this paper, we introduce ProCon, a novel framework that identifies mislabeled nodes by measuring label consistency among semantically similar peers, which are determined by feature similarity and graph adjacency. Mislabeled nodes typically exhibit lower consistency with these peers, a signal we measure using pseudo-labels derived from representational prototypes. A Gaussian Mixture Model is fitted to the consistency distribution to identify clean samples, which refine prototype quality in an iterative feedback loop. Experiments on multiple datasets demonstrate that ProCon significantly outperforms state-of-the-art methods, effectively mitigating label noise and enhancing GNN robustness.

Existing 3D generation frameworks have demonstrated notable success, their continued evolution is impeded by issues including view inconsistency and insufficient detail, which significantly hinders further methodological refinements. To address these challenges, we propose MagicGS++, an innovative framework for Image-To-3D generation that efficiently generates high-quality 3D content from a single-view image. MagicGS++ adopts a Coarse-To-Fine framework consisting of two main stages. In the frst stage, a fixed-orthogonal view diffusion model with a further Super-Resolution model are employed to generate orthogonal view images and perform initial optimization on the 3D Gaussian distribution, yielding a rough shape. In the second stage, a fixed view image of the 3D Gaussian model is rendered alongside two neighboring-view images most relevant to the fixed view. By jointly optimizing the multi-MSE loss between the neighboring view images and the orthogonal view image, and the SDS loss between the fixed-view image and the 3D model. Extensive experiments demonstrate that MagicGS++ outperforms existing methods in both geometric detail and texture quality. Moreover, by integrating with a conditional diffusion model, our method supports multi-modal 3D generation tasks, showcasing remarkable adaptability.

Blockchain enhances the robustness and user engagement of Federated Learning (FL) systems but fails to meet the throughput and real-time requirements for model transmission. While sharding architectures improve system throughput, the latency introduced by mainchain model transmission remains a performance bottleneck, compromising the QoS of FL systems. In this paper, we propose a Mainchain-Free Sharding architecture, MFSChain, featuring an adaptive sharding mechanism based on hierarchical clustering. This mechanism improves shard model performance by eliminating the need for mainchain aggregation (i.e., shard-level global models). We also introduce the Federated Learning State Tree (FLS-Tree) for client management and state migration without a mainchain, alongside a lightweight storage scheme, LiFLS-Tree. Through theoretical analysis and extensive simulations, we demonstrate that MFSChain outperforms traditional blockchain and sharding architectures. Specifically, MFSChain reduces client waiting time by 17% and 24%, increases average model accuracy by 2.5% to 10% compared to traditional global models, and boosts throughput by$464 \times$while reducing transaction processing latency by 99%.

Legal document review is a time-consuming and highly specialized task, and the capabilities of intelligent legal review systems are limited and insufficient to complete detailed reviews. Traditional methods struggle with cross-references, dependencies, and context-dependent clauses. Our work introduces a multi-round RAG framework for legal document analysis, which iteratively refines queries and aggregates context to improve recall and understanding. Experiments on diverse contracts show a recall of 78.67%, outperforming baseline (57.33%) and single-round RAG (74.67%). Our analysis shows that iterative refinement effectively filters irrelevant results despite reduced precision. The multi-round approach halves missed cross-clause dependencies but reveals limitations in numerical consistency and obligation scope detection. These insights advance RAG for legal applications and provide a foundation for future work on scalable and accurate contract review.

Graph Neural Networks (GNNs) achieve strong performance in node classification tasks but exhibit substantial performance degradation under label noise. Despite recent advances in noise-robust learning, a principled approach that exploits the node-neighbor interdependencies inherent in graph data for label noise detection remains underexplored. To address this gap, we propose GD$^2$, a noise-aware \underline{G}raph learning framework that detects label noise by leveraging \underline{D}ual-view prediction \underline{D}iscrepancies. The framework contrasts the \textit{ego-view}, constructed from node-specific features, with the \textit{structure-view}, derived through the aggregation of neighboring representations. The resulting discrepancy captures disruptions in semantic coherence between individual node representations and the structural context, enabling effective identification of mislabeled nodes. Building upon this insight, we further introduce a view-specific training strategy that enhances noise detection by amplifying prediction divergence through differentiated view-specific supervision. Extensive experiments on multiple datasets and noise settings demonstrate that \name~achieves superior performance over state-of-the-art baselines.

With the proliferation of trajectory data generated by many Internet of Things (IoT) devices in the AI-driven transportation field, trajectory representation is crucial for extracting individual behavioral characteristics from intelligent IoT systems. Neural network-based trajectory representation learning methods excel at obtaining consistent representations of individual movements and mining spatio-temporal autocorrelation features of trajectories. However, existing methods achieve high accuracy in reliable experimental data, and their high performance is not always available in real-world wild scenarios, which is not available to reveal the performance bounds of trajectory representation learning models when confronted with real-world phenomena. Given this, we propose a universal trajectory adversarial attack framework that investigates the robustness vulnerabilities of trajectory representation learning models by generating adversarial trajectory examples. We reveal the impact of the attack surface in practical deployment and the adversarial perturbation budget on the trajectory adversarial attack performance. Guided by the new framework, we propose a universal black-box adversarial attack for trajectories, named Universal Trajectory Customized Iteration Attack (UTCIA). Specifically, we simulate trajectories through single-point perturbations to obtain a vulnerability-dependent set of victim points, selecting and perturbing only a small subset of points to degrade the entire model performance. Furthermore, we aggregate the discrete set of salient target trajectory points into a high-dimensional space for iterative direction estimation, and relax the constraint region to further compress noise. We generate adversarial trajectory examples for two trajectory representation downstream tasks with fundamentally different objectives using multiple large-scale real-world datasets. Extensive experiments demonstrate the feasibility and generalizability of our proposed framework in exploring the robustness vulnerabilities of trajectory representation learning models.

In Internet of Vehicles (IoV), unmanned aerial vehicles (UAVs) assisted mobile edge computing (MEC) can improve the system performance and communication range of intelligent transportation systems (ITSs). However, the resource allocation and computation offloading in UAVs-assisted IoV systems still face huge challenges due to the growing number of vehicle terminals (VTs), potential privacy leakage, and inefficient problem-solving. Existing solutions cannot adapt to such dynamic multi-UAV scenarios and meet the real-time requirements of VTs. To address these challenges, we propose RACOMU, a novel resource allocation and collaborative offloading framework for multi-UAV-assisted IoV. First, we introduce the convex optimization theory to decouple the original problem and then obtain the near-optimal allocation of transmission power and computing resources by solving the Karush-Kuhn–Tucker (KKT) condition. Next, we design a new collaborative offloading strategy with federated deep reinforcement learning (FDRL), where the offloading requests from VTs are processed in a distributed manner to approach the global optimum while preserving data privacy. Extensive experiments verify the effectiveness of the proposed RACOMU. Compared to benchmark methods, RACOMU achieves better performance in terms of task processing latency, decision-making time, and load balancing degree under various scenarios.

The cloud-edge–terminal architecture relies on hierarchy for resource allocation but lacks global optimization. The computing power network (CPN) introduces a new distributed computing paradigm, integrating cross-domain, heterogeneous resources for global scheduling. However, most CPN research focuses on task optimization during resource allocation, while neglecting the similarity of random tasks before the allocation stage. Additionally, fragmented CPN resources and complex task demands pose challenges to global load balancing. This article proposes a deep reinforcement learning framework with task merging and congestion avoidance for on-demand resource allocation. Specifically, a low-complexity similar task merging algorithm reduces redundant resource consumption during task preprocessing. In task offloading, the principal neighborhood aggregated graph neural network captures CPN’s intricate features. Lyapunov optimization, integrated into a multithreaded training framework, minimizes resource backlog congestion. A carefully designed reward function balances multiple objectives, enhancing computing resource utilization efficiency and ensuring system stability. Theoretical analysis shows that with control parameter V, the tradeoff between resource utilization efficiency and system stability follows the relationship [O(1/V), O(V)]. Extensive experiments demonstrate a 33.5% improvement in resource utilization efficiency and a 62.7% increase in task offloading success rates with respect to those in state-of-the-art algorithms. The proposed algorithm exhibits robustness and effectiveness, particularly in high-load and real network topologies.

In the context of Internet of Things edge devices, pretrained models are often sourced directly from cloud computing platforms due to the unavailability of training data. This lack of access during the training phase makes these models susceptible to backdoor attacks. To address this challenge, we introduce a novel data-free backdoor removal method that operates effectively even when only the poisoned model is accessible. Our innovative approach employs two end-to-end generators with identical architectures to create both clean and poisoned samples. These samples are crucial for transferring knowledge from the teacher model—the fixed poisoned model—to the student model, which is initialized with the poisoned model. Our method utilizes a channel shuffling technique during the distillation process to disrupt and eliminate the backdoor knowledge embedded in the teacher model. This process involves iterative updates of the generators and meticulous distillation of the student model, leading to efficient backdoor removal. We conducted extensive experiments on five sophisticated backdoor attacks across two benchmark datasets. The results demonstrate that our method not only significantly bolsters the model’s resistance to backdoor attacks but also maintains high recognition accuracy for clean samples, thereby outperforming existing methods. Additionally, the code for our method is available athttps://github.com/gaoyafeiyoo/ADBR.

Cloud–edge collaboration and edge intelligence have greatly driven the growth of the Industrial Internet of Things (IIoT). However, the jittery network delay and limited computational resources of edge servers make it difficult to meet the stringent latency requirements in IIoT, and so far there is no good solution to solve this problem. To this end, we introduce scalable neurocomputing, which provides neural networks with different utilities and computation resource requirements, to be deployed on edge servers of cloud–edge IIoT systems. We then optimize such systems by formulating data scheduling and system computational resource allocation as an infinite horizon optimization problem, considering that the data collection from end devices is an infinite long-term process. To solve this hard problem, we design a rolling prediction-optimization framework that transforms the infinite horizon problem into a truncated finite horizon optimization that maximizes the average system utility while satisfying the stringent delay constraints. We have conducted extensive simulations and built a prototype system, which verify the feasibility and performance of our proposed scheme.

By integrating subgraph learning with federated learning, federated subgraph learning realizes collaborative learning of subgraph information among distributed Unmanned Agents (UAs) while protecting data privacy, offering a promising solution for graph modeling in Internet of Unmanned Agents (IUA). However, due to the various manners of collecting data on different UAs, graph data exhibits the features of Non-Independent and Identically Distributed (Non-IID), while the structures and features of local graph data on UAs are quite diverse. These factors lead to convergence difficulties and insufficient generalization ability of federated subgraph learning during the training process. To address these important challenges, we propose PFedSL, a novel knowledge-sharing Personalized Federated Subgraph Learning framework for IUA. First, a new personalized model aggregation is performed based on the confidence score of UAs and their similarity to reduce the interference of Non-IID data on model performance. Next, a parameter selective activation is introduced for model updating to handle the heterogeneity issue of subgraph structural features. Finally, an original personalized single-view contrastive learning is designed to optimize node embedding, thereby enhancing local representation consistency. Using real-world benchmark graph datasets, extensive experiments demonstrate the superiority of the proposed PFedSL. The results show that PFedSL achieves higher node classification accuracy than state-of-the-art methods in different scenarios. Meanwhile, the effectiveness of the core components in PFedSL is validated via ablation studies.

With the development of quantum blockchain, the quantum consensus protocols have garnered increasing attention, which play a crucial role in driving the implementation of quantum blockchains. However, existing protocols, derived from the classical consensus algorithms, face practical application challenges due to current quantum technology limitations. The first challenge is the bottleneck in generating large-scale entangled quantum states. The second challenge arises from the generation of malicious quantum states. The final challenge involves privacy concerns. To address these challenges, we propose a practical iterative QUantum consensus protocol with sharding construction, namely, Q-Union. In fact, Q-Union employs an iterative consensus algorithm where participating nodes are divided into multiple smaller shards, with the consensus process occurring within the current shard, and new shards are involved only if consensus is not achieved. Leveraging Greenberger-Horne- Zeilinge states and Aharonov states, Q-Union harnesses the advantages of quantum mechanics to achieve anonymous consensus, protecting the private information of participating nodes. Additionally, by integrating state verification, Q-Union ensures the correctness of the consensus procedure in the presence of malicious nodes generating adversarial quantum states. Finally, it is proven that Q-Union can also defend against Byzantine attacks from adversarial nodes, maintaining the same security level as traditional non-sharded consensus protocols. Specifically, it consistently outputs the correct consensus when the fraction of adversaries among participating nodes is less than 1/2 with synchronous communication. Both the theoretical analysis and performance illustration demonstrate the superior performance of the proposed Q-Union compared to state-of-the-art protocols.

With the widespread adoption of encrypted communication and anonymous networks, traditional passive traffic analysis methods face considerable limitations in tracking malicious activities. Existing active network flow watermarking schemes, exhibit insufficient robustness against packet dropping and splitting attacks. In response, this paper introducesAverage Interval-based Watermarking (AIBW), a novel technique designed to enhance watermark resilience by partitioning network flows into discrete time windows and dynamically adjusting inter-packet intervals. Specifically, AIBW categorizes packets into three segments—start,information, andend—and embeds watermark bits through maximum/minimum delay modulation within the information segment. Experimental evaluations demonstrate that AIBW outperforms state-of-the-art watermarking methods, yielding average accuracy improvements.

Federated Learning with Cloud-Edge Collaboration (FL-CEC) has emerged as a cutting-edge paradigm in distributed learning. Efficient resource investment incentive mechanisms are crucial to encouraging clients in FL-CEC to contribute the necessary data and computational resources for training. However, existing studies are inadequate in meeting the incentive design requirements under multi-level information-sharing scenarios. Moreover, current works often rely on specific functional relationships between resource investment and global model accuracy. To bridge these gaps, this paper investigates the incentive problem for data and computational resource investment under multi-level information-sharing levels. We design a resource investment incentive mechanism based on a weighted potential game without depending on any specific functional relationship between data investment and model accuracy. Furthermore, we propose four algorithms to solve resource investment strategies for different levels of information sharing. The complexity and convergence rates of the proposed algorithms are thoroughly analyzed. Finally, we construct a simulation incentive platform on the Aliyun. Extensive evaluations demonstrate that the proposed scheme effectively enhances social welfare, and improves collaborative training accuracy and efficiency.

Cloud-Edge Collaborative Architecture (CEA) is a prominent framework that provides low-latency and energy-efficient solutions for video stream processing. In Cloud-Edge Collaborative Video Streaming Systems (CEAVS), efficient online offloading strategies for video tasks are crucial for enhancing user experience. However, most existing works overlook budget constraints, which limits their applicability in real-world scenarios constrained by finite resources. Moreover, they fail to adequately address the heterogeneity of video task redundancies, leading to suboptimal utilization of CEAVS's limited resources. To bridge these gaps, we propose an Efficient Online Computing framework for CEAVS (EOCA) that jointly optimizes accuracy, energy consumption, and latency performance through adaptive online offloading and redundancy compression, without requiring future task information. Technically, we formulate computing offloading and adaptive compression under budget constraints as a stochastic optimization problem that maximizes system satisfaction, defined as a weighted combination of accuracy, latency, and energy performance. We employ Lyapunov optimization to decouple the long-term budget constraint. We prove that the decoupled problem is a generalized ordinal potential game and propose algorithms based on generalized Benders decomposition (GBD) and the best response to obtain Nash equilibrium strategies for computing offloading and task compression. Finally, we analyze EOCA's performance bound, convergence rate, and worst-case performance guarantees. Evaluations demonstrate that EOCA effectively improves satisfaction while effectively balancing satisfaction and computational overhead.

Federated Learning (FL) has emerged as a promising approach for collaborative model training without sharing private data. However, privacy concerns regarding information exchanged during FL have received significant research attention.Gradient Inversion Attacks (GIAs)have been proposed to reconstruct the private data retained by local clients from the exchanged gradients. While recovering private data, the data dimensions and the model complexity increase, which thwart data reconstruction by GIAs. Existing methods adopt prior knowledge about private data to overcome those challenges. In this article, we first observe that GIAs with gradients from a single iteration fail to reconstruct private data due to insufficient dimensions of leaked gradients, complex model architectures, and invalid gradient information. We investigate a Temporal Gradient Inversion Attack with a Robust Optimization framework, called TGIAs-RO, which recovers private data without any prior knowledge by leveraging multiple temporal gradients. To eliminate the negative impacts of outliers, e.g., invalid gradients for collaborative optimization, robust statistics are proposed. Theoretical guarantees on the recovery performance and robustness of TGIAs-RO against invalid gradients are also provided. Extensive empirical results on MNIST, CIFAR10, ImageNet and Reuters 21578 datasets show that the proposed TGIAs-RO with 10 temporal gradients improves reconstruction performance compared to state-of-the-art methods, even for large batch sizes (up to 128), complex models like ResNet18, and large datasets like ImageNet (224× 224pixels). Furthermore, the proposed attack method inspires further exploration of privacy-preserving methods in the context of FL.

Generative Adversarial Network (GAN) models have shown promise in detecting adversarial examples for classifiers by leveraging their ability to reconstruct benign and adversarial examples differently. However, existing approaches often struggle with reconstruction optimization convergence for adversarial examples, as GANs cannot perfectly reconstruct such inputs. To address this instability, we propose two novel methods: Average-RAED and Strict-RAED. Average-RAED focuses on the average reconstruction performance throughout the optimization process, with the difference in the classifier’s logit information between the candidate sample and its reconstructed version as the detection signal. This approach enhances robustness and effectiveness, particularly for complex datasets. Strict-RAED integrates classification information into the reconstruction process and imposes a strict constraint to keep the latent code within the initial latent subspace. This design enables Strict-RAED to effectively identify both manually-crafted and natural adversarial examples. We evaluated both methods in a grey-box scenario, where the adversary has full knowledge of the target classifier’s architecture and parameters but no information about the defense GAN. Our results demonstrate that both Average-RAED and Strict-RAED outperform baseline methods in distinguishing adversarial examples from benign samples. Notably, Strict-RAED achieves higher detection rates for both manually-crafted and natural adversarial examples. Furthermore, combining Strict-RAED for detection and Average-RAED for reforming candidate samples significantly improves classifier accuracy against less significant adversarial examples.

Privacy-preserving federated learning (PPFL) is a promising secure distributed learning paradigm, which enables collaborative training of a global machine learning model through sharing encrypted local models instead of sensitive raw data. PPFL, however, is vulnerable to model poisoning attacks. Most existing Byzantine-robust PPFL solutions typically employ two non-colluding servers to achieve secure model detection and aggregation by executing interactive security protocols, which incur considerable computation and communication overheads. To tackle this issue, we propose an efficient and secure federated learning (ESFL) technique to accelerate the detection of poisonous models in PPFL. First, to improve computational efficiency, we construct a lightweight non-interactive efficient decryption functional encryption (NED-FE) scheme to protect the data privacy of local models. Then, to ensure high communication performance, we elaborately design a non-interactive privacy-preserving robust aggregation strategy, which efficiently detects the blind poisonous models and aggregates benign models. Finally, we implement ESFL and conduct extensive theoretical analysis and experiments. The numerical results demonstrate that ESFL not only achieves the confidentiality and robustness design goals but also maintains high efficiency. Compared with the baseline, ESFL effectively reduces the aggregation latency by up to 88%.

Automatic nuclei segmentation and classification (NSC) is a fundamental prerequisite in digital pathology analysis as it enables the quantification of biomarkers and histopathological features for precision medicine. Nuclei appear to be small, however, global spatial distribution and brightness contrast, or color correlation between the nucleus and background, have been recognized as key rationales for accurate nuclei segmentation in actual clinical practice. Although recent great breakthroughs in medical image segmentation have been achieved by Transformer-based methods, the adaptability of segmenting and classifying nuclei from histopathological images is rarely investigated. Also, the severe overlap of nuclei and the large intra-class variability are common in clinical wild data. Prevailing methods based on polygonal representations or distance maps are limited by empirically designed post-processing strategies, resulting in ineffective segmentation of large irregular nuclei instances. To address these challenges, we propose a keypoint-guided tri-decoder Transformer (PointFormer) for NSC simultaneously. Specifically, the overall NSC task is decoupled to a multi-task learning problem, where a tri-decoder structure is employed for decoding nuclei instance, edges, and types, respectively. The nuclei detection and classification (NDC) subtask is reformulated as a semantic keypoint estimation problem. Meanwhile, introduces a novel attention-guiding strategy to capture strong inter-branch correlations and mitigate inconsistencies between multi-decoder predictions. Finally, a multi-local perception module is designed as the base building block of PointFormer to achieve local and global trade-offs and reduce model complexity. Comprehensive quantitative and qualitative experimental results on three datasets of different volumes have demonstrated the superiority of the proposed method over prevalent methods, especially for the PanNuke dataset with an achievement of 70.6% on bPQ.

As the adoption of electric vehicles continues to grow, the demand for extensive charging infrastructure in urban areas is concurrently rising. In response to the evolving charging infrastructure shortage, private charging piles have emerged as crucial supplementary energy sources, especially in areas lacking public charging infrastructure. The sharing of private charging piles, however, introduces several challenges. Notably, the variable availability time and extremely limited usage space of private charging piles pose scheduling complexities for charging pile owners. Furthermore, the completely peer-to-peer operation of private charging piles may lead to suboptimal solutions for fulfilling overall charging demand. To comprehensively address these challenges, we explore the potential for cooperation among geographically proximate charging piles. We introduce a novel online booking mechanism paired with specialized scheduling algorithms designed for scenarios involving both multiple private charging piles and single private charging piles. Our objective is to maximize the attained revenue of charging pile owners under fully dynamic conditions on both the supply and demand sides. Through meticulous theoretical proofs, we show that our mechanism achieves advantageous competitive ratios for both scenarios when compared to the offline optimal solutions. Numerous experiments, conducted with real charging sessions, consistently demonstrate that the proposed mechanism achieves the highest revenue, providing substantial evidence for its superior performance.

Modern machine learning (ML) models have grown to a scale where training them on a single machine becomes impractical. As a result, there is a growing trend to leverage federated learning (FL) techniques to train large ML models in a distributed and collaborative manner. These models, however, when deployed on new devices, might struggle to generalize well due to domain shifts. In this context, federated domain adaptation (FDA) emerges as a powerful approach to address this challenge. Most existing FDA approaches typically focus on aligning the distributions between source and target domains by minimizing their (e.g., MMD) distance. Such strategies, however, inevitably introduce high communication overheads and can be highly sensitive to network reliability. In this paper, we introduce RF-TCA, an enhancement to the standard Transfer Component Analysis approach that significantly accelerates computation without compromising theoretical and empirical performance. Leveraging the computational advantage of RF-TCA, we further extend it to FDA setting with FedRF-TCA. The proposed FedRF-TCA protocol boasts communication complexity that isindependentof the sample size, while maintaining performance that is either comparable to or even surpasses state-of-the-art FDA methods. We present extensive experiments to showcase the superior performance and robustness (to network condition) of FedRF-TCA.

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we proposeSR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposedSR-CL. Compared to benchmark methods, theSR-CLachieves superior convergence and delay performance under various scenarios.

The emerging load prediction techniques support up-front and rational resource provisioning in edge systems to enhance system efficiency and Quality-of-Service (QoS). Classic prediction methods may handle loads with apparent trends, but they cannot achieve accurate prediction for highly-variable edge loads. With the advantage of sequential data analysis, recurrent neural networks (RNNs) are often used for load prediction but reveal limited generalization ability and low training efficiency. Moreover, it is hard to obtain a well-performed prediction model by discrete single-edge training with insufficient historical data. To address these important challenges, we propose a novel Multi-edge Cooperative universal framework for load Prediction with Personalized Federated deep learning (MC-2PF), enabling multi-edge cooperative training of load prediction models. Specifically, to solve the client-drift issue in federated learning (FL) caused by distinct data distribution, we customize personalized models for each edge by independent control parameters and theoretically analyze the model convergence improvement. Meanwhile, we prove the generalization bound of the MC-2PF and its universality to RNN-based prediction models through a practical example. Using the real-world testbed and load datasets, extensive experiments verify the effectiveness and practicality of the MC-2PF for different RNN-based prediction models. Compared to state-of-the-art frameworks, the MC-2PF achieves higher prediction accuracy, faster convergence, and stronger adaptiveness.

With the rise of transportation electrification, an increasing number of charging stations have been established, forming a city-scale charging system. These charging stations serve as intermediaries that connectsupplyanddemand, drawing power from the grid and renewable energy sources to provide electricity to electric vehicles. Maintaining a delicate balance between supply and demand has emerged as a significant challenge for the charging system. On amacroscopiclevel, it impacts the power grid's peak load and reliability, whilelocally, it influences electric vehicle detour events. To comprehensively model the spatio-temporal characteristics in the charging system, we partition the charging system by adopting a supply-demand-aware approach and propose OPS, an online power scheduling algorithm based on the regularization technique. OPS aims to achieve a bi-level balance between supply and demand while constraining the power output of the charging system. We substantiate the efficacy of OPS through rigorous theoretical proofs, demonstrating its comparability to the optimal solution. Furthermore, we conduct extensive evaluation experiments with real-world data sets to establish the feasibility of the proposed methodology in alleviating the supply-demand imbalance. The results indicate that OPS attains an empirical competitive ratio of less than 1.2.

In many RFID-enabled applications, objects are classified into different categories, and the information associated with each object's category (called category information) is written into the attached tag, allowing the reader to access it later. The category information sampling in such RFID systems, which is to randomly choose (sample) a few tags from each category and collect their category information, is fundamental for providing real-time monitoring and analysis in RFID. However, to the best of our knowledge, two technical challenges, i.e., how to guarantee a minimized execution time and reduce collection failure caused by missing tags, remain unsolved for this problem. In this paper, we address these two limitations by considering how to use the shortest possible time to sample a different number of random tags from each category and collect their category information sequentially in small batches. In particular, we first obtain a lower bound on the execution time of any protocol that can solve this problem. Subsequently, we present a near-OPTimalCategory information sampling protocol (OPT-C) that solves the problem with an execution time close to the lower bound. Finally, extensive simulation results demonstrate the superiority of OPT-C over existing protocols, while real-world experiments further validate its practicality.

Edge computing significantly enhanced the growth of edge-assistant video streaming applications. However, challenges such as unpredictable wireless conditions, resource constraints, and task redundancy have intertwined impacts on the overall performance of edge video streaming systems (EVS). Therefore, it is essential to have an integrated framework that addresses resource management, computational offloading, and video task preprocessing. Existing optimization strategies often neglect the simultaneous management of computational offloading, resource allocation, and video task preprocessing, leading to a suboptimal system utility. Moreover, they struggle to handle high-dimensional decision variables. On the other hand, learning-based adaptive schemes fall short in integrating distributed decisions and ensuring the scalability of wireless devices. Additionally, current approaches lack adaptive incentives. To bridge these gaps, we propose a novel framework called AIRA, which is based on improved multi-agent reinforcement learning (MARL) and smart contracts. AIRA manages resources, video compression, and adaptive incentives in a distributed manner. It consists of a MARL-driven cooperative learning algorithm (CLA) and a smart contract-guided adaptive incentive mechanism. Leveraging an actor-critic structure, the CLA enables wireless devices to master strategies for resource allocation, video task compression, and offloading, utilizing historical data. Notably, the CLA incorporates an attention mechanism to select pivotal tuples from the observation-action pairings among different agents, ensuring improved scalability and computational prowess. Evaluations based on real-world trajectories demonstrate that AIRA enables adaptive incentives. Compared to state-of-the-art approaches, CLA effectively enhances the long-term system utility and scalability of EVS.

As a key technique for future networks, the performance of emerging multi-edge caching is often limited by inefficient collaboration among edge nodes and improper resource configuration. Meanwhile, achieving optimal cache hit rates poses substantive challenges without effectively capturing the potential relations between discrete user features and diverse content libraries. These challenges become further sophisticated when caching schemes are exposed to adversarial attacks that seriously impair cache performance. To address these challenges, we introduce RoCoCache, a resilient collaborative caching framework that uniquely integrates robust federated deep learning with proactive caching strategies, enhancing performance under adversarial conditions. First, we design a novel partitioning mechanism for multi-dimensional cache space, enabling precise content recommendations in user classification intervals. Next, we develop a new Discrete-Categorical Variational Auto-Encoder (DC-VAE) to accurately predict content popularity by overcoming posterior collapse. Finally, we create an original training mode and proactive cache replacement strategy based on robust federated deep learning. Notably, the residual-based detection for adversarial model updates and similarity-based federated aggregation are integrated to avoid the model destruction caused by adversarial updates, which enables the proactive cache replacement adapting to optimized cache resources and thus enhances cache performance. Using the real-world testbed and datasets, extensive experiments verify that the RoCoCache achieves higher cache hit rates and efficiency than state-of-the-art methods while ensuring better robustness. Moreover, we validate the effectiveness of the components designed in RoCoCache for improving cache performance via ablation studies.

Vital sign detection, based on Channel State Information (CSI) from commercial off-the-shelf (COTS) WiFi devices, has become a popular research area. Previous works in this field mainly focus on respiration, while heartbeat sensing has not been well studied yet, because its signal is very weak and overwhelmed by hardware noises and the respiration signal. Different from existing research that exploits directional antenna, the proposed WiCG ( Wi Fi C ardio G ram) system uses common antennas, and not only accurately senses heartbeat rate but also provides the heartbeat signal for further analysis in complex real-life home scenes. Specifically, we first propose an effective denoising solution for Wi-Fi CSI by exploiting its spatial structure, which exhibits strong correlation among the In-phase/Quadrature components. Leveraging this characteristic with Principal Component Analysis (PCA) achieves effective reduction of ambient noise in both the amplitude and phase of the CSI. Then, we introduce a heartbeat enhancement scheme that utilizes the periodicity of the heartbeat signal. By applying Singular Spectrum Analysis (SSA), the complex effects of residual noise and respiratory interference are effectively mitigated. Extensive experiments have proven that WiCG can effectively sense the heartbeat rate. In a real deployment environment, the average detection error can be reduced to 0.28 bpm, close to current commercial heartbeat sensors.

