Quantum entanglement can be employed in some distributed communication tasks to reduce computation and communication overheads, yielding speedups over their classical counterparts. However, existing quantum Byzantine protocols often entail excessive communication and entanglement resource consumption. Conversely, classical schemes face insecurity with the impending reality of quantum computers. To address these challenges, we present a novel quantum Detectable Weak Byzantine Agreement (DWBA) protocol with high practicability, security and optimal fault-tolerance bound. For all n players, our protocol can complete the consensus of infinite classical information via fixed n + 1 -entangled qubits and a digest function, which requires infinite entanglement resources in previous schemes. Moreover, our protocol can tolerate arbitrary t ( t < n ) faulty players without necessitating any additional initial setup information (e.g., digital signature, private random coins). In terms of efficiency, our protocol only requires O (1) rounds and O ( ( n − t ) 2 ) communication complexity, constituting an order of magnitude improvement over previous protocols. In addition, the DWBA problem solved by our protocol is inherently stronger than the 3-player Detectable Byzantine Agreement (DBA) problem solved by previous protocols, and in a weaker but sufficiently practical model, our protocol can achieve Weak Byzantine Agreement (WBA).

Federated fine-tuning (FedFT) provides an effective paradigm for fine-tuning large language models (LLMs) in privacy-sensitive scenarios. However, practical deployment remains challenging due to the limited resources on end devices. Existing methods typically utilize parameter-efficient fine-tuning (PEFT) techniques, such as Low-Rank Adaptation (LoRA), to substantially reduce communication overhead. Nevertheless, significant memory usage for activation storage and computational demands from full backpropagation remain major barriers to efficient deployment on resource-constrained end devices. Moreover, substantial resource heterogeneity across devices results in severe synchronization bottlenecks, diminishing the overall fine-tuning efficiency. To address these issues, we propose FedQuad, a novel LoRA-based FedFT framework that adaptively adjusts the LoRA depth (the number of consecutive tunable LoRA layers from the output) according to devices' computational power, while employing activation quantization to reduce memory overhead, thereby enabling efficient deployment on resource-constrained devices. Specifically, FedQuad first identifies the feasible and efficient combinations of LoRA depth and the number of activation quantization layers based on device-specific resource constraints. Subsequently, FedQuad employs a greedy strategy to select the optimal configurations for each device, effectively accommodating system heterogeneity. Extensive experiments demonstrate that FedQuad achieves a 1.4–5.3× convergence acceleration compared to state-of-the-art baselines when reaching target accuracy, highlighting its efficiency and deployability in resource-constrained and heterogeneous end-device environments.

Decentralized Federated Graph Learning (DFGL) overcomes the potential bottlenecks of the parameter server in FGL. However, extensive cross-worker communication of graph node embeddings during DFGL training introduces substantial communication costs. To improve communication efficiency, constructing sparse network topologies or applying graph sampling are potential methods. In this paper, we first reveal the bidirectional coupling between network topology construction and graph sampling, underscoring the necessity of their joint optimization. Motivated by this insight, we proposeDuplex, a unified framework that co-optimizes these two components by explicitly modeling their interdependent relationship, thereby significantly reducing communication costs while enhancing training performance in DFGL.Duplexformulates the decision-making process as a coordinated configuration$\langle \mathbf {A}, \mathbf {R} \rangle$, where$\bf {A}$is the adjacency matrix of the network topology and$\bf {R}$denotes the set of graph sampling ratios for workers. However, determining proper coordinated configurations to achieve optimal communication efficiency and training performance (e.g., model accuracy and convergence rate) is challenging due to several practical issues,e.g., statistical heterogeneity and dynamic network conditions. To overcome these challenges,Duplexintroduces a novel learning-driven algorithm to adaptively determine optimal network topologies and graph sampling ratios for workers. Experimental results demonstrate thatDuplexreduces completion time by 20.1%–48.8% and communication costs by 16.7%–37.6% to achieve target accuracy, while improving accuracy by 3.3%–7.9% under identical resource budgets compared to baselines.

Recently, aided by edge computing (EC), federated meta-learning (FML) has been proposed to adequately utilize personalized data on different edge devices by incorporating meta-learning with federated learning (FL). Unlike conventional FL which conducts several stochastic gradient descent (SGD) steps to directly update the local model in each training round, FML first performs some SGDsteps to obtain a reference model and then performs a meta-step to update the local model along the direction towards the reference model. In this way, FML can identify common patterns and structures across different clients (i.e., edge devices) and quickly adapt to new data distributions, which exhibits its advantage in dealing with statistical heterogeneity inherent in FL. However, existing FML approaches struggle to effectively cope with two key challenges: i) the disparity in dataset informativeness among clients with statistically heterogeneous local data; and ii) the disparity in computing capacities among clients with heterogeneous hardware configurations. Herein, we propose a heterogeneity-aware FML framework, termed HFML, which explores adjusting the execution frequencies of SGD step and meta-step in each training round to address the above statistical and system heterogeneity challenges simultaneously. We introduce usable information (UI) as a new metric in HFML to quantify dataset informativeness, and theoretically analyze the influence of the execution frequencies of SGD steps and meta-steps on model performance and convergence speed. Based on the theoretical analysis, we develop an efficient optimization algorithm to jointly adjust the execution frequencies for different clients to handle the heterogeneity issues, which contributes to accelerating model training and enhancing personalization performance. Experimental results demonstrate that HFML can speed up training by up to 4.02× and improve model accuracy by an average of 11.5% compared to the baselines under heterogeneous settings.

Quantum Key Distribution (QKD) is considered to be the ultimate solution to communication security. However, current QKD devices, especially quantum photon sources, are expensive, and they can generate secret keys only at a low rate. In this paper, we first consider homogeneous trusted-relay-based QKD networks where every request has the same amount of secret key requirement and every photon source has the same key distribution rate, and design an approach named RPSP to not only minimize the number of photon sources needed in a network to ensure at least one feasible relay path exists for any potential QKD requests but also save the time to complete a batch of QKD requests by jointly optimizing the routing of relay paths and the provisioning of photon sources to distribute secret keys. Then, we extend RPSP to RPSP-HN which can be applied to heterogeneous networks where requests have different secret key requirements and photon sources distribute keys at different rates. Furthermore, we also extend RPSP to RPSP-HY, which considers that some of the nodes in a network is untrusted. Compared with existing works, RPSP and its extensions focus on more practical scenarios where only some of the nodes are equipped with photon sources and they leverage optical switching to enable dynamic photon source provisioning such that we can utilize QKD devices more efficiently. Extensive simulations show that compared with baseline schemes, RPSP, RPSP-HN, and RPSP-HY can save up to 33%, 37%, and 25% of the time to complete a batch of QKD requests in homogeneous, heterogeneous, and hybrid QKD networks, respectively.

Entanglement routing plays a vital role in supporting various applications in quantum networks. Existing works on entanglement routing either ignored the Entangled Photon Source (EPS) placement issue or simply assumed a pool of EPSes at a centralized location that can provision entanglement over arbitrary quantum links. In this paper, we propose LIGHTER and fidelity-aware LIGHTER (named F-LIGHTER) to solve the joint EPS placement and entanglement routing problem based on the assumption that EPSes are co-located with quantum nodes and each EPS can send one entangled photon at a time to one of its adjacent nodes only. The salient features of LIGHTER and F-LIGHTER include (i) LIGHTER and F-LIGHTER use a demand-agnostic EPS placement scheme to maximize network throughput and fairness for all feasible Entanglement Connection EC) establishment demands, and (ii) most requested ECs can be established over Entanglement Paths (EPs) determined offline, and only a small percentage of them will be established over online calculated EPs, resulting in fast and efficient entanglement routing. Extensive simulations show that compared with schemes without proper EPS placement or entanglement routing, LIGHTER can improve the network throughput by up to 175.6% and 37.0%, respectively. When the fidelity is considered, the network throughput improvement achieved by F-LIGHTER will be up to 135.0% and 21.5%, respectively.

Enhancing defense through model ensemble is an emerging trend, where the challenge lies in how to use ensemble knowledge to counter Out-of-Distribution (OOD) attacks. In this paper, we propose the Reliable Defense Ensemble (REE) to address this issue. REE optimizes the ensemble knowledge of models through aggregation and enhances multidimensional robust performance through collaboration. It employs the Dynamic Synergy Amplification for weight allocation and strategy adjustment. Furthermore, we design a new Kernel Anomaly Smoothing Detection Module, which detects anomalous attacks using a smoothing feature function based on Gaussian kernel mean embedding and a multi-layer feedback structure. Particularly, we build a framework that uses reinforcement learning to iteratively fine-tune the parameters of inter-model communication and consensus. Extensive experimental results show that REE outperforms current state-of-the-art methods by a large margin in defending against OOD attacks.

Large language models (LLMs) rely heavily on sampling methods to generate diverse and highquality text.While existing sampling methods like top-p and min-p have identified the detrimental effects of low-probability tails in LLMs' outputs, they still fail to effectively distinguish between diversity and noise.This limitation stems from their reliance on probability-based metrics that are inherently sensitive to temperature scaling.Through empirical and theoretical analysis, we make two key discoveries: (1) the pre-softmax logits exhibit a clear statistical separation between informative tokens and noise, and (2) we prove the mathematical equivalence of min-p and top-(1-p) under uniform distribution over logits.These findings motivate the design of top-nσ, a novel sampling method that identifies informative tokens by eliminating noise directly in logit space.Unlike existing methods that become unstable at high temperatures, top-nσ achieves temperature-invariant token selection while preserving output diversity.Extensive experiments across reasoning and creative writing tasks demonstrate that our method consistently outperforms existing approaches, with particularly significant improvements in high-temperature settings.

In the field of multimodal learning, controlling the training of unimodal encoders from different perspectives is a primary approach to addressing Training Imbalance. However, the inherent capacity limitations of the modality affect the model’s capability. Therefore, generating adversarial examples that can achieve balanced transferability remains a challenging and perplexing problem. In this paper, we propose the InterModality Balanced Attack (MOBA) to address this problem. MOBA leverages Aggregated Modality Perturbation (AMP), which exploits the unbalanced effects of text and image perturbations to maximize the impact on the victim model. AMP capitalizes on the intrinsic feature connections between modalities during the optimization process, adjusting perturbations through Cross-Modality Discrepancy Loss to enhance attack success rates. Additionally, we devise the Transferability-Enhanced Evolution (TEE) to overcome the issue of diminished attack transferability due to model capacity limitations. TEE employs Transfer-Driven Optimization Loss to alleviate overfitting in single models, thereby enhancing the generalization ability.

Edge inference is a technology that enables real-time data processing and analysis on clients near the data source. To ensure compliance with the Service-Level Objectives (SLOs), such as a 30% latency reduction target, caching is usually adopted to reduce redundant computations in inference tasks on stream data. Due to task and data correlations, sharing cache information among clients can improve the inference performance. However, the non-independent and identically distributed (non-IID) nature of data across different clients and the long-tail distributions, where some classes have significantly more samples than others, will reduce cache hit ratios and increase latency. To address the aforementioned challenges, we propose an efficient inference framework, CoCa, which leverages a multi-client collaborative caching mechanism to accelerate edge inference. On the client side, the model is pre-set with multiple cache layers to achieve a quick inference. During inference, the model performs sequential lookups at cache layers activated by the edge server. On the server side, CoCa uses a two-dimensional global cache to periodically aggregate information from clients, mitigating the effects of non-IID data. For client cache allocation, CoCa first evaluates the importance of classes based on how frequently and recently their samples have been accessed. CoCa then selects frequently recurring classes to address long-tail distribution challenges. Finally, CoCa dynamically activates cache layers to balance lookup overhead and accuracy. Extensive experiments demonstrate that CoCa reduces inference latency by 23.0% to 45.2% on the VGG, ResNet and AST models with a slight loss of accuracy.

Federated fine-tuning (FedFT) has become a decentralized approach for fine-tuning pre-trained large language models(LLMs). However, due to the immense size of LLMs, there are two critical challenges for efficient FedFT in practice, i.e., resource constraints and system heterogeneity. Though existing approaches employ low-rank adaptation (LoRA) method, to mitigate fine-tuning overhead, they still suffer from high memory consumption and large training latency. According to the characteristics of FedFT, we observe that appropriate quantization and LoRA configurations can reduce resource consumption while maintaining comparable model performance. Consequently, we propose an efficient FedFT framework, termed FedQLoRA, which dynamically adjusts the quantization level and LoRA depth during training. This design enables large models to be fine-tuned on resource-limited devices while reducing training latency and preserving model performance. Extensive experimental results demonstrate that FedQLoRA achieves up to 70.34% reduction in training time and$62.71 \%-77.50 \%$savings in memory consumption compared to baseline methods.

In the developing field of visual object tracking, the robustness and resilience of detection modules against adversarial perturbations is critical. Traditional attacks have shown limitations in maintaining long-term deception, which is mainly reflected in that they are often only effective for a short period of time, or only have an impact on a specific single frame of images, rather than continuously and effectively mislead objects in continuous video sequences. Therefore, the tracker tends to quickly recover to the correct tracking state in the face of continuously changing adversarial attacks, showing robustness against occasional detection anomalies. In order to solve these problems, we propose Multi-Strategy Adversarial Attack (MSAA). MSAA imposes specific constraints on the decision-making ability of the model, regulating the priority of modification to candidate bounding boxes, including offset and size. In addition, the strategy to construct a predefined hijacking trajectory includes a direction-aware perturbation and a center matching scheme to hijack the feature-aware module to a predefined target. To the best of our knowledge, this is the first time that a unified perspective is adopted to address the problem from diffuse deviation to target hijacking. Our method not only enhances the persistence and concealment of attacks, but also achieves more precise control in multi-target scenarios, which has not been fully addressed in traditional adversarial attack methods. Experiments show that MSAA greatly outperforms state-of-the-art attack methods on multiple public datasets.

Recently, many sharding blockchain protocols have sacrificed some important attributes to improve scalability, and this makes them complicated and insecure. Moreover, achieving a constant (rather than linear) Communication Cost Per Transaction (CCPT) is still a challenge for many sharding protocols. Motivated by this, we present EZchain, a scalable blockchain protocol via “passive sharding” with proven validity and security. We redesign the Value-Centric Blockchains (VCB) framework to achieve the passive sharding that helps EZchain reach higher security than traditional sharding protocols. With fixed initialization parameters, the expected value of EZchain's communication cost reaches a constant level, and it is independent of the network's size. Moreover, the EZchain node's storage cost without beacon chains also approaches a constant and does not change with the increase in the network's size and transactions. Cross-shard transactions, network sharding algorithm, and anti-Sybil attack verification are no longer needed in passive sharding, thus EZchain is very concise and efficient. Our experiment uses a lightweight EZchain prototype and extends the experimental network size up to 100,000 nodes. The evaluation results show that EZchain satisfies all our analyses of its performance (the constant communication and non-beacon storage cost) in large networks. In addition, the comparison experiment shows that ezchain has obvious advantages over the previous protocols in long-term operation and large network environments.

Federated graph learning (FGL) has been proposed to collaboratively train the increasing graph data with graph neural networks (GNNs) in a recommendation system. Nevertheless, implementing an efficient recommendation system with FGL still faces two primary challenges, i.e., limited communication bandwidth and non-IID local graph data. Existing works typically reduce communication frequency or transmission amount, which may suffer significant performance degradation under non-IID settings. Furthermore, some researchers propose to share the underlying structure among clients, which brings massive communication cost. To this end, we propose an efficient FGL framework, named FedACS, which adaptively selects a subset of clients for model training, to alleviate communication overhead and non-IID issues simultaneously. In FedACS, the global GNN model learns significant hidden edges and the structure of graph data among selected clients, enhancing recommendation efficiency. This capability distinguishes it from the traditional FL client selection methods. To optimize the client selection process, we introduce a multi-armed bandit (MAB) based algorithm to select participating clients according to the resource budgets and the training performance (i.e., RMSE). Experimental results indicate that FedACS improves RMSE by 5.4% over baselines with the same resource budget and reduces communication costs by up to 70.7% to achieve the same RMSE performance.

Federated Learning (FL) enables distributed clients to collaboratively train models without exposing their private data. However, it is difficult to implement efficient FL due to limited resources. Most existing works compress the transmitted gradients or prune the global model to reduce the resource cost, but leave the compressed or pruned parameters under-optimized, which degrades the training performance. To address this issue, the neural composition technique constructs size-adjustable models by composing low-rank tensors, allowing every parameter in the global model to learn the knowledge from all clients. Nevertheless, some tensors can only be optimized by a small fraction of clients, thus the global model may get insufficient training, leading to a long completion time, especially in heterogeneous edge scenarios. To this end, we enhance the neural composition technique, enabling all parameters to be fully trained. Further, we propose a lightweight FL framework, called Heroes, with enhanced neural composition and adaptive local update. A greedy-based algorithm is designed to adaptively assign the proper tensors and local update frequencies for participating clients according to their heterogeneous capabilities and resource budgets. On this basis, we further propose an extension of Heroes, termed AdaHeroes, which further improves the training performance under the statistical heterogeneity scenario based on an adaptive client selection strategy. Extensive experiments demonstrate that Heroes can reduce traffic consumption by about 72.46% and provide up to$2.76\times $speedup compared to the baselines. Furthermore, with the setting of statistical heterogeneity, AdaHeroes can improve the test accuracy by about 4.77% compared with Heroes and the baselines.