Content Delivery Network (CDN) has been widely used to provide data transmission services to end users. The edge cache servers are important components in CDN, and their hit ratios significantly influence the quality of cache service. However, edge caches are shared by multiple tenants (i.e., Internet Content Providers or ICPs) and the resource contention among tenants presents a huge challenge to improve the cache performance. Cache partitioning is a common method to deal with this chal-lenge, and several approaches have been proposed but still have some drawbacks. Existing methods bring non-negligible temporal and spatial overheads while obtaining features. Although some learning based methods have reduced these costs, the learning model convergence is slow due to the large searching space. To address the above problems, we propose a lightweight Reinforcement Learning based Cache Partitioning Approach (RL-Cache), which increases overall hit ratios of edge cache servers in CDN. The core of RL-Cache is a new feature named Compulsory Miss Ratio (CMR). It can be obtained in linear complexity and reflect the tenants' demand of cache space. To demonstrate the effectiveness of our approach, we not only utilize open-source traces from industrial CDNs but also collect real-world workloads from Tencent Cloud CDN. We develop a simulator to conduct several experiments driven by various traces. The experimental results show that compared to the commonly used methods, RL-Cache reduces the upstream traffic by 12.6% on average and improves the hit ratio by up to 4%.

Serverless computing is a promising approach for edge computing since its inherent features, e.g., lightweight virtualization, rapid scalability, and economic efficiency. However, there are two challenges existing in serverless edge computing: significant cold start latency and request blocking. Previous studies have not successfully resolved these challenges, which affect the Quality of Experience. In this paper, we formulate the Serverless Function Scheduling (SFS) problem in resource-limited edge computing, aiming to minimize the average response time. To solve this intractable scheduling problem, we first consider a simplified offline form of the SFS problem and design a polynomial-time optimal scheduling algorithm. Inspired by this optimal algorithm, we propose an Enhanced Shortest Function First (ESFF) algorithm, including function creation and function replacement. To avoid frequent cold starts, ESFF selectively decides the initialization of new function instances when receiving requests. To deal with request blocking, ESFF judiciously replaces serverless functions based on the function weight at the completion time of requests. Extensive simulations based on real-world serverless request traces are conducted, and the results show that ESFF consistently and substantially outperforms existing baselines under different settings.

Cloud-edge collaboration Architecture (CEA) enables the co-training of AI models by cloud servers and edge servers, offering a promising solution for large-scale model training. An efficient data trading mechanism helps encourage edges to invest data resources to participate in training while reducing the cost of cloud servers. Existing research on data trading within CEA focuses on static scenarios, either overlooking the dynamics of data demand and the fairness of the selected edges or assuming unknown future communication overheads. To bridge these gaps and consider the long-term fairness constraints, we propose an Online Data Trading mechanism for the CEA, called ODT, to improve the long-term utility. Technically, ODT decouples the long-term fairness constraint into a series of single time-slot sub-problems using the Lyapunov optimization method and applies dynamic programming to solve the single time-slot edge selection sub-problems. We prove the NP-hardness of the sub-problems, the performance bounds, and the computational complexity of the proposed algorithm. Evaluation results demonstrate that the proposed mechanism effectively improves long-term utility and achieves an efficient trade-off between fairness and utility.

Data compression is increasingly employed to enhance throughput and space efficiency in flash-based storage systems, which are critical for data-intensive applications. Current intra-SSD compression techniques operate transparently with respect to the file system and contribute to improving the lifetime of SSDs. These approaches typically avoid compressing read-hot data to reduce the latency penalties associated with decompression. However, the read-hot data remain uncompressed even after turning into cold data, thereby reducing overall compression effectiveness and diminishing space efficiency. Moreover, when previously compressed cold data become read-hot, it necessitates frequent decompression, which increases the read latency. To address the above problems, we propose a novel Garbage Collection aided Compression (GCC) scheme, to optimize space efficiency and mitigate read latency for compression-supported SSDs. The key idea of GCC is exploiting the valid page migration during garbage collection to enable background compression and decompression. Throughout the garbage collection process, the migrated valid pages can potentially be compressed or decompressed, which progressively improves space efficiency and minimizes the need for decompression during read operations. Performance evaluations conducted using MQSim simulator demonstrate that, compared to the typical compression schemes, GCC reduces the read and write latency by 25.27% and 9.43% on average and improves the space efficiency by 15.01% on average.

Cloud storage systems often face the issues of failure and straggler nodes. Failure is characterized as a fail-stop scenario, which refers to disk failures that can result in significant data unavailability. Straggler nodes are typically those with heavy workloads or poor performance. Usually, both failure and straggler nodes coexist, posing a significant challenge to data availability in storage systems. In such failure scenarios, parallel recovery and straggler recovery methods are commonly used as separate approaches for data recovery. However, parallel recovery methods encounter bottlenecks on the recovery path due to the presence of straggler nodes. Meanwhile, straggler recovery methods face the challenge of lacking available recovery paths in cases of multiple node failures. Scenarios involving both multiple failures and stragglers are common, yet there is a lack of efficient recovery methods for these situations. In this paper, we focus on scenarios involving video data, which occupies a significant portion of cloud storage systems, to address the above issues. We propose a Hybrid Global Graph-based Recovery (HGR) method that integrates parallel and straggler recovery approaches into a single global graph. The key idea of HGR is to construct a global graph that includes global node parameter information, enabling comprehensive coordination. We partition the global graph into two subgraphs: one containing straggler nodes and the other containing failure nodes. Resources are efficiently allocated to each subgraph to schedule recovery tasks in parallel. For data that presents significant recovery challenges, exhibits poor parallelism, has substantial tail latency, or exceeds fault tolerance limits, we employ approximate recovery methods. To demonstrate HGR's effectiveness, we conducted several experiments. The results indicate that HGR can reduce recovery time by up to 45.06% and improve I/O throughput by as much as 1.79× compared to state-of-the-art recovery methods.

Transductive node prediction has been a popular learning setting in Graph Neural Networks (GNNs). It has been widely observed that the shortage of information flow between the distant nodes and intra-batch nodes (for large-scale graphs) often hurt the generalization of GNNs which overwhelmingly adopt message-passing. Yet there is still no formal and direct theoretical results to quantitatively capture the underlying mechanism, despite the recent advance in both theoretical and empirical studies for GNN's generalization ability. In this paper, the $L$-hop interplay (i.e., message passing capability with training nodes) for a $L$-layer GNN is successfully incorporated in our derived PAC-Bayesian bound for GNNs in the semi-supervised transductive setting. In other words, we quantitatively show how the interplay between training and testing sets influence the generalization ability which also partly explains the effectiveness of some existing empirical methods for enhancing generalization. Based on this result, we further design a plug-and-play ***Graph** **G**lobal **W**orkspace* module for GNNs (InterpGNN-GW) to enhance the interplay, utilizing the key-value attention mechanism to summarize crucial nodes' embeddings into memory and broadcast the memory to all nodes, in contrast to the pairwise attention scheme in previous graph transformers. Extensive experiments on both small-scale and large-scale graph datasets validate the effectiveness of our theory and approaches.

In cloud storage systems with erasure coding (EC), increased demand for data services and EC-based data recovery lead to high volumes of concurrent I/O requests, potentially causing network congestion or server overload. Network congestion or node overload significantly reduces I/O throughput and data parallelism. Various methods have been proposed to address these issues, such as fine-grained data packet partitioning, I/O scheduling, and transfer reading. However, these methods may not be effective in different scenarios. For instance, even if most I/O paths are relieved, data may still remain inaccessible.

Memory deduplication techniques are widely used to improve memory utilization in cloud computing platforms, and they can be categorized into virtualized and containerized environments. In virtualized environments, prevalent memory deduplication approaches often rely on scanning the virtual address space of different processes. However, the complexity of virtual address spaces can reduce scanning efficiency in containerized environments. Additionally, the many-to-one mapping between virtual and physical pages can decrease the efficiency of merging operations.To solve the above problems, we proposed a Container-based Kernel Samepage Merging method called CKSM. This method leverages potential duplicate candidates and efficiently performs merging operations. It employs layered sampling to construct the priority of physical pages. Additionally, a physical page scanning mechanism is designed to directly obtain valid pages within the system. CKSM uses the physical page merge mechanism to merge all virtual pages at once and release the corresponding memory directly. We conduct several experiments to demonstrate the efficiency of CKSM. It reduces the scanning overhead by up to 80.99% and increases page comparison efficiency by up to 42.51%. Besides, CKSM achieves an average of 3.02×memory usage reduction compared to UKSM and 2.79×response speedup compared to KSM in the containerized environment. In cloud computing emulation, CKSM has been proven to be optimal in high-density deployment.

In the current storage disaggregation architecture, the challenge of quickly retrieving data from storage clusters is typically addressed using caching or data pushdown strategies to accelerate data access and reduce data movement. However, most caching systems still manage data at the page granularity level. For compute-intensive applications, such as analytical applications, this coarse data management approach leads to underutilized computational resources and read-write amplification issues. Additionally, insufficient cache utilization results in inefficient data flow. By managing data at the schema granularity level and modifying the data flow path, we alleviate the read-write amplification problem and improve data transfer speeds. Managing data at the schema level also enables semantic awareness, allowing us to proactively analyze semantics for more precise cache management strategies and accurate prefetching, rather than passively waiting for cache request sequences. We also observed that native compute caches waste valuable cache space and complicate the association between original and result data. To address this, we propose a multi-grained caching model to avoid these limitations. Compared to the baseline, Turbo Table reduces computation time by 2.4% to 36.8%.

Recent advancements in 3D generation have garnered considerable interest due to their potential applications. Despite these advancements, the field faces persistent challenges in multi-conditional control, primarily due to the lack of paired datasets and the inherent complexity of 3D structures. To address these challenges, we introduce ImageBind3D, a novel framework for controllable 3D generation that integrates text, hand-drawn sketches, and depth maps to enhance user controllability. Our innovative contribution is adopting an inversion-align strategy, facilitating controllable 3D generation without requiring paired datasets. Firstly, utilizing GET3D as a baseline, our method innovates a 3D inversion technique that synchronizes 2D images with 3D shapes within the latent space of 3D GAN. Subsequently, we leverage images as intermediaries to facilitate pseudo-pairing between the shapes and various modalities. Moreover, our multi-modal diffusion model design strategically aligns external control signals with the generative model's latent knowledge, enabling precise and controllable 3D generation. Extensive experiments validate that ImageBind3D surpasses existing state-of-the-art methods in both fidelity and controllability. Additionally, our approach can offer composable guidance for any feed-forward 3D generative models, significantly enhancing their controllability.

Nowadays, unmanned aerial vehicles (UAVs) are widely applied in crowd sensing. For UAV-enabled crowd sensing (UAVCS) systems, the sensing outcome and charging cost are two primary concerns. To achieve a satisfactory sensing outcome under the charging budget, we exploit joint moving, sensing, and charging scheduling of UAVs, as they all have critical impacts on such two objectives. However, the dynamically generated sensing targets and the variety of sensing tasks a UAVCS system may face make farsighted scheduling of UAVs rather challenging. To this end, we propose a novel multi-task constrained multi-agent reinforcement learning (MARL) method to help UAVs make distributed moving, sensing, and charging decisions. Specifically, we design a multi-task MARL framework to learn a single generic policy for a large collection of tasks, and propose a primal-dual training algorithm that alternates between improving the overall sensing outcome and reducing each task's constraint violation. Theoretically, we show that our algorithm provably converges, and analyze the optimality gap and constraint violation of the trained policy on unseen tasks. Extensive experiments on an incident dataset in New York City demonstrate that our method outperforms strong baselines in sensing outcome maximization and budget satisfaction, and also generalize well to unseen tasks.

Sharding is one of the prominent solutions to solve the scalability problem of traditional blockchains. By dividing the blockchain network into independent shards, transactions in different shards can be executed in parallel, improving the throughput of the blockchain. However, sharding also brings security issues. In a hash-based transaction sharding system, the output shard of a transaction is determined by the hash value of the transaction. Unfortunately, we show that this hashbased transaction assignment strategy can be easily exploited by attackers to conduct front-running attacks. Attackers can take advantage of the execution differences between different shards and the extra processing time of cross-shard transactions to make the attacker’s transaction executed and committed before the victim’s transaction, thereby obtaining the benefits that originally belonged to the victim. We also propose a flooding front-running attack, which introduces a single-shard flooding attack to enhance the front-running attack. Specifically, injecting a large number of junk transactions into the shard where the victim’s transaction is located can significantly extend the execution time of the victim’s transaction. We demonstrate the feasibility and practical effects of these two attacks through experiments on RapidChain, which show that a single-shard flooding attack can increase the success rate of a front-running attack by 12%. Finally, we discuss two possible mitigation measures and the cost of two proposed attacks.

In Mobile Edge Computing (MEC) systems, Unmanned Aerial Vehicles (UAVs) facilitate Edge Service Providers (ESPs) offering flexible resource provisioning with broader communication coverage and thus improving the Quality-of-Service (QoS). However, dynamic system states and various traffic patterns seriously hinder efficient cooperation among UAVs. Existing solutions commonly rely on prior system knowledge or complex neural network models, lacking adaptability and causing excessive overheads. To address these critical challenges, we propose the DisOff, a novel profit-aware cooperative offloading framework in UAV-enabled MEC with lightweight Deep Reinforcement Learning (DRL). First, we design an improved DRL with twin critic-networks and delay mechanism, which solves the Q-value overestimation and high variance and thus approximates the optimal UAV cooperative offloading and resource allocation. Next, we develop a new multi-teacher distillation mechanism for the proposed DRL model, where the policies of multiple UAVs are integrated into one DRL agent, compressing the model size while maintaining superior performance. Using the real-world datasets of user traffic, extensive experiments are conducted to validate the effectiveness of the proposed DisOff. Compared to benchmark methods, the DisOff enhances ESP profits while reducing the DRL model size and training costs.

Information sharing among vehicles plays a critical role in improving driving safety and traffic loads in Internet of Vehicles (IoV). However, due to the existence of malicious vehicles, information sharing among vehicles lacks a trustworthy environment. It is challenging for a vehicle to assess the credibility of the received information. Blockchain has attracted extensive attention because of the decentralization and tamper-proof characteristics. Nevertheless, the high mobility of vehicles leads to rapid network topology changes, which makes it difficult to reach a consensus. In this article, we propose a consortium blockchain-based trust-value management approach to build a trustworthy environment for information sharing among vehicles. A new incentive mechanism is designed to encourage vehicles to actively participate in blockchain maintenance. Furthermore, an Enhanced Proof of Work (EPoW) consensus algorithm is designed to reduce the amount of information that needs to be transmitted over the network to reach consensus quickly. Comprehensive analysis and simulation results verify that the proposed trust-value management approach and EPoW consensus algorithm realize secure and efficient information sharing in IoV.

With the advancement of intelligent transportation systems, location-based services (LBS) have been widely applied in vehicular ad hoc networks (VANETs). LBS utilizes mobile devices to gather vehicle location data, which is then processed using relevant technologies. By combining this data with additional information, LBS offers users personalized and intelligent services. However, providing LBS brings critical security issues related to the exposure of vehicle positions, as well as privacy-preserving problems during the process of collecting location information in VANETs. We propose a distributed trust-based k anonymity scheme to address the aforementioned issues. Our proposed scheme adopts a trust framework among vehicles for various types of LBS. This framework involves a multiparty evaluation and consideration of trust value fluctuations to enhance the efficiency of establishing a reliable k anonymous cloaking region. Furthermore, by leveraging the tamper-proof and decentralized nature of blockchain, we employ a lightweight consortium blockchain to maintain the security of the trustworthiness data throughout the entire model. Extensive security analysis and rigorous experiments have been conducted to demonstrate that the scheme exhibits a certain degree of resilience against attacks on various trust models. Additionally, it has the ability to construct anonymous regions with limited time delay, thereby preserving the privacy of vehicle locations. In comparison to other schemes, it exhibits lower computational complexity and enhanced security.

Over-the-air computation (AirComp), as an effective method to wireless data aggregation, has attracted much attention recently. It helps to improve network efficiency and scalability by integrating communication and computation in the air. In AirComp, both signal magnitude misalignment and noise lead to computation error. In the single antenna case, by allowing misalignment in signal magnitude, a good tradeoff can be achieved between signal distortion and noise power, which leads to a minimal error. In the multi-antenna case, usually the zero-forcing policy is used to enforce signal magnitude alignment (no distortion), which, however, increases noise and affects the overall computation error. To better exploit multiple antennas at the sink, in this paper, we propose a misalignment allowed optimization (Miso) method for AirComp. Specifically, a group of nodes whose signals may be misaligned are dynamically selected, and other signals are aligned to a higher level with higher quality. On this basis, the optimization of multi-antenna AirComp is converted to a difference of convex problem and is solved iteratively. Simulations confirm that the proposed method greatly reduces computation error and scales better with the number of nodes, compared with previous methods.

In the Industrial Internet of Things (IIoT), cross-silo federated learning (CSFL) enables entities, such as manufacturers and suppliers to train global models for optimizing production processes while ensuring data privacy. A well-designed incentive mechanism is essential to persuade clients to contribute data resources. However, existing methodologies overlook the dynamic nature of the training process, where the accuracy of the globally trained model and the client’s data ownership change over time. Furthermore, the majority of previous research assumes a defined functional relationship between the data contribution and the model accuracy, which is infeasible in realistic and dynamic training environments. To address these challenges, we design a novel adaptive mechanism for CSFL that inspires organizations to contribute data resources in a dynamic training environment with the aim of maximizing their long-term payoffs. This mechanism leverages multiagent reinforcement learning (MARL) to ascertain near-optimal data contribution strategies from potential game histories without necessitating private organizational information or a precise accuracy function. Experimental results indicate that our mechanism achieves adaptive incentive in dynamic environments and effectively enhances the long-term payoffs of organizations.

With flexible mobility and broad communication coverage, Unmanned Aerial Vehicles (UAVs) have become an important extension of Multi-access Edge Computing (MEC) systems, exhibiting great potential for improving the performance of Federated Graph Learning (FGL). However, due to the limited computing and storage resources of UAVs, they may not well handle the redundant data and complex models, causing the inference inefficiency of FGL in UAV-assisted MEC systems. To address this critical challenge, we propose a novel LightWeight FGL framework, named LW-FGL, to accelerate the inference speed of classification models in UAV-assisted MEC systems. Specifically, we first design an adaptive Information Bottleneck (IB) principle, which enables UAVs to obtain well-compressed worthy subgraphs by filtering out the information that is irrelevant to downstream classification tasks. Next, we develop improved tiny Graph Neural Networks (GNNs), which are used as the inference models on UAVs, thus reducing the computational complexity and redundancy. Using real-world graph datasets, extensive experiments are conducted to validate the effectiveness of the proposed LW-FGL. The results show that the LW-FGL achieves higher classification accuracy and faster inference speed than state-of-the-art methods.

The emerging Space-Air-Ground Integrated Networks (SAGIN) empower Mobile Edge Computing (MEC) with wider communication coverage and more flexible network access. However, the fluctuating user traffic and constrained computing architecture seriously hinder the Quality-of-Service (QoS) and resource utilization in SAGIN. Existing solutions generally depend on prior knowledge or adopt static resource provisioning, lacking adaptability and resulting in serious system overheads. To address these important challenges, we propose THOAS, a novel Traffic-aware lightweight Hierarchical Offloading framework towards Adaptive Slicing-enabled SAGIN. First, we innovatively separate SAGIN into Communication Access Platforms (CAPs) and Computation Offloading Platforms (COPs). Next, we design a new self-attention-based prediction method to accurately capture the traffic changes on each platform, enabling adaptive slice resource adjustments. Finally, we develop an improved deep reinforcement learning method based on proximal clipping with dynamic confidence intervals to reach optimal offloading. Notably, we employ knowledge distillation to compress offloading policies into lightweight networks, enhancing their adaptability in resource-limited SAGIN. Using real-world datasets of user traffic, extensive experiments are conducted. The results show that the THOAS can accurately predict traffic and make adaptive resource adjustments and offloading decisions, which outperforms other benchmark methods on multiple metrics under various scenarios.

Recently, the flash-based Solid State Drive (SSD) array has been widely implemented in real-world large-scale clusters. With the increasing number of users in upper-tier applications and the burst of Input/Output requests in this data explosive era, data centers need to continuously scale up to meet real-time data storage needs. However, the classical disk array scaling methods are designed based on HDDs, ignoring the wear leveling and garbage collection characteristics of SSD. This leads to penalties due to the vast lifetime gap between extended SSDs and the original in-use SSDs while scaling the SSD array, including extra triggered wear leveling I/O, latency in average response time, etc.To address these problems, we propose an Adaptive Wear-Leveling aware data migration approach for flexible SSD array scaling in clusters. It manages the interdisk wear leveling based on Model Reference Adaptive Control, which includes an SSD behavior emulator, Kalman filter estimator, and adaptive law. To demonstrate the effectiveness of this approach, we conducted several simulations and implementations on actual hardware. The evaluation results show that Ada-WL has the self-adaptability to optimize the wear leveling management parameters for various states of SSD arrays, diverse workloads, and scaling performed multiple times, significantly improving performance for SSD array scaling.

Federated learning (FL) is a technique that enables clients to collaboratively train a model by sharing local models instead of raw private data. However, existing reconstruction attacks can recover the sensitive training samples from the shared models. Additionally, the emerging poisoning attacks also pose severe threats to the security of FL. However, most existing Byzantine-robust privacy-preserving federated learning solutions either reduce the accuracy of aggregated models or introduce significant computation and communication overheads. In this paper, we propose a novelBlockchain-basedSecure andRobustFederatedLearning (BSR-FL) framework to mitigate reconstruction attacks and poisoning attacks. BSR-FL avoids accuracy loss while ensuring efficient privacy protection and Byzantine robustness. Specifically, we first construct a lightweight non-interactive functional encryption (NIFE) scheme to protect the privacy of local models while maintaining high communication performance. Then, we propose a privacy-preserving defensive aggregation strategy based on NIFE, which can resist encrypted poisoning attacks without compromising model privacy through secure cosine similarity and incentive-based Byzantine-tolerance aggregation. Finally, we utilize the blockchain system to assist in facilitating the processes of federated learning and the implementation of protocols. Extensive theoretical analysis and experiments demonstrate that our new BSR-FL has enhanced privacy security, robustness, and high efficiency.

With breakthroughs in high-resolution image generation, applications for disentangled generative adversarial networks (GANs) have attracted much attention. At the same time, the privacy issues associated with GAN models have been raising many concerns. Membership inference attacks (MIAs), where an adversary attempts to determine whether or not a sample has been used to train the victim model, are a major risk with GANs. In prior research, scholars have shown that successful MIAs can be mounted by leveraging overfit images. However, high-resolution images make the existing MIAs fail due to their complexity. And the nature of disentangled GANs is such that the attributes are overfitting, which means that, for an MIA to be successful, it must likely be based on overfitting attributes. Furthermore, given the empirical difficulties with obtaining independent and identically distributed (IID) candidate samples, choosing the non-trivial attributes of candidate samples as the target for exploring overfitting would be a more preferable choice. Hence, in this paper, we propose a series of attribute-based MIAs that considers both black-box and white-box settings. The attacks are performed on the generator, and the inferences are derived by overfitting the non-trivial attributes. Additionally, we put forward a novel perspective on model generalization and a possible defense by evaluating the overfitting status of each individual attribute. A series of empirical evaluations in both settings demonstrate that the attacks remain stable and successful when using non-IID candidate samples. Further experiments illustrate that each attribute exhibits a distinct overfitting status. Moreover, manually generalizing highly overfitting attributes significantly reduces the risk of privacy leaks.

We are witnessing a rapid growth of electric vehicles in both individual and public transportation. Many large-scale company-built electric bus charging stations have been established to facilitate public transportation, while these electric bus charging stations are not accessible to private vehicles. The motivation for this work comes from the possibility of opening surplus resources in electric bus charging stations to alleviate the charging resource shortage and gain extra profit for the electric bus charging station. The operation facing private vehicles, however, also encounters obstacles including serious congestion induced by absorbing private vehicles and delays of bus lines due to private vehicles occupying charging points. To jointly solve these challenges, we propose a real-time control mechanism to maximize the long-term net profit of an electric bus charging station based on Lyapunov optimization theory and generalized benders decomposition, while maintaining the congestion level and ensuring timetables of buses. We demonstrate through rigorous theoretical proof that the proposed mechanism can be arbitrarily close to the optimal solution. Comprehensive evaluation experiments with real-world data sets have been conducted to show the credibility of the mechanism in reducing congestion, ensuring bus timetables, and maximizing the long-term net profit.

Through deploying computing resources at the network edge, Mobile Edge Computing (MEC) alleviates the contradiction between the high requirements of intelligent mobile applications and the limited capacities of mobile End Devices (EDs) in smart communities. However, existing solutions of computation offloading and resource allocation commonly rely on prior knowledge or centralized decision-making, which cannot adapt to dynamic MEC environments with changeable system states and personalized user demands, resulting in degraded Quality-of-Service (QoS) and excessive system overheads. To address this important challenge, we propose a novel Personalized Federated deep Reinforcement learning based computation Offloading and resource Allocation method (PFR-OA). This innovative PFR-OA considers the personalized demands in smart communities when generating proper policies of computation offloading and resource allocation. To relieve the negative impact of local updates on global model convergence, we design a new proximal term to improve the manner of only optimizing local Q-value loss functions in classic reinforcement learning. Moreover, we develop a new partial-greedy based participant selection mechanism to reduce the complexity of federated aggregation while endowing sufficient exploration. Using real-world system settings and testbed, extensive experiments demonstrate the effectiveness of the PFR-OA. Compared to benchmark methods, the PFR-OA achieves better trade-offs between delay and energy consumption and higher task execution success rates under different scenarios.

In edge computing, applications can be scheduled in the granularity of inter-dependent tasks to proximate edge servers to achieve high performance. Before execution, the edge server must initialize the corresponding runtime environment, named task startup. However, existing studies on dependent task scheduling severely ignore bandwidth constraints during task startups, which is impractical and incurs a long startup latency. To fill in this gap, we first model the task startup process with bandwidth constraints on edge servers. Then, we formulate the dependent task scheduling problem with startup latency in heterogeneous edge computing. To efficiently generate schedules and satisfy the real-time requirements in edge computing, a novel low-complexity list scheduling algorithm integrated with cloud clone, Startup-aware Dependent Task Scheduling (SDTS), is proposed. Constrained by bandwidth and computation resources, SDTS first coordinates task startup, dependent data transmission, and task execution to optimize each task’s finish time. Then, a cloud clone for each task is deployed to utilize scalable resources and initialized runtime environments. Furthermore, task scheduling refinement is designed to release the bandwidth and computation resources consumed by redundant tasks and improve the schedule. Extensive simulations based on real-world datasets show that SDTS substantially reduces 30%-60% makespan compared with existing baselines.

The performance of deep learning models highly depends on the amount of training data. It is common practice for today's data holders to merge their datasets and train models collaboratively, which yet poses a threat to data privacy. Different from existing methods such as secure multi-party computation (MPC) and federated learning (FL), we find representation learning has unique advantages in collaborative learning due to its low privacy budget, wide applicability to tasks and lower communication overhead. However, data representations face the threat of model inversion attacks. In this article, we formally define the collaborative learning scenario, and present ARS (for adversarial representation sharing), a collaborative learning framework wherein users share representations of data to train models, and add imperceptible adversarial noise to data representations against reconstruction or attribute extraction attacks. By theoretical analysis and evaluating ARS in different contexts, we demonstrate that our mechanism is effective against model inversion attacks, and can achieve great utility and low communication complexity while preserving data privacy. Moreover, the ARS framework has wide applicability, which can be easily extended to the vertical data partitioning scenario and utilized in different tasks.

While deep learning models have achieved the state-of-the-art performance on single-image rain removal, most methods only consider learning fixed mapping rules on the single synthetic dataset for lifetime. This limits the real-life application as iterative optimization may change mapping rules and training samples. However, when models learn a sequence of datasets in multiple incremental steps, they are susceptible to catastrophic forgetting that adapts to new incremental episodes while failing to preserve previously acquired mapping rules. In this paper, we argue the importance of sample diversity in the episodes on the iterative optimization, and propose a novel memory management method, Associative Memory, to achieve incremental image de-raining. It bridges connections between current and past episodes for feature reconstruction by sampling domain mappings of past learning steps, and guides the learning to trace the current pathway back to the historical environment without storing extra data. Experiments demonstrate that our method can achieve better performance than existing approaches on both inhomogeneous and incremental datasets within the spectrum of highly compact systems.

Edge computing is a highly promising technology that empowers mobile devices to offload video streaming tasks to edge servers, thereby improving the video stream analysis performance. However, most existing research on edge video streaming has failed to give adequate attention to the joint optimization of video streaming tasks with respect to dynamics, redundancy, and long-term energy constraints. To address this limitation, we propose a novel method based on a multi-agent reinforcement learning algorithm, which significantly enhances the performance of edge video stream analysis under long-term energy constraints. Specifically, our proposed method conducts video compression and offloading under long-term energy constraints to maximize the long-term rewards of video task processing. Experimental evaluations have demonstrated the convergence of the proposed method, which outperforms the baseline solutions, achieving higher long-term rewards.

Graph Neural Networks (GNNs) have demonstrated superior performance in numerous real-world applications. Despite their success, recent studies have shown that GNNs are vulnerable under edge inference attacks aimed to infer the connectivity of a given pair of nodes. However, existing methods primarily focus on the scenario when properties of target nodes are revealed. In this paper, we propose an edge inference attack in a more realistic and practical setting. In our threat model, the adversary cannot obtain properties of target nodes but can inject a single probing node and query the target GNN for its prediction. By connecting the probing and target nodes, the adversary can infer the connectivity of the target node pair based on the prediction of the probing node. Extensive experiments show that our attack performs comparably to ones that require properties of target nodes. And when given such auxiliary knowledge, our attack outperforms state-of-the-art methods.

Cross-silo federated learning (CFL) is a distributed learning paradigm that allows organizations (e.g., financial or medical entities) to train a global model on siloed data. Recent studies on mechanisms designed for CFL, however, rarely jointly consider the potential inter-organizational competition and the lack of credibility between organizations, which may discourage organizational participation. In this paper, we investigate the problem of inter-organizational competition and credibility assurance. We propose a distributed trading mechanism, called$TradeFL$, to incentivize organizations to contribute data and computational resources through mutual trading among organizations. Technically, TradeFL characterizes the competition among organizations and compensates for their damage incurred by competition. TradeFL runs on distributed organizations and provides credibility guarantees for compensation through a customized smart contract11Illustration of the prototype: https://github.com/user10963.. We prove that the interaction among organizations that contribute resources to maximize personal payoffs is a weighted potential game. Then, we propose a centralized algorithm and a distributed algorithm to determine the optimal resource contribution. Simulation results and evaluations based on real-world datasets demonstrate that our scheme achieves higher social welfare, increases the amount of contributed data by up to 64%, and improves the accuracy of the global model by at most 23.2%.

Remote driving, an emergent technology enabling remote operation of vehicles, presents a significant challenge due to the necessity of transmitting substantial volumes of image data from the vehicle to a central server. This requirement outpaces the capacity of traditional communication methods, emphasizing the need for efficient data communication. We propose a framework using semantic communication, specifically through a semantic segmentation-based method, which reduces the communication cost by transmitting meaningful semantic information rather than bit-wise data. Addressing the challenge of inconsistencies across knowledge bases in semantic communication, we present a blockchain-based, edge-assisted knowledge base management system. This system leverages edge nodes to manage multiple, geographically and contextually diverse knowledge bases while ensuring security through blockchain's tamper-resistant nature. Furthermore, blockchain sharding is employed to manage different knowledge bases for varying tasks, thereby enhancing the blockchain's throughput. Experimental results showed a great reduction in latency by sharding and an increase in model accuracy, confirming our framework's effectiveness.

Blockchain is a decentralized digital ledger system that can be used in many fields. However, the traditional approach of storing blockchain data (full repliction) incurs expensive storage costs. As a result, researchers have proposed erasure coding methods to reduce storage costs. Existing erasure coding methods are all based on RS codes. Since blockchain systems typically have a large number of nodes, it is necessary to use wide-stripe RS codes to store blocks, which results in significant overhead during the recovery processes. Because widestripe RS codes require accessing a large number of nodes for decoding, this means significant network and I/O cost.To solve the above problem, we propose DW-LRC, which is a dynamic wide-stripe Local Reconstruction Code (LRC) based methods in permissioned blockchain systems. DW-LRC predicts malicious nodes using node reputation, and then selects an appropriate variant of LRC code to lower storage and recovery costs compared to traditional erasure coding. To demonstrate the effectiveness of DW-LRC, we conduct several experiments on the open source blockchain software tendermint. The results show that, compared to the state-of-the-art erasure coding methods, DW-LRC reduced the average recovery latency by 36.4% and improved the block access throughput by 32.3%.

Previous works have proven the superior performance of ensemble-based black-box attacks on transferability. However, existing methods require significant difference in architecture among the source models to ensure gradient diversity. In this paper, we propose a Diverse Gradient Method (DGM), verifying that knowledge distillation is able to generate diverse gradients from unchangeable model architecture for boosting transferability. The core idea behind our DGM is to obtain transferable adversarial perturbations by fusing diverse gradients provided by a single source model and its distilled versions through an ensemble strategy. Experimental results show that DGM successfully crafts adversarial examples with higher transferability, only requiring extremely low training cost. Furthermore, our proposed method could be used as a flexible module to improve transferability of most of existing black-box attacks.

In the era of information explosion, we urgently need recommendation systems to filter massive amounts of information. Recent advancements in graph neural networks have led to the widespread adoption of graph collaborative filtering algorithms for recommendation systems. Despite their effectiveness, graph collaborative filtering algorithms have several limitations, such as data sparsity and long-tailed distribution. This sparse data with numerous long-tailed nodes can be viewed as an inductive scenario in which models require a robust inductive ability to learn quality representations from sparse data. Inductive graph collaborative filtering methods, such as Pin-SAGE, improve the generalization ability via random neighbor sampling. However, these inductive methods are time-consuming or ineffective in transductive scenarios because of the complicated operations and information loss in random neighbor sampling methods. We propose IDHA, an inductive dummy-based homogeneous neighborhood augmentation method for graph collaborative filtering, to address the issues above. Our method employs dummy nodes connected to all nodes to take advantage of low-degree nodes in graph structure learning. To improve the model's generalization in inductive scenarios, we adopt one-hop random-walk sampling. We propose homogeneous neighborhood augmentation via contrastive learning for inductive graph collaborative filtering. This method exploits contrastive learning in sparse data to its full potential. In addition, we employ a lightweight model design to enhance performance and practicality while reducing model complexity. Extensive experiments on three datasets demonstrate that our method outperforms the state-of-the-art transductive and inductive graph collaborative filtering recommendation methods.

In the recent storage market, Flash-based Solid State Drives (SSDs) have become high-performance alternatives to Hard Disk Drives (HDDs), dramatically increasing SSD shipments. To guarantee product reliability and quality to remain competitive, SSD manufacturers pay significant efforts in technology qualification and reliability design, especially in Manufacturing Self-Test (MST) processes. However, the cost of the MST process becomes more prominent as the memory density of SSD increases. In this paper, we study the MST data in over 20,000 SSDs and propose a novel and economical approach to dynamically reduce the MST overhead by proactive infant defect prediction based on Generative Adversarial Network-Attention based Spatial-Temporal Sequence-to-Sequence network (GAN-ASTSeq). It reduces the temporal cost by 80.2% (i.e., improves the efficiency by 4×) while maintaining an outstanding detection rate of defects.

Wi-Fi-based contactless activity recognition is of great importance to computer–human interaction, accounting for convenience concerns. However, it remains challenging to recognize activities from multiple users due to the multipath distortion and disruption of Wi-Fi signals. In this article, we propose a highly universal framework, namely, WISDOM, for Wi-Fi-based multiuser activity recognition. Specifically, we first leverage an existing model to identify the number of users from the input Wi-Fi signals. Then, we develop a subcarrier correlation and inversion-based sorting algorithm to extract the signal for each user. Finally, we design a neural network, i.e., WISDOM-Net, which is built on a bidirectional gated recurrent unit network incorporated with the attention mechanism and the one dimension convolutional neural network, to recognize the corresponding user activities. Experimental results show that our proposed WISDOM-Net outperforms the existing baselines on both the public and our own data sets. In particular, WISDOM-Net can reach an average recognition accuracy of up to 98.19% and 90.77% in 2-user and 3-user scenarios, respectively.

Federated learning models are collaboratively developed upon valuable training data owned by multiple parties. During the development and deployment of federated models, they are exposed to risks including illegal copying, re-distribution, misuse and/or free-riding. To address these risks, the ownership verification of federated learning models is a prerequisite that protects federated learning model intellectual property rights (IPR) i.e., FedIPR. We propose a novel federated deep neural network (FedDNN) ownership verification scheme that allows private watermarks to be embedded and verified to claim legitimate IPR of FedDNN models. In the proposed scheme, each client independently verifies the existence of the model watermarks and claims respective ownership of the federated model without disclosing neither private training data nor private watermark information. The effectiveness of embedded watermarks is theoretically justified by the rigorous analysis of conditions under which watermarks can be privately embedded and detected by multiple clients. Moreover, extensive experimental results on computer vision and natural language processing tasks demonstrate that varying bit-length watermarks can be embedded and reliably detected without compromising original model performances. Our watermarking scheme is also resilient to various federated training settings and robust against removal attacks.

In Mobile Edge Computing (MEC), latency-sensitive mobile applications comprising dependent tasks can be scheduled to edge or cloud servers to reduce latency and execution costs. However, existing algorithms based on deadline distribution can hardly satisfy tight application deadlines in heterogeneous MEC due to lacking a global view of the future impacts on descendant tasks. To fill in this gap, we formulate the deadline-constrained cost optimization problem for dependent task scheduling in MEC and propose a low-complexity scheduling algorithm that considers a single task's future impacts in two stages. Specifically: (1) In the edge scheduling stage, each task is scheduled according to its successors’ latest start times instead of its sub-deadline to alleviate the lateness of its successors. An edge-only schedule plan is generated by scheduling tasks only on edge servers to save execution costs. (2) In the cloud offloading stage, in order to utilize the powerful cloud resources to satisfy the deadline, the edge-only schedule plan missing the deadline is efficiently modified by properly offloading multiple successive tasks to the cloud. Simulation results show the substantial advantage of the proposed algorithm over baselines in both online and offline scenarios.

Semi-supervised learning acts as an effective way to leverage massive unlabeled data. In this paper, we propose a novel training strategy, termed asSemi-supervised Contrastive Learning (SsCL), which combines the well-known contrastive loss in self-supervised learning with the cross entropy loss in semi-supervised learning, and jointly optimizes the two objectives in an end-to-end way. The highlight is that different from self-training based semi-supervised learning that conducts prediction and retraining over the same model weights, SsCL interchanges the predictions over the unlabeled data between the two branches, and thus formulates a co-calibration procedure, which we find is beneficial for better prediction and avoids being trapped in local minimum. Towards this goal, the contrastive loss branch models pairwise similarities among samples, using the pseudo labels generated from the cross entropy branch, and in turn calibrates the prediction distribution of the cross entropy branch with the contrastive similarity. We show that SsCL produces more discriminative representation and is beneficial to semi-supervised learning. Notably, on ImageNet with ResNet50 as the backbone, SsCL achieves$\bm {60.2\%}$and$\bm {72.1\%}$top-1 accuracy with 1% and 10% labeled samples respectively, which significantly outperforms the baseline, and is better than previous semi-supervised and self-supervised methods.

Decentralized cryptocurrency systems have become primary targets for attackers due to substantial profit gain and economic rewards. A number of attack models have been proposed during last few years. However, the evaluation and comparison of those attack models remain problematic due to the lack of systematic framework to analyze them. In this work, we propose a general quantitative analysis framework for attack models in the network and consensus layer of blockchain. We identify the problem statement and evolution process. And we show how to apply our general framework in previous attacks such as selfish mining and bribery attack. We also explained that the framework is suitable for other attacks in blockchain. For further exploration, we simulate the success rate and benefits of different attacks through experiments. We provide several defensive strategies, and study how these strategies against previous attack models.

Edge computing has been introduced as a promising technology for real-time video streaming systems. However, due to the lack of automatic incentives and the limitation of resources, traditional edge computing performs poorly in nowadays scenarios. To handle these two challenges, we propose a framework ofJointIncentive design andResourceAllocation (JIRA) for edge-based real-time video streaming systems. Technically, to ensure the trust and automatic distribution of incentives, we develop a novel smart contract based incentive mechanism and implement a prototype. Meanwhile, we propose an efficient online algorithm, i.e., JIRA, which dynamically adjusts compression ratio, offloading decision, and resource allocation to achieve performance optimization for video streaming under long-term latency and resource constraints. Specifically, JIRA is based on Lyapunov optimization, which decomposes the challenging long-term decision problem into a series of real-time optimization problems. Then we propose a multi-cut Generalized Benders Decomposition based algorithm (MGA) to tackle the non-convexity of the decomposed problem. Through rigorous theoretical analysis, we prove the performance bound of JIRA. Extensive simulations demonstrate that the proposed schemes can achieve an efficient trade-off between accuracy performance and energy consumption.

Although randomized smoothing has demonstrated high certified robustness and superior scalability to other certified defenses, the high computational overhead of the robustness certification bottlenecks the practical applicability, as it depends heavily on the large sample approximation for estimating the confidence interval. In existing works, the sample size for the confidence interval is universally set and agnostic to the input for prediction. This Input-Agnostic Sampling (IAS) scheme may yield a poor Average Certified Radius (ACR)-runtime trade-off which calls for improvement. In this paper, we propose Input-Specific Sampling (ISS) acceleration to achieve the cost-effectiveness for robustness certification, in an adaptive way of reducing the sampling size based on the input characteristic. Furthermore, our method universally controls the certified radius decline from the ISS sample size reduction. The empirical results on CIFAR-10 and ImageNet show that ISS can speed up the certification by more than three times at a limited cost of 0.05 certified radius. Meanwhile, ISS surpasses IAS on the average certified radius across the extensive hyperparameter settings. Specifically, ISS achieves ACR=0.958 on ImageNet in 250 minutes, compared to ACR=0.917 by IAS under the same condition. We release our code in https://github.com/roy-ch/Input-Specific-Certification.

How to achieve better results with fewer labeling costs remains a challenging task. In this paper, we present a new active learning framework, which for the first time incorporates contrastive learning into recently proposed one-bit supervision. Here one-bit supervision denotes a simple Yes or No query about the correctness of the model's prediction, and is more efficient than previous active learning methods requiring assigning accurate labels to the queried samples. We claim that such one-bit information is intrinsically in accordance with the goal of contrastive loss that pulls positive pairs together and pushes negative samples away. Towards this goal, we design an uncertainty metric to actively select samples for query. These samples are then fed into different branches according to the queried results. The Yes query is treated as positive pairs of the queried category for contrastive pulling, while the No query is treated as hard negative pairs for contrastive repelling. Additionally, we design a negative loss that penalizes the negative samples away from the incorrect predicted class, which can be treated as optimizing hard negatives for the corresponding category. Our method, termed as ObCP, produces a more powerful active learning framework, and experiments on several benchmarks demonstrate its superiority.

Blockchain and edge computing have been widely applied in video streaming systems. However, previous works lack a joint consideration of video redundancy and full utilization of edge resources (bandwidth resources, CPU frequency), resulting in suboptimal performance of video streaming systems. In this paper, we propose a computing offloading framework for blockchain-enabled video streaming systems to fully exploit edge resources and reduce energy consumption. Specifically, we formulate computing offloading, resource allocation, and adaptive compression as a joint optimization problem. We transform and decompose the original non-convex problem and propose an algorithm based on the alternating direction method of multipliers (ADMM) to solve the decomposed problem in a distributed manner. Simulation results demonstrate that our scheme can effectively reduce energy consumption and fully utilize the bandwidth and computational resources.

Traffic flow prediction is of particular interest since its massive applications in intelligent transportation systems (ITS). The problem is challenging due to the complex spatio-temporal correlations and nonlinearities of traffic flows. However, existing methods based on the graph neural networks cannot efficiently extract the dynamic and long-range spatial correlations, thus producing unsatisfactory prediction results. In this paper, we propose an AdaBoost Spatio-temporal Network (Ada-STNet). Similar to AdaBoost, Ada-STNet stacks several base neural networks as "layers" which capture spatial and temporal correlations simultaneously. Each layer learns an adaptive adjacency matrix from weights and embedding of nodes. The adjacency matrix is layer-wise adjusted to extract information from distant neighbors and adapt to dynamic correlations. Experiments are conducted on three real-world benchmark datasets, demonstrating that the Ada-STNet outperforms the state-of-the-art methods.

Deep generative networks have achieved great success on distorted image restoration. However, existing deep learning approaches mainly focus on delicate module structure while ignoring the saturation problem. In this paper, we study the influence of different learning schemes on fitting capability and tackle the problem by proposing a novel iterative learning scheme. It accumulates weight importance from past episodes and guides the network to search for the optimal of current episodes based on obtained knowledge. Since public available datasets contain very few distortion types, we also release a new benchmark to explore this task. Extensive experimental evaluations on the benchmarks demonstrate that our learning approach significantly outperforms all other methods and achieves new state-of-the-art results.

The straggler problem is one of the most significant problems in cloud computing systems, in which a large number of parallel processes are blocked by a small set of straggler tasks with a long waiting time. This problem is crucial in erasure coded storage systems, where the recovery processes require to retrieve a set of multiple chunks among different nodes. With skewed data accesses from various applications, several nodes with a high workload could easily become stragglers during the recovery process, leading to unacceptable long tail latency. To address the above problems, we propose a Redirection Computational Scheduling method called RCS, to accelerate the data recovery under straggler scenarios. The key idea of RCS is transferring the computational and network workload from one node to another, which can avoid the adverse effects caused by the stragglers. To demonstrate the effectiveness of RCS, we conduct several experiments in a cluster. The results show that, compared to the state-of-the-art recovery methods, RCS saves the recovery time by up to 72.1%, and speeds up the recovery throughput by up to a factor of 1.4X, respectively.

Erasure coding (EC) has been widely used in cloud storage systems to provide both high reliability and low storage cost. Previous literatures show that the cross-rack update operations are prevalent for many applications in erasure-coded cloud storage systems, which introduces significant I/O amplification, load imbalance and high latency. Several existing methods have been proposed to mitigate these problems. However, they ignore the correlations among chunks when performing data placement. Thus numerous stripes and racks participate in the update leading to extra I/Os and cross-rack traffic. Moreover, they don’t take into account the parallelism of network transmission which loses the potential update performance gains.To address the issues, we propose a novel Graph-based cross-Rack Parallel Update (GRPU) scheme to improve the update performance for erasure-coded cloud storage systems. The key idea of GRPU is to place the correlated chunks in the same stripe and rack, and transmit the chunks in parallel based on the network distance. The data placement and transmission paths selection are guided by two kinds of graphs. To demonstrate the effectiveness of GRPU, we conduct several experiments in a local cluster. The results show that, compared to the state-of-the-art methods, GRPU reduces the cross-rack traffic by up to 34.66% and the average response time by up to 61.69%, respectively.

Backdoor attacks have been proved to be seriously threatening to deep neural networks. Many defending methods against backdoor attack have been proposed and reduced attack success rate significantly. However, most existing defending methods are empirical, and might be later broken by stronger attack methods. To avoid such a cat-and-mouse game, We proposed CRAB, a defense that can guarantee the robustness of an image classifier against poisoning-based backdoor attack with triggers bounded in a contiguous region. We analyze two ways of adding triggers: fixed-region and randomized-region. For fixed-region setting, we train a set of models on benign dataset for different image ablation positions and give robustness guarantee to both training and testing datasets. Whilst for random position triggers, we train a universal model on dataset with triggers, and give robustness guarantee to testing datasets. Our excellent experimental results demonstrate that CRAB exhibits strong robustness against patched backdoor attack, while maintaining comparable high clean accuracies.

Limited by the incomprehensive training samples, existing scene graph generation (SGG) methods perform poorly on predicting zero-shot (i.e., unseen) subject-predicate-object triples. To address this problem, we propose a general SGG framework to improve their zero-shot performance. The main idea of our method is to generate the information of zero-shot triples before the training of the predicate classifier and thus make the original zero-shot triples non-zero-shot. Specifically, the missing information of zero-shot triples is generated by our proposed knowledge graph completion strategy and then integrated with visual features of images. Therefore, the predicate classification of zero-shot triples is no longer just regarded as a single visual classification task but also transformed into a prediction task of missing links in a knowledge graph. The experiments on the dataset Visual Genome demonstrate that our proposed method outperforms the state-of-the-art methods in popular zero-shot metrics (i.e., zR@N, ng-zR@N) for all popular SGG tasks.

Bootstrap aggregating (bagging) is an effective ensemble protocol, which is believed can enhance robustness by its majority voting mechanism. Recent works further prove the sample-wise robustness certificates for certain forms of bagging (e.g. partition aggregation). Beyond these particular forms, in this paper, we propose the first collective certification for general bagging to compute the tight robustness against the global poisoning attack. Specifically, we compute the maximum number of simultaneously changed predictions via solving a binary integer linear programming (BILP) problem. Then we analyze the robustness of vanilla bagging and give the upper bound of the tolerable poison budget. Based on this analysis, we propose hash bagging to improve the robustness of vanilla bagging almost for free. This is achieved by modifying the random subsampling in vanilla bagging to a hash-based deterministic subsampling, as a way of controlling the influence scope for each poisoning sample universally. Our extensive experiments show the notable advantage in terms of applicability and robustness. Our code is available at https://github.com/Emiyalzn/ICML22-CRB.

Data deduplication is an effective technique to reduce the amount of redundant data, which is widely used in backup systems. To reconstruct the original backup data, restore is a typical procedure in inline deduplication, which brings read amplification when duplicate chunks are shared among various data streams. Rewrite is a cost-efficient method to improve the inline deduplication restore performance by writing the fragmented duplicate chunks repeatedly. Although several rewrite schemes are proposed to improve the restore performance, they either decrease the deduplication ratio or increase the temporal overhead of the rewrite procedure. This is because existing rewrite methods select inappropriate number of containers, or ignore the inter-container redundancy information. To address the above problems, we propose an E ffective –Region -Partitioning based rewrite scheme (ERP), which improves the restore performance in backup systems and ensures a high deduplication ratio. The key idea of ERP is to effectively narrow the selection range and choose a flexible number of containers by investigating the inter-container redundancy information. To demonstrate the effectiveness of ERP, we conduct several experiments in a deduplication backup system. Compared to the state-of-the-art rewrite schemes, the results show that ERP reduces the rewrite cost by up to 97.71%.

Existing scene graph generation (SGG) methods are far from practical, primarily due to their poor performance on predicting zero-shot (i.e., unseen) subject-predicate-object triples. We observe that these SGG methods treat images along with the triples in them independently and thus fail to consider the complex and hidden information that is inherently implicit in the triples of other images. To this effect, our paper proposes a novel encoder-decoder SGG framework to leverage the semantic correlations between the triples of different images into the prediction of a zero-shot triple. Specifically, the encoder aggregates the triples in each image of training set into a large knowledge graph and learns the entity embeddings that capture the features of their neighborhoods with a relational graph neural network. The neighborhood-aware embeddings are then fed into the vision-based decoder to predict the predicates in images. Extensive experiments on the popular benchmark Visual Genome demonstrate that our proposed method outperforms the state-of-the-art methods in popular zero-shot metrics (i.e., zR@N, ngzR@N) for all SGG tasks.

Distributed deep learning is a typical machine learning method running in distributed environment such as cloud computing systems. The corresponding training, validation and test datasets are very large in general (e.g., several TBs), which need to be stored across multiple data nodes. Due to the high disk failure ratio in cloud storage systems, one of the critical issues for distributed deep learning is how to efficiently tolerate disk failures in the training procedures. These failures can lead to a large amount of data loss, which decreases the training accuracy and slows down the training process. Although several recovery methods are proposed to accelerate the data reconstruction, the related overhead is extremely high, such as high CPU/GPU utilization, a large number of I/Os, etc.To address the above problems, we propose a novel Partial-Recovery Method (called PRM) , which is an adaptive recovery method to accelerate data reconstruction for distributed deep learning applications in cloud storage systems. The key idea of PRM is combining the advantages of erasure coding’s ability to obtain global information on the data distribution with the AI’s ability to recover partial lost data, which can sharply reduce the overhead with acceptable training accuracy. To demonstrate the effectiveness of the PRM approach, we conduct several experiments. The results show that, compared to the state-of-the-art full or approximate recovery methods, PRM decreases the average network transmission time overhead by up to 64.50%, and reduces the recovery time by up to 55.90%, respectively.

Internet of Things enables devices to communicate, collect and exchange data with the network. As the number of IoT devices keeps growing, the volume of data they produce is also increasing exponentially. Given the feature of limited computing and storage resources of IoT, it is inevitable to store data in the cloud for better services. However, for users to effectively and efficiently inspect those data over the cloud is a critical and open problem. Most public integrity auditing over the cloud schemes requires the user to do a sheer amount of preprocessing work on the local devices, which is unsuitable for IoT devices. With the development of edge computing extending cloud computing, it can provide computing capability for resource-constrained devices in close geographic proximity. In this paper, we design an auditing scheme based on secure computation outsourcing assisted by edge computing, in which the data preprocessing work can be offloaded to the edge server. The experiments show that it reduces the computing load on the devices and improves the efficiency of task processing.

Nowadays wide stripe erasure codes (ECs) become popular as they can achieve low monetary cost and provide high reliability for cold data. Generally, wide stripe erasure codes can be generated by extending traditional erasure codes with a large stripe size, or designing new codes. However, although wide stripe erasure codes can decrease the storage cost significantly, the construction of lost data is extraordinary slow, which stems primarily from high cross-rack overhead. It is because a large number of racks participate in the construction of the lost data, which results in high cross-rack traffic. To address the above problems, we propose a novel erasure code called XOR-Hitchhiker-RS (XHR) code, to decrease the cross-rack overhead and still maintain low storage cost. The key idea of XHR is that it utilizes a triple dimensional framework to place more chunks within racks and reduce global repair triggers. To demonstrate the effectiveness of XHR-Code, we provide mathematical analysis and conduct comprehensive experiments. The results show that, compared to the state-of-the-art solutions such as ECWide under various failure conditions, XHR can effectively reduce cross-rack repair traffic and the repair time by up to 36.50%.

The applications of Internet of Things have emerged in every aspect of people’s life. The volume of data gathered can be enormous. Enterprises and personal consumers are increasingly reliant on cloud storage services instead of local storage. While they enjoy the convenience of cloud storage services, they also worry about the integrity of the cloud-stored data since they do not physically own the data. To enable public integrity auditing, third-party auditors as trusted ones verify data integrity on behalf of the data owner. However, the vulnerability of auditors should also be considered. We propose a consortium blockchain-based public integrity verification system (CBPIV). In CBPIV, the auditor behaviors are recorded in the consortium blockchain so that authorized parties can audit the auditor to see if the verification results are correct. A smart contract is deployed to check the behavior of the auditor automatically, which can trigger alerts for unusual behaviors. The evaluation on both security and performance shows that our proposed scheme is secure and alleviates the burden on data owners of limited computation capability.

Recent years have witnessed considerable progress in the application of Internet of Things (IoT) technology in smart transportation systems. The wider presence of Wi-Fi networks in subway gates allows passengers to use the quick response (QR) code of mobile phone applications for entrance. The network established by gates has become a medium which connects stations and passengers. However, in addition to directly monitoring the passenger flow, the potential application of the smart card usage data collected by the gates remains an open topic. Although there are several clustering-based works devoted to revealing passengers’ travel behavior patterns, research on the social attributes of subway passengers is very limited. To fill the gap, this article proposes a novel method to mine similarity information of passengers by leveraging passengers’ communication behaviors hidden in subway card usage data. Passengers are first organized as a graph, which not only reflects the interactions between them but also incorporates the context information of subway stations. Then, the node embedding is used to encode the information contained in the graph and with the use of cosine similarity, the similarity between two passengers is measured. Extensive experiments on two real-world location-based social network data sets and extended experiments on a Shanghai subway data set are conducted. The results show that the proposed method can effectively improve the accuracy of similarity measurement and provide social features that are distinguishable from travel behavior patterns.

Computation offloading has become popular in recent years as it is an effective way to reduce the energy consumption and enhance the performance of smartphones. To deal with the heterogeneous architectures between the smartphone and the server, and to simplify deployment of the server, we propose and implement a lightweight offloading framework which supports offloading of compute-intensive tasks and deploying the server efficiently. Based on this framework, generic and developer-customized offloading services could be provided for different third-party applications. Furthermore, we design a multi-task offloading tactic for the framework to deal with intensive offloading requests from various mobile devices. When receiving an offloading request, the master node in server-side determines whether this task should be offloaded or not and which VM should handle this task, so that the overall execution time and energy consumption are optimized. We implement this framework and evaluate it by comparing the execution time, energy consumption and CPU utilization rate among three execution modes with three applications. We also conduct experiments of the multi-task offloading tactic in simulation environment. Experimental results indicate that this framework effectively reduces energy consumption and boosts performance for compute-intensive tasks, and the multi-task offloading tactic is valid for intensive offloading requests.

For academic research and business intelligence, trajectory data has been widely collected and analyzed. Releasing trajectory data to a third party may lead to serious privacy leakage, which has spawned considerable researches on trajectory privacy protection technology. However, existing work suffers from several shortcomings. They either focus on point-based location privacy, ignoring the spatio-temporal correlations among locations within a trajectory, or they protect the privacy of each user separately without considering privacy leakage of the social relationship between trajectories of different users. Besides, they fail to balance privacy protection and data utility. Motivated by these limitations, in this article, we propose S 3 T -Trajectory, which is a utility-aware and privacy-preserving trajectory synthesis model that Resists social relationship privacy attacks. Specifically, we first develop a time-dependent Markov chain based on an adaptive spatio-temporal discrete grid to efficiently and accurately capture human mobility behavior. Then, we propose three mobility feature metrics from spatio-temporal, semantic, and social dimensions. On the basis of the metrics, we construct a bi-level optimization problem to accomplish the utility-aware and privacy-preserving trajectory synthesizing. The upper-level objective guarantees data utility and the lower-level optimization problems (or upper-level constraints) provides two-layer privacy protection for S 3 T -Trajectory, i.e., resisting location inference attacks and social relationship privacy attacks. We conduct extensive experiments on large-scale real-world datasets loc-Gowalla and loc-Brightkite. The experimental results demonstrate the effectiveness and robustness of S 3 T Trajectory. Compared with the baseline models, S 3 T Trajectory achieves between 7.8% and 23.8% performance improvement in resisting social relationship privacy attacks and achieves at least 5.19% improvement regarding data utility.

With the commoditization of private data, data trading in consideration of user privacy protection has become a fascinating research topic. The trading for private web browsing histories brings huge economic value to data consumers when leveraged by targeted advertising. And the online pricing of these private data further helps achieve more realistic data trading. In this paper, we study the trading and pricing of multiple correlated queries on private web browsing history data at the same time. We propose CTRADE, which is a novel online data CommodiTization fRamework for trAding multiple correlateD queriEs over private data. CTRADE first devises a modified matrix mechanism to perturb query answers. It especially quantifies privacy loss under the relaxation of classical differential privacy and a newly devised mechanism with relaxed matrix sensitivity, and further compensates data owners for their diverse privacy losses in a satisfying manner. CTRADE then proposes an ellipsoid-based query pricing mechanism according to a given linear market value model, which exploits the features of the ellipsoid to explore and exploit the close-optimal dynamic price at each round. In particular, the proposed mechanism produces a low cumulative regret, which is quadratic in the dimension of the feature vector and logarithmic in the number of total rounds. Through real-data based experiments, our analysis and evaluation results demonstrate that CTRADE balances total error and privacy preferences well within acceptable running time, indeed produces a convergent cumulative regret with more rounds, and also achieves all desired economic properties of budget balance, individual rationality, and truthfulness.