Quantum Networks (QNs) typically leverage teleportation to send quantum bits (called qubits) to their destinations. To teleport a data qubit from Alice to Bob, one Entanglement Connection (EC) between Alice and Bob needs to be established. Accordingly, we have to concurrently establish as many requested ECs as possible in order to maximize the network throughput. Conventional methods either assumed a known traffic matrix and calculated the Entanglement Paths (EPs) for all requests in one batch or maximized the number of ECs established between all Source-Destination (SD) pairs without considering the amount of data qubits to be teleported. These methods are not scalable in large scale QNs since it is time consuming to calculate the EPs for a batch of requests. In addition, there may be only very few data qubits to be teleported between some SD pairs. Accordingly, the latter method may establish many useless ECs. To address these issues, we propose a Dynamic Entanglement Routing (DER) scheme which determines the EPs based on stream processing. By introducing a method to derive an appropriate purification scheme along each EP, we further extend DER to Dynamic Entanglement Routing with Purification (DERP) that provides fidelity guarantee to the established ECs. Through extensive simulations, we demonstrate that DER outperforms two representative heuristics by up to 52.79% and 61.27%, respectively, in terms of average request completion time and when we have to ensure the fidelity of the established ECs, this performance improvement will become 21.05% and 48.67%, respectively, if DERP is adopted.

Entanglement routing plays a vital role in distributed quantum computing and quantum networks. Previous works on entanglement routing have addressed several design challenges due to limited quantum resources, failures to establish entanglement, and decoherence of established entanglement, but considered neither the limitations imposed by having a limited number of entangled photon sources (EPSes), nor the benefits of using all-optical (or quantum) switching to distribute entangled photons. In this paper, we first explore the problem of jointly optimizing entanglement routing and EPS assignment, assuming no all-optical switching capability. In other words, a pair of entangled photons generated by one EPS can be distributed to two neighboring quantum nodes. We then relax the above assumption so as to allow a pair of entangled photons generated by one EPS to be distributed to non-adjacent nodes using all-optical switching. We propose two corresponding solutions, namely, entanglement routing and EPS assignment (or ERSA), and ERSA with all-optical switching-based distribution (or ERSA+D) that aim to maximize the number of entanglement connections between the given set of source-destination (SD) pairs while avoiding starvation and achieving fairness among the SD pairs. In order to obtain efficient solutions in a large discrete solution space in a timely manner, we first formulate each optimization problem as an Integer Linear Programming (ILP), and then propose efficient algorithms to derive near-optimal solutions based on relaxation, Lagrangian decomposition, duality iteration, and rounding techniques. Extensive simulations show that ERSA+D can increase network throughput by up to 1652% and 113%, respectively, thanks to EPS assignment optimization, and all-optical switching based entanglement distribution.

Entanglement routing in quantum data networks plays a vital role to support various quantum applications. Existing works on entanglement routing either ignored the Entangled Photon Source (EPS) placement issue or simply assumed a pool of EPSes at a centralized location that can provision entanglement over arbitrary quantum links. In this paper, we propose LightER to solve the joint EPS placement and entanglement routing problem based on the assumption that EPSes are distributed over a quantum network, where an EPS, which is co-located with a quantum node, can send one entangled photon at a time to one of the adjacent nodes only. The salient features of LightER include (i) LightER uses a demand-agnostic EPS placement scheme to maximize network throughput and fairness for all feasible entanglement connection (EC) establishment demands, and (ii) most of the requested ECs can be established over their corresponding Entanglement Paths (EPs) determined offline, and only a small percentage of the ECs will be established over EPs that need to be calculated online, resulting in fast and efficient entanglement routing. Extensive simulations show that compared with schemes without proper EPS placement or entanglement routing, LightER can improve the network throughput by up to 215% and 56.2%, respectively.

Efficient entanglement routing in Quantum Data Networks (QDNs) is essential in order to concurrently establish as many Entanglement Connections (ECs) as possible, which in turn maximizes the network throughput. In this work, we consider a new class of QDNs with wavelength division multiplexed (WDM) quantum links where each quantum repeater will perform entanglement swapping by measuring two photonic qubits coming from some entangled photon sources directly on the same wavelength. To address unique challenges in achieving a high network throughput in such QDNs, we propose QuRWA to jointly optimize the entanglement routing and wavelength assignment. To this end, we introduce a key concept named Co-Path to improve fault-tolerance: all ELs in a Co-Path set will be assigned the same wavelength and this may serve as backup for some other ELs in the same Co-Path when establishing ECs. We design efficient algorithms to optimize the Co-Path selection and wavelength assignment to maximize resource utilization and fault tolerance. Extensive simulations demonstrate that compared with the methods without introducing Co-Path, QuRWA improves the network throughput by up to 122%.

Quantum Key Distribution (QKD) is considered to be an ultimate solution to communication security. However, current QKD devices, especially quantum photon sources, are expensive, and they can generate secret keys only at a low rate. In this paper, we design a system named RPSP for trusted relay-based QKD networks to not only minimize the number of photon sources needed in a network to ensure at least one feasible relay path exists for any potential QKD requests but also save the time to complete a batch of end-to-end QKD requests by jointly optimizing the routing of relay paths and the provisioning of photon sources along each relay path. Compared with existing works, RPSP focuses on a more practical scenario where only some of the nodes are equipped with photon sources and it leverages optical switching to enable dynamic photon source provisioning such that we can utilize such QKD devices in a more efficient way. Extensive simulations show that compared with baseline schemes, RPSP can save up to 87% of the photon sources needed in a trusted relay based QKD network, and 36% of the time to complete a batch of QKD requests.

Mobile devices contribute more than half of the world's web traffic, providing massive and diverse data for powering various federated learning (FL) applications. In order to avoid the communication bottleneck on the parameter server (PS) and accelerate the training of large-scale models on resource-constraint workers in edge computing (EC) system, we propose a novel split federated learning (SFL) framework, termed ParallelSFL. Concretely, we split an entire model into a bottom submodel and a top submodel, and divide participating workers into multiple clusters, each of which collaboratively performs the SFL training procedure and exchanges entire models with the PS. However, considering the statistical and system heterogeneity in edge systems, it is challenging to arrange suitable workers to specific clusters for efficient model training. To address these challenges, we carefully develop an effective clustering strategy by optimizing a utility function related to training efficiency and model accuracy. Specifically, ParallelSFL partitions workers into different clusters under the heterogeneity restrictions, thereby promoting model accuracy as well as training efficiency. Meanwhile, ParallelSFL assigns diverse and appropriate local updating frequencies for each cluster to further address system heterogeneity. Extensive experiments are conducted on a physical platform with 80 NVIDIA Jetson devices, and the experimental results show that ParallelSFL can reduce the traffic consumption by at least 21%, speed up the model training by at least 1.36X, and improve model accuracy by at least 5% in heterogeneous scenarios, compared to the baselines.

Quantum Data Networks (QDNs) are vital to building Distributed Quantum Computing (DQC) systems. Though several communication protocols have been proposed for QDNs, most of them are at the network layer or below. The only transport layer protocol [1] used batch processing of requests for End-to-End (E2E) quantum data transmission. It not only limits the quantum resource utilization, more importantly, it cannot guarantee reliable E2E quantum data transmission. In this paper, we propose the first asynchronous transportation layer protocol, called AQTP, for QDNs to achieve high-speed and reliable E2E quantum data transmission. AQTP has several distinct features: (i) each quantum node locally allocates quantum resources in order to improve scalability; (ii) requests are processed in an asynchronous manner, which results in a higher quantum resource utilization; and (iii) it ensures reliable data transmission even if the teleportation operations fail. Extensive simulations show that compared with a batch processed transport layer protocol, AQTP can increase the network throughput by up to 82.97%, and reduce the Average Task Completion Time (ATCT) of DQC tasks by up to 94.69%.

Traditional machine learning-based steganalysis methods on compressed speech in VoIP applications have achieved great success. However, in these methods, there is a dilemma between the effectiveness of modeling the steganographic carrier and the high dimensionality of extracted features. Especially for small-sized and low embedding rate samples, most existing methods do not perform well enough. To deal with this issue, we present MDoIF— an Adaptive Multi-Rate (AMR) steganalysis of compressed speech based on multi-domain information fusion. In order to fully extract the information reflecting the change of carrier correlation before and after VoIP steganography, we construct a Bayesian network with FCB parameters in compressed speech as the vertices, and quantify link strength between codebook parameters. On this basis, we design a multi-domain feature extraction algorithm, supplemented by an information-theoretic measure-based feature selection algorithm for dimensionality reduction, which can significantly improve the performance of MDoIF. To evaluate the performance of our method, we conduct comprehensive experiments on MDoIF and existing models. Experimental results show that MDoIF performs effectively on various AMR steganalysis tasks with excellent detection accuracy. Particularly for small-sized and low embedding rate samples, MDoIF surpasses the state-of-the-art methods.

Federated learning (FL) is an effective approach to train models collaboratively among distributed edge nodes (i.e., workers) while facing three crucial challenges, edge heterogeneity, resource constraint, and Non-IID data. Under the parameter server (PS) architecture, a single parameter server may become the system bottleneck and cannot well deal with the edge heterogeneity, while the peer-to-peer (P2P) architecture causes significant communication consumption to achieve satisfactory training performance. To this end, hierarchical aggregation (HA) architecture is proposed to cluster workers to tackle the edge heterogeneity and reduce communication consumption for FL. However, the existing researches on HA architecture cannot provide a unified clustering approach for various inter-cluster aggregation patterns (e.g., centralized or decentralized structure, synchronous or asynchronous mode). In this paper, we explore the quantitative relationship between the convergence bounds of different inter-cluster patterns and several factors, e.g., data distribution, frequency of clusters participating in inter-cluster aggregation (for asynchronous modes), and inter-cluster topology (for decentralized structures). Based on the convergence bounds, we design a unified clustering algorithm FedUC to organize workers for different patterns. Experimental results on classical models and datasets show that FedUC can greatly accelerate the model training of different patterns by 1.79-7.39× compared with the state-of-the-art clustering methods.

Federated learning (FL) has been widely applied to collaboratively train deep learning (DL) models on massive end devices (i.e., clients). Due to the limited storage capacity and high labeling cost, the data on each client may be insufficient for model training. Conversely, in cloud datacenters, there exist large-scale unlabeled data, which are easy to collect from public access (e.g., social media). Herein, we propose theAda-FedSemisystem, which leverages both on-device labeled data and in-cloud unlabeled data to boost the performance of DL models. In each round, local models are aggregated to produce pseudo-labels for the unlabeled data, which are utilized to enhance the global model. Considering that the number of participating clients and the quality of pseudo-labels will have a significant impact on the training performance, we introduce a multi-armed bandit (MAB) based online algorithm to adaptively determine the participating fraction and confidence threshold. Besides, to alleviate the impact of stragglers, we assign local models of different depths for heterogeneous clients. Extensive experiments on benchmark models and datasets show that given the same resource budget, the model trained by Ada-FedSemi achieves 3%$\sim$14.8% higher test accuracy than that of the baseline methods. When achieving the same test accuracy, Ada-FedSemi saves up to 48% training cost, compared with the baselines. Under the scenario with heterogeneous clients, the proposed HeteroAda-FedSemi can further speed up the training process by$1.3\times \sim 1.5\times$.

There are two conventional methods to establish an entanglement connection in a Quantum Data Networks (QDN). One is to create single-hop entanglement links first and then connect them with quantum swapping, and the other is forwarding one of the entangled photons from one end to the other via all-optical switching at intermediate nodes to directly establish an entanglement connection. The two methods both have pros and cons. Respectively, the former method has a higher success probability of constructing entanglement link, but it would consume more quantum resources. The latter method, however, has a lower success probability to deliver a photon across multiple quantum links with fewer quantum resources. Accordingly, we are expecting to establish significantly more entanglement connections with limited quantum resources by first creating entanglement segments, each spanning multiple quantum link, using all-optical switching, and then connecting them with quantum swapping. In this paper, we design SEE, a Segmented Entanglement Establishment approach that seamlessly integrates quantum swapping and all-optical switching to maximize quantum network throughput. SEE first creates entanglement segments over one or multiple quantum links with all-optical switching, and then connect them with quantum swapping. Accordingly, SEE can theoretically outperform conventional entanglement link-based approaches. Large scale simulations show that SEE can achieve up to 100.00% larger throughput compared with the state-of-the-art entanglement link-based approaches, e.g., Redundant Entanglement Provisioning and Selection (REPS).

Data generated at the network edge can be processed locally by leveraging the emerging technology of Federated Learning (FL). However, non-IID local data will lead to degradation of model accuracy and the heterogeneity of edge nodes inevitably slows down model training efficiency. Moreover, to avoid the potential communication bottleneck in the parameter-server-based FL, we concentrate on the Decentralized Federated Learning (DFL) that performs distributed model training in Peer-to-Peer (P2P) manner. To address these challenges, we propose an asynchronous DFL system by incorporating neighbor selection and gradient push, termed AsyNG. Specifically, we require each edge node to push gradients only to a subset of neighbors for resource efficiency. Herein, we first give a theoretical convergence analysis of AsyNG under the complicated non-IID and heterogeneous scenario, and further design a priority-based algorithm to dynamically select neighbors for each edge node so as to achieve the trade-off between communication cost and model performance. We evaluate the performance of AsyNG through extensive experiments on a physical platform. Evaluation results show that AsyNG can reduce the communication cost by 60% and the completion time by about 30% for achieving the same test accuracy, compared to the baselines.

Entanglement routing plays a vital role in quantum networks. Previous works on entanglement routing did not note that all-optical switching capacity can be used to improve the network throughput or ignored that the placement of Entangled Photon Sources (EPS), another type of precious (and costly) quantum source, which also limits the network throughput. In this paper, we propose OptEPS to jointly optimize all-optical switching and EPS placement in entanglement routing to maximize the quantum network throughput. The main challenge of OptEPS lies in two folds: i). an entanglement may fail to be created; ii). the joint optimization problem suffers from a large time complexity. To overcome these challenges, we first formulate the joint optimization problem as a link-path based model and prune out some candidate paths in order to reduce the time complexity. Then, efficient algorithms are proposed to derive a near-optimal solution in a timely manner. Extensive simulations show that compared with the solutions ignoring EPS placement or without introducing all-optical switching, OptEPS will increase the network throughput by 1557% and 109%, respectively.

In the field of object detector attacks, previous methods primarily rely on fixed gradient optimization or patch-based cover techniques, often leading to suboptimal attack performance and excessive distortions. To address these limitations, we propose a novel attack method, Interactive Reinforcement-based Sparse Attack (IRSA), which employs Reinforcement Learning (RL) to discover the vulnerabilities of object detectors and systematically generate erroneous results. Specifically, we formulate the process of seeking optimal margins for adversarial examples as a Markov Decision Process (MDP). We tackle the RL convergence difficulty through innovative reward functions and a composite optimization method for effective and efficient policy training. Moreover, the perturbations generated by IRSA are more subtle and difficult to detect while requiring less computational effort. Our method also demonstrates strong generalization capabilities against various object detectors. In summary, IRSA is a refined, efficient, and scalable interactive, iterative, end-to-end algorithm.

Blockchain technology has gained widespread use. However, it faces several challenges including throughput, transaction delay, security, and decentralization. This paper presents the Avalon protocol based on a novel Proof-of-Market (PoM) consensus mechanism to address these issues. PoM is a type of Proof-of-Work (PoW) consensus that incorporates market-driven leader election and shifts PoW from mining pools to consumers based on transactions. The matching incentive mechanism makes PoM incentive compatible. PoM decouples the scalability and security of Bitcoin, which means that Avalon can optimize the capacity and interval of blocks without compromising other performance goals. Our analysis shows that Avalon can tolerate malicious nodes possessing up to$\bf{1/3}$of the network's total computational power. Furthermore, the implementation of Avalon is similar to Bitcoin and is highly concise. We evaluate the performance of Avalon through a simulated network of over$\bf{1,000}$nodes. Experimental results demonstrate that Avalon can achieve a throughput of$\bf{4,000}$TPS (transactions per second), which is significantly better than state-of-the-art schemes ($\bf{10\boldsymbol{\times}}$Bitcoin-NG,$\bf{5\boldsymbol{\times}}$ByzCoin, and$\bf{4\boldsymbol{\times}}$Algorand). Additionally, it has a transaction confirmation delay of up to$\bf{40}$s, which is twice better than Bitcoin-NG and ByzCoin while experiencing only minimal blockchain splits and maintaining excellent decentralization.

Falling is a common health problem for elderly people. Early detection of falls allows earlier rescue measures to be implemented. Most existing Wi-Fi-based fall detection systems employ learning-based methods, which require large amounts of labeled data for prior training. To address this issue, we in this paper present AFall, a robust model-based fall detection system that does not require prior training for a single person based on Wi-Fi Channel State Information (CSI). Different from previous Wi-Fi-based fall detection systems, we model the relationship between human falls and changes of Angle of Arrival (AoA) of Wi-Fi signals reflected from human body by multiple signal classification (MUSIC) algorithm. In particular, we deploy two receivers in orthogonal spatial layouts to capture diversified AoA information. Since AoA reflected from human body is independent of environments and subjects, the performance of AFall can remain stable when the environment changes slightly, which can meet the daily needs of the elderly people. We implement AFall using commodity Wi-Fi devices and evaluate it in five different indoor environments. The experimental results demonstrate that AFall achieves an average accuracy of 84.31% and an average F1 score of 84.56%.