LiDAR point cloud analysis is a core task for 3D computer vision, especially for autonomous driving. However, due to the severe sparsity and noise interference in the single sweep LiDAR point cloud, the accurate semantic segmentation is non-trivial to achieve. In this paper, we propose a novel sparse LiDAR point cloud semantic segmentation framework assisted by learned contextual shape priors. In practice, an initial semantic segmentation (SS) of a single sweep point cloud can be achieved by any appealing network and then flows into the semantic scene completion (SSC) module as the input. By merging multiple frames in the LiDAR sequence as supervision, the optimized SSC module has learned the contextual shape priors from sequential LiDAR data, completing the sparse single sweep point cloud to the dense one. Thus, it inherently improves SS optimization through fully end-to-end training. Besides, a Point-Voxel Interaction (PVI) module is proposed to further enhance the knowledge fusion between SS and SSC tasks, i.e., promoting the interaction of incomplete local geometry of point cloud and complete voxel-wise global structure. Furthermore, the auxiliary SSC and PVI modules can be discarded during inference without extra burden for SS. Extensive experiments confirm that our JS3C-Net achieves superior performance on both SemanticKITTI and SemanticPOSS benchmarks, i.e., 4% and 3% improvement correspondingly.

Variational AutoEncoder (VAE) is a popular deep generative framework with a solid theoretical basis. There are many research efforts on improving VAE. Among the existing works, a recently proposed deterministic Regularized AutoEncoder (RAE) provides a new scheme for generative modeling. RAE fixes the variance of the inferred Gaussian approximate posterior distribution as a hyperparameter, and substitutes the stochastic encoder by injecting noise into the input of a deterministic decoder. However, the deterministic RAE has three limitations: 1) RAE needs to fit the variance; 2) RAE requires ex-post density estimation to ensure sample quality; 3) RAE employs an additional gradient regularization to ensure training smoothness. Thus, it raises an interesting research question: Can we maintain the flexibility of variational inference while simplifying VAE, and at the same time ensuring a smooth training process to obtain good generative performance? Based on the above motivation, in this paper, we propose a novel Semi-deterministic and Contrastive Variational Graph autoencoder (SCVG) for item recommendation. The core design of SCVG is to learn the variance of the approximate Gaussian posterior distribution in a semi-deterministic manner by aggregating inferred mean vectors from other connected nodes via graph convolution operation. We analyze the expressive power of SCVG for the Weisfeiler-Lehman graph isomorphism test, and we deduce the simplified form of the evidence lower bound of SCVG. Besides, we introduce an efficient contrastive regularization instead of gradient regularization. We empirically show that the contrastive regularization makes learned user/item latent representation more personalized and helps to smooth the training process. We conduct extensive experiments on three real-world datasets to show the superiority of our model over state-of-the-art methods for the item recommendation task. Codes are available at https://github.com/syxkason/SCVG.

Nowadays, Solid State Drive (SSD) arrays have been widely used in commercial big data centers and high-performance storage services. Meanwhile, in the era of explosive data growth, data centers need to implement the array scaling schemes to meet the increasing storage capacity requirements. The existing state-of-the-art scaling methods, such as Round-Robin (RR) and FastScale, aim at ensuring a uniform data redistribution. However, most of them are designed for Hard Disk Drive (HDD) arrays, ignoring lifetime difference among extended and former-used disks, which leads to several additional penalties in SSD arrays. Furthermore, due to the sudden interdisk lifetime disparity, the extended SSD disks trigger frequently wear-leveling operations for controlling the wearing balance into the predefined threshold. These reactions result in inefficient scaling and I/O performance degradation. To address the above problem, we propose a Lazy W ear-L eveling (Lazy-WL) mechanism to reduce the conventional wear-leveling overhead during the scaling process. Its core idea is to reduce the unnecessary intensive wear-leveling migration significantly, via narrowing the difference of program/erase (P/E) cycles among new-added and former deployed disks smoothly and gradually. To demonstrate the effectiveness of this approach, we conduct several simulation via Disksim and real implementation via a Hadoop cluster. The evaluation results show that, compared to the typical inter and intra disk wear leveling methods, Lazy-WL could lower the triggered wear-leveling operations by up to 92.9% and achieve a maximal 85.2% response time reduction, which suggests that Lazy-WL performs a balanced I/O distribution, and maintains high performance of SSD array with high scaling efficiency.

Material identification is playing an increasingly important role in our daily lives such as public security checks. X-ray-based technologies are highly radioactive because they rely on specialized devices to transmit high-frequency signals. Ultrasound-based technologies are cumbersome due to their large size. RF-based approaches necessitate the use of RFID which is usually expensive to be used in home and office environments. To this end, WiFi-based material identification approach has emerged recently as a low-cost yet effective alternative. In this paper, we propose WiMate, a noncontact material identification system leveraging only off-the-shelf WiFi devices. The key enabler of WiMate is a novel theoretical model we build to characterize how the electromagnetic wave decays when penetrating different materials. Our model identifies a unique feature for each material that only depends on the material itself. Consequently, we can leverage this feature coupling with the machine learning techniques for robust and accurate material identification. We prototype WiMate using low-cost commodity WiFi devices and evaluate its performance in real-world. The empirical study shows that WiMate can identify six different materials, i.e., board, paperboard, nickel, wood chip, iron and titanium, with an average accuracy of 96.20%.

With the growth of data scale in the mobile edge computing (MEC) network, data security of the MEC network has become a burning concern. The application of blockchain technology in MEC enhances data security and privacy protection. However, throughput becomes the bottleneck of the blockchain-enabled MEC system. Hence, this paper proposes a novel hierarchical and partitioned blockchain framework to improve scalability while guaranteeing the security of partitions. Next, we model the joint optimization of throughput and security as a Markov decision process (MDP). After that, we adopt deep reinforcement learning (DRL) based algorithms to obtain the number of partitions, the size of micro blocks and the large block generation interval. Finally, we analyze the security and throughput performance of proposed schemes. Simulation results demonstrate that proposed schemes can improve throughput while ensuring the security of partitions.

Lung cancer has been the leading death cause in modern society. Early detection of pulmonary nodules can significantly improve the survival rate of lung cancer. In this paper, we propose a novel pulmonary nodule detection framework and a novel 3D dense attention module (DAM) which can efficiently exploit the abundant 3D spatial features. The attention module, which integrates the improved dense block and the conv attention block, focuses on three dimensions, plane attention, depth attention, and channel attention. And the whole framework consists of two phases: Nodule Candidate Generation (NCG) and False Positive Reduction (FPR). In NCG phase, we construct a detection network based on DAM. Due to the wide distribution of the nodule diameters, we propose a 3D Feature Pyramid Network (3DFPN) to better handle the scale-varying problem. In FPR phase, we design a 3D DCNN to erase the false positives. Sliding-window based data augment methods are adopted to deal with the unbalance problem of the data. Comprehensive experiments show that our scheme outperforms the existing methods.

Flow-based approaches for video frame interpolation typically consist of multiple networks that are responsible for feature extraction, optical flow estimation, and image synthesis, respectively. However, they are usually computationally expensive, and can hardly be employed in devices with limited computing resources. In this work, we propose an All-in-one Grid Frame Interpolation Network (AGFIN) to address this problem. AGFIN is a light-weight network with multiple rows and columns. In each row, we estimate the contextual features and optical flows, then the image synthesis module reconstructs the results from the warped frames and features. Each row serves as a coarser or finer auxiliary for the nearest row. In contrast to using multiple networks, our model integrates feature extraction, optical flow estimation, and image synthesis into a compact network. The experimental results show that our approach has better or comparable performance comparing to representative state-of-the-art approaches with less computational cost.

Flow-based video frame interpolation approaches typically adopt forward or backward warping to approximate the intermediate frames. And a synthesis network is used to refine the interpolation results. Optical flows indicate motion between two input frames, but both forward and backward warping only utilize the first frame. In this work, we propose bilateral warping to make full use of optical flows. Specifically, the proposed bilateral warping yields intermediate candidates from not only the first frame but also the second frame. Our model first applies bilateral warping on the input frames and contextual features. Then, we add skip connections from the input frames and contextual features to the synthesis network. Finally, the synthesis network generates the interpolation results by integrating the original and warped representations. The experimental results on a wide variety of datasets demonstrate the superiority of the proposed approach over the state-of-the-art video frame interpolation methods.

Cloud storage services offer flexible, convenient solutions for business and personal users to store data. Traditionally, Third Party Auditors (TPAs) are introduced to ensure data integrity for public auditing. However, TPAs may also be untrusted for forging the auditing results or colluding with cloud storage servers to deceive users. In this paper, we propose a novel Blockchain-based Public Auditing Outsourcing system without TPAs (BPAO), in which the computationally expensive operations in public auditing are outsourced through blockchain to the cloud servers without risking users' privacy. Our security analysis indicates that BPAO achieves soundness and robustness. The experimental results show that BPAO is computationally efficient for cloud storage user.

Within the cloud computing scenario, each server usually carries multiple service processes, which intensifies the concurrency pressure of the system. As a result, the process of memory management during page allocation and deallocation becomes a significant bottleneck. Although several methods such as Buddy System and Inverse Buddy System (iBuddy) have been proposed to improve the performance of memory management, they cannot adapt to the highly concurrent environment of cloud computing, because they either force the memory allocation/deallocation requests to be serialized or bring extra fragmentation. To address the above problem, we propose Spring Buddy, which improves the concurrency of both memory allocation and deallocation and avoids unnecessary fragmentation. It can detect the changes of system- and process-level memory request patterns and dynamically adjust the organization of page frames. Inventively, Spring Buddy uses the spring core layer to provide both concurrent response and resource aggregation capability which is adapted to the system's concurrency pressure, and also uses the spring lazy layer to further mitigate the system resource contention through process behavior prediction. To demonstrate the effectiveness of Spring Buddy, we implement it in the Linux kernel. The results demonstrate that Spring Buddy can reduce memory allocation latency by 71.47 % and deallocation latency by 93.20% on average compared to the existing methods.

In cloud storage systems, disk arrays are widely used because of their high reliability and low monetary cost. Due to the burst of I/O in sprinting computing scenarios (i.e. online retailer services on Black Friday or Cyber Monday), large scale cloud storage systems such as AWS S3 and GFS need to afford 10XI/O workloads. Therefore, rack level scaling for cloud disk arrays becomes urgent for sprinting services. Although several existing methods, such as Round-Robin(RR) and Scale-RS, are proposed to accelerate the scaling processes, the efficiencies of these approaches are limited. It is because that the cross-rack data migrations are ill-considered in their designs. To address the above problem, in this paper, we propose Rack-Scaling, a novel data redistribution method to accelerate rack level scaling process in cloud storage systems. The basic idea of Rack-Scaling is migrating appropriate data blocks within and among racks to achieve a uniform data distribution while minimizing the cross-rack migration, which costs more than intra-rack migration. We conduct simulations via Disksim and we also implement Rack-Scaling on Hadoop to demonstrate the effectiveness of Rack-Scaling. The results show that, compared to typical methods such as Round-Robin (RR), Semi-RR, Scale-RS and BDR, Rack-Scaling reduces the number of I/O operations and the data amount of cross-rack transmission by up to 90.4% and 99.9%, respectively, and speeds up the scaling by up to 8.77X.

Microservice system is an architectural style to develop a single application as a suite of small services running in its process and communicating with lightweight message mechanisms. Although microservice architecture enables rapid, frequent and reliable delivery of large, complex applications, it is increasingly challenging for operational staffs to locate the root cause of a microservice fault, which usually occurs on a service node and propagates to affect the entire system. To this end, in this paper, we first introduce the concept of deployment graph and service dependency graph to depict the deployment status and calling relationship between service nodes. Then we formulate the root cause localization problem in microservice systems based on the constructed graphs, in which fault model is defined to capture the characteristics of a fault’s root cause. A fault model based automatic root cause localization framework called ModelCoder is later developed to figure out the root cause of unknown faults by comparing with the predefined fault models. We evaluate ModelCoder on a real-world microservice system monitoring data set spanning 15 days. Through extensive experiments, it is revealed that ModelCoder can localize the fault root cause nodes within 80 seconds on average and improve the root cause localization accuracy (to 93%) by 12% compared with the state-of-the-art root cause localization algorithm.

Erasure codes (EC) have become a typical technology for distributed storage systems in place of data replication, providing similar data availability but lower storage cost. However, a great number of data computations and migrations during the EC recovery process bring high I/O and network latency penalties. Although several EC recovery methods have been designed to compromise the recovery penalty with high parallelism, the performance of these schemes was usually bounded by the straggler problems due to the various (I/O) performance among different nodes in the storage system. Moreover, the variation of the access popularity from the upper layer application causes the dynamic load fluctuation and asymmetry upon different nodes, which makes the scheduling more difficult during the recovery. To address the above problem, we propose a dynamic load-balanced scheduling algorithm for straggler recovery called EC-Scheduler. EC-Scheduler adjusts the recovery schedule dynamically with the awareness of continuous load fluctuation on the nodes, guaranteeing high parallelism and load balance ability simultaneously. To demonstrate the effectiveness of EC-Scheduler, we conduct several experiments in a cluster. The results show that, compared to typical recovery schemes such as Fast-PR and EC-Store, EC-Scheduler could achieve a 1.3X speed-up in the recovery process and 10X improvement in recovery load imbalance factor.

Abstract Data trading has attracted increasing attention over the years as a cost-effective business paradigm, probably producing a tremendous amount of economic value. However, the study of query-based trading in the user data market is still in the initial stage. To design a practical user data trading mechanism, we have to consider three major challenges: privacy concern, compensation cost minimization and revenue maximization in a Bayesian environment. By jointly considering these challenges, we propose a profit-maximizing mechanism for user data trading with personalized differential privacy, called READ, which comprised two components, READ-COST for cost minimization and READ-REV for revenue maximization. Especially, READ adopts personalized differential privacy to satisfy each data owner’s diverse privacy preferences. READ-COST greedily selects the most cost-effective data owner to achieve the sub-optimal data query cost. Given this query cost, READ-REV calculates the maximum expected revenue in a Bayesian setting. Through rigorous theoretical analysis and real-data based experiments, we demonstrate that READ achieves all desired properties and approaches the optimal profit.

Summary Cloud computing has been an essential technology for providing on‐demand computing resources as a service on the Internet. Not only enterprises but also individuals can outsource their data to the cloud without worrying about purchase and maintenance cost. The cloud storage system, however, is not fully trustable. Cloud data integrity auditing is crucial for defending against the security threats of data in the untrusted multicloud environment. Storing multiple replicas is a commonly used strategy for the availability and reliability of critical data. In this paper, we summarize and analyze the state‐of‐the‐art multiple‐replica integrity auditing schemes in cloud data storage. We present the system model and security threats of outsourcing data to the cloud with classification of ongoing developments. We also summarize the existing data integrity auditing schemes for multicloud data storage. The important open issues and potential research directions are addressed.

The problem of initialization of active learning is considered in this paper. Especially, this paper studies the problem in an imbalanced data scenario, which is called as class-imbalance active learning cold-start. The novel method is two-stage clustering-based active learning cold-start (ALCS). In the first stage, to separate the instances of minority class from that of majority class, a multi-center clustering is constructed based on a new inter-cluster tightness measure, thus the data is grouped into multiple clusters. Then, in the second stage, the initial training instances are selected from each cluster based on an adaptive candidate representative instances determination mechanism and a clusters-cyclic instance query mechanism. The comprehensive experiments demonstrate the effectiveness of the proposed method from the aspects of class coverage, classification performance, and impact on active learning.

WiFi-based identity recognition is predominant because of its noninvasive and ubiquitous advantages. However, existing approaches show slow training speed and limited applicability. In this article, we propose a lightweight deep learning model, named as lightweight WiFi-based identification (LW-WiID), to address these technical challenges. LW-WiID reconstructs original data of channel state information into frequency energy graph, which contains not only the temporal feature of the gait but also the spatial feature among subcarriers, ensuring the accuracy of identity recognition. Furthermore, a novel Balloon mechanism is designed to achieve the lightweight. Through information integration crossing both layers and channels, the Balloon mechanism effectively reduces the number of model parameters. Experimental results demonstrate that LW-WiID achieves an accuracy of 99.7% on a 50-person gait data set while the model size is compressed to 5.53% of the existing identity recognition approaches with the same accuracy.

In the practical applications of supervised learning methods, the high cost of obtaining labeled data for learning tasks is a critical problem. One promising research area for solving the problem is transfer learning, which aims to learn a task in target domain by utilizing the training data in a different but related source domain. In this article, we propose a novel heterogeneous transfer learning algorithm called Heterogeneous Daily Living Activity Learning (HDLAL) which derives domain invariant feature representation space from cross-domain data distributions by projecting both domain data into the derived space in the close proximity of each other using Maximum Mean Discrepancy. Within the new feature space, we utilize ensemble classification algorithm to train multi-label classifier using the projected data to predict the labels in the target domain. We show the effectiveness of our approach by experimenting on real-world smart home datasets. The results shows that our HDLAL algorithm outperforms the common direct learning approaches in the context of predicting labels of activities of daily living (ADL).

Due to the complexity of myocardial infarction (MI) waveform, most traditional automatic diagnosis models rarely detect it, while those able to detect MI often require high computing and storage capacity, rendering them unsuitable for portable devices. Therefore, in order for convenient real-time MI detection, it is essential to design lightweight models suitable for resource-limited portable devices. This paper proposes a novel multi-channel lightweight model (ML-Net), that provides a new solution for portable detection devices with limited resources. In ML-Net, each electrocardiogram (ECG) lead is assigned an independent channel, ensuring data independence and preserve the ECG characteristics of different angles represented by different leads. Moreover, convolution kernels of heterogeneous sizes are utilized to achieve accurate classification with only a small amount of lead data. Extensive experiments over actual ECG data from the PTB diagnostic database are conducted to evaluate ML-Net. The results show that ML-Net outperforms comparable schemes in diagnosing MI, and it requires lower computational cost and less memory, so that portable devices can be more widely used in the field of Internet of Medical Things(IoMT).

Network function virtualization (NFV) is a new network architecture concept that simplifies the deployment of network services and improves service management. However, it is challenging for a network service provider (NSP) to decide where to place virtual network functions (VNFs). Most previous studies have considered only single-chain services, wherein the VNFs for a request are executed in sequence. In contrast to previous approaches, we consider more general and practical situations in which the VNFs of a request can be executed in parallel and are represented as a forwarding graph. Our objective is to maximize the profits earned by providing network services while satisfying the delay requirements of requests. We formulate the VNF placement problem as an integer linear programming (ILP) problem. Due to the complexity of this problem, we propose a probability-based approach called PBP, in which the placements of the VNFs are determined based on their probabilities of contributing to the profit. Furthermore, we propose a heuristic reliability-aware algorithm to guarantee service reliability, in which each VNF of a request is assigned a backup that can be shared with other requests. Simulation experiments show that PBP achieves a much shorter computation time than previous algorithms while earning higher profit, and furthermore, our reliability-aware algorithm provides the same reliability as a previous algorithm while yielding much higher profit.

Fuzzy clustering algorithm has become a relevant research field of unsupervised learning due to that the uncertainties between patterns can be described more accurately. Based on objective function, fuzzy clustering algorithm uses a constrained optimization mathematical problem to represent the clustering problem. It then determines the division of data sets and fuzzy clustering results by solving the optimization problem. Fuzzy c-means (FCM) is one of the best known for its simplicity and efficiency. However, it shows some weaknesses, particularly its tendency to fall into local optima and dependence on initial values. Particle Swarm Optimization (PSO) is one of the heuristic methods that usually implemented on function optimization problems since it has a robust global search capability. In this paper, a new concept of worst position is introduced to PSO that gives a chance for particles to change flying directions. Moreover, new hybrid algorithms based on FCM and improved PSO with worst position (PSOWP) both in L1norm and L2norm are proposed, which avoid falling into local optimum with faster convergence speed.

Fast wireless data aggregation is a critical design challenge in Internet-of-Things (IoT) networks. In this paper, we consider a real-time status update IoT network, where an access point (AP) aims to aggregate data from multiple IoT devices using over-the-air computation (AirComp). To evaluate the freshness of the aggregated data at the AP, we propose the metric of age of aggregated information (AoAI), extended from the age of information (AoI), which is defined as the time elapsed since the generation of the latest valid aggregated data received at the AP. An aggregated status update is considered to be valid if the AirComp distortion, quantified by the mean-squarederror (MSE), is smaller than a pre-determined threshold. We formulate a constrained Markov decision process (MDP) problem for minimizing the average AoAI subject to the average transmit power constraint of each IoT device. The formulated constrained MDP problem is then reformulated as an unconstrained MDP problem by using the Lagrangian approach. By analyzing the structure of the MDP, we propose a state aggregation procedure to reduce the computational complexity. We further propose both offline and online scheduling algorithms to solve the problem. Simulation results show that the proposed algorithms significantly outperform the baseline algorithm with a fixed scheduling threshold in terms of the AoAI, and also strike a good balance between the AoAI and the total power consumption.

With the explosive growth of data in cloud storage systems, Erasure Codes (ECs) have become a typical data redundancy technology because of its low storage cost and high reliability. However, due to a large amount of complex computations and transmissions among massive data and parities, the recovery of lost data in erasure coded storage systems incurs high I/O latency. Although several fast recovery approaches devote to mitigating the recovery time from the application level or device level, the performance of file level recovery is still restricted. It is because a part of the complicated relationships among data, parity and files are ignored in the design of recovery process. To address the above problems, we propose a novel File-aware Graph Recovery (FAGR) scheme, to improve the file level recovery performance during the reconstruction process. The key idea of FAGR is establishing a graph with the mappings among files, blocks, stripes, parities, nodes and the access frequencies of files, and guides the recovery process from file point of view. A corresponding model is established to analyze the cost efficiency of recovery process, which guarantees that FAGR reconstructs the popular files in advance to accelerate the recovery. To demonstrate the effectiveness of FAGR, we conduct several numerical analysis and experiments in clusters. The results show that, compared to typical fast recovery methods, FAGR reduces the average response time of files by up to 81.63 % and improves the throughput by up to 4.44 ×.

In edge computing, computation offloading of video stream tasks and collaboration processing among edge nodes is a huge challenge. The previous research mainly focuses on the selection of computing modes and resource allocation, but taking no joint consideration of computation offloading and collaborative processing of edge node groups. In order to jointly tackle these issues in edge computing, we propose an innovative distributed collaborative video stream processing framework for edge computing(DCVP), where the video tasks are assigned to mobile edge computing (MEC) nodes or edge groups based on the offloading decision. First, we design a method for the group formation, which matches video subtasks to appropriate edge groups. In addition, we present two offloading modes for video streaming tasks, e.g., offloading to MEC nodes or edge groups, to handle computationally intensive video tasks. Furthermore, we formulate the joint optimization problem for offloading decision and collaborative processing of video subtasks into a distributed optimization problem. Finally, we employ an alternating direction method of multipliers (ADMM)-based algorithm to solve the problem. Simulation results under multiple parameters show the proposed schemes outperform other typical schemes.

With the development of deep convolutional neural networks (CNNs), the object detection accuracy has been greatly improved. But the performance of small object detection is still far from satisfactory, mainly because small objects are so tiny that the information contained in the feature map is limited. Existing methods focus on improving classification accuracy but still suffer from the limitation of bounding box prediction. To solve this issue, we propose a detection framework by generative and discriminative learning. First, a reconstruction generator network is designed to reconstruct the mapping from low frequency to high frequency for anchor box prediction. Then, a detector module extracts the regions of interest (ROIs) from generated results and implements a RoI-Head to predict object category and refine bounding box. In order to guide the reconstructed image related to the corresponding one, a discriminator module is adopted to tell from the generated result and the original image. Extensive evaluations on the challenging MS-COCO dataset demonstrate that our model outperforms most state-of-the-art models in detecting small objects, especially the reconstruction module improves the average precision for small object (APs) by 7.7%.

With the commoditization of private data, data trading in consideration of user privacy protection has become a fascinating research topic. The trading for private web browsing histories brings huge economic value to data consumers when leveraged by targeted advertising. In this paper, we study the trading of multiple correlated queries on private web browsing history data. We propose TERBE, which is a novel trading framework for correlaTed quEries based on pRivate web Browsing historiEs. TERBE first devises a modified matrix mechanism to perturb query answers. It then quantifies privacy loss under the relaxation of classical differential privacy and a newly devised mechanism with relaxed matrix sensitivity, and further compensates data owners for their diverse privacy losses in a satisfying manner. Through real-data based experiments, our analysis and evaluation results demonstrate that TERBE balances total error and privacy preferences well within acceptable running time, and also achieves all desired economic properties of budget balance, individual rationality, and truthfulness.

Nowadays erasure coding is one of the most significant techniques in cloud storage systems, which provides both quick parallel I/O processing and high capabilities of fault tolerance on massive data accesses. In these systems, triple disk failure tolerant arrays (3DFTs) is a typical configuration, which is supported by several classic erasure codes like Reed-Solomon (RS) codes, Local Reconstruction Codes (LRC), Minimum Storage Regeneration (MSR) codes, etc. For an online recovery process, the foreground application workloads and the background recovery workloads are handled simultaneously, which requires a comprehensive understanding on both two types of workload characteristics. Although several techniques have been proposed to accelerate the I/O requests of online recovery processes, they are typically unilateral due to the fact that the above two workloads are not combined together to achieve high cost-effective performance.To address this problem, we propose Erasure Codes Fusion (EC-Fusion), an efficient hybrid erasure coding framework in cloud storage systems. EC-Fusion is a combination of RS and MSR codes, which dynamically selects the appropriate code based on its properties. On one hand, for write-intensive application workloads or low risk on data loss in recovery workloads, EC-Fusion uses RS code to decrease the computational overhead and storage cost concurrently. On the other hand, for read-intensive or frequent reconstruction in workloads, MSR code is a proper choice. Therefore, a better overall application and recovery performance can be achieved in a cost-effective fashion. To demonstrate the effectiveness of EC-Fusion, several experiments are conducted in hadoop systems. The results show that, compared with the traditional hybrid erasure coding techniques, EC-Fusion accelerates the response time for application by up to 1.77×, and reduces the reconstruction time by up to 69.10%.

In many real-time RFID-enabled applications (e.g., logistic tracking and warehouse controlling), a subset of wanted tags is often selected from a tag population for monitoring and querying purposes. How this subset of tags is rapidly selected, which is referred to as the subset selection problem, becomes pivotal for boosting the efficiency in RFID systems. Current state-of-the-art schemes result in high communication latencies, which are far from the optimum, and this degrades the system performance. This problem is addressed in this paper by using a simple Bit-Counting Function BCF(), which has also been employed widely by other protocols in RFID systems. In particular, we first propose a near-OPTimal SeLection protocol, denoted by OPTSL, to rapidly solve this problem based on the simple function BCF(). Second, we prove that the communication time of OPTSL is near-optimal with rigorous theoretical analysis. Finally, we conduct extensive simulations to verify that the communication time of the proposed OPT-SL is not only near-optimal but also significantly less than that of benchmark protocols.

As massive data in modern cloud storage systems grow dramatically, it is a common method to partition and store data in multiple Availability Zones (AZs). Multiple AZs not only provide high reliability, but also reduce the network latency. Erasure Codes (ECs) are widely used in multiple AZs to provide high reliability at low storage cost. However, the recovery cost of EC is extremely high in multiple AZs' environment, which is mainly because a normal EC needs to reconstruct the lost data via transferring the data/parities across AZs. Although existing fast recovery approaches can save the I/O cost or network bandwidth in an effective manner, they are not suitable for multiple AZs. The reasons include low flexibility on various complex network scenarios, less consideration on crossing-AZ bandwidth, low capabilities on multiple disk/node failures, etc. To address the above problem, in this paper, we propose a crossing $\underline{\mathrm{A}}$vailability Zone Recovery (AZ-Recovery) method to efficiently improve the recovery performance for multiple AZs. AZ-Recovery investigates the complex homogeneous/heterogeneous network topologies, and finds an optimal data transmission path. Using this method, AZ-Recovery can significantly reduce the recovery cost and save the crossing AZ bandwidth in various failure scenarios. To demonstrate the effectiveness of AZ-Recovery, we evaluate various erasure codes via mathematical analysis and simulations in Network Simulator-3. The results show that, compared to the traditional erasure coding methods, AZ-Recovery saves the recovery bandwidth by up to 77.47%.

We consider the multicast transmission of a real-time Internet of Things (IoT) system, where a server transmits time-stamped status updates to multiple IoT devices. We apply a recently proposed metric, named age of information (AoI), to capture the timeliness of the information delivery. The AoI is defined as the time elapsed since the generation of the most recently received status update. Different from the existing studies that considered either multicast transmission without hard deadlines or unicast transmission with hard deadlines, we enforce a hard deadline for the service time of multicast transmission. This is important for many emerging multicast IoT applications, where the outdated status updates are useless for IoT devices. Specifically, the transmission of a status update is terminated when either the hard deadline expires or a sufficient number of IoT devices successfully receive the status update. We first calculate the distributions of the service time for all possible reception outcomes at IoT devices, and then derive a closed-form expression of the average AoI. Simulations validate the performance analysis, which reveals that: 1) the multicast transmission with hard deadlines achieves a lower average AoI than that without hard deadlines; and 2) there exists an optimal value of the hard deadline that minimizes the average AoI.

In this article, we consider the multicast transmission of a real-time Internet-of-Things (IoT) system, where an access point (AP) transmits timestamped status updates to multiple IoT devices. Different from the existing studies that only considered multicast transmission without deadlines, we enforce a deadline for the service time of each multicast status update, taking into account both the fixed and randomly distributed deadlines. In particular, a status update is dropped when either its deadline expires or it is successfully received by a certain number of IoT devices. Considering deadlines is important for many emerging IoT applications, where the outdated status updates are of no use to IoT devices. We evaluate the timeliness of the status update delivery by applying a recently proposed metric, named the Age of Information (AoI), which is defined as the time elapsed since the generation of the most recently received status update. After deriving the distributions of the service time for all possible reception outcomes at IoT devices, we manage to obtain the closed-form expressions of both the average AoI and the average peak AoI. Simulations validate the performance analysis, which reveals that the multicast transmission with deadlines achieves a lower average AoI than that without deadlines and there exists an optimal value of the deadline that can minimize the average (peak) AoI. Results also show that the fixed and random deadlines have respective advantages in different deadline regimes.