The emerging Federated Learning (FL) enables IoT devices to collaboratively learn a shared model based on their local datasets. However, due to end devices’ heterogeneity, it will magnify the inherent synchronization barrier issue of FL and result in non-negligible waiting time when local models are trained with the identical batch size. Moreover, the useless waiting time will further lead to a great strain on devices’ limited battery life. Herein, we aim to alleviate the negative impact of synchronization barrier through adaptive batch size during model training. When using different batch sizes, stability and convergence of the global model should be enforced by assigning appropriate learning rates on different devices. Therefore, we first study the relationship between batch size and learning rate, and formulate a scaling rule to guide the setting of learning rate in terms of batch size. Then we theoretically analyze the convergence rate of global model and obtain a convergence upper bound. On these bases, we propose an efficient algorithm that adaptively adjusts batch size with scaled learning rate for heterogeneous devices to reduce the waiting time and save battery life. We conduct extensive simulations and testbed experiments, and the experimental results demonstrate the effectiveness of our method.

In edge computing (EC), federated learning (FL) enables massive devices to collaboratively train AI models without exposing local data. In order to avoid the possible bottleneck of the parameter server (PS) architecture, we concentrate on the decentralized federated learning (DFL), which adopts peer-to-peer (P2P) communication without maintaining a global model. However, due to the intrinsic features of EC, e.g., resource limitation and heterogeneity, network dynamics and non-IID data, DFL with a fixed P2P topology and/or an identical model compression ratio for all workers results in a slow convergence rate. In this paper, we propose an efficient algorithm (termed CoCo) to accelerate DFL by integrating optimization of topology Construction and model Compression. Concretely, we adaptively construct P2P topology and determine specific compression ratios for each worker to conquer the system dynamics and heterogeneity under bandwidth constraints. To reflect how the non-IID data influence the consistency of local models in DFL, we introduce the consensus distance, i.e., the discrepancy between local models, as the quantitative metric to guide the fine-grained operations of the joint optimization. Extensive simulation results show that CoCo achieves 10× speedup, and reduces the communication cost by about 50% on average, compared with the existing DFL baselines.

Smartphones have been the necessities for us due to their advanced computing capabilities and ubiquitous connectivity to our daily lives. However, they also produce many negative influences, such as ring noise, nuisance calls, which interrupt people’s attention when working. It would be user-friendly if smartphones can automatically sense the surroundings and dynamically work at an appropriate profile to prevent their ringing from disturbing people in some special circumstances. To address this issue, in this paper, we propose a novel smartphone profile switching system, called AutoProfile, which combines the techniques of acoustic sensing, walk detection and machine learning to automatically and dynamically change smartphones’ profiles in different scenarios. We develop a new compact ambient sound scheme for feature extraction, named DWT & MFCC fingerprint, which can effectively distinguish between different social scenarios and outperforms the existing method. To evaluate the performance of AutoProfile, we conduct experiments in 8 scenarios and take multiple influence factors into consideration. The results demonstrate that AutoProfile can realize overall recognition accuracies of$91.4 \;\%$and$90.6 \;\%$when using Random Forest and$k$-nearest Neighbors classifiers, respectively. Moreover, since AutoProfile senses the ambient sound passively, it does not create additional noise compared with some active acoustic sensing schemes. In addition, the power consumption of AutoProfile is acceptable, and thus AutoProfile can be tailored as a background service of smartphones to make them become “smarter”.

With the broad deployment of distributed applications on clouds, east-west traffic is now dominating the majority of cloud networks. The existing communication solutions are tightly coupled with either the control plane (e.g., preprogrammed model) or the location of compute nodes (e.g., conventional gateway model). As a result, it is difficult to flexibly respond to the rapidly expanding networks and frequent abnormal events (e.g., burst traffic and device failures). Accordingly, they may not provide high-performance east-west forwarding while ensuring scalability and robustness. To address this issue, we design Zeta, a scalable and robust east-west forwarding framework with gateway clusters for hyperscale clouds. Zeta abstracts the traffic forwarding capability as a Gateway Cluster Layer, decoupled from the logic of control plane and the location of compute nodes. Specifically, Zeta adopts gateway clusters to support large-scale networks and cope with burst traffic. Moreover, a transparent Multi IPs Migration is proposed for fast recovery from unpredictable failures. We implement Zeta based on eXpress Data Path (XDP) and evaluate its scalability and robustness through comprehensive experiments with up to 100k container instances. Our evaluation shows that Zeta reduces the 99% RTT by$5.1 {\times }$in burst video traffic, and reduces the gateway pure recovery delay by$10.8 {\times }$compared with the state-of-the-art solutions.

Recently, mobile and embedded devices worldwide generate a massive amount of data at the network edge. To efficiently exploit the data from distributed devices, we concentrate on decentralized machine learning (DML), where the workers collaboratively train models under the peer-to-peer (P2P) setting. DML avoids the bottleneck of the parameter server (PS) by enabling the workers to exchange local models with their neighbors rather than the PS. However, DML still faces some key challenges, i.e., resource limitation, system heterogeneity, network dynamics and non-IID data. In this article, we design and implement MOTOR, an efficient DML mechanism that simultaneously addresses these challenges by applying model pruning and topology construction, thus accelerating DML. Specifically, MOTOR assigns different pruning ratios to heterogeneous workers. After model pruning, each worker will train and transmit a sub-model that fits its capabilities, reducing both computation and communication overhead. Besides, MOTOR dynamically constructs the network topology considering the time-varying network conditions and non-IID data distributions. We theoretically analyze the impact of pruning ratio and network topology on model training performance. Guided by the theoretical analysis, we develop a joint optimization algorithm for pruning ratio decision and topology construction to achieve the trade-off between resource overhead and training performance. We implement MOTOR on commercial devices and evaluate the performance with different DML tasks. Extensive experiments show that MOTOR achieves up to 4.2× speedup compared to the existing DML approaches.

Deep neural networks are extremely vulnerable to malicious input data. As 3D data is increasingly used in vision tasks such as robots, autonomous driving and drones, the internal robustness of the classification models for 3D point cloud has received widespread attention. In this paper, we propose a novel method named SPGA (Shape Prior Guided Attack) to generate adversarial point cloud examples. We use shape prior information to make perturbations sparser and thus achieve imperceptible attacks. In particular, we propose a Spatially Logical Block (SLB) to apply adversarial points through sliding in the oriented bounding box. Moreover, we design an algorithm called FOFA for this type of task, which further refines the adversarial attack in the process of breaking down complicated problems into sub-problems. Compared with the methods of global perturbation, our attack method consumes significantly fewer computations, making it more efficient. Most importantly of all, SPGA can generate examples with a higher attack success rate (even in a defensive situation), less perturbation budget and stronger transferability.

The training process of federated learning is known to be vulnerable to adversarial attacks (e.g., backdoor attack). Previous works showed that differential privacy (DP) can be used to defend against backdoor attacks, yet at the cost of vastly losing model utility. To address this issue, we in this paper propose a defense method based on differential privacy, called Clip Norm Decay (CND), to maintain utility when defending against backdoor attacks with DP. CND reduces the injected noise by decreasing the clipping threshold of model updates through the whole training process. In particular, our algorithm bounds the norm of malicious updates by adaptively setting the appropriate thresholds according to the current model updates. Empirical results show that CND can substantially enhance the accuracy of the main task when defending against backdoor attacks. Moreover, extensive experiments demonstrate that our method performs better defense than the original DP, further reducing the attack success rate, even in a strong assumption of threat model.

Federated learning (FL) has been widely applied to collaboratively train deep learning (DL) models on massive end devices (i.e., clients). Due to the limited storage capacity and high labeling cost, there are always insufficient data stored and annotated on each client. Conversely, in cloud datacenters, there exist large-scale unlabeled data, which are easy to collect from public access (e.g., social media). Herein, upon the federated semi-supervised learning (FSSL) technology, we propose the Ada-FedSemi system, which leverages both on-device labeled data and in-cloud unlabeled data to boost the performance of DL models. Given the limited communication and massive quantity of the clients, in each training round, we decide to select partial clients to participate in FL, and their local models are aggregated by the parameter server (PS) to produce pseudo-labels for the unlabeled data, which are utilized to enhance the global model. Considering that the number of participating clients and the quality of pseudo-labels will have a significant impact on the training performance (e.g., efficiency and accuracy), we introduce a multi-armed bandit (MAB) based online algorithm to adaptively determine the participating fraction and confidence threshold during federated model training. Extensive experiments on benchmark models and datasets show that, given the same resource budget, the model trained by Ada-FedSemi achieves 3%-14.8 % higher test accuracy than that of the baseline methods. Besides, when achieving the same test accuracy, Ada-FedSemi saves up to 48% training cost, compared with the baselines.

Image generation from text is a challenging and ill-posed task. Images generated from previous methods usually have low semantic consistency with texts and the achieved resolution is limited. To generate semantically consistent high-resolution images, we propose a novel method named AtHom, in which two attention modules are developed to extract the relationships from both independent modality and unified modality. The first is a novel Independent Modality Attention Module (IAM), which is presented to find out semantically important areas in generated images and to extract the informative context in texts. The second is a new module named Unified Semantic Space Attention Module (UAM), which is utilized to find out the relationships between extracted text context and essential areas in generated images. In particular, to bring the semantic features of texts and images closer in a unified semantic space, AtHom incorporates a homomorphic training mode by exploiting an extra discriminator to distinguish between two different modalities. Extensive experiments show that our AtHom surpasses previous methods by large margins.

Current multi-object tracking and segmentation (MOTS) methods follow the tracking-by-detection paradigm and adopt 2D or 3D convolutions to extract instance embeddings for instance association. However, due to the large receptive field of deep convolutional neural networks, the foreground areas of the current instance and the surrounding areas containing the nearby instances or environments are usually mixed up in the learned instance embeddings, resulting in ambiguities in tracking. In this paper, we propose a highly effective method for learning instance embeddings based on segments by converting the compact image representation to un-ordered 2D point cloud representation. In this way, the non-overlapping nature of instance segments can be fully exploited by strictly separating the foreground point cloud and the background point cloud. Moreover, multiple informative data modalities are formulated as point-wise representations to enrich point-wise features. For each instance, the embedding is learned on the foreground 2D point cloud, the environment 2D point cloud, and the smallest circumscribed bounding box. Then, similarities between instance embeddings are measured for the inter-frame association. In addition, to enable the practical utility of MOTS, we modify the one-stage instance segmentation method SpatialEmbedding for instance segmentation. The resulting efficient and effective framework, named PointTrackV2, outperforms all the state-of-the-art methods including 3D tracking methods by large margins (4.8 percent higher sMOTSA for pedestrians over MOTSFusion) with the near real-time speed (20 FPS evaluated on a single 2080Ti). Extensive evaluations on three datasets demonstrate both the effectiveness and efficiency of our method. Furthermore, as crowded scenes for cars are insufficient in current MOTS datasets, we provide a more challenging dataset named APOLLO MOTS with a much higher instance density.

With the increasing quantity of private cars, curb parking has evolved into an important approach to mitigate parking pressure in urban cities. While some efforts have been made for the demand analysis of point-of-interest (POI) and pattern analysis of human mobility, which may indirectly reflect the parking situation in urban area, there is a lack of comprehensive models for the parking demand, so as to make a prediction for the road sections without parking lots. In this paper, by focusing on curb parking and designing a systemic framework, namedCurb Parking Demand Estimation(CPDE), we model the public parking demand in urban area, w.r.t. parking durations and regional characteristics. Specifically, we use taxi destinations and the distribution of POIs to quantitatively analyze the regional characteristics, designing corresponding features, and propose aK-means-basedLeast Square (KLS) method to relate parking characteristics, namely, the temporal parking durations and the corresponding demands, with these features. In this way, we effectively avoid the geographical sparsity of road parking sections and can finely estimate parking durations and demands for newly developed districts without parking data. Moreover, we give a strategy, namedParking Types Estimation(PTE), which projects estimated parking durations and demands onto Gaussian Mixture Model (GMM) to accurately measure the distribution of demands over different parking durations for a road section. At last, we conduct experiments on a real-world curb parking dataset in Hefei, a provincial city in China. This dataset contains parking orders of 2016 over the urban area of Hefei. The experimental results validate the effectiveness of our methods, and show that our framework outperforms the state-of-the-art baseline schemes.

Cloud computing is motivating data owners to outsource their databases to the cloud. However, for privacy concerns, the sensitive data has to be encrypted before outsourcing, which inevitably posts a challenging task for effective data utilization. Existing work either focuses on keyword searches, or suffers from inadequate security guarantees or inefficiency. In this paper, we concentrate on multi-dimensional range queries over dynamic encrypted cloud data. We first propose a tree-based private range query scheme over dynamic encrypted cloud data (TRQED), which supports faster-than-linear range queries and protects single-dimensional privacy. Then, we discuss the defects of TRQED in terms of privacy-preservation. We modify the framework of the system by adopting a two-server model and put forward a safer range query scheme, called TRQED$^{+}$. By newly designed secure node query (SNQ) and secure point query (SPQ), we propose the perturbation-based oblivious R-tree traversal (ORT) operation to preserve both path pattern and stronger single-dimensional privacy. Finally, we conduct comprehensive experiments on real-world datasets and perform comparisons with existing works to evaluate the performance of the proposed schemes. Experimental results show that our TRQED and TRQED$^+$surpass the state-of-the-art methods in privacy-preservation level and efficiency.

Most existing Wi-Fi-based gait recognition systems consider gait cycle detection as a critical process. However, the noise mixed in dynamic measurements obtained from commercial Wi-Fi devices makes it hard to detect gait cycles. Herein, we adopt the attention-based Recurrent Neural Network (RNN) encoder-decoder and propose a cycle-independent human gait recognition and walking direction estimation system, termed AGait, in Wi-Fi networks. For capturing more human walking dynamics, two receivers together with one transmitter are deployed in different spatial layouts. The Channel State Information (CSI) from different receivers are first assembled and refined to form an integrated walking profile. Then, the RNN encoder reads and encodes the walking profile into primary feature vectors. Given a specific gait or direction sensing task, a corresponding and particular attention vector is computed by the decoder and is finally used to predict the target. The attention scheme motivates AGait to learn to adaptively align with different critical clips of CSI data for different tasks. We implement AGait on commercial Wi-Fi devices in three different indoor environments, and the experimental results demonstrate that AGait can achieve average$F_1$scores of 97.32 to 89.77 percent for gait recognition from a group of 4 to 10 subjects and 97.41 percent for direction estimation from 8 walking directions.

The past decades have seen a proliferation of middlebox deployment in various scenarios, including backbone networks and cloud networks. Since flows have to traverse specific service function chains (SFCs) for security and performance enhancement, it becomes much complex for SFC routing due to routing loops, traffic dynamics and scalability requirement. The existing SFC routing solutions may consume many resources (e.g., TCAM) on the data plane and lead to massive overhead on the control plane, which decrease the scalability of middlebox networks. Due to SFC requirement and potential routing loops, solutions like traditional default paths (e.g., using ECMP) that are widely used in non-middlebox networks will no longer be feasible. In this paper, we present and implement a scalable and flexible middlebox policy enforcement (SAFE-ME) system to minimize the TCAM usage and control overhead. To this end, we design the smart tag operations for construction of default SFC paths with less TCAM rules in the data plane, and present lightweight SFC routing update with less control overhead for dealing with traffic dynamics in the control plane. We implement our solution and evaluate its performance with experiments on both physical platform (Pica8) and Programming Protocol-independent Packet Processors (P4) based data plane, as well as large-scale simulations. Both experimental and simulation results show that SAFE-ME can greatly improve scalability (e.g., TCAM cost, update delay, and control overhead) in middlebox networks, especially for large-scale clouds. For example, our system can reduce the control traffic overhead by about 85% while achieving almost the similar middlebox load, compared with state-of-the-art solutions.

In the age of 5G, everything is connected. The departure site, destination site, departure time and other information of all vehicles on the road can be known by the unified dispatching center. Therefore, on this basis, the vehicle routing model in the traffic network is established. In the scheduling model, the real world vehicle driving situation is simulated by setting the road vehicle scheduling strategy and the intersection passing strategy. By considering the deadlock state, the problem of priority of vehicles passing through the intersection is solved. Considering the congestion in the road and the frequent use of the road in the network center, the whole road grid is layered. The heuristic algorithm with path congestion is used to calculate the route selection of vehicles in real time. The experimental results show that the total scheduling time and waiting time can be effectively reduced when the route scale and vehicle scale are large.

In this paper, we propose VK-Net, a neural network that learns to discover a set of category-specific keypoints from a single point cloud in an unsupervised manner. VK-Net is able to generate semantically consistent and rotation invariant keypoints across objects of the same category and different views. Particularly, we find that utilizing learned keypoints for the task of point cloud registration outperforms other traditional and learning-based approaches. Given the paired source and target point clouds, we can construct keypoint correspondences from learned keypoints using VK-Net. These keypoint correspondences are then employed to calculate a good pose initialization, after which an ICP is utilized to refine the registration. Extensive experiments on the ShapeNet dataset demonstrate that our model outperforms the state-of-the-art methods by a large margin.