Software-Defined Networking (SDN) is an innovational network architecture which gives network administrators the ability to directly control the whole network by programming on a centralized controller. Due to network complexity, networks are unlikely to be bug-free. The ability to verify data plane properties will make network management easier for network administrators in SDN. In this paper, we present a novel atomic predicates based data plane properties verification method for SDN using Spark which is a big data processing framework. First, we verify packet reachability which is a fundamental data plane property. Then, we verify other data plane properties such as loop-freedom and nonexistence of black holes. In addition, the proposed method can detect a security threat existing in SDN called firewall bypass threat with packet reachability verification. By adopting atomic predicates, we achieve less computational and storage overhead. We implement the methods and study the performance. The results of experiments show that we can efficiently and accurately detect loops, black holes and firewall bypass threats.

Sleep is a major event of our daily lives. Its quality constitutes a critical indicator of people's health conditions, both mentally and physically. Existing sensor-based or vision-based sleep monitoring systems either are obstructive to use or fail to provide adequate coverage. With the fast expansion of wireless infrastructures nowadays, channel data, which is pervasive and transparent, emerges as another alternative. To this end, we propose Sleepy, a wireless channel data driven sleep monitoring system leveraging commercial WiFi devices. The key idea of Sleepy is that the energy feature of the wireless channel follows a Gaussian Mixture Model (GMM) derived from the accumulated channel data over a long period. Therefore, a GMM based foreground extraction method has been designed to adaptively distinguish motions like rollovers (foreground) from background (stationary postures), leading to certain major merits, e.g., no calibrations or target-dependent training needed. We prototype Sleepy and evaluate it in two real environments. In the short-term controlled experiments, Sleepy achieves 95.65 percent detection accuracy (DA) and 2.16 percent false negative rate (FNR) on average. In the 60-minute real sleep studies, Sleepy demonstrates strong stability, i.e., 0 percent FNR and 98.22 percent DA. Considering that Sleepy is compatible with existing WiFi infrastructure, it constitutes a low-cost yet promising solution for sleep monitoring.

The standardization process of the fifth generation (5G) wireless communications has recently been accelerated and the first commercial 5G services would be provided as early as in 2018. The increasing of enormous smartphones, new complex scenarios, large frequency bands, massive antenna elements, and dense small cells will generate big datasets and bring 5G communications to the era of big data. This paper investigates various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modeling. We propose a big data and machine learning enabled wireless channel model framework. The proposed channel model is based on artificial neural networks (ANNs), including feed-forward neural network (FNN) and radial basis function neural network (RBF-NN). The input parameters are transmitter (Tx) and receiver (Rx) coordinates, Tx-Rx distance, and carrier frequency, while the output parameters are channel statistical properties, including the received power, root mean square (RMS) delay spread (DS), and RMS angle spreads (ASs). Datasets used to train and test the ANNs are collected from both real channel measurements and a geometry based stochastic model (GBSM). Simulation results show good performance and indicate that machine learning algorithms can be powerful analytical tools for future measurement-based wireless channel modeling.

The papers in this special section focus on wireless big data. Big data, which has been following the exponential growth rates in different commercial areas, has profoundly changed the way we live. It has received considerable attention in both academic and industrial communities, in contexts such as mobile communications, distributed computing, e-health, intelligent transportation systems, wireless sensor networks, etc. In the meantime, the Internet of Things (IoT) scenarios considered in the Fifth Generation (5G) wireless communication systems are expected to create many novel applications and services with various requirements [1]. These new directions bring a dramatic increase and change in the amount and types of wireless data, thus driving wireless communications into a new era. Therefore, an in-depth analysis and understanding of wireless big data can greatly facilitate better system design and performance optimization, which will certainly benefit equipment vendors, network operators and service providers.

Mobile edge computing (MEC) raises the issue of resisting selfish edge attackers that use less computation resources than promised to process offloading tasks or provide faked computation results. In this paper, we present a blockchain based trust mechanism to help MEC address selfish edge attacks and faked service record attacks. This mechanism evaluates the computational performance of the edge devices and broadcasts such information to the neighboring edge devices and mobile devices. By building a reputation assignment method for the edge devices, the edge reputation system chooses the miner of the blockchain, which applies the joint Proof-of-Work and Proof-of-Stake consensus protocol to append a block recording the new service reputations onto the MEC blockchain. We propose a reinforcement learning (RL) based edge central processing unit (CPU) allocation algorithm without knowing the mobile service generation model and the network model in the dynamic edge computing process and a deep RL version to further improve the computational performance. The security performance is analyzed and the performance bound of the edge utility is provided. Experimental results show that this framework suppresses the selfish edge attacks, decreases the response latency and saves the energy compared with a benchmark MEC scheme.

Log anomaly detection is a critical step towards building a secure and trustworthy cloud system. As more corporations turn to cloud system to store and process their most valuable data, the risk of a potential breach of those systems increases exponentially. However, conventional top-n log candidates anomaly detection methods, such as Deeplog and N-gram, often suffer from the limited scope of the top-n list, which rules out many potentially suitable candidates. In this paper, we propose Discounted Cumulative Gain (DCG) discriminative algorithm that ranks all the log candidates and calculates the dcg score to determine the number of log candidates. To demonstrate the effectiveness of our algorithm, we conduct comprehensive experiments under different log workloads. Experimental evaluations show that DCG has outperformed Deeplog and N-gram methods in cloud systems, and improved the F-score of Deeplog and N-gram by up to 3.8% and 11.6% respectively.

Highly efficient and dependable large-scale DDoS attack detection scheme is critical for network anomaly detection. Typical machine learning algorithms such as Decision Tree and Adaboost work well on flow level analysis but cannot perform fine- grained detection of packet levels. Since these algorithms require more packets information for detection, resulting in higher detection delay and relatively lower accuracy. To address the problem, we propose DeepDDoS which is a deep learning method focusing on both period- wise and packetwise attack detection. First, the network packets are modeled in time dimension to discover the potential abnormal time period. Second, the network packets are grouped by 5 tuples (flow), the packets inside the group are sorted according to their arrival time. Then the data packet level sequence modeling is performed in each group. Comprehensive performance evaluation shows that the detection accuracy of DeepDDoS reach 99%. Furthermore, only 5 consecutive packets are needed for packet-wise detection, greatly reducing detection delay and computational overhead. Comparative experiments show that DeepDDoS outperforms existing typical attack detection methods.

How can we effectively log a fine-grained sleep record consisting of still postures and in-place motions for the sleep disorder diagnosis without any specialized hardware? Existing sensor-based or vision-based solutions are either obstructive to use or rely on particular devices. This paper introduces SleepGuardian, a Radio Frequency (RF) based sleep monitoring system leveraging only omnipresent WiFi signals to provide a silent (unobtrusive and free of privacy concerns) yet loyal (finegrained and reliable) logging service. The key to SleepGuardian is to model the energy feature of wireless channel as a Gaussian Mixture Model (GMM) to adaptively recognize motions happened during sleep. We prototype SleepGuardian with off-the-shelf WiFi devices and evaluate it in an office. Experimental results over 11 subjects with several artificial and real periods of sleep demonstrate that SleepGuardian is effective since it achieves 100% overall accuracy (ACC), 0% false negative rate (FNR) and 0.64 s mean absolute error (MAE) on average. Considering that SleepGuardian is compatible with existing WiFi infrastructure, it constitutes a low-cost yet promising solution for sleep monitoring.

Falls are the second leading cause of injury deaths worldwide, inducing over 0.6 million accidental deaths per year. Among various prevention strategies, fall-related research has been prioritized. However, conventional fall detection solutions rely on computer vision or wearable sensors embody several inherent limitations such as scalability, coverage, and privacy issues. To this end, we present FallSense, a transparent and real-time fall sensing system driven by wireless channel data. FallSense is built on a Dynamic Template Matching (DTM) algorithm, which can start with a light training set and keep updating on usage. FallSense has been realized on commodity WiFi devices and evaluated in real environments. Experimental results show that FallSense outperforms another state-of-the-art approach WiFall in terms of detection precision, false alarm rate and complexity.

Nowadays massive video data are stored in cloud storage systems, which are generated by various applications such as autonomous driving, news media, security monitoring, etc. Meanwhile, erasure coding is a popular technique in cloud storage to provide both high reliability and low monetary cost, where triple disk failure tolerant arrays (3DFTs) is a typical choice. Therefore, how to minimize the storage cost of video data in 3DFTs is a challenge for cloud storage systems. Although there are several solutions like approximate storage technique, they cannot guarantee low storage cost and high data reliability concurrently.

In large scale distributed systems such as cloud storage systems, erasure coding is a fundamental technique to provide high reliability at low monetary cost. Compared with the traditional disk arrays, cloud storage systems use an erasure coding scheme with both flexible fault tolerance and high scalability. Thus, Reed-Solomon (RS) Codes or RS-based codes are popular choices for cloud storage systems. However, the decoding performance for RS-based codes is not as good as XOR-based codes, which are optimized via investigating the relationships among different parity chains or reducing the computational complexity of matrix multiplications. Therefore, exploring an efficient decoding method is highly desired. To address the above problem, in this paper, we propose an Advanced Parity-Check Matrix (APCM) based approach, which is extended from the original Parity-Check Matrix based (PCM) approach. Instead of improving the decoding performance of XOR-based codes in PCM, APCM focuses on optimizing the decoding efficiency for RS-based codes. Furthermore, APCM avoids the matrix inversion computations and reduces the computational complexity of the decoding process. To demonstrate the effectiveness of the APCM, we conduct intensive experiments by using both RS-based and XOR-based codes under cloud storage environment. The results show that, compared to typical decoding methods, APCM improves the decoding speed by up to 32.31% in the Alibaba cloud storage system.

The trading of social media data has attracted wide research interests over years. Especially the trading for web browsing histories probably produces tremendous economic value for data consumers when being applied to targeted advertising. However, the disclosure of entire browsing histories, even in form of anonymous datasets poses a huge threat to user privacy. Although some existing solutions have investigated privacy-preserving outsourcing of social media data, unfortunately, they neglected the impact on the data consumer's utility. In this paper, we propose PEATSE, a new Privacy-prEserving dAta Trading framework for web browSing historiEs. It takes users' diverse privacy preferences and the utility of their web browsing histories into consideration. PEATSE perturbs users' detailed browsing times on released browsing records to protect user privacy, while balancing the privacy-utility tradeoff. Through real-data based experiments, our analysis and evaluation results demonstrate PEATSE indeed achieves user privacy protection, the data consumer's accuracy requirement, and truthfulness, individual rationality as well as budget balance.

As data in modern cloud storage system grows dramatically, it's a common method to partition data and store them in different Availability Zones (AZs). Multiple AZs not only provide high fault tolerance (e.g., rack level tolerance or disaster tolerance), but also reduce the network latency. Replication and Erasure Codes (EC) are typical data redundancy methods to provide high reliability for storage systems. Compared with the replication approach, erasure codes can achieve much lower monetary cost with the same fault-tolerance capability. However, the recovery cost of EC is extremely high in multiple AZ environment, especially because of its high bandwidth consumption in data centers. LRC is a widely used EC to reduce the recovery cost, but the storage efficiency is sacrificed. MSR code is designed to decrease the recovery cost with high storage efficiency, but its computation is too complex. To address this problem, in this paper, we propose an erasure code for multiple availability zones (called AZ-Code), which is a hybrid code by taking advantages of both MSR code and LRC codes. AZ-Code utilizes a specific MSR code as the local parity layout, and a typical RS code is used to generate the global parities. In this way, AZ-Code can keep low recovery cost with high reliability. To demonstrate the effectiveness of AZ-Code, we evaluate various erasure codes via mathematical analysis and experiments in Hadoop systems. The results show that, compared to the traditional erasure coding methods, AZ-Code saves the recovery bandwidth by up to 78.24%.

Abstract The advent of big data era has given rise to the big data trading market because of the potentially enormous economic value. However, designing an effective trading mechanism for the data trading market is still in its infancy. Existing several incentive mechanisms have neglected the important fact that data consumers have both preferences and complex conflicts of interest (CoI) relations among them. In response to the limitations of existing trading mechanisms, we propose DTPCI, a truthful double auction mechanism for a Data Trading market with two unique characteristics of consumers’ Preferences and complex CoI relations among them. However, three major challenges have to be addressed, i.e. diverse market preferences, the complex CoI relations of data consumers and the strategic behaviors of both two sides. To jointly address the three challenges, we propose DTPCI to achieve nonnegative social welfare, which features a group rule and a data trading rule. The group rule generates all conflict-free virtual groups based on the CoI graph. The data trading rule adopts the group buying to share data and expense. Through rigorous theoretical analysis and real-data based experiments, we demonstrate that DTPCI achieves all the desired economic properties.

In the Service-Function-Chaining (SFC) enabled networks, various sophisticated policy-aware network functions, such as intrusion detection, access control and unified threat management, can be realized in either physical middleboxes or virtualized network function (VNF) appliances. In this paper, we study the service chaining towards the hybrid SFC clouds, where both physical appliances and VNF appliances provide services collaboratively. In such hybrid SFC networks, the challenge is how to efficiently steer the service chains for traffic demands while matching their individual policy chains concurrently such that a utility associated with the total admitted traffic rate and the induced overheads can be maximized. We find such problem has not been well solved so far. To this end, we devise a Markov Approximation (MA) based algorithm. The approximation property of the proposed algorithm is also proved. Extensive evaluation results show that the proposed MA algorithm can yield near-optimal solutions and outperform other benchmark algorithms significantly.

The actual queuing delay in satellite networks is hard to get due to long propagation. So, most existing routing algorithms take the expected queuing delay as the routing metrics so that links with short-time light traffic are often chosen when setting up routing tables, which results in that more packets could be sent to the nodes with short-time light traffic. In this paper, we propose a Queue State based Dynamical Routing (QSDR) mechanism for NGEO satellite networks. Instead of expected queuing delay, we model effective queuing delay through filtering short-time light traffic based on the proposed forgotten factor, which considers not only the traffic load but also their duration. To balance traffic load, we propose a dynamical route updating algorithm based on real-time queue states with route state model, which ensures that each satellite sends out packets as soon as possible and avoids congestion at current node. We develop a NS2-based simulation system to evaluate our QSDR. The results demonstrate that our QSDR outperforms related TLR and ELB in terms of packet drop rate, throughput and end-to-end delay.

In this paper, we present WoSense, a device-free and real-time behavior analysis system leveraging only WiFi infrastructures. WoSense aims to remotely recognize various human behaviors like surfing, gaming and working around computers, which are considered to be an essential part of our daily lives both at work and at home. The key of WoSense is to exploit the signal distortions on channel data caused by gestures like finger and hand movements, and then identify possible behaviors via the composite of gestures. Therefore, two critical challenges need to be tackled: how to enhance such insignificant distortions led by micro gestures, how to segment the continuous signals according to different gestures in a real-time manner? For the former, instead of relying on empirical studies like our rivals, WoSense offers a Fresnel zone based model with theoretic understandings between the gestures and signal distortions. For the latter, WoSense employs a light-weight automatic segmentation algorithm exploring the variance feature of channel data. We prototype WoSense on the commodity low-cost WiFi devices and evaluate its performance in extensive real- world experiments. WoSense achieves an average 96.77% accuracy for distinguishing the typing and mousing gestures, and 92.5% accuracy for recognizing four different behaviors, i.e., stationary, surfing, gaming and working.

Emotion is a unique feature of human beings. Recent research in emotion sensing has already revealed its potentials in enhancing our living experiences through applications like emotion companion and autism treatment. However, existing solutions exploring audiovisual clues or psychological sensors have several critical concerns such as the availability (specialized hardware), reliability (illumination and line-of-sight constraints) and privacy issues (being watched). To this end, we present EmoSense, a first-of-its-kind WiFi-based emotion sensing system leveraging the temporal and frequency fingerprints on the wireless channel data induced by the physical expression of emotion. EmoSense has been prototyped with off- the-shelf WiFi devices and evaluated by comparing with the main-stream sensor-based approach in real environments. Experimental results demonstrate its effectiveness and robustness. Considering that EmoSense is compatible with existing WiFi infrastructures, it constitutes a low-cost yet promising solution for emotion sensing.

Access control is an important mechanism in cloud computing. The Key Policy Attribute Based Encryption (KP-ABE) is an important method to implement the access control in cloud service. However, conventional user revocation scheme in KP-ABE costs huge computational overhead. In this paper, we focus on the important user revocation issue in KP-ABE. We introduce several control parameters, including version value, check value and user list. We combine KP-ABE with salt encryption for the implementation. We provide a novel user revocation scheme for KP-ABE to improve the user revocation issue, which can reduce the heavy computational overhead when user being revoked. The performance evaluation shows that the proposed user revocation scheme gives good performance with KP-ABE.

Software-Defined networking (SDN), as a new paradigm, fixes the shortage that traditional network does not support the dynamic, scalable computing and storage needs of more computing environments. SDN, however, also faces security problems such as vulnerable to DDoS attacks. DDoS attacks are well-known and powerful attacks. DDoS detection and DDoS traffic separation for SDN environments are still an open research issue. DDoS attacks in SDN environments will not only bring damage to target server, but also takes exact impact on SDN system. In this paper, we identify a new type DDoS attack, specifically aiming SDN environment, which is harder to be detected. We propose a novel real-time DDoS detection scheme for SDN environment, by using Principal Component Analysis (PCA) scheme to analyze the network status on traffic packets data. We separate the network into different parts, to reduce the total calculation burden. We compare our scheme with sample entropy, showed our scheme achieves better detecting ability for DDoS attacks.

In order to cope with the increasing number of cyber attacks, network operators must monitor the whole network situations in real time. Traditional network monitoring method that usually works on a single machine, however, is no longer suitable for the huge traffic data nowadays due to its poor processing ability. In this paper, we propose a machine-learning based online Internet traffic monitoring system using Spark Streaming, a stream- processing-based big data framework, to detect DDoS attacks in real time. The system consists of three parts, collector, messaging system and stream processor. We use a correlation-based feature selection method and choose 4 most necessary network features in our machine- learning-based DDoS detection algorithm. We verify the result of feature selection method by a comparative experiment and compare the detection accuracy of 3 machine learning methods - Naïve Bayes, Logistic Regression and Decision Tree. Finally, we conduct experiments in a cluster with the standalone mode, showing that our system can detect 3 typical DDoS attacks - TCP flooding, UDP flooding and ICMP flooding at the accuracy of more than 99.3%. It also shows the system performs well even for large Internet traffic.

Owing to the explosive growth of Internet traffic, network operators must be able to monitor the entire network situation and efficiently manage their network resources. Traditional network analysis methods that usually work on a single machine are no longer suitable for huge traffic data owing to their poor processing ability. To cope with high speed streaming data, various stream-processing-based big data frameworks, such as Storm, Flink, and Spark Streaming, have been proposed. In this paper, we treat network traffic as a streaming data, and propose an online Internet traffic monitoring framework based on Spark Streaming and Flink, respectively. The framework could be used for real-time TCP performance monitoring and DDoS detection. We conduct typical experiments to compare the performance of Spark Streaming and Flink. The experiments show that our framework performs well for large Internet traffic measurement and monitoring.

Sleep is a major event of our daily lives. Its quality constitutes a critical indicator of people's health conditions, both mentally and physically. Existing sleep monitoring systems either are obstructive to use or fail to provide adequate coverage. To overcome these shortages, we propose Sleepy, an adaptive and noninvasive sleep monitoring system leveraging channel response in the commercial WiFi devices. Sleepy needs no calibrations or target-dependent training to recognize posture changes during sleep. To achieve that, a Gaussian Mixture Model (GMM) based foreground extraction method has been designed to adaptively distinguish motions like rollovers (foreground) from background (stationary postures). We prototype Sleepy and evaluate it in two real environments. In the short-term controlled experiments, Sleepy achieves 95.04% detection accuracy and 4.07% false negative rate. In the 60-minute real sleep studies, Sleepy demonstrates strong stability. Considering that Sleepy is compatible with existing WiFi infrastructures, it constitutes a low-cost yet promising solution for sleep monitoring.

Due to overshadow and channel fading, many mobile users are unable to receive the signal transmitted from satellite directly. Hence, some relay stations should be set to help this type of users to receive signals reliably. In this paper, we present a novel cognitive hybrid satellite-terrestrial model, where two cognitive relays forward their received signal for a mobile user successively. Furthermore, we address its achievable rate maximization. We first convert the co-channel interference threshold into transmit power constraints, and then formulate the maximization of the achievable rate as an optimization problem. Based on Karush-Kuhn-Tucker conditions, the optimization problem is decomposed into four cases, each of which is solved in closed form. Simulation study with different system settings is presented, and the efficiency of the proposed power allocation scheme is shown.

Load-balanced flow scheduling for big data centers in clouds, in which a large amount of data needs to be transferred frequently among thousands of interconnected servers, is a key and challenging issue. The OpenFlow is a promising solution to balance data flows in data center networks through its programmatic traffic controller. Existing OpenFlow based scheduling schemes, however, statically set up routes only at the initialization stage of data transmissions, which suffers from dynamical flow distribution and changing network states in data centers and often results in poor system performance. In this paper, we propose a novel dynamical load-balanced scheduling (DLBS) approach for maximizing the network throughput while balancing workload dynamically. We first formulate the DLBS problem, and then develop a set of efficient heuristic scheduling algorithms for the two typical OpenFlow network models, which balance data flows time slot by time slot. Experimental results demonstrate that our DLBS approach significantly outperforms other representative load-balanced scheduling algorithms Round Robin and LOBUS; and the higher imbalance degree data flows in data centers exhibit, the more improvement our DLBS approach will bring to the data centers.

Link stability significantly suffers from dynamical primary user activities and random node movement in mobile cognitive networks (MCNs). In multihop MCNs, it will become even worse due to potential interference among multiple links so that stable routing will become more important and also more challenging than that in traditional wireless networks. In this paper, we first formulate the joint topology control and stable routing (JTCSR) problem based on primary user (PU) activity prediction. Then, we propose a PU prediction model to reveal channel utilization patterns of PUs. Next, we propose a novel routing metric PU prediction-based stability metric (PPSM), which quantitatively measures PU activities and node mobility, and design a min-max PPSM matrix construction algorithm. Finally, we propose and develop a PU prediction-based (PP) JTCSR algorithm for maximizing network throughput, which can find out the most stable and the shortest path. Theoretical analysis validates the effectiveness and efficiency of our approach. NS2-based simulation results further demonstrate that our PP-JTCSR can generate stable and efficient topology through predicting PU activities quantitatively, and outperforms related proposals in terms of path stability, average throughput, and packet loss rate.

Arbitrary flow arrival and satellite communication hot spot cause uneven traffic distribution, which breaks load balancing even results in congestion in partial nodes. In this paper, we propose a State-Aware and Load-Balanced (SALB) routing model for LEO (low earth orbit) satellite networks. We firstly propose a mechanism to quantitatively estimate link states and dynamically adjust the weight of queuing delay. SALB divides the occupancy rate of each queue into n levels and each level corresponds to a link state. Then, we develop the SALB model that considers various situations including load change, and link and node failure and recovery. Routing tables are reset up at the beginning of each handover and are dynamically updated through an efficient shortest path tree algorithm between two successive handovers, which significantly lower routing overhead. We evaluate our SALB model through a NS2-based system. The results demonstrate that our SALB outperforms related proposals in terms of system throughput, end-to-end delay, and packet drop rate.

Due to the explosive growth of Internet traffic, network operators must be able to monitor the whole network situations and manage their network resources in an efficient way. Traditional network analysis method that works on a single machine are no longer suitable for this huge traffic data due to its poor processing ability. Some big data frameworks, such as Hadoop and Spark, can handle such analysis job even for large network traffic, but they are inherently designed for offline data analysis. In this paper, we treat the online network analysis as a stream analysis problem and use Spark Streaming to cope with the high-speed Internet traffic data in real time. The system consists of two parts, collector and stream processor. Firstly, several collectors capture network traffic data from switches through mirrored ports and send the packet information to a central stream processor which is a cluster running Spark Streaming. Then, the stream processor analyzes the input data streams and calculates Internet performance metrics. We take TCP performance monitoring as an example to show how network measurement can be done using the stream processing platform. Finally, we conducted typical experiments in a cluster of 3 computers with the standalone mode, showing that our system performs well in huge Internet traffic measurement and monitoring.

In an Information-Centric Internet of Things (ICIoT) environment for big data sharing, IoT data can be cached throughout the network. Such distributed data caching poses a challenge on flexible authorization and identity verification. For fine-grained data access authorization in a distributed manner, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been identified as a promising approach. However in the existing CP-ABE based scheme, each publisher would need to retrieve the attributes from the centralized server for encrypting data, resulting in high communication overhead. Moreover, valid authorization period and distributed authentication are still not addressed and seamlessly incorporated. In this paper, we propose a Verifiable and Flexible Data Sharing (VFDS) mechanism for ICIoT, which exploits CP-ABE for authorization and Identity-Based Signature (IBS) for the distributed verification of the identities. In VFDS, publishers retrieve the attributes from the nearby cache holders. In addition, the Attribute Manifest (AM) and the Automatic Attribute Update (AAU) realize efficient attribute updates within the distributed caches to achieve valid authorization period. Meanwhile, VFDS provides the public parameters of IBS in local domain, which enables the efficient identity verifications. Our system evaluations show that the VFDS can achieve lower bandwidth cost compared to the existing schemes for both authentication and flexible authorization.

Memory deduplication is a technique to eliminate redundant data, save memory space and improve the performance of the whole system. There are several effective deduplication algorithms, which identify replicated data via comparing the content of different pages. However, although a few literatures utilize spatial locality to improve the efficiency of memory deduplication [1][2], they still have several limitations, such as low ratio of continuous distribution and high failure rate of prediction under unstable environments. To address these problems, in this paper, we design a new memory deduplication algorithm called “Loc-K”. On one hand, it utilizes logical addresses of different pages to ensure better continuity, which can gain better spatial locality. On the other hand, Loc-K predicts K potential duplication locations as the targets for page scanning, which improves the prediction hit ratio. Furthermore, Loc-K merges the duplicated pages directly to avoid regular searching routines. To demonstrate the effectiveness of our algorithm, we conduct several experimentations via implementation in Linux Kernel. The results show that, compared to the state-of-the-art memory deduplication algorithms, Loc-K increases the predictable opportunity by up to 97.8%, increases the prediction hit ratio by up to 96.5%, and reduces the duplication identification time by at least 34.3% respectively.

With the development of cloud computing, disk arrays tolerating triple disk failures (3DFTs) are receiving more attention nowadays because they can provide high data reliability with low monetary cost. However, a challenging issue in these arrays is how to efficiently reconstruct the lost data, especially for partial stripe errors (e.g., sector and chunk errors). It is one of the most significant scenarios in practice. However, existing cache strategies are not efficient for partial stripe reconstruction in 3DFTs, which is because the complex relationships among data and parities are usually ignored during the recovery process. To address this problem, in this paper, we proposed a comprehensive cache policy called Favorable Block First (FBF), which can speed up the partial stripe reconstruction of 3DFTs. FBF investigates the relationships among parity chains via allocating various priorities of shared chunks. Thus in the recovery process, by giving higher priorities to the chunks which are shared by more parities chains, FBF can dynamically hold the significant data in buffer cache for partial stripe reconstruction. Obviously, it increases the cache hit ratio and reduces the reconstruction time. To demonstrate the effectiveness of FBF, we conduct several simulations via Disksim. The results show that, compared to typical recovery schemes by combining with classic cache policies (e.g., LRU, LFU and ARC), FBF improves hit ratio by up to 2.47 times and accelerates the reconstruction process by 14.90%, respectively.

Motion is a critical indicator of human presence and activities. Recent developments in the field of indoor motion detection have revealed their potentials in enhancing our living experiences through applications like intrusion detection and sleep monitoring. However, existing solutions still face several critical downsides such as the availability (specialized hardware), reliability (illumination and line-of-sight constraints), and privacy issues (being watched). To overcome such shortages, a radio frequency (RF) based device-free motion detection system (MoSense) is designed via leveraging the attenuation of ubiquitous WiFi signals induced by motions to deliver a reliable and transparent detection service in realtime. The design and implementation of MoSense face two challenges: 1) characterizing stationary states and 2) the noisy subcarriers. For the first challenge, a silence analysis model is proposed to characterize stationary states for distinguishing motions. For the second challenge, we design a distance-based mechanism to select certain subcarriers that better capture the impact of motions from the noisy channel through measuring the similarity between subcarriers. A prototype of MoSense is realized and evaluated in real environments. By comparing MoSense with other two state-of-the-art systems, i.e., FIMD and FRID, we have shown that MoSense is superior in terms of precision, false negative rate and computational complexity. Considering that MoSense is compatible with existing WiFi infrastructure, it constitutes a low-cost yet promising solution for motion detection.

In Information-Centric Internet of Things (ICIoT), Internet of Things (IoT) data can be cached throughout a network for close data copy retrievals. Such a distributed data caching environment, however, poses a challenge to flexible authorization in the network. To address this challenge, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been identified as a promising approach. However, in the existing CP-ABE scheme, publishers need to retrieve attributes from a centralized server for encrypting data, which leads to high communication overhead. To solve this problem, we incorporate CP-ABE and propose a novel Distributed Publisher-Driven secure data sharing for ICIoT (DPD-ICIoT) to enable only authorized users to retrieve IoT data from distributed cache. In DPD-ICIoT, newly introduced attribute manifest is cached in the network, through which publishers can retrieve the attributes from nearby copy holders instead of a centralized attribute server. In addition, a key chain mechanism is utilized for efficient cryptographic operations, and an automatic attribute self-update mechanism is proposed to enable fast updates of attributes without querying centralized servers. According to the performance evaluation, DPD-ICIoT achieves lower bandwidth cost compared to the existing CP-ABE scheme.