Current light field (LF) occlusion removal approaches usually select only a part of sub-aperture images (SAIs) or simply stack all SAIs to reconstruct the center view, which destroys the spatial layout of SAIs. In this paper, we present a simple yet effective LF occlusion removal method name Mask4D, which is a 4D convolution-based encoder-decoder network. We propose to keep the spatial layout of SAIs and construct all SAIs as a 5D input tensor to fully exploit the spatial connection information between SAIs. In particular, except for center view reconstruction, we jointly predict the occlusion mask to disentangle the occlusion mask from the occluded content. Extensive evaluations demonstrate that our Mask4D surpasses the state-of-the-art approaches across different datasets. Moreover, visualizations show that Mask4D predicts the occlusion mask precisely and the reconstructed center view looks more realistic than other approaches. Our code will be publicly available.

Deep convolutional neural networks are widely witnessed vulnerable to adversarial attacks. Recently, great progress has been achieved in attacking object detectors. However, current attacks neglect the practical utility and rely on global perturbations on the target image with a large number of patches or pixels. In this paper, we present a novel attack framework named DTTACK to fool both one-stage and two-stage object detectors with limited perturbations. A novel divergent patch shape consisting of four intersecting lines is proposed to effectively affect deep convolutional feature extraction with limited pixels. In particular, we introduce an instance-aware heat map as a self-attention module to help DTTACK focus on salient object areas, which further improves the attacking performance. Extensive experiments on PASCAL-VOC, MS-COCO, as well as an online detection system demonstrate that DTTACK surpasses the state-of-the-art methods by large margins.

Current text spotting methods perform text detection and text recognition separately. However, in complex scenes where bounding boxes of texts with various shapes are often overlapped, text detection becomes error-prone. By contrast, character detection is more non-ambiguous and easier to learn. In this paper, we present a highly efficient one-stage method named PointerNet for arbitrary-shaped text spotting. Unlike previous methods, PointerNet does not rely on text detection and opens a novel spotting-by-character-detection paradigm. In particular, to connect characters to texts, we propose a simple yet highly effective strategy named pointer that learns the 2D offset from the center of the current character to the center of the subsequent character. Evaluations demonstrate that our PointerNet achieves state-of-the-art performance and is more efficient than current methods (75ms vs. 133ms compared with FOTS). Our code will be publicly available.

Current self-supervised depth estimation algorithms mainly focus on either stereo or monocular only, neglecting the reciprocal relations between them. In this paper, we propose a simple yet effective framework to improve both stereo and monocular depth estimation by leveraging the underlying complementary knowledge of the two tasks. Our approach consists of three stages. In the first stage, the proposed stereo matching network termed StereoNet is trained on image pairs in a self-supervised manner. Second, we introduce an occlusion-aware distillation (OA Distillation) module, which leverages the predicted depths from StereoNet in non-occluded regions to train our monocular depth estimation network named SingleNet. At last, we design an occlusion-aware fusion module (OA Fusion), which generates more reliable depths by fusing estimated depths from StereoNet and SingleNet given the occlusion map. Furthermore, we also take the fused depths as pseudo labels to supervise StereoNet in turn, which brings StereoNet’s performance to a new height. Extensive experiments on KITTI dataset demonstrate the effectiveness of our proposed framework. We achieve new SOTA performance on both stereo and monocular depth estimation tasks.

Current one-step multi-object tracking and segmentation (MOTS) methods lag behind recent two-step methods. By separating the instance segmentation stage from the tracking stage, two-step methods can exploit non-video datasets as extra data for training instance segmentation. Moreover, instances belonging to different IDs on different frames, rather than limited numbers of instances in raw consecutive frames, can be gathered to allow more effective hard example mining in the training of trackers. In this paper, we bridge this gap by presenting a novel data augmentation strategy named continuous copy-paste (CCP). Our intuition behind CCP is to fully exploit the pixel-wise annotations provided by MOTS to actively increase the number of instances as well as unique instance IDs in training. Without any modifications to frameworks, current MOTS methods achieve significant performance gains when trained with CCP. Based on CCP, we propose the first effective one-stage online MOTS method named CCPNet, which generates instance masks as well as the tracking results in one shot. Our CCPNet surpasses all state-of-the-art methods by large margins (3.8% higher sMOTSA and 4.1% higher MOTSA for pedestrians on the KITTI MOTS Validation) and ranks 1st on the KITTI MOTS leaderboard. Evaluations across three datasets also demonstrate the effectiveness of both CCP and CCPNet. Our codes are publicly available at: https://github.com/detectRecog/CCP.

Depth completion aims to recover the dense depth map from sparse depth data and RGB image respectively. However, due to the huge difference between the multi-modal signal input, vanilla convolutional neural network and simple fusion strategy cannot extract features from sparse data and aggregate multi-modal information effectively. To tackle this problem, we design a novel network architecture that takes full advantage of multi-modal features for depth completion. An effective Pre-completion algorithm is first put forward to increase the density of the input depth map and to provide distribution priors. Moreover, to effectively fuse the image features and the depth features, we propose a multi-modal deep aggregation block that consists of multiple connection and aggregation pathways for deeper fusion. Furthermore, based on the intuition that semantic image features are beneficial for accurate contour, we introduce the deformable guided fusion layer to guide the generation of the dense depth map. The resulting architecture, called MDANet, outperforms all the stateof-the-art methods on the popular KITTI Depth Completion Benchmark, meanwhile with fewer parameters than recent methods. The code of this work will be available at https://github.com/USTC-Keyanjie/MDANet_ICRA2021.

While machine learning brings convenience, it also faces the issue of data privacy. For privacy issues, most researches focus on implementing homomorphic encryption or differential privacy to protect data, while ignoring the potential threats caused by the leakage of model parameters. However, a malicious attacker can still recover sensitive data information through model parameters. On the one hand, traditional methods cannot take both high accuracy and low computation time into account. On the other hand, they cannot resist the reconstruction attack from the model's parameter. In order to address this problem, this paper designs a privacy protection framework named FLI, which is inspired by public key infrastructure. In FLI, all participants and the server are trained and aggregated under one framework based on federated learning, which includes key exchange and shares with the idea of homomorphic encryption. Under the algorithm we design, the malicious adversary cannot recover effective information after obtaining the transformed parameters, while the server can still perform effective parameter aggregation. To evaluate the performance of FLI, we conduct extensive experiments. The experimental results show that the computation time is within an acceptable range while ensuring high accuracy.

Without appealing to exhaustive labeled data, self-supervised monocular depth estimation (MDE) plays a fundamental role in computer vision. Previous methods usually adopt a one-stage MDE network, which is insufficient to achieve high performance. In this paper, we dig deep into this task to propose an aggressive framework termed AggNet. The framework is based on a training-only progressive two-stage module to perform pseudo counter-surveillance as well as a simple yet effective dual-warp loss function between image pairs. In particular, we first propose a residual module, which follows the MDE network to learn a refined depth. The residual module takes both the initial depth generated from MDE and the initial color image as input to generate refined depth with residual depth learning. Then, the refined depth is leveraged to supervise the initial depth simultaneously during the training period. For inference, only the MDE network is retained to regress depth from a single image, which gains better performance without introducing extra computation. In addition to self-distillation loss, a simple yet effective dual-warp consistency loss is introduced to encourage the MDE network to keep depth consistency between stereo image pairs. Extensive experiments show that our AggNet achieves state-of-the-art performance on the KITTI and Make3D datasets.

Existing Few Shot Segmentation (FS-Seg) methods mostly study a restricted setting where only foreground and background are required to be discriminated and fall short at discriminating multiple classes. In this paper, we focus on a challenging but more practical variant: Generalized Few Shot Segmentation (GFS-Seg), where all SEEN and UNSEEN classes are segmented simultaneously. Previous methods treat the background as a regular class, leading to difficulty in differentiating UNSEEN classes from it at the test stage. To address this issue, we propose Adaptive Background Modeling and Prototype Query Network (ABPNet), in which the background is formulated as the complement of the set of interested classes. With the help of the attention mechanism and a novel meta-training strategy, it learns an effective set difference function that predicts task-specific background adaptively. Furthermore, we design a Prototype Querying (PQ) module that effectively transfers the learned knowledge to UNSEEN classes with a neural dictionary. Experimental results demonstrate that ABPNet significantly outperforms the state-of-the-art method CAPL on PASCAL-5i and COCO-20i, especially on UNSEEN classes. Also, without retraining, ABPNet can generalize well to FS-Seg.

Federated learning (FL) involves training machine learning models over distributed edge nodes (i.e., workers) while facing three critical challenges, edge heterogeneity, Non-IID data and communication resource constraint. In the synchronous FL, the parameter server has to wait for the slowest workers, leading to significant waiting time due to edge heterogeneity. Though asynchronous FL can well tackle the edge heterogeneity, it requires frequent model transfers, resulting in massive communication resource consumption. Moreover, the different relative frequency of workers participating in asynchronous updating may seriously hurt training accuracy, especially on Non-IID data. In this paper, we propose a semi-asynchronous federated learning mechanism (FedSA), where the parameter server aggregates a certain number of local models by their arrival order in each round. We theoretically analyze the quantitative relationship between the convergence bound of FedSA and different factors,e.g., the number of participating workers in each round, the degree of data Non-IID and edge heterogeneity. Based on the convergence bound, we present an efficient algorithm to determine the number of participating workers to minimize the training completion time. To further improve the training accuracy on Non-IID data, FedSA deploys adaptive learning rates for workers by their relative participation frequency. We extend our proposed mechanism to the dynamic and multiple learning tasks scenarios. Experimental results on the testbed show that our proposed mechanism and algorithms address the three challenges more effectively than the state-of-the-art solutions.

Traditional machine learning-based steganalysis methods on compressed speech have achieved great success in the field of communication security. However, previous studies lacked mathematical modeling of the correlation between codewords, and there is still room for improvement in steganalysis for small-sized and low embedding rate samples. To deal with the challenge, we use Bayesian networks to measure different types of correlations between codewords in linear prediction code and present F3SNet—a four-step strategy: embedding, encoding, attention, and classification for quantization index modulation steganalysis of compressed speech based on the hierarchical attention network. Among them, embedding converts codewords into high-density numerical vectors, encoding uses the memory characteristics of LSTM to retain more information by distributing it among all its vectors, and attention further determines which vectors have a greater impact on the final classification result. To evaluate the performance of F3SNet, we make a comprehensive comparison of F3SNet with existing steganography methods. Experimental results show that F3SNet surpasses the state-of-the-art methods, particularly for small-sized and low embedding rate samples.

Fine-grained flow management is useful in many practical applications, e.g., resource allocation, anomaly detection and traffic engineering. However, it is difficult to provide fine-grained management for a large number of flows in SDNs due to switches' limited flow table capacity. While using wildcard rules can reduce the number of flow entries needed, it cannot fully ensure fine-grained management for all the flows without degrading application performance. In this article, we design and implement hybrid rule placement for fine-grained flow management (to be referred to as HiFi here after). HiFi achieves fine-grained management with a minimal number of flow entries through taking a two-step approach: wildcard entry installment and application-specific exact-match entry installment. How to optimally install wildcard and exact-match flow entries, however, is intractable. Therefore, we design approximation algorithms with bounded factors to solve these problems. We consider how to achieve network-wide load balancing via fine-grained flow management as a case study. Both experiment on a testbed built with open virtual switches and extensive simulation show that HiFi can reduce the number of required flow entries by about 45-69 percent and reduce the control overhead by about 28-50 percent compared with the state-of-the-art approaches for achieving fine-grained flow management.

In Mobile Edge Computing (MEC), many tasks require specific service support for execution and in addition, have a dependent order of execution among the tasks. However, previous works often ignore the impact of having limited services cached at the edge nodes on (dependent) task offloading, thus may lead to an infeasible offloading decision or a longer completion time. To bridge the gap, this article studies how to efficiently offload dependent tasks to edge nodes with limited (and predetermined) service caching. We formally define the problem of offloading dependent tasks with service caching (ODT-SC), and prove that there exists no algorithm with constant approximation for this hard problem. Then, we design an efficient convex programming based algorithm (CP) to solve this problem. Moreover, we study a special case with a homogeneous MEC and propose a favorite successor based algorithm (FS) to solve this special case with a competitive ratio of O(1)O(1). Extensive simulation results using Google data traces show that our proposed algorithms can significantly reduce applications' completion time by about 21-47 percent compared with other alternatives.

3D object detection is an essential task in autonomous driving and robotics. Though great progress has been made, challenges remain in estimating 3D pose for distant and occluded objects. In this paper, we present a novel framework named ZoomNet for stereo imagery-based 3D detection. The pipeline of ZoomNet begins with an ordinary 2D object detection model which is used to obtain pairs of left-right bounding boxes. To further exploit the abundant texture cues in rgb images for more accurate disparity estimation, we introduce a conceptually straight-forward module – adaptive zooming, which simultaneously resizes 2D instance bounding boxes to a unified resolution and adjusts the camera intrinsic parameters accordingly. In this way, we are able to estimate higher-quality disparity maps from the resized box images then construct dense point clouds for both nearby and distant objects. Moreover, we introduce to learn part locations as complementary features to improve the resistance against occlusion and put forward the 3D fitting score to better estimate the 3D detection quality. Extensive experiments on the popular KITTI 3D detection dataset indicate ZoomNet surpasses all previous state-of-the-art methods by large margins (improved by 9.4% on APbv (IoU=0.7) over pseudo-LiDAR). Ablation study also demonstrates that our adaptive zooming strategy brings an improvement of over 10% on AP3d (IoU=0.7). In addition, since the official KITTI benchmark lacks fine-grained annotations like pixel-wise part locations, we also present our KFG dataset by augmenting KITTI with detailed instance-wise annotations including pixel-wise part location, pixel-wise disparity, etc.. Both the KFG dataset and our codes will be publicly available at https://github.com/detectRecog/ZoomNet.

Generative Adversarial Network (GAN) has already made a big splash in the field of generating realistic "fake" data. However, when data is distributed and data-holders are reluctant to share data for privacy reasons, GAN’s training is difficult. To address this issue, we propose private FL-GAN, a differential privacy generative adversarial network model based on federated learning. By strategically combining the Lipschitz limit with the differential privacy sensitivity, the model can generate high-quality synthetic data without sacrificing the privacy of the training data. We theoretically prove that private FL-GAN can provide strict privacy guarantee with differential privacy, and experimentally demonstrate our model can generate satisfactory data.

Mobile edge computing(MEC), as an emerging computing paradigm, pushes services away from centralized remote cloud to distributed edge servers deployed by multiple service providers(SPs), improving user experience and reducing the communication burden on core network. However, this distributed computing architecture also brings some new challenges to the network. In multi-SP MEC system, a SP prefers to use edge servers deployed by itself instead of others, which not only improves service quality but also reduces processing cost. The service placement and request scheduling strategies directly affect the revenue of SPs. Since the service popularity changes over time and the resources of edge servers are limited, the network system needs to make decisions about service placement and request scheduling dynamically to provide better service for users. Owing to the lack of long-term prior knowledge and involving binary decision variables, how to place services and schedule requests to boost the profit of SPs is a challenging problem. We formally formalize this joint optimization problem and propose an efficient online algorithm. First, we invoke Lyapunov optimization technology to convert the long-term optimization problem into a series of subproblems, then a dual-decomposition algorithm is utilized to solve the subproblem. Experimental results show that the algorithm proposed in this paper achieves nearly optimal performance, and it raises 25% and 70% profit compared to greedy and Top-K algorithms, respectively.

Attributed graph data is powerful to describe relational information in various areas, such as social links through numerous web services and citation/reference relations in the collaboration network. Taking advantage of attributed graph data, service providers can model complex systems and capture diversified interactions to achieve better business performance. However, privacy concern is a huge obstacle to collect and analyze user's attributed graph data. Existing studies on protecting private graph data mainly focus on edge local differential privacy(LDP), which might be insufficient in some highly sensitive scenarios. In this paper, we present a novel privacy notion that is stronger than edge LDP, and investigate approaches to analyze attributed graphs under this notion. To neutralize the effect of excessively introduced noise, we propose PrivAG, a privacy-preserving framework that protects attributed graph data in the local setting while providing representative graph statistics. The effectiveness and efficiency of PrivAG framework is validated through extensive experiments.

An SDN overlay network is a legacy network improved through SDN and overlay technology. It has some traits including the cheap upgrade cost, flexible network management and the sharing of physical network resources which has brought huge benefits to the multi-tenant cloud platform. Link failure is an important issue that shoulde be solved in any large network. In SDN overlay networks, link failure recovery brings new challenges different from the legacy network, such as how to maintain the performance of overlay networks in the post-recovery network. Thus, in the case of single link failure, we devise a recovery approach to guarantee the performance of overlay networks by the coordination between SDN switches and traditional switches. We formulate the link failure recovery (LFR) problem as an integer linear program and prove its NP-hardness. A rounding-based algorithm with bounded approximation factors is devised to solve the LFR problem. The simulation results show that the devised scheme can guarantee the performance of the overlay network after restoration. The results also show that, compared with SPR and IPFRR, the designed method can reduce the maximum link load rate by approximately 41.5% and 51.6%.

Network Function Virtualization (NFV) is a new paradigm to enable service innovation through virtualizing traditional network functions. To construct a new NFV-enabled network, there are two critical requirements: minimizing server deployment cost and satisfying switch resource constraints. However, prior work mostly focuses on the server deployment cost, while ignoring the switch resource constraints (e.g., switch's flow-table size). It thus results in a large number of rules on switches and leads to massive control overhead. To address this challenge, we propose an incremental server deployment (INSD) problem for construction of scalable NFV-enabled networks. We prove that the INSD problem is NP-Hard, and there is no polynomial-time algorithm with approximation ratio of (1- ε) ·ln m, where ε is an arbitrarily small value and m is the number of requests in the network. We then present an efficient algorithm with an approximation ratio of 2 · H(q · p)1, where q is the number of VNF's categories and p is the maximum number of requests through a switch. We evaluate the performance of our algorithm with experiments on physical platform (Pica8), Open vSwitches, and large-scale simulations. Both experiment and simulation results show high scalability of the proposed algorithm. For example, our solution can reduce the control and rule overhead by about 88% with about 5% additional server deployment, compared with the existing solutions.

Fine-grained flow management is useful in many practical applications, e.g., resource allocation, anomaly detection and traffic engineering. However, it is difficult to provide fine-grained management for a large number of flows in SDNs due to switches' limited flow table capacity. While using wildcard rules can reduce the number of flow entries needed, it cannot fully ensure fine-grained management for all the flows without degrading application performance. In this paper, we design and implement HiFi, a system that achieves fine-grained management with a minimal number of flow entries. To this end, HiFi takes a two-step approach: wildcard entry installment and application-specific exact-match entry installment. How to optimally install wildcard and exact-match flow entries, however, is intractable. Therefore, we design approximation algorithms with bounded factors to solve these problems. We consider how to achieve network-wide load balancing via fine-grained flow management as a case study. Both experimental and simulation results show that HiFi can reduce the number of required flow entries by about 45%-69% and reduce the control overhead by 28%-50% compared with the state-of-the-art approaches for achieving fine-grained flow management.

In Mobile Edge Computing (MEC), many tasks require specific service support for execution and in addition, have a dependent order of execution among the tasks. However, previous works often ignore the impact of having limited services cached at the edge nodes on (dependent) task offloading, thus may lead to an infeasible offloading decision or a longer completion time. To bridge the gap, this paper studies how to efficiently offload dependent tasks to edge nodes with limited (and predetermined) service caching. We formally define the problem of offloading dependent tasks with service caching (ODT-SC), and prove that there exists no algorithm with constant approximation for this hard problem. Then, we design an efficient convex programming based algorithm (CP) to solve this problem. Moreover, we study a special case with a homogeneous MEC and propose a favorite successor based algorithm (FS) to solve this special case with a competitive ratio of O(1). Extensive simulation results using Google data traces show that our proposed algorithms can significantly reduce applications' completion time by about 27-51% compared with other alternatives.

6DoF object pose estimation is essential for many real-world applications. Although great progress has been made, challenges still remain in estimating 6D pose for occluded objects. Current RGB-D approaches predict 6DoF pose directly, which is sensitive to occlusion in cluttered scenes. In this work, we propose DCNet, an end-to-end framework for estimating 6DoF object poses. DCNet first converts pixels in the image plane to point clouds in the camera coordinate system and then establishes dense correspondences between the camera coordinate system and the object coordinate system. Based on these two systems, we fuse 2D appearance and 3D geometric features by pixel-wise concatenation to construct dense correspondences, from which the pose is calculated through the least-squares fitting algorithm. Dense correspondences guarantee enough point pairs for a robust 6DoF pose estimation, even if the occlusion is heavy. Experimental results demonstrate that DCNet outperforms the state-of-the-art methods on LINEMOD, Occlusion LINEMOD and YCB-Video datasets, especially in terms of the robustness to occlusion scenes.

Along with the generation of Internet of Things (IoT), the values of tremendous volumes of sensing data will be slowly unlocked. Thus, crowd-sensed data trading as a new business paradigm has recently attracted increasing attention. A typical data trading system contains a platform, data consumers, and crowd workers. The platform recruits crowd workers to collect data and then sells the data to consumers. In this article, we design a differentially private crowd-sensed data trading mechanism, called DPDT, to preserve the identity privacy of consumers and the task privacy against crowd workers during the data collection process, simultaneously. DPDT consists of a differentially private auction-based data pricing algorithm and a differentially private data collection algorithm. The data pricing algorithm achieves a good approximation to the maximum revenue. Meanwhile, it guarantees (e2- 1)ϵ-truthfulness and 2ϵ-differential privacy, where ϵ > 0 is a small constant. The data collection algorithm is able to effectively protect the data collection task privacy against crowd workers. We prove that this data collection algorithm achieves δ-approximate ϵ-differential privacy, where δ <; 1/e is a small constant, and meanwhile guarantees a tight bound of the expected approximation ratio. At last, extensive simulations are conducted to verify the significant performance of DPDT.

Most user-generated data in online services are presented as set-valued data, e.g., visited website URLs, recently used Apps by a person, and etc. These data are of great value to service providers, but also bring privacy concerns if collected and analyzed directly. To tackle potential privacy threatens, local differential privacy (LDP) attracts increasing attention nowadays. However, existing approaches only provide sub-optimal error bound for set-valued data distribution estimation with LDP. Besides, it is computational expensive and communication expensive to use for high dimensional set-valued data, considering large domains in real scenarios. Thus, existing approaches are unpractical to use on resource-constrained user-side devices (e.g., smartphones and wearable devices). In this paper, we propose a utility-optimal and efficient set-valued data publication method (i.e., wheel mechanism ). On the user side, each user contributes only one numerical value to represent their privatized data. The computational complexity is O (min{ m log m , me ɛ }) and communication cost is O (log( me ɛ )) bits, while existing approaches usually depend on O ( d ) or O (log d ), where m is the number of items in the set-valued data ( m ≡ 1 for categorical data), d is the domain size (usually d ≫ m ) and ɛ is the privacy budget. On the server side, the estimator takes numerical values from users as input and derives an unbiased distribution estimation. Theoretical results show that estimation error bounds are improved from previously known [EQUATION] to the optimal rate [EQUATION]. Results on extensive experiments demonstrate that our proposed wheel mechanism is 3-100× faster than existing approaches, meanwhile has optimal statistical efficiency.

In the cloud environment, it brings better reliability and robustness with geographically distributed datacenters. As the growth of large-scale applications in geo-distributed cloud systems, the resource demand from different areas increases violently, and researchers pay more attention to meet as many cloud users' VM demands as possible by using limited cloud resources. However, there exist many issues for cloud users in existing works, such as the VM demands being refused and high response latency. In this paper, we present a cloud system model for the cloud provider to dynamically expand the scale of geo-distributed date centers. In our model, the cloud provider rents hardware resources from other resource owners (ROs), who have redundant resources and are willing to lease them. Since the ROs possess vast resources and spread all over the global, our system model can deploy more cloud users' VMs and effectively reduce the bandwidth cost. We propose an optimization problem for the cloud provider to maximize the profit, and carefully solve it in different conditions. Our simulation results show that our system model and algorithms can effectively improve the user satisfaction and the total revenue and reduce the average latency of users' requests.

With the proliferation of mobile devices, mobile crowd sensing (MCS) has emerged as a new data collection paradigm, which allows the crowd to act as sensors and contribute their observations about entities. Unfortunately, users with varied skills and motivations may provide conflicting information for the same entity. Existing work solves this problem by estimating user reliability and inferring the correct observations (i.e., truths). However, these methods assume that users' expertise degrees are dependent on the truths, but ignore the finer clusters that exist even in the entities with the same truths. To capture users' fine-grained reliability on different entity clusters, we propose a novel Bayesian co-clustering truth discovery model for the task of observation aggregation. This model enables us to produce a more precise estimation while taking into account the entity clusters and the user clusters. Experiments on four real-world datasets reveal that our method outperforms the state-of-the-art approaches in terms of accuracy and F1-score.

We study the min-cost seed selection problem in online social networks for viral marketing, where the goal is to select a set of seed nodes with the minimum total cost such that the expected number of influenced nodes in the network exceeds a predefined threshold. We propose several algorithms that outperform the previous studies both on the theoretical approximation ratio and on the experimental performance. In the case where the nodes have heterogeneous costs, our algorithms are the first bi-criteria approximation algorithms with polynomial running time and provable approximation ratio. In the case where the users have uniform costs, our algorithms achieve logarithmic approximation ratio and provable time complexity which is smaller than that of the existing algorithms in orders of magnitude. We conduct extensive experiments using real social networks. The experimental results show that, our algorithms significantly outperform the existing algorithms both on the total cost and on the running time, and also scale well to billion-scale networks.

Mobile crowdsensing is a new paradigm in which a requester can recruit a group of mobile users via a platform and coordinate them to perform some sensing tasks by using their smartphones. In mobile crowdsensing, each user might perform multiple tasks with different sensing qualities. Meanwhile, the users participating in the crowdsensing will ask for sufficient rewards to compensate for their expenditures. Hence, an important problem is how to recruit the users with minimum cost while achieving a satisfactory sensing quality for each task. Furthermore, in order to ease users' worries about privacy disclosures, the user recruitment process needs to protect each user's sensing quality and recruitment cost information from being revealed to other users or to the platform. In this paper, we propose two secure user recruitment problems for the cases where the recruitment costs of users are homogeneous and heterogeneous. After proving the NP-hardness of the problems, we design two secure user recruitment protocols by using secret sharing scheme. Both of the proposed protocols adopt greedy strategies, which can recruit nearly optimal users while ensuring that the total sensing quality of each task is no less than a given threshold. The difference lies in that the two greedy strategies are based on two unique utility functions. We analyze the approximation ratios of the two protocols and prove the security under the semi-honest model. Finally, we demonstrate the significant performance of the proposed protocols through extensive simulations and executions on real smartphones.

Sketches have been widely used to record traffic statistics using sub-linear space data structure. Most sketches focus on the traffic estimation of elephant flows (i.e., heavy hitters) due to their importance to many network optimization tasks, e.g., traffic engineering and load balancing. In fact, the information of aggregate mice flows (e.g., all the mice flows with the same source IP) is also crucial to many security-associated tasks, e.g., DDoS detection and network scan detection. However, the previous solutions, e.g., measuring each individual flow or using multiple sketches for independent measurement tasks, will result in worse estimation error or higher computational overhead. To conquer the above disadvantages, we propose an accurate traffic measurement framework with multiple filters, called Sketchtree, to efficiently measure both elephant flows and aggregate mice flows. These filters in Sketchtree are organized in a hierarchical manner, and help to alleviate the hash collision and improve the measurement accuracy, as the number of flows through hierarchical filters in turn will be decreased gradually. We also design some mechanisms to improve the resource utilization efficiency. To validate our proposal, we have implemented Sketchtree and conducted experimental evaluation using real campus traffic traces. The experimental results show that Sketchtree can increase the processing speed by 100 percent, and reduce the measurement error by over 30 percent compared with state-of-the-art sketches.

As information security is increasingly valued, privacy-preserving data mining has become a research hotspot in the field of big data and signal processing. We propose a new differentially private greedy decision forest algorithm called DPGDF to help improve the accuracy of privacy-preserving data mining. Unlike previous algorithms that only employed greedy decision trees or random forests, our algorithm uses a combination of greedy trees and parallel combination theory to construct a greedy decision forest and coordinate privacy protection and prediction accuracy to achieve the best balance. Combined with smooth sensitivity, the introduction of noise is minimized, making the prediction accuracy of the algorithm notably better than the current state-of-the-art algorithms. Experiments on the UCI datasets show that the prediction accuracy of our algorithm is about 10% higher than that of those algorithms.

Local differential privacy (LDP), as a strong and practical notion, has been applied to deal with privacy issues in data collection. However, existing LDP-based strategies mainly focus on utility optimization at a single privacy level while ignoring various privacy preferences of data providers and multilevel privacy demands for statistics. In this poster, we for the first time propose a framework to optimize the utility of histogram estimation with these two privacy requirements. To clarify the goal of privacy protection, we personalize the traditional definition of LDP. We design two independent approaches to minimize the utility loss: Advanced Combination, which composes multilevel results for utility optimization, and Data Recycle with Personalized Privacy, which enlarges sample size for an estimation. We demonstrate their effectiveness on privacy and utility. Moreover, we embed these approaches within a Recycle and Combination Framework and prove that the framework stably achieves the optimal utility by quantifying its error bounds. On real-world datasets, our approaches are experimentally validated and remarkably outperform baseline methods.

The past decades have seen a proliferation of middlebox deployment in various networks, including backbone networks and datacenters. Since network flows have to traverse specific service function chains (SFCs) for security and performance enhancement, it becomes much complex for SFC routing due to routing loops, traffic dynamics and scalability requirement. The existing SFC routing solutions may consume many resources (e.g., TCAM) on the data plane and lead to massive overhead on the control plane, which decrease the scalability of middlebox networks. Due to SFC requirement and potential routing loops, solutions like traditional default paths (e.g., using ECMP) that are widely used in non-middlebox networks will no longer be feasible. In this paper, we present and implement a scalable and flexible middlebox policy enforcement (SAFE-ME) system to minimize the TCAM usage and control overhead. To this end, we design the smart tag operations for construction of default SFC paths with less TCAM rules in the data plane, and present lightweight SFC routing update with less control overhead for dealing with traffic dynamics in the control plane. We implement our solution and evaluate its performance with experiments on both physical platform (Pica8) and Open vSwitch (OVS), as well as large-scale simulations. Both experimental and simulation results show that SAFE-ME can greatly improve scalability (e.g., TCAM cost, update delay, and control overhead) in middlebox networks. For example, our system can reduce the control traffic overhead by about 83% while achieving almost the similar middlebox load, compared with state-of-the-art solutions.

Many important functions in software defined networks can benefit from fine-grained traffic measurement at flow level. Because TCAM-based flow entries only provide aggregate traffic statistics, prior research has suggested to perform flow-level measurement in SRAM and balance the measurement load across the network through collaborative traffic measurement. The key problem of collaborative measurement is to provide a mechanism to distribute flows to switches such that each switch can identify its subset of flows to measure. We observe that the prior work has focused on optimizing flow distribution among switches, but overlooked their high space and per-packet processing overhead introduced to the data plane, which becomes a serious issue in large SDN systems. In this paper, we propose a new lightweight solution to the flow distribution problem. It follows the design principle of alleviating complexity of the data plane by minimizing the data-plane space and processing overhead. At the control plane, we formulate flow distribution as optimization problems under two scenarios that implement collaborative measurement by edge switches only and by edge/core switches together, respectively. Our extensive simulations demonstrate that, comparing with the best existing work, the proposed lightweight solution achieves a comparable performance in terms of load balancing, while drastically reducing both space overhead and per-packet processing overhead, making it more practical in real-world systems that are sensitive to the additional overhead introduced by flow distribution.

Rain observations with fine spatio-temporal granularity are significant for professional researches, decision-making, and our daily lives. However, the existing rain gauges can only cover less than 1% of the earth surface, and its amount is still decreasing. Even with the help of several other limited and immature supplementary techniques, rain observations today are still not precise enough. In such context, crowdsourcing paves the avenues toward a fault-tolerant rain observation network with unprecedented resolution and coverage, based on an alternative, nowadays omnipresent source, smartphones, which are integrated with abundant advanced sensors and are becoming more and more ubiquitous around us. In this paper, we propose Chaac, a novel system that exploits opportunistically crowdsourced audio clips from smartphone users to achieve precise detection and intensity measurement of rain. The evaluation results of performing Chaac on 1-s long audio segments demonstrate that it can detect and measure rain with 92.0% and 93.9% true positive rates, respectively.

Recently, local differential privacy (LDP), as a strong and practical notion, has been applied to deal with privacy issues in data collection. However, existing LDP-based strategies mainly focus on utility optimization at a single privacy level while ignoring various privacy preferences of data providers and multilevel privacy demands for statistics. In this paper, we for the first time propose a framework to optimize the utility of histogram estimation with these two privacy requirements. To clarify the goal of privacy protection, we personalize the traditional definition of LDP. We design two independent approaches to minimize the utility loss: Advanced Combination, which composes multilevel results for utility optimization, and Data Recycle with Personalized Privacy, which enlarges the sample size for an estimation. We demonstrate their effectiveness on privacy and utility, respectively. Moreover, we embed these approaches within a Recycle and Combination Framework and prove that the framework stably achieves the optimal utility by quantifying its error bounds. On real-world datasets, our approaches are experimentally validated and remarkably outperform baseline methods.

For the purpose of improving the quality of services, softwares or online services are collecting various of user data, such as personal information and locations. Such data facilitates mining statistical knowledge of users, but threatens users' privacy as it may reveal sensitive information (e.g., identities and activities) about individuals. This work considers distribution estimation over user-contributed data meanwhile providing rigid protection of their data with local ε-differential privacy (ε-LDP), which sanitizes each user's data on the client's side (e.g, on the user's mobile device). Our privacy protection covers both qualitative data (e.g., categorical data) and discrete quantitative data (e.g., location data). Specifically, for categorical data, we derive an optimal ε-LDP mechanism (termed as k-subset mechanism) from mutual information perspective, and further show its optimality over existing approaches within the context of discrete distribution estimation; for discrete quantitative data that have arbitrary distance metric, we provide an efficient extension of k-subset mechanism by proposing a variant of the popular Exponential Mechanism (EM) to tackle the asymmetry issue on the data domain. Experiments on real-world datasets and simulated scenarios show that our mechanism is highly efficient and reduces nearly a fraction of exp(- ε/2) error for distribution estimation when compared to existing approaches.