Cognitive radio significantly mitigates the spectrum scarcity for various applications built on wireless communication. Current techniques on mobile cognitive ad hoc networks (MCADNs), however, cannot be directly applied to time-critical applications due to channel interference, node mobility as well as unexpected primary user activities. In multichannel multiflow MCADNs, it becomes even worse because multiple links potentially interfere with each other. In this paper, we propose a delay-minimized routing (DMR) protocol for multichannel multiflow MCADNs. First, we formulate the DMR problem with the objective of delay minimization. Next, we propose a delay prediction model based on a conflict probability. Finally, we design the minimized path delay as a routing metric, and propose a heuristic joint routing and channel assignment algorithm to solve the DMR problem. Our DMR can find out the path with a minimal end-to-end (e2e) delay for time-critical data transmission. NS2-based simulation results demonstrate that our DMR protocol significantly outperforms related proposals in terms of average e2e delay, throughput, and packet loss rate.

In this paper, we address the challenging and important cooperative spectrum sensing (CSS) problem with M-ary quantized data under spectrum sensing data falsification (SSDF) attacks. We introduce a probabilistic SSDF attack model to characterize the attacks by a malicious secondary user (SU). We analyze the attack behavior and derive the condition to nullify the detection capability of the fusion center (FC). To defend against the SSDF attacks, we propose a novel attack-proof CSS scheme with M-ary quantized data, mainly including a malicious SU identification method and an adaptive linear combination rule. By using the malicious SU identification approach, FC identifies malicious SUs and removes them from the data fusion process. The adaptive linear combination rule adjusts the weighted coefficients with the distribution parameter sets of identified normal SUs estimated using a maximum likelihood-based estimator. FC performs the spectrum sensing process with M-ary quantized data from the identified normal SUs. Comprehensive evaluation is conducted. Evaluation results show that the proposed malicious SU identification method can remove malicious SUs successfully and the proposed CSS scheme with M-ary quantized data is robust against the SSDF attacks.

It is a fundamental goal but a tough task to fully utilize various resources in wireless networks. In multi-hop cognitive radio ad hoc networks (CRAHNs), it becomes more challenging due to primary users' uncertain activities, varying available channels, arbitrary traffic arrivals and rate requirements, and channel interference among multiple links. In this paper, we propose a Joint Rate adaptation, Channel assignment and Routing (J-RCR) approach to maximize social welfare by optimizing the resource utility in multi-channel multi-hop CRAHNs. Our J-RCR, jointly and dynamically adjusts the data transmission rate based on network states and rate requirements, assigns interference-free channels, and selects a route when a new data flow arrives or any primary node activates. The routing mechanism in our J-RCR considers the relay workload, the distance between the relay and the destination node, and co-channel interference to primary and secondary nodes. To show the efficiency of our J-RCR, we conduct both rigorous theoretical analysis and comprehensive performance evaluations. We derive the performance bound of our J-RCR on social welfare and compare it with the ideal approach that can precisely predict future network states. Numerical results further demonstrate that our J-RCR outperforms the related solutions (e.g., Robust Route, BPR and GPSR) in terms of social welfare, average throughput, network stability, and end-to-end delay.

The benefits of network layer cooperation cognitive radio networks have been gradually recognized in recent years. In this paper we consider the network layer cooperation in cognitive radio network, whereby primary users select some secondary users to relay packets, in return for more favourable spectrum access rules for secondary users. Under this cooperation scheme, we investigate how to enlarge the throughput of the whole network, where a QoS(Quality of Service)-guaranteed adaptive cooperation scheme is developed. Our scheme can guarantee the QoS demand of primary users and update its frequency division cooperation scheme dynamically according to the status of nodes. Our algorithm requires knowledge of only instantaneous queue lengths at secondary nodes and the predictable end-to-end delay. Simulation results reveal that our proposed scheme significantly outperforms previous works in terms of throughput.

In the context of Software-Defined Networking (SDN), various sophisticated policy-aware network functions such as intrusion detection, access control and load balancer, can be realized via specified middlebox devices. However, high congestions may occur in specific bottleneck links if middlebox selection and traffic routing are not well jointly planed. To this end, we study a joint optimization of MiddleBox Selection and Routing (MBSR) problem with the objective to maximize the throughput for a specified set of sessions in an SDN network. In order to solve this NP-hard problem, we design a polynomial algorithm using the Markov approximation technique. Numerical results show that the proposed Markov approximation based algorithm outperforms other benchmark algorithms significantly and generates near-optimal solutions.

In large scale data centers, with the increasing amount of user data, Triple Disk Failure Tolerant arrays (3DFTs) gain much popularity due to their high reliability and low monetary cost. With the development of cloud computing, scalability becomes a challenging issue for disk arrays like 3DFTs. Although previous solutions improves the efficiency of RAID scaling, they suffer many problems (high I/O overhead and long migration time) in 3DFTs. It is because that existing approaches have to cost plenty of migration I/Os on balancing the data distribution according to the complex layout of erasure codes. To address this problem, we propose a novel Balanced Data Redistribution scheme (BDR) to accelerate the scaling process, which can be applied on XOR-based 3DFTs. BDR migrates proper data blocks according to a global point of view on a stripe set, which guarantees uniform data distribution and a small number of data movements. To demonstrate the effectiveness of BDR, we conduct several evaluations and simulations. The results show that, compared to typical RAID scaling approaches like Round-Robin (RR), SDM and RS6, BDR reduces the scaling I/Os by up to 77.45%, which speeds up the scaling process of 3DFTs by up to 4.17×, 3.31×, 3.88×, respectively.

Data deduplication is a technique to eliminate duplicated copies of data. It can save the storage space, reduce the amount of disk I/Os, then improve the system performance. There have been several popular deduplication algorithms such as SISL [30], Extreme Binning [1], Sparse Indexing [14], etc. These schemes use containers to aggregate data chunks for better performance. However, they either suffer from low cache hit ratios or inefficient cache utilization. To address this problem, we design Zero-Chunk, a new cache algorithm that balances the cache hit ratio and memory usage. In our method, we choose chunks whose fingerprints have all-zero remainders as pointers (called zero chunks), and aggregate the following chunks into their corresponding containers. And then, when the access patterns change, our method can eliminate cold data chunks and containers to maintain a low overhead. To demonstrate the effectiveness of Zero-Chunk, we conduct several simulations. The results show that, compared to Sparse Indexing (the most popular implementation method in data deduplication), Zero-Chunk improves the cache hit ratio by up to 5.2%, saves the memory consumption by more than 50.7%, and decreases the total number of I/Os by up to 17.3%, respectively.

This paper presents a novel WLAN-based indoor localization algorithm (i.e., HED) to combat the environmental dynamics by tolerating the sequence disorders caused by AP (access point) changes, while harvesting from the bursting number of available wireless resources. Via extensive real-world experiments lasting for over 6 months, we show the superiority of our HED algorithm in terms of accuracy and complexity over two state-of-the-art solutions that are also designed to resist the dynamics, i.e., FreeLoc and LCS (Longest Common Subsequences). Moreover, experimental results not only confirm the benefits brought by environmental dynamics, but also provide valuable investigations and hand-on experiences on the real-world localization system.

The stability of links in mobile cognitive networks (MCNets) is significantly affected by primary user activities and node mobility, which makes topology control and stable routing more challenging than that in traditional wireless networks. In multi-channel multi-hop MCNets, it will become worse. In this paper, we propose a primary user activity prediction model to reveal channel utilization patterns of primary users. Next, we put forward a novel routing metric Primary user activity Prediction based Stability Metric (PPSM) to quantitatively capture the affect of primary user activities and node mobility. Finally, we propose and implement a Primary user activity Prediction based Joint Topology Control and Stable Routing (PP-JTCSR) protocol for maximizing network throughput based on our primary user activity prediction model, which can find out the most stable and the shortest path between a source and a destination. NS2-based simulation results demonstrate that our PP-JTCSR protocol can generate stable topology through predicting link and path duration quantitatively, and outperforms related proposals in terms of path stability and average throughput.

Indoor human activity recognition remains a hot topic and receives tremendous research efforts during the last few decades. However, previous solutions either rely on special hardware, or demand the cooperation of subjects. Therefore, the scalability issue remains a great challenge. To this end, we present an online activity recognition system, which explores WiFi ambient signals for received signal strength indicator (RSSI) fingerprint of different activities. It can be integrated into any existing WLAN networks without additional hardware support. Also, it does not need the subjects to be cooperative during the recognition process. More specifically, we first conduct an empirical study to gain in-depth understanding of WiFi characteristics, e.g., the impact of activities on the WiFi RSSI. Then, we present an online activity recognition architecture that is flexible and can adapt to different settings/conditions/scenarios. Lastly, a prototype system is built and evaluated via extensive real-world experiments. A novel fusion algorithm is specifically designed based on the classification tree to better classify activities with similar signatures. Experimental results show that the fusion algorithm outperforms three other well-known classifiers [i.e., NaiveBayes, Bagging, and k-nearest neighbor (k-NN)] in terms of accuracy and complexity. Important sights and hands-on experiences have been obtained to guide the system implementation and outline future research directions.

In this special issue, we cover some recent results in the following four emerging areas: 5G cellular systems, big data systems, bio/nano/molecular networks, and smart grids. In the past several years, there are various technologies emerging, which are either directly or indirectly related to communication. Some of them are over the evolution of traditional communication systems, while others are over new systems such as smart grids, molecular networks, and big data systems.

In this paper, we consider the problem of concurrent transmissions in a wireless network consisting of multiple radio access technologies (multi-RATs). That is, a single flow of packets is dispatched over multiple RATs so that the complementary advantages of different RATs can be exploited. One of the challenging issues arising in concurrent transmissions is the packet out-of-order problem due to diverse wireless channel states and scheduling policies of different RATs, leading to substantial performance degradation to delay sensitive applications. To address this problem, we first propose a state-independent packet dispatching (SIPD) policy, which attempts to find the traffic dispatching ratios over multiple RATs to minimize the maximum average delay across different RATs in the long run. We further propose a state-dependent packet dispatching (SDPD) policy, which achieves fine-grained packet dispatching in the short-term. We use the value function as a measure of the admittance cost for packet dispatching given the current queueing states, and formulate the SDPD problem as a convex optimization problem. We derive the closed-form solutions for both problems for the special case of two RATs, and adopt the dual decomposition technique as the solution for the general cases. Simulation results are presented to compare the performance of the proposed schemes with existing solutions.

Abstract Vehicular ad hoc networks (VANETs) are expected in improving road safety and traffic conditions, in which security is essential. In VANETs, the authentication of the vehicular access control is a crucial security service for both inter‐vehicle and vehicle–roadside unit communications. Meanwhile, vehicles also have to be prevented from the misuse of the private information and the attacks on their privacy. There is a number of research work focusing on providing the anonymous authentication with preserved privacy in VANETs. In this paper, we specifically provide a survey on the privacy‐preserving authentication (PPA) schemes proposed for VANETs. We investigate and categorize the existing PPA schemes by their key cryptographies for authentication and the mechanisms for privacy preservation. We also provide a comparative study/summary of the advantages and disadvantages of the existing PPA schemes. Lastly, the open issues and future objectives are identified for PPA in VANETs. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract We propose a novel vertical handoff scheme with the support of the software‐defined networking technique for heterogeneous wireless networks. The proposed scheme solves two important issues in vertical handoff:network selectionandhandoff timing. In this paper, the network selection is formulated as a 0‐1 integer programming problem, which maximizes the sum of channel capacities that handoff users can obtain from their new access points. After the network selection process is finished, a user will wait for a time period. Only if the new access point is consistently more appropriate than the current access point during this time period, will the user transfer its inter‐network connection to the new access point. Our proposed scheme ensures that a user will transfer to the most appropriate access point at the most appropriate time. Comprehensive simulation has been conducted. It is shown that the proposed scheme reduces the number of vertical handoffs, maximizes the total throughput, and user served ratio significantly. Copyright © 2016 John Wiley & Sons, Ltd.

Nowadays massive log streams are generated from many Internet and cloud services. Storing log streams consumes a large amount of disk space and incurs high cost. Traditional compression methods can be applied to reduce storage cost, but are inefficient for log analysis, because fetching relevant log entries from compressed data often requires retrieval and decompression of large blocks of data. We propose a column-wise compression approach for well-formatted log streams, where each log entry can be independently compressed or decompressed for analysis. Specifically, we separate a log entry into several columns and compress each column with different models. We have implemented our approach as a library and integrated it into two applications, a log search system and a log joining system. Experimental results show that our compression scheme outperforms traditional compression methods for decompression times and has a competitive compression ratio. For log search, our approach achieves better query times than using traditional compression algorithms for both in-core and out-of-core cases. For joining log streams, our approach achieves the same join quality with only 30% memory of uncompressed streams.

Nowadays RAID is widely used due to its large capacity, high performance and high reliability. With the increasing requirement of reliability in storage systems and fast development of cloud computing, RAID-6, which can tolerate concurrent failures of any two disks, receives more attention than ever. However, the write performance of RAID-6 systems is a bottleneck to serve various applications. In the last two decades, many approaches are proposed to enhance the write performance of RAID-6, but they have several limitations, such as unbalanced I/O distribution and high I/O cost. To address this problem, in this paper, we propose a Balanced Partial Stripe (BPS) write scheme to improve the write performance of RAID-6 systems. The basic idea of BPS is reorganizing the distribution of write data blocks according to a global point of view on modified parities, and flushing these blocks to storage devices at once. Therefore, it can significantly reduce the total number of parity updates and balance the I/O workload. BPS has three main advantages: 1) BPS decreases the number of I/O operations and aggregate the fragmented I/Os, which improves the I/O performance, 2) BPS provides a balanced partial stripe write approach for RAID-6, 3) BPS can be applied with various erasure codes. To demonstrate the effectiveness of our scheme, we conduct simulations on DiskSim to evaluate different partial stripe write approaches. The results show that, compared to typical partial stripe write approaches, BPS reduces the average access time by up to 37.14%, and decreases the number of write operations by up to 26.24%.

With the rapid expansion of data storages and the increasing risk of data failures, triple Disk Failure Tolerant arrays (3DFTs) become popular and widely used. They achieve high fault tolerance via erasure codes. One class of erasure codes called Maximum Distance Separable (MDS) codes, which aims to offer data protection with minimal storage overhead, is a typical choice to enhance the reliability of storage systems. However, existing 3DFTs based on MDS codes are inefficient in terms of update complexity, which results in poor write performance. In this paper, we present an efficient MDS coding scheme called TIP-code, which is purely based on XOR operations and can tolerate triple disk failures. It uses three independent parities (horizontal, diagonal and anti-diagonal parities), and offers optimal update complexity. To demonstrate the effectiveness of TIP-code, we conduct several quantitative analysis and experiments. The results show that, compared to typical MDS codes for 3DFTs (i.e., Cauchy-RS and STAR codes), TIP-code improves the single write performance by up to 46.6%.

Dynamic spectrum reallocation, under which the spectrum owners temporarily share the underutilized spectrum to secondary users for economic profit, is an important approach to improve the spectrum utilization ratio. Auction is believed to be a natural marketing tool to incentivize the spectrum owners, and thus redistribute the idle spectrum efficiently. Extensive researches have been done in the problem of truthful spectrum auction, in which the bidders bid based on their true valuations of the spectrum. The true valuation of the individual bidder, however, is a private information which should be protected against exposure. In this paper, we propose a privacy preserving truthful spectrum auction scheme by utilizing homomorphic encryption. The proposed scheme reveals the group bids but hides the users' bids even from the auctioneer. The evaluation results show that the proposed scheme achieves good spectrum utilization efficiency with low communication and computation overheads.

We study the problem of maximizing the lifetime of data-gathering tree for wireless sensor networks (WSNs). Both data routing and aggregation are considered at the same time to improve the energy efficiency for data collection in WSNs. With different data-aggregation methods, three aggregation modes are studied: full aggregation, non-aggregation, and a hybrid partialaggregation using Compressive Sensing. For each mode, an exact solution based on Mixed-integer linear programming (MIP) is proposed to find the optimal data-gathering tree. Although nonlinear relation exists between the sensor node lifetime and the number of data units that receives or transmits in each time slot, we succeed to express it by a set of linear equations. Performance results demonstrate that the lifetime of data-gathering tree can be increased tenfold with efficient data aggregation methods.

Channel interference and node mobility cause significant performance degradation to wireless networks. In multichannel multi-flow mobile cognitive ad hoc networks, it becomes even worse due to both unexpected primary user activities and potential interference among multiple flows. In this paper, we propose a Joint Routing and Channel Assignment (JRCA) approach based on delay prediction. Firstly, it formulates the JRCA problem with the objective of delay minimization. Next, a delay prediction model is proposed based on the channel collision probability. Then, a heuristic algorithm joints routing and channel assignment is designed to solve the JRCA problem. the JRCA algorithm can find out the path with minimal end- toend (e2e) delay. NS2-based simulation results demonstrate that the JRCA approach significantly outperforms related proposals in terms of average e2e delay.

With the sharp growth of cloud services and their possible combinations, the scale of data center network traffic has an inevitable explosive increasing in recent years. Software defined network (SDN) provides a scalable and flexible structure to simplify network traffic management. It has been shown that Ternary Content Addressable Memory (TCAM) management plays an important role on the performance of SDN. However, previous literatures, in point of view on rule placement strategies, are still insufficient to provide high scalability for processing large flow sets with a limited TCAM size. So caching is a brand new method for TCAM management which can provide better performance than rule placement. In this paper, we propose FDRC, an efficient flow-driven rule caching algorithm to optimize the cache replacement in SDN-based networks. Different from the previous packet-driven caching algorithm, FDRC is characterized by trying to deal with the challenges of limited cache size constraint and unpredictable flows. In particular, we design a caching algorithm with low-complexity to achieve high cache hit ratio by prefetching and special replacement strategy for predictable and unpredictable flows, respectively. By conducting extensive simulations, we demonstrate that our proposed caching algorithm significantly outperforms FIFO and least recently used (LRU) algorithms under various network settings.

In this paper, we consider the problem of concurrent transmissions in a wireless network consisting of multiple radio access technologies (multi-RATs). That is, a single stream of packets are split to transmit over multiple RATs simultaneously so that the complementary advantages of different RATs can be exploited. One of the challenging issues is the packet out-of-order problem due to different wireless channel states and transmission scheduling policies over different RATs, leading to substantial performance degradation to delay sensitive applications. To address this problem, we adopt a M/G/1 queueing model to characterize the delay experienced by batch arrival packets in a RAT. A convex optimization problem is formulated. It attempts to find the optimal traffic splitting over multiple RATs such that maximum delay across different RATs is minimized. We derive the close-form solution for the problem under the special condition of two RATs, and the dual decomposition technique is adopted to solve the optimization problem for general cases. Numerical results are presented to show the performance of the proposed scheme and compare with existing solutions.

With the rapid growth of data storage, the demand for high reliability becomes critical in large data centers where RAID-5 is widely used. However, the disk failure rate increases sharply after some usage, and thus concurrent disk failures are not rare, therefore RAID-5 is insufficient to provide high reliability. A solution is to convert an existing RAID-5 to a RAID-6 (a type of "RAID level migration") to tolerate more concurrent disk failures via erasure codes, but existing approaches involve complex conversion process and high transformation cost. To address these challenges, we propose a novel MDS code, called "Code 5-6", to combine a new dedicated parity column with the original RAID-5 layout. Code 5-6 not only accelerates online conversion from a RAID-5 to a RAID-6, but also demonstrates several optimal properties of MDS codes. Our mathematical analysis shows that, compared to existing MDS codes, Code 5-6 reduces new parities, decreases the total I/O operations, and speeds up the conversion process by up to 80%, 48.5%, and 3.38×, respectively.

Fountain codes are linear codes with low complexities. LT (Luby Transform) codes, which are a special class of Fountain codes, are widely used in Wireless Sensor Networks (WSNs) to increase the robustness of data storage and efficiency of data retrieval. In this paper, we propose a novel LT codes based Distributed Coding (LTDC) scheme for efficient distributed storage in WSNs. In the proposed LTDC scheme, we use random walks to disseminate sensed data from a source sensor node to a random subset of sensor nodes by multicast. As long as a data packet stops at an ending sensor node of a random walk, the ending sensor node encodes this data packet in a main packet (an encoded data packet) with a certain probability. By adjusting the main packet with the un-encoded data packets, the number of data packets encoded in the main packet follows the distribution of LT codes. The data collector is able to decode the original data by querying any subset of sensor nodes. The theoretical analysis and simulation results have demonstrated that the proposed LTDC scheme has lower data dissemination cost and lower storage overhead, while maintains the same level of fault tolerance as the original LT codes.

This paper investigates the secure information transfer issue for cognitive radio networks that have multiple non-altruistic primary users, secondary users and eavesdroppers. The design objective is to improve the secrecy rates of the primary users, and create the transmission opportunities for the secondary users. To achieve this goal, we propose to incentivize the non-altruistic users to cooperate by a barter-like exchange. Specifically, the primary users leverage the assist of the secondary users in the form of cooperative transmitting or friendly jamming, and in return, yield certain licensed spectrum accessing time to the aided secondary users. We propose a truthful Double Auction mechanism for Secure Information transfer in cognitive radio networks, namely DASI, to jointly formulate the cooperator/jammer assignment and the corresponding resource allocation problems. We prove that DASI preserves nice economic properties that are critical for the auction design, including truthfulness, individual rationality and budget balance. We also evaluate DASI in terms of aggregated throughput and spectrum utilization ratio by simulations.

In large storage systems, erasure codes is a primary technique to provide high reliability with low monetary cost. Among various erasure codes, a major category called XORbased codes uses purely XOR operations to generate redundant data and offer low computational complexity. These codes are conventionally implemented via matrix based method or several specialized non-matrix based methods. However, these approaches are insufficient on decoding performance, which affects the reliability and availability of storage systems. To address the problem, in this paper, we propose a novel Parity-Check Matrix based (PCM) approach, which is a general-purpose method to implement XOR-based codes, and increases the decoding performance by using smaller and sparser matrices. To demonstrate the effectiveness of PCM, we conduct several experiments by using different XOR-based codes. The evaluation results show that, compared to typical matrix based decoding methods, PCM can improve the decoding speed by up to a factor of 1.5× when using EVENODD code (an erasure code for correcting double disk failures), and accelerate the decoding process of STAR code (an erasure code for correcting triple disk failures) by up to a factor of 2.4×.

This paper addresses the spectrum leasing issue in cognitive radio networks by exploiting the primary user's cooperative ARQ (automatic repeated-request). To incentivize the otherwise non-cooperative users, we propose a novel trading model to foster the cooperation in the context of cooperative retransmitting. By formulating the network as a Stackelberg game, we maximize the utilities of both primary and secondary users in terms of transmission rates and revenues. We analyze the existence of the unique Nash equilibrium of the game, and give the optimal solutions with corresponding constraints. Numerical results demonstrate the efficiency of the proposed framework, under which the performance of the whole system could be substantially improved.

Most existing routing algorithms assume wireless nodes use maximal transmission power or set up the power at the beginning of the network configuration. These static approaches potentially introduce signal interference that can be mitigated through power control. In this paper, we propose a Joint Channel assignment, stable Routing and adaptive Power control (JCRP) approach that dynamically controls the transmission power to avoid the channel interference for improving the channel utility. Our JCRP allows a node to control its transmission power to a certain value at which it has a longest channel conflict-free time. Besides, we propose a novel routing metric integrated selecting stability (ISS) to measure the quality of links, which considers node mobility and channel interference, together with the dynamical power control. The simulation results demonstrate that our JCRP significantly outperforms the related routing algorithms in terms of network throughput.

One of challenging issues for task allocation problem in wireless sensor networks (WSNs) is distributing sensing tasks rationally among sensor nodes to reduce overall power consumption and ensure these tasks finished before deadlines. In this paper, we propose a soft real-time fault-tolerant task allocation algorithm (FTAOA) for WSNs in using primary/backup (P/B) technique to support fault tolerance mechanism. In the proposed algorithm, the construction process of discrete particle swarm optimization (DPSO) is achieved through adopting a binary matrix encoding form, minimizing tasks execution time, saving node energy cost, balancing network load, and defining a fitness function for improving scheduling effectiveness and system reliability. Furthermore, FTAOA employs passive backup copies overlapping technology and is capable to determinate the mode of backup copies adaptively through scheduling primary copies as early as possible and backup copies as late as possible. To improve resource utilization, we allocate tasks to the nodes with high performance in terms of load, energy consumption, and failure ratio. Analysis and simulation results show the feasibility and effectiveness of FTAOA. FTAOA can strike a good balance between local solution and global exploration and achieve a satisfactory result within a short period of time.

Cooperative wireless networking, which is promising in improving the system operation efficiency and reliability by acquiring more accurate and timely information, has attracted considerable attentions to support many services in practice. However, the problem of secure cooperative communication has not been well investigated yet. In this paper, we exploit physical layer security to provide secure cooperative communication for wireless ad hoc networks (WANETs) where involve multiple source-destination pairs and malicious eavesdroppers. By characterizing the security performance of the system by secrecy capacity, we study the secrecy capacity optimization problem in which security enhancement is achieved via cooperative relaying and cooperative jamming. Specifically, we propose a system model where a set of relay nodes can be exploited by multiple source-destination pairs to achieve physical layer security. We theoretically present a corresponding formulation for the relay assignment problem and develop an optimal algorithm to solve it in polynomial time. To further increase the system secrecy capacity, we exploit the cooperative jamming technique and propose a smart jamming algorithm to interfere the eavesdropping channels. Through extensive experiments, we validate that our proposed algorithms significantly increase the system secrecy capacity under various network settings.

In Vehicular Ad hoc NETworks (VANETs), authentication is a crucial security service for both inter-vehicle and vehicle-roadside communications. On the other hand, vehicles have to be protected from the misuse of their private data and the attacks on their privacy, as well as to be capable of being investigated for accidents or liabilities from non-repudiation. In this paper, we investigate the authentication issues with privacy preservation and non-repudiation in VANETs. We propose a novel framework with preservation and repudiation (ACPN) for VANETs. In ACPN, we introduce the public-key cryptography (PKC) to the pseudonym generation, which ensures legitimate third parties to achieve the non-repudiation of vehicles by obtaining vehicles' real IDs. The self-generated PKCbased pseudonyms are also used as identifiers instead of vehicle IDs for the privacy-preserving authentication, while the update of the pseudonyms depends on vehicular demands. The existing ID-based signature (IBS) scheme and the ID-based online/offline signature (IBOOS) scheme are used, for the authentication between the road side units (RSUs) and vehicles, and the authentication among vehicles, respectively. Authentication, privacy preservation, non-repudiation and other objectives of ACPN have been analyzed for VANETs. Typical performance evaluation has been conducted using efficient IBS and IBOOS schemes. We show that the proposed ACPN is feasible and adequate to be used efficiently in the VANET environment.

Cooperative communication, which utilizes nearby terminals to relay the overhearing information to achieve the diversity gains, has a great potential to improve the transmitting efficiency in wireless networks. To deal with the complicated medium access interactions induced by relaying and leverage the benefits of such cooperation, an efficient Cooperative Medium Access Control (CMAC) protocol is needed. In this paper, we propose a novel cross-layer distributed energy-adaptive location-based CMAC protocol, namely DEL-CMAC, for Mobile Ad-hoc NETworks (MANETs). The design objective of DEL-CMAC is to improve the performance of the MANETs in terms of network lifetime and energy efficiency. A practical energy consumption model is utilized in this paper, which takes the energy consumption on both transceiver circuitry and transmit amplifier into account. A distributed utility-based best relay selection strategy is incorporated, which selects the best relay based on location information and residual energy. Furthermore, with the purpose of enhancing the spatial reuse, an innovative network allocation vector setting is provided to deal with the varying transmitting power of the source and relay terminals. We show that the proposed DEL-CMAC significantly prolongs the network lifetime under various circumstances even for high circuitry energy consumption cases by comprehensive simulation study.

We propose a novel Quality of Service (QoS) vertical handoff scheme with the support of the Software-Defined Network (SDN) technique for the heterogeneous wireless networks. The proposed scheme solves two important issues of the vertical handoff: network selection and handoff timing. In this paper, the network selection is formulated as an 0-1 integer programming problem, which maximizes the overall QoS and avoids the network congestion. After the network selection process is finished, a mobile will wait for a time period for implementing the vertical handoff. The selected network should be consistently more appropriate than the current network during the time period, the mobile will transfer its inter-network connection to the selected network. Our proposed scheme ensures that, a mobile will transfer to the most appropriate network at the most appropriate time. Comprehensive simulation has been conducted. It is shown that the proposed scheme reduces the number of vertical handoffs, and maximizes the overall QoS significantly comparing with existing schemes.

With the enormous and increasing user demand, I/O performance is one of the primary considerations to build a data center. Several new technologies in data centers, such as tiered storage [33], prompt the widespread usage of multi-level cache techniques. In these storage systems, the upper level storage typically serves as a cache for the lower level, which forms a distributed multi-level cache system. However, although many excellent multi-level cache algorithms are proposed to improve the I/O performance, they still have potential to be enhanced by investigating the history information of hints [28]. To address this challenge, in this paper, we propose a novel Hint Frequency-based Approach (HFA), to improve the overall multi-level cache performance of storage systems. The main idea of HFA is using hint frequencies (the total number of demotions/promotions by employing demote/promote hints) to efficiently explore the valuable history information of data blocks among multiple levels. HFA can be applied with several popular multi-level cache algorithms, such as Demote, Promote, Hint-K, etc. Simulation results show that, compared to original multi-level cache algorithms such as Demote, Promote and Hint-K, HFA can improve the I/O performance by up to 20% under different I/O workloads.

Providing lightweight authentication and resisting Denial of Service (DoS) attacks are challenging problems in wireless ad hoc networks, such as wireless sensor networks (WSNs). We introduce two improved protocols based on fault-tolerant protocol and DoS-resistant protocol in Multilevel μTESLA to overcome these difficulties. The proposed Efficient Fault-Tolerant Protocol contributes in shortening the recovery time when highlevel packets are lost, and hence reduces the risk of memory-based DoS attacks. The proposed Enhanced DoS-Resistant Protocol enhances the resistance to DoS attacks by offering packet-loss recovery of authentication message.

This paper deals with the retransmission protocol design for cognitive radio networks by exploiting the primary hybrid ARQ. In contrast with previous work that focuses on cancellation based retransmissions, we propose a novel cooperative coding based retransmission protocol for cognitive radio networks. The design objective is to improve the throughput of the primary user and create the transmission opportunity for the secondary user. By exploiting the primary retransmission appropriately, the knowledge on primary packet which is required by the cooperative coded retransmission can be obtained without any non-causal assumption. Performances on the proposed protocol are analyzed mathematically, and verified by numerical results.