There are more and more structured traffic flows (a.k.a coflow) in today's data center networks. Completing a coflow is extremely important for various applications, e.g., MapReduce. To reduce the coflow completion time or CCT, one may increase the link capacity by applying advanced optical circuit switches in data center networks. Due to special features of optical circuit switches, both traffic scheduling and circuit scheduling will influence the CCT. However, previous solutions have some significant limitations: they consider either coflow scheduling, or circuit scheduling for only one optical circuit switch, which are both insufficient. In this paper, we study the integrated coflow and circuit scheduling (GCCS) problem with the objective to minimize the CCT, and prove its NP-hardness. We present an integrated algorithm which includes two steps, coflow scheduling and circuit scheduling, respectively. We also analyze that the proposed algorithm can achieve the approximation ratio O(h) in most practical situations, where h is the maximum number of ports among all lightpaths. Through large-scale simulations, we demonstrate that the integrated solution can significantly reduce the CCT by about 43-70 percent compared with the state-of-the-art coflow scheduler for optical networks.

Traditional word embedding approaches learn semantic information at word level while ignoring the meaningful internal structures of words like morphemes.Furthermore, existing morphology-based models directly incorporate morphemes to train word embeddings, but still neglect the latent meanings of morphemes.In this paper, we explore to employ the latent meanings of morphological compositions of words to train and enhance word embeddings.Based on this purpose, we propose three Latent Meaning Models (LMMs), named LMM-A, LMM-S and LMM-M respectively, which adopt different strategies to incorporate the latent meanings of morphemes during the training process.Experiments on word similarity, syntactic analogy and text classification are conducted to validate the feasibility of our models.The results demonstrate that our models outperform the baselines on five word similarity datasets.On Wordsim-353 and RG-65 datasets, our models nearly achieve 5% and 7% gains over the classic CBOW model, respectively.For the syntactic analogy and text classification tasks, our models also surpass all the baselines including a morphology-based model.

With the rapid development of mobile technologies, LTE is turning to be a wonderful carrier for covert channels. Existing detection for covert storage channel (CSC) are almost packet analysis based methods. In this paper, we present an entropy-based method for detecting CSC in Sequence Number (SN) fields of PDCP and RLC layer, which is seen as the most difficult to be detected. We simulate the LTE network in NS3 platform, and propose a Protocol Data Unit (PDU) based blind method to calculate the distance between the SN of PDU and its first left neighbor, instead of analyzing the packets or extracting the value of SN from the PDU. Our experimental results have demonstrated that the proposed detection method is sensitive to the hidden information in the SN fields of PDCP and RLC layer. It can detect them in an accurate manner, and can be conducted in both real-time online and offline storage detection.

License plate recognition is an essential step in automatic license plate recognition since it is a key technology to recognize detected license plates. Though there are extensive researches on license plate recognition, it is still challenging to recognize license plates under conditions like great tilt angles, uneven illuminations, and distortions. Based on the observation that an accurate shape correction can significantly improve the recognition accuracy on these images, this paper proposes a robust methodology named LCR for license plate recognition free of conventional image analysis operations. This approach is based on three neural networks for three different purposes: (i) predicting the locations of four vertices; (ii) predicting cutting locations; (iii) character classification. To the best of our knowledge, LCR is the first to address shape correction by designing neural networks to accurately predict the coordinates of license plates vertices. Experiments on over 250,000 unique images show that LCR significantly outperforms several state-of-the-art license plate recognition approaches. Moreover, in evaluations, the application of shape correction significantly improve the recognition accuracy.

Set-valued data is useful for representing a rich family of information in numerous areas, such as market basket data of online shopping, apps on mobile phones and web browsing history. By analyzing set-valued data that are collected from users, service providers could learn the demographics of the users, the patterns of their usages, and finally, improve the quality of services for them. However, privacy has been an increasing concern in collecting and analyzing users' set-valued data, since these data may reveal sensitive information (e.g., identities, preferences and diseases) about individuals. In this work, we propose a privacy preserving aggregation mechanism for set-valued data: PrivSet. It provides rigorous data privacy protection locally (e.g., on mobile phones or wearable devices) and efficiently (its computational overhead is linear to the item domain size) for each user, and meanwhile allowing effective statistical analyses (e.g., distribution estimation of items, distribution estimation of set cardinality) on set-valued data for service providers. More specifically, in PrivSet, within the constraints of local e-differential privacy, each user independently responses with a subset of the set-valued data domain with calibrated probabilities, hence the true positive/false positive rate of each item is balanced and the performance of distribution estimation is optimized. Besides presenting theoretical error bounds of PrivSet and proving its optimality over existing approaches, we experimentally validate the mechanism, the experimental results illustrate that the estimation error in PrivSet has been reduced by half when compared to state-of-the-art approaches.

The combination of Network Function Virtualization (NFV) and Software Defined Networking (SDN) possesses a great potential in accommodating dynamic network control via cloning/migration of virtualized NFs and steering of traffic flows. A great challenge is the lack of the proprietary internal NF state information to the control system (including SDN controller and NFV orchestrator), which may lead to incorrect packet/flow processing at the newly created NF instances. In this work, we design a light-weight approach which can function either independently or as a plug-in to the network control system to reveal the internal NF states. Unlike the previous work, we propose to learn the internal NF states through normal network functions instead of designing extra APIs for certain NFs. Moreover, we propose a feasible way to detect state violations and even correct them automatically. Our approach is tested by experiments, and the results confirm its efficiency and practicability.

There is a fundamental and critical problem in modern mobile applications, in which the battery life of mobile devices is usually limited. Recently, some researchers prolong the life of batteries by offloading computation tasks to edge-servers which are deployed near the mobile devices. However, computing offloading causes extra delay, which may severely downgrade the user experience especially for the delay-sensitive applications. Moreover, the dynamic nature of mobile devices and the limited computation capacity of edge-servers also bring another challenges for tradeoff optimization between energy consumption and task completion latency. In this paper, we propose a dynamic computing offloading (DCL) problem, which aims to minimize the maximum energy consumption of the mobile devices with constraints on computation tasks latency in a Mobile Edge-Computing (MEC) network. To solve the problem, we consider two complementary cases: offline case (we sacrifice response time to achieve better service results) and online case (where we have to make immediate offloading decision for each computation task arrived online). For the offline case, we propose an efficient RMCL algorithm, and prove that our RMCL method achieves at least O((log m)/α + 1) of the optimum with high probability, where m is the number of computation tasks in a time slot, and α is a value depending on the minimum edge-server capacity and the maximum computation task demand, with α ≥ 1 under most practical situations. For the online case, we propose an algorithm, named OMCL, which considers a trade off between the latency and energy consumption. The performance of our proposed algorithms is evaluated by formal analysis and simulation on a small-scale system. The simulation results show that the algorithm can reduce the maximum energy consumption in a set of mobile devices by 40% compared with executing computation tasks locally.

Forecasting taxi demand is of great significance to the intelligent transportation systems in a smart city. Traditional demand prediction methods mostly considered about inter-regional traffic, events, activities, and weather, while they overlooked the influence of other travel modes, such as metro. In this paper, we propose a Deep Taxi-Metro Spatial-Temporal Network framework, namely TMST-Net, to model the spatiotemporal relationships between the taxi demand and the metro crowd flows. In detail, we apply residual neural networks to model temporal (current, day, and week) properties of the taxi demand in each area. For each feature, we apply residual convolutional units to handle the spatial properties of taxi demand. Likewise, we apply the same method to model the metro crowd flows. TMST-Net learns to assign different weights between taxi and metro by aggregating the output of the three residual neural networks and the external factors to forecast the final taxi demand for each area in the next timestamp. Experimental results on real taxi trajectory and the automatic fare collection (AFC) data in Shanghai show that our approach outperforms the state-of-the-art methods.

For online conversations with top privacy, we often need to erase the existing contact behavior. Thus we want communications in which adversaries can not link you to the person you contact, namely communications with unlinkability. However, most current communication systems including variations of Mix networks fail to maintain unlinkability against global active adversaries (GAA) who can monitor global traffic and easily compromise clients and infrastructures. Therefore, designing an unlinkable communication system against GAA is challenging. By analyzing limitations of current communication systems, we propose two other features to assure unlinkability: covertness and deniability. In this paper, we design HTor, a novel and practical communication system with unlinkability, via a single web server. HTor interpolates the server to cut off the direct connection between two people in one communication and exploits covert channels (CCs) to hide communications between clients and the server. Considering servers might be corrupted, HTor utilizes a group mechanism to protect the receiver for each message. By extensive large-scale evaluations, we show that communications over HTor are robust and difficult to detect. Besides, HTor is easily implemented and, with multiple servers, it can provide enough bandwidth and relatively low latency for chatting.

Covert channels transmit secret information by using the existing resources which were not designed for communication. As a major approach to information leakage, covert channels are rapidly gaining popularity with the exponentially growth of cloud and network resources. Long Term Evolution Advance (LTE‐A) has dominated the mobile telecommunication networks, which brings an elevation of the risk of covert channels. In this study, the authors propose a supervised learning scheme based on support vector machine (SVM) for the covert channel detection in LTE‐A. Based on the fact that the covert channel using the header fields of LTE‐A protocol would change the regularity, goodness of fit or correlation of the data traffic, they present behaviour characteristics statistics index (CSI) in the LTE‐A protocol to evaluate the changes. According to CSI, they extract the classification feature vectors from the data traffic stream, based on which an SVM classifier used for classifying the channel as covert or overt is trained for testing on the channel under investigation. Experiment results show that the authors' proposed detection scheme is high‐efficiency in terms of detection accuracy, sensitivity and specificity, which has great potential to serve as a new idea for the detection of covert channel in LTE‐A.

In[1], reference [16] should be replaced as follows: [16] P. Chao, Y. Tan, and L. T. Yang, “New algorithms for the minimum-cost single-source unsplittable flow problem,” inProc. 21st Int. Conf. Adv. Inf. Netw. Appl. Workshops (AINAW), Niagara Falls, ON, Canada, May 2007.

To better serve a diversity of flows, load balancing is crucial to ensure operational efficiency. However, previous works for load balancing have several disadvantages: 1) limited applicability with sub-flow scheduling (e.g., LetFlow); 2) hash collision (e.g., ECMP); or 3) transient network congestion due to reactive scheduling for traffic dynamics (e.g., Hedera and DevoFlow). An important reason for the above disadvantages is that it is difficult to provide fully fine-grained flow control for load balancing in an SDN as the flow table size of each SDN switch is usually limited. Inspired by the fact that a virtual switch (vswitch) has more powerful processing capacity and more flow entries compared with a physical switch, the previous work (e.g., Presto) deploys one vswitch for each ingress switch, and achieves the load balancing through efficient flow routing. However, this mechanism may lead to high cost and not well deal with topology asymmetry. Thus, this paper proposes to achieve the load balancing by incrementally deploying a certain number of vswitches in an SDN. We formulate the joint optimization of vswitch deployment and routing (JVR) problem as an integer linear program, and prove its NP-hardness. A rounding-based algorithm with bounded approximation factors is proposed to solve the JVR problem. We implement the proposed algorithm on an SDN testbed for experimental studies and use simulations for large-scale investigation. The experimental results and simulation results show high efficiency of our algorithm. For example, our proposed algorithm can reduce the link load ratio by about 41.5% compared with ECMP by deploying a small number of virtual switches.

In this paper, we focus on the incentive mechanism design for a vehicle-based, nondeterministic crowdsensing system. In this crowdsensing system, vehicles move along their trajectories and perform corresponding sensing tasks with different probabilities. Each task may be performed by multiple vehicles jointly so as to ensure a high probability of success. Designing an incentive mechanism for such a crowdsensing system is challenging since it contains a non-trivial set cover problem. To solve this problem, we propose a truthful, reverse-auction-based incentive mechanism that includes an approximation algorithm to select winning bids with a nearly minimum social cost and a payment algorithm to determine payments for all participants. Moreover, we extend the problem to a more complex case in which the Quality of sensing Data (QoD) of each vehicle is taken into consideration. For this problem, we propose a QoD-aware incentive mechanism, which consists of a QoD-aware winning-bid selection algorithm and a QoD-aware payment determination algorithm. We prove that the proposed incentive mechanisms have truthfulness, individual rationality, and computational efficiency. Moreover, we analyze the approximation ratios of the winning-bid selection algorithms. The simulations, based on a real vehicle trace, also demonstrate the significant performances of our incentive mechanisms.

Software defined networking (SDN) is becoming increasingly prevalent for its programmability that enables centralized network configuration and management. With the growth of SDNs, a cluster of controllers cooperatively manages more and more switches/flows in a network to avoid the single-controller congestion/failure and improve the control-plane robustness. Under the architecture with multiple controllers, it is expected to minimize the maximum response time on these controllers to provide better QoS for users. To achieve this target, two previous methods are mainly used, the static scheme and the dynamic scheme. However, these methods may lead to an increase of the control-plane communication overhead/delay. In this paper, we propose to minimize the maximum response time on controllers through flow redirecting, which is implemented by installing wildcard rules on switches. We formulate the minimum controller response time problem, which takes the flow-table size and link capacity constraints into account, as an integer linear program, and prove its NP-Hardness. Two algorithms with bounded approximation factors are designed to solve this problem. We implement the proposed methods on our SDN testbed. The testing results and extensive simulation results show that our proposed algorithm can reduce the maximum controller response time by about 50%-80% compared with the static/dynamic methods under the same controller cost, or reduce the number of controllers by 30% compared with the dynamic method while preserving almost the same controller response time.

Traditional networks rely on aggregate routing and decentralized control to achieve scalability. On the contrary, software-defined networks achieve near optimal network performance and policy-based management through per-flow routing and centralized control, which, however, face scalability challenge due to: 1) limited ternary content addressable memory and on-die memory for storing the forwarding table and 2) per-flow communication/computation overhead at the controller. This paper presents a novel hybrid switching (HS) design, which integrates traditional switching and software-defined networking (SDN) switching for the purpose of achieving both scalability and optimal performance. We show that the integration also leads to unexpected benefits of making both types of switching more efficient under the hybrid design. We also design the general optimization framework via HS and propose an approximation algorithm for load-balancing optimization as a case study. Testing and numerical evaluation demonstrate the superior performance of HS when comparing with the state-of-the-art SDN design.

In a software-defined network (SDN), statistics information is of vital importance for different applications, such as traffic engineering, flow rerouting, and attack detection. Since some resources, e.g., ternary content addressable memory, SRAM, and computing capacity, are often limited on SDN switches, traffic measurements based on flow tables or sampling become infeasible. In fact, sketches provide a promising building block for filling this void by monitoring every packet with fixed-size memory. Although many efficient sketches have been designed, our analysis shows that existing sketch-based measurement solutions may suffer from severe computing overhead on switches especially under high traffic load that significantly interferes with switch's basic functions, such as flow rule setup and modification. In this paper, we present CountMax, a lightweight and cooperative sketch for traffic measurement, which can achieve low-amortized processing overhead and tight estimation bounds, to track large flows in SDNs. We also discuss how to apply CountMax to support a variety of applications. We have implemented the proposed algorithm on our open switches. Testbed experiments and extensive simulation results show that CountMax consumes only 1/3-1/2 computing overhead and reduces the average estimation error by 20%-30%, compared with the existing solutions under the same memory size.

Cooperative communication (CC) is shown to be a promising technology to improve the spatial diversity without additional equipments and antennas. The choice of relay nodes can significantly affect the performance of wireless cooperative networks, e.g. energy- efficiency. Most of existing works focused on dedicating one relay node to a transmission pair in the network. However, the energy-efficiency can be improved by sharing each relay node among more than one transmission pair. In this paper, we first introduce the feasibility of shared relay assignment to improve the energy-efficiency while achieving the required bandwidth for each transmission pair. Then, the Minimum Energy Consumption (MEC) problem of CC with shared relay is defined, and we formulate it as a mixed integer optimization program and prove it is NP-hard. A Simulated Annealing (SA) algorithm is designed to solve this problem. We conduct abundant simulation experiments to evaluate the performance of our proposed algorithm. The simulation results show that the SA algorithm can save about 15 energy consumption on average than the previous work in wireless networks.

With the increasing prevalence of cloud computing, data owners prefer to outsource their databases to the cloud. For the protection of data privacy, sensitive data have to be encrypted before outsourcing, which introduces much difficulty into effective data utilization. Most previous studies either suffer from privacy disclosure and low efficiency, or do not support personalized multidimensional range queries. In this paper, we focus on personalized private range queries over outsourced data. We propose a personalized and privacy-preserving private range query protocol (PPP), which uses bounding-box PIR (bbPIR) to trade access pattern privacy for flexible privacy and high efficiency, and satisfies various quality of service (QoS) requirements. To our best knowledge, PPP is the first to achieve personalized search according to owner-specified privacy-cost tradeoff. Furthermore, PPP is secure against semi-honest adversaries under known ciphertext model. Experimental results on real-world datasets show that PPP is efficient and able to achieve diverse QoS requirements.

Software Defined Network (SDN) is an architectural trend in networking towards the use of the centralized controller to get better performance. However, due to limited resources (especially limited flow table size and controller processing capacity), it may result in low-throughput and long-delay for a set of bursty flows. In this paper, we first combine the flow table size constraint and the controller processing capacity constraint to define the Throughput Maximization with Limited Resources (TMLR) problem. Then we prove TMLR is NP-Hard and design an approximation algorithm to solve the TMLR problem. The approximation factor of the proposed algorithm is also analyzed. The simulation results on the SDN platform (Mininet [1]) show that our algorithm can improve the network throughput about 39% on average compared with the existing algorithms.

Software Defined Networking (SDN) separates the control plane from the data plane to ease network management and provide flexibility in packet routing. The control plane interacts with the data plane through the forwarding tables, usually including a flow table and a group table, at each switch. Due to high cost and power consumption of Ternary Content Addressable Memory (TCAM), commodity switches can only support flow/group tables of limited size, which presents serious challenge for SDN to scale to large networks. One promising approach to address the scalability problem is to deploy aggregate default paths specified by wildcard forwarding rules. However, the multi-dimensional interaction among numerous system parameters and performance/scalability considerations makes the problem of setting up the flow/group tables at all switches for optimal overall layout of default paths very challenging. This paper studies the joint optimization of flow/group tables in the complex setting of large-scale SDNs. We formulate this problem as an integer linear program, and prove its NP-Hardness. An efficient algorithm with bounded approximation factors is proposed to solve the problem. The properties of our algorithm are formally analyzed. We implement the proposed algorithm on an SDN testbed for experimental studies and use simulations for large-scale investigation. The experimental results and simulation results demonstrate high efficiency of our proposed algorithm.

The categorical data that have natural ordering between categories are termed ordinal data, which are pervasive in numerous areas, including discrete sensor readings, metering data or preference options. Though aggregating such ordinal data from the population is facilitating plenty of crowdsourcing applications, contributing such data is privacy risky and may reveal sensitive information (e.g. locations, identities) about individuals. This work studies ordinal data aggregation for distribution estimation meanwhile locally preserving individuals' data privacy (such as on their mobile devices). Under ε-geo-indistinguishable constraints, which capture intrinsic dissimilarity between ordinal categories in the framework of differential privacy, we provide an efficient and effective locally private mechanism: Subset Exponential Mechanism (SEM) for ordinal data distribution estimation. The mechanism randomly responds with a fixed-size subset of the categories with calibrated probability assignment. Specially for uniform ordinal data, we propose a circling technique to symmetrically randomizing categories and estimating frequencies of categories, hence the computational/space costs and estimation performance of SEM are further optimized. Besides contributing theoretical error bounds of SEM, we also evaluate the mechanism on extensive scenarios, the evaluation results show that SEM reduces distribution estimation error on average by exp(ϵ/2) factor over existing private mechanisms.

To take advantage of software defined networking (SDN) within a limited budget constraint, a natural strategy is to incrementally deploy a few SDN switches (and a limited amount of additional link bandwidth) into the legacy optical network. In such a hybrid optical network, operators can only change the routes of flows that traverse SDN switches. Therefore, to optimize SDN deployment, it is essential to decide the best places to deploy SDN resources (including SDN switches and link bandwidth) while taking the network traffic into consideration. In this paper, we propose a new SDN deployment scheme, called duplicated deployment, to provide a simple and efficient way for a hybrid network. Based on the proposed deployment scheme, we for the first time define the joint duplicated deployment and routing (DDR) problem for throughput maximization (or optimal deployment) with a given budget constraint on the additional SDN resource cost. Due to the NP-Hardness of the DDR problem, we then present an approximation algorithm based on the traffic mapping and randomized rounding methods, and prove that the approximation factor is (O(log n);O(log n)) in the worst case and (O(1);O(1)) under most practical situations for link capacity and flow-table size constraints, where n is the number of devices (including SDN switches and legacy routers) in the hybrid network. Through extensive simulations, we demonstrate high efficiency of our joint deployment and routing algorithm. For example, our proposed algorithm can improve the network throughput by about 26% compared with existing routing mechanisms with the same amount of extra resources.

In urban road systems, it is a challenging task to investigate traffic status of all intersections due to the scarce distribution of road surveillance cameras. Previous research mostly focuses on how to use historical data of camera-equipped intersections to infer their future traffic statuses. However, as far as we know, there does not exist an effective algorithm to infer the real-time traffic statuses of those camera- free intersections by using the traffic information from some other road video cameras in urban road system. In this paper, we first study the spatial- temporal variation characteristics of urban traffic flows from a macroscopic view, including turning ratio models and traveling time models of individual road segments. And then we build a novel traffic impact tree model to calculate the real-time traffic volume for specific camera-free intersections. We evaluate our solutions on real-world taxicab and road surveillance system data-set. The experimental results show that our proposed method outperforms alternative solutions in terms of the accuracy of the reckoned future traffic flow.

Cooperative communication has great potential to enhance the performance of wireless networks by exploiting relay nodes' spatial diversity. Relay assignment plays a vital role in making the most of this potential. However, most previous studies investigate relay assignment in a rather static manner which may lead to the under‐utilisation of relay nodes, especially in the scenarios where source nodes' demands are time varying. Hence, in this study, the authors consider the relay assignment problem for cooperative networks using auction model with access flexibility, i.e. providing sufficient flexibility for source nodes to access relay nodes in time domain. They divide relay nodes' access time into multiple smaller access units and permit each source node to bid for a bundle of desired access units in the auction. This model is formulated as a combinational auction whose winner determination problem is NP hard. They thus present an auction mechanism with an elaborated greed strategy which not only achieves adorable properties such as truthfulness, individual rationality and computational efficiency, but also guarantees near‐optimal social welfare. Finally, they conduct extensive evaluations to verify the performance of their mechanism.

Software defined networking (SDN) separates the data plane and control plane on independent devices. Since the data plane, consisting of switches, is responsible for packets forwarding, previous work often considers the different constraints (e.g., data link capacity and flow-table size) only in the data plane to provide better QoS for users. However, due to limited CPU processing power and low speed of flow-table updating on each switch, the control channels/links between switches and the controller often have very limited capacity, which will cause QoS performance (e.g., response time and throughput) degradation when the switch should handle a high traffic load. The goal of our paper is to achieve better QoS by jointly considering the control link constraint and other different constraints of the data plane in SDNs. We formally define the control link load balancing and low delay route deployment problems, and prove the NP-Hardness. We present two algorithms with bounded approximation factors for each problem and implement the proposed methods on our SDN testbed. Extensive simulation results and experimental results show that our algorithms can reduce control link load by about 50% and response time by about 60%, and increase the network throughput by 65% compared with previous methods.

Mobile crowdsensing is a new paradigm in which a crowd of mobile users exploit their carried smart phones to conduct complex sensing tasks. In this paper, we focus on the makespan sensitive task assignment problems for the crowdsensing in mobile social networks, where the mobility model is predicable, and the time of sending tasks and recycling results is non-negligible. To solve the problems, we propose an Average makespan sensitive Online Task Assignment (AOTA) algorithm and a Largest makespan sensitive Online Task Assignment (LOTA) algorithm. In AOTA and LOTA, the online task assignments are viewed as multiple rounds of virtual offline task assignments. Moreover, a greedy strategy of small-task-first-assignment and earliest-idle-user-receive-task is adopted for each round of virtual offline task assignment in AOTA, while the greedy strategy of large-task-first-assignment and earliest-idle-user-receive-task is adopted for the virtual offline task assignments in LOTA. Based on the two greedy strategies, both AOTA and LOTA can achieve nearly optimal online decision performances. We prove this and give the competitive ratios of the two algorithms. In addition, we also demonstrate the significant performance of the two algorithms through extensive simulations, based on four real MSN traces and a synthetic MSN trace.

To explore the advantages of software defined network (SDN), while preserving the legacy networking systems, a natural deployment strategy is to deploy a hybrid SDN incrementally to improve the network performance. In this paper, we address two technical challenges: an incremental deployment strategy and a throughput-maximization routing, for deploying a hybrid network incrementally. For incremental deployment, we propose a heuristic algorithm for deploying a hybrid SDN under the budget constraint, and prove the approximate factor of 1- 1/e. For throughput-maximization routing, we apply a depth-first-search method and a randomized rounding mechanism to solve the multi-commodity h-splittable flow routing problem in a hybrid SDN, where h ≥ 1. We also prove that our method has approximation ratio O(1/log N), where N is the number of links in a hybrid SDN. We then show, by both analysis and simulations, that our algorithms can obtain significant performance gains and perform better than the theoretical worst-case bound. For example, our incremental deployment scheme helps to enhance the throughout about 40% compared with the previous deployment scheme by deploying a small number of SDN devices, and the proposed routing algorithm can improve the throughput about 31% compared with ECMP in hybrid networks.

Due to flow dynamics, a software defined network (SDN) may need to frequently update its data plane so as to optimize various performance objectives, such as load balancing. Most previous solutions first determine a new route configuration based on the current flow status, and then update the forwarding paths of existing flows. However, due to slow update operations of Ternary Content Addressable Memory-based flow tables, unacceptable update delays may occur, especially in a large or frequently changed network. According to recent studies, most flows have short duration and the workload of the entire network will vary significantly after a long duration. As a result, the new route configuration may be no longer efficient for the workload after the update, if the update duration takes too long. In this paper, we address the real-time route update, which jointly considers the optimization of flow route selection in the control plane and update scheduling in the data plane. We formulate the delay-satisfied route update problem, and prove its NP-hardness. Two algorithms with bounded approximation factors are designed to solve this problem. We implement the proposed methods on our SDN test bed. The experimental results and extensive simulation results show that our method can reduce the route update delay by about 60% compared with previous route update methods while preserving a similar routing performance (with link load ratio increased less than 3%).

In a software-defined network (SDN), the control plane needs to frequently collect flow statistics measured at the data plane switches for different applications, such as traffic engineering, QoS routing, and attack detection. However, existing solutions for flow statistics collection may result in large bandwidth cost in the control channel and long processing delay on switches, which significantly interfere with the basic functions, such as packet forwarding and route update. To address this challenge, we propose a cost-optimized flow statistics collection (CO-FSC) scheme and a cost-optimized partial flow statistics collection (CO-PFSC) scheme using wildcard-based requests, and prove that both the CO-FSC and CO-PFSC problems are NP-hard. For CO-FSC, we present a rounding-based algorithm with an approximation factor f, where f is the maximum number of switches visited by each flow. For CO-PFSC, we present an approximation algorithm based on randomized rounding for collecting statistics information of a part of flows in a network. Some practical issues are discussed to enhance our algorithms, for example, the applicability of our algorithms. Moreover, we extend CO-FSC to achieve the control link cost optimization FSC problem, and also design an algorithm with an approximation factor f for this problem. We implement our designed flow statistics collection algorithms on the open virtual switch-based SDN platform. The testing and extensive simulation results show that the proposed algorithms can reduce the bandwidth overhead by over 39% and switch processing delay by over 45% compared with the existing solutions.

Due to the explosive proliferation of mobile cloud computing applications, much data needs to be transmitted between mobile users and clouds, incurring a huge traffic demand on cellular networks. Mobile offloading is a promising approach to address this challenge. In this paper, we focus on the problem of offloading many deadline-sensitive data items to some WiFi networks with capacity constraints; that is, how to schedule each data item to the WiFi networks, so that we can offload as many data items before their deadlines as possible, while taking the constraints of transmission capacity into consideration. This problem involves a probabilistic combination of multiple 0-1 knapsack constraints, which differs from existing problems. To solve this problem, we propose a greedy oFfline Data Offloading (FDO) algorithm, achieving an approximation ratio of 2. Also, we propose an oNline Data Offloading (NDO) algorithm, which has a competitive ratio of 2. Additionally, we extend our problem to a more general scenario where WiFi transmission costs are heterogeneous. We design a Heterogeneous Data Offloading (HDO) algorithm to solve the extended problem, and give its performance analysis. Finally, we demonstrate the significant performances of our algorithms through extensive simulations based on some real-world and synthetic WiFi datasets.

In recent literatures, WiFi signals have been widely used to "sense" people's locations and activities. Researchers have exploited the characteristics of wireless signals to "hear" people's talk and "see" keystrokes by human users. Inspired by the excellent work of relevant scholars, we turn to explore the field of human-computer interaction using finger-grained gestures under WiFi environment. In this paper, we present Wi-Finger - the first solution using ubiquitous wireless signals to achieve number text input in WiFi devices. We implement a prototype of WiFinger on a commercial Wi-Fi infrastructure. Our scheme is based on the key intuition that while performing a certain gesture, the fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time series of Channel State Information (CSI) values. WiFinger is deigned to recognize a set of finger-grained gestures, which are further used to realize continuous text input in off-the-shelf WiFi devices. As the results show, WiFinger achieves up to 90.4% average classification accuracy for recognizing 9 digits finger-grained gestures from American Sign Language (ASL), and its average accuracy for single individual number text input in desktop reaches 82.67% within 90 digits.

Due to high mobility and intermittent connections in vehicular networks, reliable and efficient vehicular communication is a challenging task. Previous research on Vehicle-to-Vehicle (V2V) communication mostly focuses on achieving reliable transmissions from a given source to a given destination by mining moving patterns of taxicabs. However, to the best of our knowledge, none of them considered the habit-driven regularities of individual taxicabs as well as the urban-layout-driven time-varying regularities of crowds of taxicabs synthetically. With this insight, we model both individual and holistic driving patterns by Markov Chain models, then devise a new method to predict possible driving routes for every single taxicab. In addition, we design a new method to evaluate the probability that a single taxicab retrieves information of a specific road segment while it drives through another road segment during a given time period, and also to quantify the expected probability that a single taxicab obtains the information of a given road segment in the near future. With such information, our solution enables the selection of the optimal data packet transmission scheme. We evaluate our solution on a real-world taxicab dataset. Experimental results demonstrate that our approach outperforms alternative solutions in terms of diffusion speed and success ratio of data retrieval.

Mobile crowdsensing is a new paradigm in which a group of mobile users exploit their carried smart devices to cooperatively perform a large-scale sensing job over urban environments. In this paper, we focus on the Deadline-sensitive User Recruitment (DUR) problem for probabilistically collaborative mobile crowdsensing, in which mobile users perform sensing tasks with certain probabilities, and multiple users might be recruited to cooperatively perform a common task, ensuring that the expected completion time be no larger than a deadline. In order to solve this problem, we propose a greedy approximation algorithm, which can achieve the logarithmic approximation ratio.

Recent advances in vehicular networks, GPS and smartphone technologies have changed the paradigm of intelligent taxicab systems. Indeed, taxicab trajectories and online calling information have enabled us to provide more efficient and personalized services. However, existing approaches are not sufficient in exploiting cooperative scheduling techniques and utilizing real time calling information. To this end, in this paper, we model the time-varying regularities of traffic flows, activity ratios of passengers, and unoccupied taxicabs of road segments by mining statistical data on taxicab trajectories. Along this line, we propose a novel approach to calculate the expected revenue of possible routes for individual taxicabs while considering the influence of others, and at the same time, advance a dynamic taxicab scheduling mechanism with online taxicab calling information. Finally, we evaluate our algorithm on real-world taxicab data. Experimental results demonstrate that our approach outperforms existing alternative solutions in terms of average revenue of taxi drivers.

Mobile crowdsensing is a new paradigm in which a group of mobile users exploit their smart devices to cooperatively perform a large-scale sensing job over urban environments. In this paper, we focus on the Deadline-sensitive User Recruitment (DUR) problem for probabilistically collaborative mobile crowdsensing. Unlike previous works, mobile users in this problem perform sensing tasks with probabilities, and multiple users might be recruited to cooperatively perform a common task, ensuring that the expected completion time is no larger than a deadline. Owing to such a probabilistic collaboration, DUR can be formalized as a non-trivial set cover problem with non-linear programming constraints and an optimization objective of real function. We first prove that the DUR problem is NP-hard. Then, we propose a greedy DUR algorithm, called gDUR, to solve this problem. Next, we prove that the gDUR algorithm can achieve a logarithmic approximation ratio. Furthermore, we extend the problem to a more complex case where sensing duration is taken into consideration, and we propose a sensing-duration-aware user recruitment algorithm, called dDUR. Finally, we validate the performance of the proposed algorithms through extensive simulations, based on a real mobile social network trace and a synthetic trace.

Due to flow dynamics, a software defined network (SDN) may need to frequently update its data plane so as to optimize various performance objectives, such as load balancing. Most previous solutions first determine a new route configuration based on the current flow status, and then update the forwarding paths of existing flows. However, due to slow update operations of Ternary Content Addressable Memory (TCAM) based flow tables, unacceptable update delays may occur, especially in a large or frequently changed network. According to recent studies, most flows have short duration and the workload of the entire network may vary after a long duration. As a result, the new route configuration may be no longer efficient for the workload after the update, if the update duration takes too long. In this paper, we address the real-time route update, which jointly considers the optimization of flow route selection in the control plane and update scheduling in the data plane. We formulate the delay-satisfied route update (DSRU) problem, and prove its NP-Hardness. Two algorithms with bounded approximation factors are designed to solve this problem. We implement the proposed methods on our SDN testbed. The experimental results and extensive simulation results show that our method can reduce the route update delay by about 60% compared with previous route update methods while preserving a similar routing performance (with link load ratio increased less than 3%).

Nowadays, vehicles have shown great potential in crowdsensing. To guarantee a good Quality of Service (QoS), stimulating enough vehicles to participate in crowdsensing is very necessary. In this paper, we focus on the incentive mechanism design in the vehicle-based nondeterministic crowdsensing. Different from existing works, we take into consideration that each vehicle performs sensing tasks along some trajectories with different probabilities, and each task must be successfully performed with a joint probability no less than a threshold. Designing an incentive mechanism for such a nondeterministic crowdsensing system is challenging, which contains a non-trivial set cover problem with non-linear constraints. To solve the problem, we propose a truthful incentive mechanism based on reverse auction, including an approximation algorithm to select winning bids with a nearly minimum social cost, and a payment algorithm to determine the payments for all participants. Through theoretical analysis, we prove that our incentive mechanism is truthful and individual rational, and we give an approximation ratio of the winning bid selection algorithm. In addition, we conduct extensive simulations, based on a real vehicle trace, to validate the performances of the proposed incentive mechanism.