This paper investigates the secure information transfer issue for cognitive radio networks by exploiting the spectrum leasing technique. The design objective is to improve the secrecy rate of the primary user, and meanwhile, create the transmission opportunities for the secondary users. To achieve this goal, we consider a system model where the primary user harnesses the assist of the secondary users in the form of cooperative transmitting. And in return, the primary user provides certain transmission opportunities over licensed spectrum for the cooperating secondary users. We propose an auction-based spectrum leasing scheme to jointly formulate the optimal cooperator selection and resource allocation problems. By analyzing and solving the dominant strategy equilibrium for the proposed scheme, we present reliable predictions for the system behavior and the achievable performances. Simulation results reveal that the proposed scheme could provide substantial gains for both the primary user and the cooperating secondary user.

Indoor localization remains a hot topic during the last few decades. Previous research mainly relies on wireless fingerprints and thus demands a large number of APs or labor-intensive site survey. To this end, by exploring Bluetooth features and leveraging user motions, we introduce a novel localization scheme that needs neither APs nor site survey. More specifically, through a systematic experimental study, we gain in-depth understandings of Bluetooth characteristics e.g., The impact of various factors such as distance, orientation, and obstacles on the Bluetooth RSSI (Received Signal Strength Indicator). With the empirical experiences, a novel motion-assisted localization model is built to describe the relationship between RSSI and device location. Based on the model, we design a localization scheme that iteratively adjusts the search directions according to RSSI changes to approach the target device. We prototype and evaluate our system in several real-world scenarios. Extensive experiments show that the proposed scheme is efficient in terms of localization accuracy, searching time and energy consumption.

Next-generation wireless networks integrate multiple wireless access technologies to provide seamless wireless connectivity for mobile nodes (MNs). When MNs move in wireless heterogeneous networks, they may suffer from the great degradation of received signal strength (RSS) and further quality of services (QoS), if randomly selecting an access point (AP). We address vertical handoff with game theory to enable MNs to trigger the handoff and select an appropriate network from multiple wireless access technologies. On the other hand, the existing vertical handoff schemes lack of jointly considering the behaviors of MNs and APs for approaching the reality. To solve this problem, we propose a repeated game based scheme for vertical handoff. Each sub-game is formulated as a non-cooperative strategic game between a MN and an AP in which the Nash equilibrium is the solution of each strategic game. The proposed repeated game is to optimize the utility functions of the whole network by finding an equilibrium point. We perform the performance analysis, which shows that proposed scheme can achieve better bandwidth utilization and throughput of the network compared to the AP random selection scheme.

Since the era of Internet of Vehicles (IoVs) is coming in next few years, real-time data transmission between vehicles and road-side sensor nodes has many application scenarios. However, due to different characteristics of IoVs and WSNs (Wireless Sensor Networks), real-time traffic data transmission is too complicated and slow when emergencies occurred in many RSUs-sparse (Road Side Units) areas. We build a simple integrated network model and propose a broadcast authentication protocol, namely Paralleling Broadcast Authentication Protocol (PBAP), aiming at enhance energy efficiency and providing network security in the direct communication between vehicles and WSNs. The simulation results demonstrate that the protocol can effectively extend lifetime of WSNs by improving the utilization rate of the keys and show nice properties in different channel loss ratio and different degrees of DoS attacks.

We study the impact of the dynamics of the end-user playout buffer on the quality of experience (QoE) of the HTTP progressive video over wireless networks in this paper, especially the impact of the block size and the finite video length on the playback interruption during wireless video watching. The playout buffer at the end-user is formulated as a G/D/1 queue with arbitrary packet arrival and deterministic playback. An analytical framework is presented to investigate the impact of the playout buffer dynamics on the wireless HTTP progressive video QoE by analyzing the transient queue length of the buffer with the diffusion approximation method. The close-form expressions for the user-perceived video quality, in terms of the buffering delay, the playback duration and the playback interruption probability, are obtained. The results show that both the buffering delay and the interruption probability decrease as the average arrival rate increases for a given buffer threshold. The higher the video quality, the greater the impact of the dynamics of the arrival process on the buffering delay and interruption probability is. Moreover, a larger playback threshold leads to a more rapidly decreasing of the interruption probability along with the increase of the average packet arrival rate. The proposed analytical framework is validated by simulations.

Device-to-device (D2D) communication underlaying a cellular infrastructure has been proposed as a means of facilitating rich local services and offloading the base station traffic. However, D2D communication presents a challenge in radio resource management due to the potential interference it may cause to the cellular network. In this paper, the joint optimization problem of D2D mode selection, modulation and coding schemes (MCSs) assignment, radio resources and power allocation is formulated to minimize the overall power consumption under minimum required rate guarantee. The problem is decoupled into two sub-problems which are solved by Lagrangian relaxation and tabu search methods, respectively. Simulation results show its performance superiority over other schemes, especially in the scenarios with high required rate and limited resources.

We address the route optimization problem for a nested mobile IPv6 NEtwork MObility (NEMO) environment. We propose an adaptive scheme which can optimize the routing process of the data communication, and minimize the end-to-end delay. The adaptive scheme consists of two sub-schemes: mobility-transparency sub-scheme and time-saving sub-scheme. The mobility-transparency sub-scheme performs well for the high mobility scenarios, while the time-saving sub-scheme performs well for the low mobility and large communication traffic scenarios. A threshold is used to determine which sub-scheme should be applied for the current situation. Theoretical analysis and simulation results demonstrate that the proposed scheme can reduce the end-to-end delay of data communication for nested mobile IPv6 NEMO environment significantly.

Data communication in mobile ad hoc cognitive networks (MACNets) significantly suffers from link instability and channel interference. The availability and stability of each link in MACNets highly depends on not only the relative movement of neighbor nodes but also the adjacent communication among primary nodes and among cognitive nodes. In multihop and multiflow MACNets, this problem becomes even worse because multiple links potentially interfere with each other. In this paper, we propose a cross-layer distributed approach, called mobility-prediction-based joint stable routing and channel assignment (MP-JSRCA), to maximize the network throughput by jointly selecting stable routes and assigning channels avoiding inter- and intra-flow interferences based on mobility prediction. To quantitatively measure the communication quality of links, we propose a new metric data transmission cost (DTC) that captures node mobility, impact to primary nodes, and channel conflict among cognitive nodes. In our MP-JSRCA, each relay node selects the best link with the smallest DTC as the next hop, within a specified sector region towards the destination. NS2-based simulation results demonstrate that our MP-JSRCA algorithm significantly improves network throughput, and the higher degree of interference MACNets experience, the more improvement can be achieved.

In this article, we present a novel distributed separate coding (DSC) scheme for continuous data collection in wireless sensor networks with a mobile base station (mBS). By separately encoding a certain number of data segments in a combined segment and doing decoding-free data replacement in the buffers of each sensor node, the DSC scheme is shown as an efficient method for continuously collecting data segments with a high success ratio. The proposed DSC scheme has a salient feature: with a minimum buffer size 2 in each sensor node, by querying any m −1 sensor nodes, the mBS can reconstruct the m latest data segments with high probability, where m is the number of latest data segments in a time interval t in which n ( t ) ( m ≤ n ( t )) data segments are generated. The necessary storage space in each sensor node can be adjusted by changing the number of sensor nodes queried by the mBS. Furthermore, the transmission cost for data submission to the mBS can be reduced with some additional storage space in each sensor node. The comprehensive performance evaluation has been conducted through computer simulation. It is shown that the proposed DSC scheme outperforms the existing scheme significantly.

Secure data transmission is a critical issue for wireless sensor networks (WSNs). Clustering is an effective and practical way to enhance the system performance of WSNs. In this paper, we study a secure data transmission for cluster-based WSNs (CWSNs), where the clusters are formed dynamically and periodically. We propose two secure and efficient data transmission (SET) protocols for CWSNs, called SET-IBS and SET-IBOOS, by using the identity-based digital signature (IBS) scheme and the identity-based online/offline digital signature (IBOOS) scheme, respectively. In SET-IBS, security relies on the hardness of the Diffie-Hellman problem in the pairing domain. SET-IBOOS further reduces the computational overhead for protocol security, which is crucial for WSNs, while its security relies on the hardness of the discrete logarithm problem. We show the feasibility of the SET-IBS and SET-IBOOS protocols with respect to the security requirements and security analysis against various attacks. The calculations and simulations are provided to illustrate the efficiency of the proposed protocols. The results show that the proposed protocols have better performance than the existing secure protocols for CWSNs, in terms of security overhead and energy consumption.

Cooperative communication, which utilizes neighboring nodes to relay the overhearing information, has been employed as an effective technique to deal with the channel fading and to improve the network performances. Network coding, which combines several packets together for transmission, is very helpful to reduce the redundancy at the network and to increase the overall throughput. Introducing network coding into the cooperative retransmission process enables the relay node to assist other nodes while serving its own traffic simultaneously. To leverage the benefits brought by both of them, an efficient Medium Access Control (MAC) protocol is needed. In this paper, we propose a novel network coding aware cooperative MAC protocol, namely NCAC-MAC, for wireless ad hoc networks. The design objective of NCAC-MAC is to increase the throughput and reduce the delay. Simulation results reveal that NCAC-MAC can improve the network performance under general circumstances comparing with two benchmarks.

Sensing coverage is one of fundamental problems in wireless sensor networks. In this paper, we investigate the polytype target coverage problem in heterogeneous wireless sensor networks where each sensor is equipped with multiple sensing units and each type of sensing unit can sense an attribute of multiple targets. How to schedule multiple sensing units of a sensor to cover multiple targets becomes a new challenging problem. This problem is formulated as an integer linear programming problem for maximizing the network lifetime. We propose a novel energy-efficient target coverage algorithm to solve this problem based on clustering architecture. Being aware of the coverage capability and residual energy of sensor nodes, the clusterhead node in each cluster schedules the appropriate sensing units of sensor nodes that are in the active status to cover multiple targets in an optimal way. Extensive simulations have been carried out to validate the effectiveness of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we study the network Lifetime Maximization problem in Mobile healthcare sensor systems (LMM). For the healthecare system, we consider a dynamic scenario where users are mobile at their own wills and periodically report their personal health information (PHI) to a static sink, e.g. a powerful server, for further processing and distributing. The objective is to optimize the network lifetime by flow scheduling. The major difficulty lies in the time-dependent network topologies. Therefore, we propose a novel temporal-spatial network modeling method by extending current model with time dimension. Based on this model, we show that if the movement of users are known in advance (i.e. offline case), the problem can be optimally solved in polynomial time by a linear programming. However, the online LMM problem is much more difficult to tackle, since we prove that there exists no online algorithm with a constant performance ratio to the offline optimal algorithm in terms of the network lifetime. We further design simulations to show the performance gap between online and offline LMM. Considering the user mobility within a given scenario follows some certain patterns, we show the potential improvements of using a prediction-based method. This investigation provides certain insights on designing efficient online algorithms for the LMM problem.

Physical layer security has emerged as a key technique for providing trustworthy and reliable future wireless networks and has witnessed a significant growth in the past few years. In this paper, we aim to improve the physical layer security and provide secure cooperative communication through cooperative relay assignment. By characterizing the security performance of the system by secrecy capacity, we study the secrecy capacity maximization problem in cooperative ad hoc networks with the involvement of multiple malicious eavesdroppers. Specifically, we propose a system model where a set of relay nodes can be exploited by multiple source-destination pairs to achieve physical layer security. We theoretically present a corresponding formulation for the secrecy capacity maximization problem. Then we develop an optimal relay assignment algorithm to solve the problem in polynomial time. The basic idea behind our proposed algorithm is to boost the capacity of the primary channel by simultaneously decreasing the capacity of the eavesdropping channel. Through extensive experiments, we validate that our proposed relay assignment algorithm significantly increase the system secrecy capacity under various network settings.

This paper investigates secure cooperative communication with the involvement of multiple malicious eavesdroppers. By characterizing the security performance of the system by secrecy capacity, we study the secrecy capacity maximization problem in cooperative communication aware ad hoc networks. Specifically, we propose a system model where secrecy capacity enhancement is achieved by the assignment of cooperative relays. We theoretically present a corresponding formulation for the problem and discuss the security gain brought by the relay assignment process. Then, we develop an optimal relay assignment algorithm to solve the secrecy capacity maximization problem in polynomial time. The basic idea behind our proposed algorithm is to boost the capacity of the primary channel by simultaneously decreasing the capacity of the eavesdropping channel. To further increase the system secrecy capacity, we exploit the jamming technique and propose a smart jamming algorithm to interfere the eavesdropping channels. Analysis and experimental results reveal that our proposed algorithms significantly increase the system secrecy capacity under various network settings.

Relaying and cooperation have emerged as important research topics in wireless communication over the past half-decade. During cooperative communication, spatial diversity can be achieved by exploiting the relaying capabilities of the involved relay nodes, which may vastly enhance the achieved system capacity. The potential gains largely depend on the location of relay nodes. In this paper, we study the relay node placement problem for multi-pair cooperative communication in wireless networks, where a finite number of candidate relay nodes can be placed to help the transmission of multiple source-destination pairs. Our objective is to maximize the system capacity. After formulating the relay node placement problem, we comprehensively study the effect of relay location on cooperative link capacity and show several attractive properties of the considered problem. As the main contribution, we develop a geographic aware relay node placement algorithm which optimally solves the relay node placement problem in polynomial time. The basic idea is to place a set of relay nodes to the optimum locations so as to maximize the system capacity. The efficiency of our proposed algorithm is evaluated by the results of series experimental studies.

In this paper, we present a novel Group Data Collection (GDC) scheme for wireless sensor networks with a mobile base station by using coding for data storage in the sensor nodes. By separately encoding a certain number of data segments in a combined segment and doing data replacement in each sensor node, the proposed GDC scheme not only provides an efficient storage method for group data, but also achieves a high success ratio of data collection. The number of necessary buffers in each sensor node can be adjusted by changing the frequency of performing data collection. The performance evaluation has been conducted through comprehensive computer simulations. It further demonstrates the feasibility and superiority of the proposed GDC scheme.

This paper exploits sink mobility to prolong the network lifetime in wireless sensor networks where the information delay caused by moving the sink should be bounded. Due to the combinational complexity of this problem, most previous proposals focus on heuristics and provable optimal algorithms remain unknown. In this paper, we build a unified framework for analyzing this joint sink mobility, routing, delay, and so on. We discuss the induced subproblems and present efficient solutions for them. Then, we generalize these solutions and propose a polynomial-time optimal algorithm for the origin problem. In simulations, we show the benefits of involving a mobile sink and the impact of network parameters (e.g., the number of sensors, the delay bound, etc.) on the network lifetime. Furthermore, we study the effects of different trajectories of the sink and provide important insights for designing mobility schemes in real-world mobile WNNs.

ABSTRACT This paper deals with the partial target coverage problem in wireless sensor networks under a novel coverage model. The most commonly used method in previous literature on the target coverage problem is to divide continuous time into discrete slots of different lengths, each of which is dominated by a subset of sensors while setting all the other sensors into the sleep state to save energy. This method, however, suffers from shortcomings such as high computational complexity and no performance bound. We showed that the partial target coverage problem can be optimally solved in polynomial time. First, we built a linear programming formulation, which considers the total time that a sensor spends on covering targets, in order to obtain a lifetime upper bound. Based on the information derived in previous formulation, we developed a sensor assignment algorithm to seek an optimal schedule meeting the lifetime upper bound. A formal proof of optimality was provided. We compared the proposed algorithm with the well‐known column generation algorithm and showed that the proposed algorithm significantly improves performance in terms of computational time. Experiments were conducted to study the impact of different network parameters on the network lifetime, and their results led us to several interesting insights. Copyright © 2011 John Wiley & Sons, Ltd.

This paper exploits sink mobility to prolong the network lifetime in wireless sensor networks (WSNs) where the information delay caused by moving the sink should be bounded. We build a unified framework for analyzing this joint sink mobility and routing problem. We offer a mathematical modeling that is general and captures diversified issues, e.g. sink mobility, routing, delay, etc. We discuss the induced subproblems and present efficient solutions for them. Then, we generalize these solutions and propose a polynomial-time optimal algorithm for the origin problem. In simulations, we show the benefits of involving a mobile sink. We also show that the impact of the delay bound on the network lifetime.

In this paper, we present a novel data collection scheme for wireless sensor networks by using separate network coding (SNC). By separately encoding a certain number of data segments in a combined data segment and doing decoding-free data replacement, SNC not only provides efficient storage method for continuous data, but also maintains a high success ratio of data collection. The performance evaluation has been conducted through comprehensive computer simulation. It is shown that SNC outperforms the exiting scheme significantly.

In the resource constrained Wireless Sensor Networks (WSNs), the broadcast authentication is widely fulfilled by the emerging delayed authentication technique. The one level backward one-way key chain in the delayed authentication technique limits heavily the duration of the broadcast authentication. In this paper, we propose a long duration broadcast authentication scheme for WSNs. The proposed scheme uses a novel hierarchical key chain to extend the duration of broadcast authentication to be as long as the whole lifetime of WSNs. Moreover, the overhead of the proposed scheme is significantly reduced compared to that with one level key chain. Extensive experimental results show that the novel hierarchical key chain fully outperforms the one level key chain in terms of memory and computation.

Time synchronization is crucial to Wireless Sensor Networks (WSNs) due to the requirement of coordination between sensor nodes. Existing secure time synchronization protocols of WSNs introduce high overhead when used for global time synchronization. In this paper, we propose a lightweight secure global time synchronization protocol for WSNs. In the proposed protocol, a broadcast synchronization packet makes all sensor nodes in the network synchronize with the trusted source. The synchronization packet is protected by a proposed broadcast authentication algorithm, which introduces asymmetry by transmitting hash values of secret keys in advance. It achieves immediate authentication and does not require the loose time synchronization. To defend the pulse-delay attacks, the arrival time of the synchronization packet is checked according to the estimated arrival time interval. The upper bound on the skew of the proposed protocol is proved. The message complexity in one period of the proposed protocol is O(n) where n represents the number of sensor nodes. The simulation results show that the maximum skew is within tens of milliseconds.

Cooperative communication, which utilizes neighboring nodes to relay the overhearing information, has been employed as an effective technique to deal with the channel fading and to improve the network performances. And network coding, which combines several packets together for transmission, is very helpful to reduce the redundancy at the network and to increase the overall throughput. Introducing network coding into the cooperative retransmission process, enables the relay node to assist other nodes while serving its own traffic simultaneously. To leverage the benefits brought by both of them, an efficient Medium Access Control (MAC) protocol is needed. In this paper, we propose a novel network coding aware cooperative MAC protocol, namely NCAC-MAC, for wireless networks. The design objective of NCAC-MAC is to increase the throughput and reduce the delay of the network. Simulation results reveal that our NCAC-MAC can improve the network performance under general circumstances.

One of the challenging issues for supporting emergency services in wireless networks is coordinating the network under emergent situations. Cooperative communication (CC) is a promising approach which can offer significant enhancements in multi-hop wireless networks. This paper investigates the potential issues in using this communication paradigm to support emergency services. We focus on promoting energy-efficient and congestion-aware cooperative networking for emergency services based on the idea of Do-It-Yourself. We propose a novel cross-layer design which jointly considers the problems of route selection in network layer, congestion and non-cooperation avoidance among multiple links in MAC layer under cooperative multi-hop wireless environments. We formulate the multi-hop cooperative flow routing and relay node selection process as an optimization problem. Based on the formulations and models, we propose a self-supported networking scheme including three novel components that make the solution procedure highly efficient. Analysis and simulation results show that our approaches significantly achieve better network performance and typically satisfy the requirements for emergency services in multi-hop wireless networks.

Throughput capacity in mobile ad hoc networks has been studied extensively under many different mobility models. However, most previous research assumes global mobility, and the results show that a constant per-node throughput can be achieved at the cost of very high delay. Thus, we are having a very big gap here, i.e., either low throughput and low delay in static networks or high throughput and high delay in mobile networks. In this paper, employing a practical restricted random mobility model, we try to fill this gap. Specifically, we assume that a network of unit area with n nodes is evenly divided into cells with an area of n^{-2\alpha }, each of which is further evenly divided into squares with an area of n^{-2\beta} (0 \le \alpha \le \beta \le {1\over 2} ). All nodes can only move inside the cell which they are initially distributed in, and at the beginning of each time slot, every node moves from its current square to a uniformly chosen point in a uniformly chosen adjacent square. By proposing a new multihop relay scheme, we present smooth trade-offs between throughput and delay by controlling nodes' mobility. We also consider a network of area n^\gamma (0\le \gamma \le 1) and find that network size does not affect the results obtained before.

As a promising way in surveillance applications, wireless sensor networks (WSNs) often encounter the target coverage (TC) problem, i.e., scheduling energy-limited sensors to monitor physical targets to prolong the network lifetime. Due to the complexity of the problem (scheduling in time domain), previous proposals mainly focus on heuristics and provable optimal algorithms remain unknown. In this paper, we fill in the research blank by providing several theoretical results. First, we present a mathematical formulation and several investigations of the problem in time domain. Such time-related results provide fundamental understandings of the problem and serve as a basis. Second, we offer an upper bound on the network lifetime derived from the time-dependant formulation. The bound, which is solvable in polynomial-time, serves as a performance benchmark. Third, we verify the set cover-based method, which is widely used by previous studies, via a transformation of the problem from time to space domain. Lastly, we offer a specialized nonlinear column generation (CG) based approach to solve the problem in space domain optimally. Simulation results show that not only the bound is effective, but also the CG-based approach offers significant improvement on the network lifetime over a brutal search algorithm and a state-of-art heuristic.

Public Key Cryptography (PKC) is widely used for broadcast authentication. Intensive use of PKC for broadcast authentication, however, is thought to be expensive to resource constrained sensor nodes. In this paper, we propose a novel PKC based broadcast authentication scheme using signature amortization for Wireless Sensor Networks (WSNs). The proposed scheme exploits only one Elliptic Curve Digital Signature Algorithm (ECDSA) signature to authenticate all broadcast messages. Thus, the overhead for the signature is amortized over all broadcast messages. Besides low overhead, the proposed scheme retains high security that is as strong as conventional PKC based broadcast authentication schemes. Moreover, the proposed scheme can achieve immediate authentication and does not require time synchronization. For the implementation of the proposed scheme, an efficient public key distribution protocol is also presented in this paper. Experimental results of a testbed show that the overhead for authenticating a broadcast message is reduced significantly.

Abstract A mobile ad hoc network (MANET) is a self‐organized and adaptive wireless network formed by dynamically gathering mobile nodes. Since the topology of the network is constantly changing, the issue of routing packets and energy conservation become challenging tasks. In this paper, we propose a cross‐layer design that jointly considers routing and topology control taking mobility and interference into account for MANETs. We called the proposed protocol as Mobility‐aware Routing and Interference‐aware Topology control (MRIT) protocol. The main objective of the proposed protocol is to increase the network lifetime, reduce energy consumption, and find stable end‐to‐end routes for MANETs. We evaluate the performance of the proposed protocol by comprehensively simulating a set of random MANET environments. The results show that the proposed protocol reduces energy consumption rate, end‐to‐end delay, interference while preserving throughput and network connectivity. Copyright © 2010 John Wiley & Sons, Ltd.

Sink scheduling is shown to be a promising scheme in wireless sensor networks. However, previous approaches on this topic suffer from poor performance due to lack of joint considerations. Therefore, in this paper, we aim to fill in the research blank. First, we develop a novel notation Placement Pattern (PP) to bound time-varying routes with placement of sinks. This bounding technique transforms time-related constraints into pattern-based ones and allows us to formulate this optimization in pattern domain. If there is only one sink, we develop a polynomial time algorithm to solve it optimally. If there are multiple sinks, we develop a column generation based approach to solve it efficiently. Simulations not only demonstrate the efficiency of proposed algorithms but also substantiate the importance of sink mobility for energy-constrained sensor networks.

One of the challenging issues for supporting emergency services in wireless ad hoc networks (WANETs) is coordinating the network under emergency situations. It may lead to inefficient use of the network resources by increasing congestion, as well as affect the network connectivity due to the non-cooperation behaviors of some selfish users. In this paper, we focus on promoting self-supported and congestion-aware networking for emergency services in WANETs based on the idea of Do-It-Yourself1. We model network congestion and non-cooperation behaviors according to the relations between nodes in the constructed dependency graph. Then we propose an energy-efficient and congestion-aware routing protocol for the emergency services of WANETs. Based on the proposed model and routing protocol, we design two novel movement schemes, called Direct Movement to potential selfish/busy Relays (DMR) scheme and Iterative Movement to potential selfish/busy Relays (IMR) scheme for urgent sources to support themselves and to avoid congestion and non-cooperation. Analysis and simulation results show that our approaches significantly achieve better network performance and typically satisfy the requirements for emergency services in WANETs.

A forest fire has long been a severe threat to the forest resources and human life. The threat could effectively be mitigated by timely and accurate detection. In this paper, we propose a novel accurate forest fire detection system using Wireless Sensor Networks (WSNs). In the proposed system, the detection accuracy is increased by applying the multi-criteria detection that an alarm decision depends on multiple attributes of a forest fire. The multi-criteria detection is implemented by the artificial neural network which fuses sensing data corresponding to multiple attributes of a forest fire into an alarm decision. Due to the utilization of the artificial neural network, the proposed system enjoys low overhead and the self-learning capability. Furthermore, we have developed a prototype consisting TelosB sensor nodes and carried out extensive experiments to study the performance of the proposed system. We have also developed a solar battery in order to persistently power the unattended sensor node deployed in the forest.

Directional antennas in wireless ad hoc networks (WANETs) offer great potential to reduce the radio interference, and improve the communication throughput. Using directional antennas, however, introduces a new problem in the wireless media access control (MAC), deafness, which may cause severe performance degradation. To solve the deafness problem, in this paper, we propose a novel CRDMAC protocol by using a sub-transmission channel and RTR (Ready To Receive) packets, which modifies the IEEE 802.11 distributed coordinated function. The sub-channel avoids collisions to other ongoing transmission and the RTR packets notify the neighbors that the mutual transmission has finished. The proposed MAC protocol decreases the binary exponential back off time of the waiting nodes. We evaluate our protocol through simulations. Simulation results show that the proposed protocol outperforms the existing DMAC (directional MAC) protocol and the CRCM (Circular RTS and CTS MAC) protocol in terms of throughput and packet drop rate.

We address the optimal sink scheduling problem in wireless sensor networks (WSNs). The problem is inherently difficult since sink scheduling and data routing are tightly coupled. Previous approaches either have questionable performance due to no joint considerations, or are based on relaxed constraints. Our aim is to fill in this blank in the research. First, by discretizing continuous time, we develop a novel bound technique to connect time-varying routes with the placement of sinks. This bounding technique transforms time-related constraints into pattern-based ones and allows us to mathematically formulate this optimization in a pattern-based way. The complexity of directly solving this optimization is intractable; therefore, on the basis of column generation (CG), a computationally efficient algorithm is developed to reduce the complexity by decomposing the problem into sub-problems and iteratively solving them to approach optimality. Simulations demonstrate the efficiency of the algorithm and substantiate the importance of sink mobility in energy-constrained sensor networks.

In this paper, we study the secure routing for cluster-based sensor networks where clusters are formed dynamically and periodically. We point out the deficiency in the secure routing protocols with symmetric key pairing. Along with the investigation of ID-based cryptography for security in WSNs, we propose a new secure routing protocol with ID-based signature scheme for cluster-based WSNs, in which the security relies on the hardness of the Diffie-Hellman problem in the random oracle model. Because of the communication overhead for security, we provide analysis and simulation results in details to illustrate how various parameters act between security and energy efficiency.

Recently, sink deployment, in the form of deploying the sink among different sites so as to leverage traffic burden, is shown to be a promising scheme to save energy and prolong network lifetime in wireless sensor networks. For this paradigm, the choice of sink sites plays a critical role in the overall system performance. In this paper, we address the optimal deployment problem for the sink in wireless sensor networks, where routing issues are naturally involved. The major contribution of this paper is the development of an efficient grid-based algorithm to solve this problem. By dividing the continuous search space into a limited number of so-called communication intersections, computational complexity has been significantly reduced. A formal proof of optimality for this algorithm is given and several interesting properties have been revealed by theoretic analysis as well as experimental results.

Throughput capacity in wireless ad hoc networks has been studied extensively under many different mobility models such as i.i.d. mobility model, Brownian mobility model, random walk model, and so on. Most of these research works assume global mobility, i.e., each node moves around in the whole network, and the results show that a constant per-node throughput can be achieved at the cost of very high expected average end-to-end delay. Thus, we are having a very big gap here, either low throughput and low delay in static networks or high throughput and high delay in mobile networks. In this paper, employing a more practical restricted random mobility model, we try to fill in this gap. Specifically, we assume a network of unit area with n nodes is evenly divided into n2¿cells with an area of n-2¿where 0 ¿ ¿ ¿ 1/2, each of which is further evenly divided into squares with an area of n-2ßwhere 0 ¿ ¿ ¿ ß ¿ 1/2. All nodes can only move inside the cell which they are initially distributed in, and at the beginning of each time slot, every node moves from its current square to a uniformly chosen point in an uniformly chosen adjacent square. Proposing a new multi-hop relay scheme, we present an upper bound and a lower bound on per-node throughput capacity and expected average end-to-end delay, respectively. We finally explicitly show smooth trade-offs between throughput and delay by controlling nodes' mobility.

Stochastic network calculus requires special care in the search of proper stochastic traffic arrival models and stochastic service models. Tradeoff must be considered between the feasibility for the analysis of performance bounds, the usefulness of performance bounds, and the ease of their numerical calculation. In theory, transform between different traffic arrival models and transform between different service models are possible. Nevertheless, the impact of the model transform on performance bounds has not been thoroughly investigated. This paper is to investigate the effect of the model transform and to provide practical guidance in the model selection in stochastic network calculus.

This paper addresses the polytype target coverage problem for heterogeneous wireless sensor networks (HWSNs) with clustered configurations. This problem is formulated as an integer programming (IP) problem for maximizing the whole lifetime of HWSNs. We present an energy-efficient target coverage algorithm (ETCA) to solve this problem and balance the energy consumption of sensor nodes. Each sensor node first calculates its sensing capability with neighbors' and sends a message with its current status information to a clusterhead. Then, the clusterhead decides which sensing units should be turned on to cover the targets in the most optimized way based on the information received from all its member nodes. Simulation results show that the performance of ETCA is close to the IP-solution that is an optimal coverage scheme regarding the energy efficiency. Moreover, ETCA can prolong 16% network lifetime compared with the energy first (EF) algorithm.

This paper addresses the secure data aggregation for wireless sensor networks (WSNs) with both static tree architecture and dynamic cluster-based architecture. For WSNs with static tree architecture, we propose the Leaf Node Representation (LNR) scheme to solve the Id problem and make the key stream-based encrypted data aggregation feasible and practical for large scale networks. For WSNs with dynamic cluster-based architectures, we propose the Delayed Hop-by-hop Authentication (DHA) scheme to provide hop-by-hop data integrity and data freshness only using individual keys. Analytical results show that the proposed scheme can reduce the communication overhead significantly compared to a well known existing scheme.

This paper deals with the partial target coverage (PTC) problem in wireless sensor networks with the objective of optimizing network lifetime. We first build a linear programming formulation, which takes total time a sensor spends on covering some targets into consideration, in order to obtain a lifetime upper bound. Then, based on the information of this formulation, we develop a sensor assignment algorithm to seek an optimal time table meeting the lifetime upper bound. A formal proof of optimality is given. We compare the proposed algorithm with a state-of-the-art algorithm: column generation approach and show that the proposed algorithm significantly outperforms in terms of computational time. Experiments have been conducted to study the effect of network parameters on network lifetime and interesting insights have been offered.

Sensing coverage and network connectivity are two fundamental issues in wireless sensor networks (WSNs). Due to resource constraints of sensor nodes, it may not be possible, or necessary, to provide full coverage and/or connectivity in WSNs. Under a certain coverage and connectivity requirement, the

The target coverage problem is one of the most fundamental challenges in wireless sensor networks. Due to the complexity of the problem (time-dependent network topology and coverage constraints), previous studies have mainly focused on heuristic algorithms and the theoretical bound remains unknown. In this paper, we aim to fill in this gap by providing fundamental results. First, we investigate the properties of a problem in time domain via an example topology and build a novel transformation to connect a problem in the time domain with a corresponding problem in the space domain while maintaining the same network lifetime. Based on this transformation, we mathematically formulate the problem and build a column-generation based algorithm, which decomposes the original formulation into two sub-formulations and iteratively solves them in a way that approaches the optimal solution. We prove that the network lifetime that can be guaranteed by the proposed algorithm is at least (1-¿) of the optimum, where ¿ can be made arbitrarily small depending on the required precision.

Nowadays efficient scheduling with high fairness has attracted much attention in wireless cellular systems. In this paper we consider the downlink transmission for multiple-base-station scenario, where the proportional fairness of multiple users is taken into account. Compared with previous works in the literature, the main contributions of this paper are three-fold: (1) The proportional fairness rule is firstly employed in the multiple-base-station cooperation case. Here we propose a cooperative proportional scheduling (CPF) scheme which maximizes the sum-log utility function using gradient descent rule. (2) We show that when adopting CPF scheduling, the limiting behavior of the throughput converges to an ordinary differential equation (ODE). The limit of each scheduler-user pair's throughput is obtained by solving a fixed-point problem. (3) We propose a distributed implementation of CPF. In the developed distributed mode, the base stations are allowed to exchange their messages of local throughput in a completely distributed and asynchronous way, which makes it realizable in practice.

A wireless ad hoc network (WANET) is a collection of wireless terminals that communicate with each other without predetermined topology. Since WANET devices are power-limited, network protocols should be designed to prolong the battery lifetime of these devices. In this paper, we propose a cross-layer integration approach for power efficient routing protocol. The proposed cross-layer integration between power control in link layer and routing protocol in network layer aims to maximize the network lifetime. We implement our proposed protocol as an extension to AODV routing protocol. We evaluated the proposed protocol by comprehensively simulating a set of random WANET environments. We simulated six different metrics comparing our proposed protocol with AODV protocol. The results showed that the proposed protocol maximizes the network lifetime, reduces the end-to-end delay, and saves the total energy consumption while achieving the throughput requirement.

Target coverage problem in wireless sensor networks remains a challenge. Due to nonlinear nature, previous work has mainly focused on heuristic algorithms, which remain difficult to characterize and have no performance guarantee. To solve the problem, this paper offers two important contributions. The first contribution is to have two lifetime upper bounds, which could be used to justify performance of previously proposed heuristic algorithms. One upper bound is based on the relaxation and reformulation technique while the other is derived by relaxing coverage constraints. We study the interesting connection between those two bounds and thus endow them with physical meanings. The second contribution is proposing a column generation based (CG) approach. The objective is to find an optimal schedule, defined as a time table specifying from what time up to what time which sensor watches which targets while the maximum lifetime has been obtained. We also offer an in-depth theoretic analysis as well as several novel techniques to further optimize the approach. Numerical results not only demonstrate that the lifetime upper bounds are very tight, but also verify that the proposed CG based approach constantly yields the optimal or near optimal solution.

Free-rider problem greatly influences the performance of unstructured networks (like ad-hoc or peer-to-peer networks). To solve such problem, we focus on trust management framework, which is intended to stimulate nodes to cooperate with each other. Currently, the existing trust management framework can be classified into trust establishment framework and reputation-based framework. However, none of them was explicitly designed with the considerations on neutrality, which is indispensable issue when devising a network system. In this paper, we investigate the relation between neutrality and trust definition, and then focus on trust management of one kind of typical unstructured networks, mobile ad hoc network (MANET). We propose a neutral trust management framework of MANET from the several aspects of neutrality characteristics, objectiveness, fairness and variegation. Then, we perform analysis on our proposed framework, which shows our proposal can achieve neutrality under the location-dependent attack of free-rider.

Because wireless network coding technology may increase the total throughput in wireless networks, it has attracted a lot of attentions. However, there is few work focusing on wireless network coding over fading channels. In fact, signal fading usually occurs in wireless communications, resulting in the performance degradation of wireless networks seriously in some scenarios. To improve the throughput of wireless networks, we analyze network coding over Rayleigh fading channels, and formulate the fading compensation as an optimization problem. By solving the optimization problem, the optimal data rate is obtained. Numerical results and simulation results indicate that if the relay node transmits packets at the optimal data rate, the total throughput will increase. We also consider the selection of relay nodes, and give the optimal assignment location of relay nodes, which will increase the total throughput. In addition, based on the concept of optimal data rate proposed in this paper, an opportunistic optimal network coding (OONC) scheme is presented, which performs well under various situations.

This paper studies the precision-constraint data aggregation problem for dynamic cluster-based wireless sensor networks. The goal is to extend the network lifetime while keeping reasonable data quality. To achieve the target, we propose the dynamical precision allocation algorithm, which splits the application error bound which users can tolerate into individual local error bounds. We differentiate the sensor nodes in clustering architecture to cluster-heads and leaf nodes, arrange the error bounds to the nodes that can really reduce their transmitting messages. In order to reduce the overhead, our algorithm is merged to the cluster-head reelection process. Experimental results show that our scheme significantly improves the network lifetime compared to the existing methods.

This paper constructs an energy efficient secure data aggregation framework for wireless networks, especially for wireless sensor networks with femtocells. We propose the Leaf Node Representation (LNR) scheme and the Hop-by-hop MAC Authentication (HMA) scheme, in order to provide a balance between the security and the communication cost. The ideas in this paper are not restricted to wireless sensor network, it can be used in other kind of wireless network after modification. Under the proposed scheme, keystream-based encrypted data aggregation is feasible and practical for large scale implementations. Analytical results show that the proposed scheme can reduce the communication overhead significantly compared to existing approaches.

Abstract Wireless sensor networks have emerged recently as an effective way of monitoring remote or inhospitable physical targets, which usually have different quality of service (QoS) constraints, i.e., different targets may need different sensing quality in terms of the number of transducers, sampling rate, etc. In this paper, we address the problem of optimizing network lifetime while capturing those diversified QoS coverage constraints in such surveillance sensor networks. We show that this problem belongs to NP‐complete class. We define a subset of sensors meeting QoS requirements as acoverage pattern, and if the full set of coverage patterns is given, we can mathematically formulate the problem. Directly solving this formulation however is difficult since number of coverage patterns may be exponential to number of sensors and targets. Hence, a column generation (CG)‐based approach is proposed to decompose the original formulation into two subproblems and solve them iteratively. Here a column corresponds to a feasible coverage pattern, and the idea is to find a column with steepest ascent in lifetime, based on which we iteratively search for the maximum lifetime solution. An initial feasible set of patterns is generated through a novel random selection algorithm (RSA), in order to launch our approach. Experimental data demonstrate that the proposed CG‐based approach is an efficient solution, even in a harsh environment. Simulation results also reveal the impact of different network parameters on network lifetime, giving certain guidance on designing and maintaining such surveillance sensor networks. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract The practical success of pervasive services running in mobile wireless networks relies largely on its flexibility in providing adaptive and cost‐effective services. Service discovery is an essential mechanism to achieve this goal. As an enhancement to our previous work for service discovery, that is, model‐based service discovery (MBSD), this paper proposes a location‐based service advertisement (SA) algorithm named as MBSD‐sa. MBSD‐sa advocates the importance of service location to the service availability and integrates the service location information together with the service semantic information into service information for advertisement. MBSD‐sa utilizes prediction to estimate the service location so as to reduce the number of SA messages (SAMs). Two complementary types of SA mechanisms (Types 1 and 2) are employed by MBSD‐sa to strike the balance between the SAM overhead and the accuracy of service information. The performance of MBSD‐sa is analyzed both numerically and using simulations. Copyright © 2008 John Wiley & Sons, Ltd.

Icebergs denote data items whose total frequency of occurrence is greater than a given threshold. When data items are scattered across a large number of network nodes, searching for global icebergs becomes a challenging task especially in bandwidth limited wireless networks. Existing solutions require a central server for ease of algorithm design and/or use random sampling to reduce bandwidth cost. In this paper, we present a new distributed algorithm to search for global icebergs without any centralized control or random sampling. A new type of Bloom filter, called linked counting Bloom filter, is designed to check the membership of a set and to store the accumulative frequency of data items. We evaluate the performance of our distributed algorithm with real data sets.

One of the most important challenges in Mobile IPv6 is to provide the service for a mobile node to maintain its connectivity to the Internet when it moves from one domain to another, which is referred to as handover. Here we deal with the fast handover problem, which is to provide rapid handover service for the delay-sensitive and real-time applications. In this paper, we propose an enhanced fast handover scheme for Mobile IPv6. In our scheme, each AR (Access Router) maintains a CoA (Care of Address) table and generates the new CoA for the MN that will move to its domain. At the same time, the binding updates to home agent and correspondent node are to be performed from the time point when the new CoA for MN is known by PAR (Previous AR). Also the localized authentication procedure cooperated with the proposed scheme is provided. For the comparison with the existing fast handover scheme, detailed performance evaluation is performed. From the evaluation results, we can see that the proposed enhanced fast handover scheme can achieve low handover latency and low packet delay.

In mobile ad hoc networks (MANETs), each node should not only work for itself, but should be cooperative with other nodes. Under such environment, some nodes may misbehave for individual interests. Currently two categories of trust management frameworks, reputation-based framework and trust establishment framework, are used to guarantee nodes to perform normal behavior. However, in reputation-based framework, it is unreasonable that only one parameter, trust value, is considered. Meanwhile, the trust establishment framework is vulnerable under the selective misbehavior attack, by which the attacker performs different behaviors to different nodes. To solve these problems, we propose an objective trust management framework (OTMF) for MANETs, by which one node evaluates the trustworthiness of another node objectively based not only on direct observations, but on second-hand information. To compare the OTMF with the existing frameworks, we provide performance evaluation. The evaluation results show that the OTMF can obtain more reliable trust than the reputation-based framework and can prevent the selective misbehavior attack more effectively than the trust establishment framework.

In this paper, we present a novel 2-way handshake authentication protocol to locally authorize intra-domain roaming users for efficient authentication in mobile IP networks, which is based on authentication, authorization and accounting (AAA) architecture. We develop a detailed procedure to establish local security associations (SAs) for re-authentication using commitment schemes. By considering the traffic and mobility patterns of a mobile user (MU), as well as the message transmission time between the MU and its home AAA server, we provide a performance study for comparing the authentication latency of existing authentication protocol with our approach. The result shows that our protocol outperforms the existing authentication protocol.

For mobile ad hoc networks (MANETs) and sensor networks, due to the battery recovery effect, a node with longer idle period after finishing a transmission task will recover more over-consumed energy; however, longer recovery period requires shorter transmission period and hence higher transmission rate which leads to more energy consumption for transmission. Therefore, there is a tradeoff between the energy consumption for transmission and the energy recovery when minimizing the overall energy depletion at each node. This paper firstly defines a new metric termed actual energy depletion (AED), which quantifies the actual energy consumption during a transmission and recovery time unit of a node. We then describe the rate selection problem into an optimizing model to minimize AED subject to the channel's capacity constrain. We propose a novel energy efficient rate selection (EERS) approach for wireless battery operated networks. We analyze the mathematical procedure on how to obtain the required optimal rate for the proposed model and provide its implementation method in real networks in a distributive way. Numerical results are provided to show the efficiency of EERS in prolonging network lifetime.

Can-follow concurrency control permits a transactionto read (write) an item write-locked (read-locked) by anothertransaction with almost no delays. By combining the merits of2PL and 2V2PL, this approach mitigates the lock contention notonly between update and read-only transactions, but also betweenupdate and update transactions.

This paper begins with a review of CDMA based 2-3G systems from a network perspective. In particular, the challenges faced by current CDMA technologies are addressed. We will show that many advanced operational requirements in future 4G networks, such as high-speed burst traffic, cross layer network design, seamless integration of different networks with and without infrastructure, will make the current CDMA technologies unsuitable due to their rigid and voice-centric physical (PHY) layer architecture. To respond to the call for CDMA innovation, a new physical layer architecture, namely Orthogonal Complementary Coded CDMA with Offset Stacking (OCC-CDMA/OS) spreading scheme, is proposed in this paper for its possible applications in futuristic wireless networks. One of the most important features of OCC-CDMA/ OS PHY architecture is its unique elasticity, which facilitates implementation of a fully-adaptive CDMA transceiver to work harmonically with various advanced upper-layer designs in 4G networks. Many other technical issues on CDMA based 4G networks will also be discussed. It is concluded that OCC-CDMA/OS has a great potential for its applications in future 4G wireless.

Power control is a critical issue to implement Mobile Ad Hoc networks. This paper presents a novel power control protocol, namely the optimistic power MAC control (OPCM) protocol for its possible application in Mobile Ad Hoc networks. The OPCM protocol works by increasing power level in the retransmission stage to guarantee the DATA reception, rather than controlling the power in the initial transmission stage. It will be shown through comprehensive computer simulations that the proposed OPCM protocol is very energy efficient, while still being able to maintain a high throughput in a Mobile Ad Hoc network.

The application of multi-hop wireless Ad Hoc networks (WANETs) has been facing a great challenge to implement cooperative control over user transmission rates under the resource restrictions. In this paper, we propose an efficient bandwidth allocation scheme called Active-Time based Bandwidth Allocation Scheme (ATBAS) that can ensure fair bandwidth allocation among the users in a multi-hop WANET model. This scheme operates at each hop and fairly allots each competing flow a share of channel time, according to which each hop then computes the updating rate for each flow traversing the hop and records their updated rates into each data packet's special control header. The source of each flow in this scheme can eventually adjust its sending rate to reach its fair share. An algorithm is developed to implement the above mechanism. We will show through simulation results that our proposed algorithm is able to fairly distribute bandwidth among multi-hop flows.

Mobile IPv6 is the next generation wireless internet protocol to support IP mobility. One of the most important challenges in Mobile IPv6 is to provide the service for a mobile node to maintain its connectivity to the internet when it moves from one domain to another, which is referred to as handover. Because when performing the handover scheme, there is a period that the packets cannot reach the MN (Mobile Node) in time, the fast handover scheme is proposed to reduce the handover latency and packet delay. In this paper, we propose an enhanced fast handover scheme for Mobile IPv6. In our scheme, each AR (Access Router) maintains a CoA (Care of Address) table and generates the new CoA for the MN who will move to its domain. At the same time, the binding updates to home agent and correspondent node are proposed to be performed from the time point that the new CoA for MN is known by PAR (Previous AR). The performance analysis is provided in the paper. After the comparison with the existing fast handover scheme, we can see that the proposed enhanced fast handover scheme can achieve low handover latency and low packet delay.

This paper introduces an approach to generate generalized pairwise complementary (GPC) codes, which offer a uniform interference free windows (IFWs) across the entire code set. The GPC codes work in pairs and can fit extremely power efficient quadrature carrier modems. The characteristic features of the GPC codes include: the set size is 2K, the processing gain is 4NK, and the IFW's width is 8N identically for all codes in a set, where K is the times to perform Walsh-Hadamard expansions and N is element code length of seed complementary codes. Therefore, by using different N, the IFW width of a GPC code set can be adjusted with its set size unchanged. Each GPC code set consists of two code groups, with each having K codes, and they have sparsely and uniformly distributed autocorrelation side lobes and cross-correlation levels outside the IFWs.

Abstract A mobile ad hoc network (MANET) is the cooperative engagement of a collection of wireless mobile nodes without the aid of any established infrastructure or centralized administration. The conventional security solutions to provide key management through accessing trusted authorities or centralized servers are infeasible for this new environment since mobile ad hoc networks are characterized by the absence of any infrastructure, frequent mobility, and wireless links. In this paper, we propose an on‐demand, fully localized, and hop‐by‐hop public key management scheme for MANETs. It can be performed by generating public/private key pairs by nodes themselves, issuing certificates to neighboring nodes, holding these certificates in their certificate repositories, and providing authentication service adaptive quickly to the dynamic topology of the network without relying on any servers. Also, our scheme can be performed successfully as long as there is a physical communication line between two nodes, and it is accustomed well to the on‐demand routing for MANETs. Copyright © 2006 John Wiley & Sons, Ltd.

To efficiently and effectively achieve the hierarchical access control for multimedia networks, in this paper we propose a distributed key management scheme whereby each SG (service group) maintains an SG server and give detailed analysis. In the proposed scheme, the server for a SG is utilized to manage the key tree and provide the related session keys for all the users in this SG. A detailed case study is provided, and we show that the communication overhead can be greatly reduced by O (n/sub O/ /spl middot/ log/sub d/n/sub O/), where n/sub O/ is the number of users in a SG, compared with employing an integrated key graph to the hierarchical access control problem. At the same time, the storage overhead for all users can be reduced by O(N), where N is the total number of users.

This paper addresses the dynamic location management for personal communication service (PCS) networks with consideration of mobility patterns. The popular hexagonal cellular architecture is considered. In this paper, we first introduce a coordinate system for the hexagonal cellular architectures. Then, we develop an all-direction mobility model based on the coordinate system for the hexagonal cellular architecture to cope with the mobility patterns of mobile terminals (MTs). The shape and size of the local area (LA) in the dynamic location management scheme is determined by minimizing the total location management cost with bounding the paging cost The optimization problem is transferred to maximize expected number of cells traversed by the MT in the LA with a given size of the LA. The analytic model of calculating the probabilities of any MT's moving is established. An algorithm is proposed to solve the optimization problem efficiently.

The inherent uncertainty in wireless mobile ad hoc networks (MANET), due to nodal mobility, unstable links, and limited resources, frequently renders routing paths unusable. Thus, recurrent route discoveries detrimentally affect network performance. The most promising solution is to use multiple redundant paths for routing. However, selecting an optimal path set is a NP hard problem. Most current multi-path routing protocols do not concentrate on the uncertainty in MANET. They choose an "optimal" multi-path set by considering only one single route selection parameter, such as the least number of intermediate hops or the maximal remaining battery power. As a result, they miss the correlations among the multiple route selection parameters. This paper proposes the genetic fuzzy multi-path routing protocol (GFMRP), which is a multi-path routing protocol based on fuzzy set theory and evolutionary computing. GFMRP naturally deals with the uncertainty in MANET and adaptively constructs a set of highly reliable paths by considering the interplays among multiple route selection parameters. GFMRP takes into account four important factors as the selection parameters; which are the energy consumption rate, queue occupancy rate, link stability, and the number of intermediate nodes. The performance of GFMRP is evaluated in terms of packet delivery ratio, average end-to-end delay, and the frequency of route rediscovery in ns2 context. Simulation results demonstrate that GFMRP is well suited to the ad hoc environment and outperforms DSR, SMR and SBMR.

A mobile ad hoc network (MANET) is the cooperative engagement of a collection of wireless mobile nodes without aid of any established infrastructure or centralized administration. The conventional security solutions to provide key management through accessing trusted authorities or centralized servers are infeasible for this new environment since mobile ad hoc networks are characterized by the absence of any infrastructure, frequent mobility, and wireless links. In this paper, we propose an on-demand, fully localized, and hop-by-hop public key management scheme for MANETs. It can be performed by generating public/private key pairs by nodes themselves, issuing certificates to neighboring nodes, holding these certificates in their certificate repositories, and providing an authentication service quickly adaptive to the dynamic topology of the network without relying on any servers. Also, our scheme can be performed successfully as long as there is a physical communication line between two nodes, and it is accustomed well to the on-demand routing of MANETs.

In this paper, a dynamic HLR (home location register) scheme for location management in PCS (personal communications service) networks is presented. The proposed scheme provides a dynamic copy of mobile terminal location information in the nearest (current) HLR database. A modified table lookup procedure is also proposed for determining the current HLR easily. It allows the location registration and call delivery to be performed efficiently. An analytical model is developed for studying the performance of the proposed scheme. The performance study shows that the proposed scheme significantly reduces the system overhead for location management in PCS networks.

We present a novel stochastic model to study the performances of the two-phase dynamic locking in database systems with no-waiting policy. It is a general stochastic model to describe the database environment and transaction states in detail. It deals with the nonuniform access, write-locking, read-locking, and multiple transaction classes in a unique way. In the analysis, we first solve the steady-state probability of the system. Then, we give the mean number of transactions with k locks, the mean total number of locks held by all transactions, the mean number of data granules locked by a transaction, the mean number of writelocks and readlocks held by a transaction, and the mean number of locked data granules in a database. These parameters provide more insight into the detailed behavior of transactions and database systems. Finally, we calculate the system throughput and restart rate, which are the two principal performance measures.

Location management is a key issue in personal communication service networks to guarantee the mobile terminals to continuously receive services when moving from one place to another. We study two location management schemes, a dynamic movement-based scheme (DYNAMIC-3G) and a static scheme (STATIC-3G), for 3G cellular networks where home location registers, gateway location registers (GLRs), and visitor location registers form a three-level hierarchical mobility database structure. For both schemes, the cost functions are formulated analytically. We prove that there is an optimal movement threshold that minimizes the total cost function of DYNAMIC-3G and propose a binary search algorithm to find the optimal threshold. Furthermore, we present performance evaluation and comparison of the proposed schemes with the previous schemes in 2G cellular networks where the GLR is not present. Our studies validate the optimality of the DYNAMIC-3G scheme and show that the proposed schemes outperform the previous schemes, especially when the remote-local-cost ratio is high. The comparison results between DYNAMIC-3G and STATIC-3G indicate that DYNAMIC-3G should be adopted when the mobility rate is low, and STATIC-3G should be adopted otherwise. Furthermore, DYNAMIC-3G tends to perform better than STATIC-3G when the paging cost is high or the number of cells in a location area is large.

We propose a dynamic fractional movement-based location management scheme for 3G networks where home location registers (HLRs), gateway location registers (GLRs), and visitor location registers (VLRs) are formed into a three-level hierarchical mobility database structure. The cost functions of location updates and paging are formulated analytically. We prove analytically that there is an optimal fractional movement threshold that minimizes the total cost function. Our study shows that the proposed scheme outperforms the previous two-tier mobility database scheme, especially when the remote-local-cost ratio is high.

Computing a distance map (distance transform) is an operation that converts a 2D image consisting of black and white pixels to an image where each pixel has a value or a pair of coordinates that represents the distance to or location of the nearest black pixel. It is a basic operation in image processing and computer vision fields, and is used for expanding, shrinking, thinning, segmentation, clustering, computing shape, object reconstruction, etc. This paper examines the possibility of implementing the problem of finding a distance map for an image efficiently using an optical bus. The computational model considered is the linear array with a reconfigurable pipelined bus system (LARPBS), which has been introduced recently based on current electronic and optical technologies. It is shown that the problem for an n /spl times/ n image can be implemented in O(log n log log n) bus cycles deterministically or in O(log n) bus cycles with high probability on an LARPBS with n/sup 2/ processors. We also show that the problem can be solved in O(log log n) bus cycles deterministically or in O(l) bus cycles with high probability on an LARPBS with n/sup 3/ processors. Scalability of the algorithms is also discussed briefly. The algorithm compares favorably to the best known parallel algorithms for the same problem in the literature.

This paper presents a dynamic database management method for location management of personal communications service (PCS) networks. The proposed method provides dynamics copies of user location information in the nearest home location register (HLR) database, which allows mobile users to access the system efficiently.

Wavelet transforms have proven to be useful tools for several applications, including signal analysis, signal coding, and image compression. In this paper, faster parallel algorithms for computing the continuous wavelet transform are designed for reconfigurable meshes. An -time algorithm for computing the continuous wavelet transform with signals and an integer grid on a 3-D reconfigurable mesh is proposed, where is the number of bits used to represent the values in calculation. A constant-time algorithm 3-D reconfigurable mesh is also proposed. To the best knowledge of the author, this is the first constanttime algorithm for continuous wavelet transform on any parallel architecture.

This letter presents: 1) a delay analysis model, which is specially for the admission control of real-time multicast connections in ATM networks; 2) a distributed multicast routing algorithm, which generates suboptimal routing trees under real-time constraints; and 3) a connection setup method that integrates multicast routing with admission control.

The performance of parallel transaction processing systems can be degraded significantly due to data skew, a phenomenon of unbalanced data distribution over the nodes of a system. Rebalancing the data load of a system with data skew by redistributing its data is known to be an effective approach to cope with data skew. Unfortunately, for most of the existing approaches, the data being redistributed is unavailable (off-line). Numerous applications, such as those for reservations, finance, process control, hospitals, police and the armed forces, however, cannot afford off-line data for any significant amount of time. These applications call for the ability of balancing data load online. In this paper, a new online data redistribution approach is proposed. A prototype of the approach has been implemented, and experiments have been conducted. Experimental results confirm the substantial performance gains of the approach.

We study a dynamic mobility management scheme: the movement-based location update scheme. An analytical model is applied to formulate the costs of location update and paging in the movement-based location update scheme. The problem of minimizing the total cost is formulated as an optimization problem that finds the optimal threshold in the movement-based location update scheme. We prove that the total cost function is a convex function of the threshold. Based on the structure of the optimal solution, an efficient algorithm is proposed to find the optimal threshold directly. Furthermore, the proposed algorithm is applied to study the effects of changing important parameters of mobility and calling patterns numerically.

The movement based dynamic location update scheme is studied. An analytical model is applied to formulate the costs of location update and paging in the movement based location update scheme. The problem of minimizing the total cost is formulated as an optimization problem that finds the optimal threshold in the movement based location update scheme. We prove that the total cost function is a convex function of the threshold. Based on the structure of the optimal solution, an efficient algorithm is proposed to find the optimal threshold directly. Furthermore, the proposed algorithm is applied to study the effects of changing important parameters of mobility and calling patterns numerically.

Load balancing problems for multiclass jobs in distributed/parallel computer systems with general network configurations are considered. We construct a general model of such a distributed/parallel computer system. The system consists of heterogeneous host computers/processors (nodes) which are interconnected by a generally configured communication/interconnection network wherein there are several classes of jobs, each of which has its distinct delay function at each host and each communication link. This model is used to formulate the multiclass job load balancing problem as a nonlinear optimization problem in which the goal is to minimize the mean response time of a job. A number of simple and intuitive theoretical results on the solution of the optimization problem are derived. On the basis of these results, we propose an effective load balancing algorithm for balancing the load over an entire distributed/parallel system. The proposed algorithm has two attractive features. One is that the algorithm can be implemented in a decentralized fashion. Another feature is simple and straightforward structure. Models of nodes, communication networks, and a numerical example are illustrated. The proposed algorithm is compared with a well-known standard steepest-descent algorithm, the FD algorithm. By using numerical experiments, we show that the proposed algorithm has much faster convergence in terms of computational time than the FD algorithm.

We present an analytic model to study the reliability of some important disk array organizations that have been proposed by others in the literature. These organizations are based on the combination of two options for the data layout, regular RAID-5 and block designs, and three alternatives for sparing: hot sparing, distributed sparing and parity sparing. Uncorrectable bit errors have big effects on reliability but are ignored in traditional reliability analysis of disk arrays. We consider both disk failures and uncorrectable bit errors in the model. The reliability of disk arrays is measured in terms of MTTDL (Mean Time To Data Loss). A unified formula of MTTDL has been derived for these disk array organizations. The MTTDLs of these disk array organizations are also compared using the analytic model.

We study the static load balancing problem in a distributed computer system with the tree hierarchy configuration. It is formulated as a nonlinear optimization problem. After studying the conditions that the solution to the optimization problem of the tree hierarchy network satisfies, we demonstrate that the special structure of the optimization problem leads to an interesting decomposition technique. A new effective decomposition algorithm to solve the optimization problem is presented. The proposed algorithm Is compared with two other well known algorithms: the Flow Deviation (FD) algorithm and the Dafermos-Sparrow (D-S) algorithm. It is shown that the amounts of the storage required for the proposed algorithm and the FD algorithm are O(n) for load balancing of an n-node system. However, the amount of the storage required for the D-S algorithm is O(n log(n)). By using numerical experiments, we show that both the proposed algorithm and the D-S algorithm have much faster convergence in terms of central processing unit (CPU) time than the FD algorithm.>