It is a common view in Software Defined Network (SDN) that the flow table plays the most significant role in SDN architecture, but suffers from the limited TCAM chips. The shortage of flow table storage strongly impacts the quality of service (QoS) provided by SDN, but requires rational solutions. In this paper, we present a practical on-line approach based on the decision tree structure to solve this problem. Our performance is evaluated by the comparison with other existing technologies.

Due to high mobility and intermittent connections in vehicular networks, reliable and efficient Vehicle-to-Vehicle (V2V) communication is a challenging task. The Named Data Networking (NDN) paradigm is recently being applied to achieve efficient V2V communication, however, proactive V2V information diffusion conflicts with the receiver-initiated nature of NDN. This paper bridges this gap by exploiting hierarchical data names to achieve efficient and proactive V2V information diffusion. We first identify a popular subgroup of vehicles, then select them as the diffusion seeds with 3G/4G capability, while others are only equipped with short-range V2V communication. We also design a namespace-based method to optimize data transmission when vehicles are close, in order to maximize the information distribution across geographical space. We evaluate our solution via a real-world taxicab dataset. Experimental results demonstrate that our approach significantly outperforms state-of-the-art solutions in terms of diffusion speed and success rate of data retrieval.

In this work, we focus on designing high-throughput anycast mechanisms in a software Defined Network (SDN), as anycast routing serves as a fundamental building block for many services. Specifically, we investigate two main challenges for efficient implementation of a SDN-based anycast system: high throughput anycast routing (HTAR) and congestion-free anycast reconfiguration (CFAR). For the first challenge, we design an anycast routing (ARFU) algorithm, based on the fully polynomial time approximation scheme and the single-source unsplittable flow routing method. We prove that ARFU produces a route that can achieve a throughput at least 1/2(1+ξ) of the optimum, where ξ > 0 is an arbitrarily small constant. We then propose an anycast reconfiguration algorithm with transitive configurations, called ARTC, for congestion-free. This algorithm tries to find a routing reconfiguration procedure consisting of at most T ≥ 1 transitive configurations while meeting all the flow demands. When traffic demands can not be satisfied, ARTC maximizes the minimum throughput using one transitive configuration. Our simulations demonstrate high-efficiency of our algorithms.

Truthful spectrum auctions make bidders reveal their true valuations for spectrum to maximize their utilities. However, disclosure of one's true value causes numerous security vulnerabilities. Moreover, as a distinguished property of spectrum auction compared to classical auctions, spectrum reutilisation requires that the bidder locations be disclosed to the auctioneer to run the auction. We investigate the impact of disclosing bidder locations and demonstrate that such disclosure can be exploited by a malicious auctioneer to gain extra profit and significantly degrade bidders' utility.

With the explosive proliferation of smartphones, many mobile cloud computing applications have emerged in recent years. These applications generally involve many data transmissions between mobile users and the cloud side. In order to reduce the monetary cost of these data transmissions, an effective approach is to offload partial data traffic from cellular networks to WiFi networks, when mobile users pass by some WiFi Access Points (APs). In this paper, we focus on the problem of offloading many deadline-sensitive data items to some WiFi APs with capacity constraints; that is, how to schedule each data item to the WiFi APs, so that we can offload as many data items before their deadlines as possible, while taking the constraints of transmission capacity into consideration. This problem involves a probabilistic combination of multiple 0-1 knapsack constraints, which differs from existing problems. To solve this problem, we propose a greedy oFfline Data Offloading (FDO) algorithm, and prove that this algorithm can achieve an approximation ratio of 2. Moreover, we extend our data offloading strategy to the online decision case, and propose an oNline Data Offloading (NDO) algorithm, which has a competitive ratio of 2. Finally, we demonstrate the significant performances of our algorithms through extensive simulations.

Surface transportation is of a great importance in urban life. Fueled by the promise of Smart City, it is becoming more and more common to provide WiFi services in urban Public Transportation System (PTS). In this paper, we present a practical method to make use of the WiFi service provided on buses to estimate real-time urban traffic condition. We validate the proposed method in a city district and show that it is an excellent alternative or supplement of the existing traffic condition estimation services.

Dynamic secondary spectrum markets have gained tremendous attentions recently, which can provide significant flexibility for trading spectrum by conducting auctions periodically. The high price of spectrum necessitates a thorough consideration on secondary users' budget constraint, i.e. the total money they could pay. However, previous studies rarely deal with the practical issue of budget constraint, and concentrate on maximizing social welfare greedily in a single round auction, which may not guarantee their performance after multiple rounds of auctions. In this paper, we investigate the techniques for budget constrained periodic spectrum auction, which ensures approximate social welfare maximization from a long- term perspective. With the celebrated primal-dual method, we present a Periodic Spectrum Auction framework (PSA) that runs a tailored One Round Spectrum Auction (ORSA) in each round. In the ORSA, we achieve critical properties such as truthfulness, individual rationality and computational efficiency. Due to the dual fitting technique, ORSA not only achieves approximate social welfare maximization in one round, but also guarantees only a small loss of approximation ratio when runs in multiple rounds under the PSA framework. Finally, we conduct extensive simulations to demonstrate the performance of our schemes.

In a number of networking applications, preserving the privacy of user-related data in data aggregation schemes is a fundamental issue. As a fact, many privacy-preserving protocols can be guaranteed with security against individual attacks, but they may be threatened by collusion between participants. Therefore, security analysis, especially for collusion attack analysis, plays an essential role in privacy-preserving data aggregation protocols. There do exist a few collusion attack schemes on data aggregation protocols, but none study the internal security mechanism of these protocols. In this paper, to our best knowledge, we are the first to propose a new kind of collusion attack analysis tool, which is named CAE (Collusion Attack Emulator). We employ it to check and judge the security of several existing privacy-preserving data aggregation schemes. We first show that for an aggregation scheme which has been known to be vulnerable under collusion attack, we can use CAE to explain why it is insecure. Then we demonstrate the blind detection function of CAE, i.e., we do not know whether an aggregation protocol is secure beforehand, and employ CAE to check its security and (if the protocol cannot pass the CAE test and thus to be insecure) to find its loophole.

Due to perceived fairness and allocation efficiency, cloud auctions for resource allocation and pricing have recently attracted significant attention. As an important economic property, truthfulness makes bidders reveal their true valuations for cloud resources to maximize their utilities. However, disclosure of one's true value causes numerous security vulnerabilities. Therefore, privacy-preserving cloud auctions are called for to prevent such information leakage. In this paper, we demonstrate how to perform privacy-preserving auctions in clouds that do not leak any information other than the auction results to anyone. Specifically, we design a privacy-preserving cloud auction framework that addresses the challenges posed by the cloud auction context by leveraging the techniques in garbled circuits and homomorphic encryption. As foundations of our privacy preserving cloud auction framework, we develop data-oblivious cloud auction algorithm and basic operations (e.g., comparison, swapping etc.), such that the execution path does not depend on the input. In practical systems with a large number of users and constrained resources, we develop an improved version with a computational complexity of O(n log2 n) in the number of bidders n. We further fully implement our framework and theoretically and experimentally show that it preserves privacy by incurring only limited computation and communication overhead.

Identity-based signature is an important technique for light-weight authentication. Recently, many efforts have been made to construct identity-based signatures over lattice assumptions since they would remain secure in future quantum age. In this paper we present a new identity-based signature scheme from lattice problems. This scheme is more efficient than other lattice-based identity-based signature schemes in terms of both computation and communication complexities. We prove its security in the random oracle model under short integer solution assumption that is as hard as approximating several worst-case lattice problems. We also extend the scheme to an identity-based message recovery signature scheme that has better performance.

Wireless sensor networks (WSNs) are increasingly deployed for mission-critical applications such as emergency management and health care, which impose stringent requirements on the communication performance of WSNs. To support these applications, it is crucial to model and measure the effect of wireless interference, which is the major factor that limits WSN performance. Accurate modeling and measurement of interference faces two key challenges. First, as shown in our experimental results, interference yields considerable spatial and temporal variations of WSN performance, which poses a major challenge for measurement at rum-time. Second, in the unlicensed band, the communication of WSN is interfered by coexisting wireless devices such as smartphones and laptops equipped with 802.11 radios, which lead to cross-technology interference that are difficult to characterize due to the heterogeneous PHY. To tackle these challenges, this paper presents a novel accuracy-aware approach to interference modeling and measurement for WSNs. First, we propose a new regression-based interference model and analytically characterize its accuracy based on statistics theory. Second, we develop a novel protocol called accuracy-aware interference measurement for measuring the proposed interference model with assured accuracy at run time. Third, building on interference modeling, we propose an algorithm that accurately forecasts the performance of WSNs in the presence of cross-technology interference. Our extensive experiments on a testbed of 17 TelosB motes show that the proposed approaches achieve high accuracy of interference modeling and WSN performance forecasting with significantly lower overhead than state-of-the-art approaches.

Relay assignment significantly affects the performance of the cooperative communication, which is an emerging technology for the future mobile system. Previous studies in this area have mostly focused on assigning a dedicated relay to each source-destination pair for one-to-one (121) traffic. However, many-to-one (M21) traffic, which is also common in many situations (for example, several users associate with one access point in a wireless access network such as a WLAN), hasn't been well studied. This paper addresses the shared relay assignment (SRA) problem for M21 traffic. We formulate two new optimization problems: one is to maximize the minimum throughput among all the sources (hereafter called M21-SRA-MMT), and the other is to maximize the total throughput over all the sources while maintaining some degree of fairness (hereafter called M21-SRA-MTT). As the optimal solutions to the two problems are hard to find, we propose two approximation algorithms whose performance factors are 5.828 and 3, respectively, based on the rounding mechanism. Extensive simulation results show that our algorithms for M21-SRA-MMTcan significantly improve the minimum throughput compared with existing algorithms, while our algorithm for M21-SRA-MTTcan achieve the close-to-optimal performance.

Virtual multi-input-multi-output vMIMO technology is becoming a promising way to improve the energy efficiency of wireless networks. Previous research always builds up the vMIMO-based routing on the fixed structure such as clusters, and the MIMO mode is omitted in most cases. So, they cannot fully explore the advantage of vMIMO in routing. In this paper, we study a general routing scheme in which no fixed structure is required, and any communication mode of vMIMO is allowed for sake of the energy efficiency. We define two vMIMO-based routing problems aiming to energy-minimization and lifetime-optimization. The first problem can be solved by our distributed energy-minimum vMIMO-based algorithm. The algorithm constructs the virtual cooperative graph, and applies the shortest path method on the virtual cooperative graph to solve this problem. The second problem is non-deterministic polynomial-time hard, and we design the distributed lifetime-oriented vMIMO-based algorithm, which is based on the modified Bellman-Ford method. It can reach approximation ratio of four. The simulations show that our algorithms can work well in many situations. For example, distributed lifetime-oriented vMIMO-based algorithm can prolong the lifetime about 20.2% in dense topologies compared with the cooperative routing algorithm on average. Copyright © 2015 John Wiley & Sons, Ltd.

Spatial Crowdsourcing (SC) nowadays has become a popular research topic studying how to outsource a set of spatial-temporal tasks to workers at specific locations. However, there exists a significant security concern: existing location privacy techniques are not applicable to SC. In this paper, we focus on protecting the worker location privacy against the semi-honest adversaries model while preserving the functionality of SC system. By introducing a semi-honest third party and using additive homomorphic encryption, we present a secure task assignment protocol for SC. More specifically, we propose an efficient protocol to securely compute the worker travel cost and select minimum cost worker in the encrypted domain, which reveals nothing about location privacy. We theoretically analyze that our protocol is secure as all encrypted private data are computationally indistinguishable. Extensive experimental results on real-world and synthetic datasets show that the proposed protocol can protect worker location privacy while keeping high task assignment rate.

Histogram estimation is one of the fundamental tasks in crowdsourcing data aggregation. Since contributing data reveal more or less information about individuals' identifications and activities, participants need to preserve privacy of data according to their own levels of privacy concern. However, most of the existing work only aggregates data with an identical privacy level. In this paper, we propose an aggregation scheme for histogram estimation, wherein participants can publish their data at personalized differential-privacy levels. The aggregator also benefits from potential wider engagement or more honest data. Specially, since privacy levels under personalized privacy policy are sensitive information for participants, our scheme permits participants to keep their privacy levels secret even from the aggregator. We also show how to further optimize the estimation accuracy under given privacy levels by choosing specific randomization strategies.

Software Defined Network (SDN) is facilitating rapid innovation of network by providing a programmable network infrastructure. However, managing SDN flow rules, especially among multiple modules and administrators, has become complex and error-prone. Different controller modules with diverse objectives may be installed on the SDN controller, which can lead to anomalies among policies and rules. In this paper, we propose ADRS(Anomaly Detecting and Resolving for SDN) to solve this problem. Firstly, we analyse the rule-level anomalies that may occur in SDN based on OpenFlow protocol. Then we present an interval tree model for rapid rule scanning and a share model for network privilege allocating. By applying these models, we provide an automatic algorithm to detect and resolve the anomalies among SDN modules. Moreover, a rule-recovery mechanism is presented to avoid modification faults. We also implement and evaluate our system in the OpenDayLight controller.

The software defined network (SDN) has been developing tremendously in recent years. Numerous studies are proposed on the performance of SDN in academia. The idea of programmable network which is the foundation of SDN ensures dynamic network management by separating the control plane away from the network switches. The focus of this paper is the flow table's size in OpenFlow switches which is a significant bottleneck in SDN's practical application. We propose a mechanism named “Flow Table Reduction Scheme”(FTRS) for reducing the flow table size and maintaining the omnipotent controller's functions at the same time. We test the performance of FTRS both in simulation and experiment and the results show that FTRS is able to reduce 98% at most of the size of flow table with no impact on network's normal functions.

To tackle the challenging secrecy communication problem in energy harvesting cognitive radio networks, this paper considers an overlay system with one energy harvesting secondary user (SU) to assist primary transmission under the assumption that the primary channel at primary receiver is worse than the eavesdropper. Under such scenario, we optimize the secrecy rate of the PU transmitter by jointly investigating energy harvesting slot, cooperative transmission slot and so on. Given the transmission rate requirement between SUs, the optimization problem is formulated as a mixed integer non-linear (MINLP) program. Due to the special features, we design a polynomial time algorithm SRMA to optimally solve this problem. The algorithm computes the lower bound and upper bound of the transmission power in a secondary transmitter, which are relative with the QoS requirement and energy harvesting parameters. Then SRMA determines its optimal transmission power by iteratively searching between two bounds. Numerical results demonstrate that the primary secrecy rate grows with the increasing energy save ratio and optimal energy save ratio is inversely proportional to the energy harvesting rate.

Cooperative cognitive radio network (CCRN) is a promising paradigm to increase spectrum utilization and exploit spatial diversity. The allocation of two related resources, i.e. spectrum and relay nodes, plays a fundamental role in the performance of CCRNs. However, previous works either lack of incentives for both primary users (PUs) and relay nodes to participate in or consider spectrum auction and relay auction separately. In this paper, we consider a static cooperative cognitive radio network scenario with several PUs and multiple secondary user coteries, each of which consists of a set of secondary users who are interested in sharing the same secondary relay node. We model the problem of joint spectrum allocation and relay allocation as a hierarchical auction and propose TERA, which is the first Truthful auction mechanism for Efficient Resource Allocation in CCRNs. We show that TERA satisfies critical economic properties such as truthful, individual rationality, budget balance, supply limits and computational efficiency. Furthermore, we theoretically prove TERA can achieve near-optimal revenue with high probability. Finally, extensive simulation results show that TERA is efficient and able to improve the utility of PUs and relay nodes significantly up to 125% and 151% respectively.

Despite the clear benefits of electric vehicles (EVs) in terms of reducing greenhouse gas emissions and traditional energy consumptions, the popularization of EVs remains a challenge in the short run. When considering electric taxis, urban planners must face the additional issue of providing battery swapping services. While previous studies focused on planning battery swapping stations for private EVs, we investigate ways of supporting the upgrade of an entire urban taxi system, with demands differing both in scale and nature. With this insight, we analyze the historical sensing data of taxi routes, and evaluate the battery swapping demand profile, as well as the driving time between positions in the road network. Based on these inputs, we propose a method to calculate an optimized battery swapping station scheme. Our strategies are then evaluated via a real world 366-day, 3,976-taxi dataset. The results show that compared to uniform deployment, our planning scheme reduces the average time-cost by 67.2%.

The application-layer covert channels have been extensively studied in recent years. Information-hiding in ubiquitous application packets can significantly improve the capacity of covert channels. However, the undetectability is still a knotty problem, because the existing covert channels are all frustrated by proper detection schemes. In this paper, we propose LiHB, a behavior-based covert channel in HTTP. When a client is browsing a website and downloading webpage objects, we can reveal some fluctuation behaviors that the distribution relationship between the ports opening and HTTP requests are flexible. Based on combinatorial nature of distributing N HTTP requests over M HTTP flows, such fluctuation can be exploited by LiHB channel to encode covert messages, which can obtain high stealthiness. Besides, LiHB achieves a considerable and controllable capacity by setting the number of webpage objects and HTTP flows. Compared with existing techniques, LiHB is the first covert channel implemented based on the unsuspicious behavior of browsers, the most important application-layer software. Because most HTTP proxies are using NAPT techniques, LiHB can also operate well even when a proxy is equipped, which poses a serious threat to individual privacy. Experimental results show that LiHB covert channel achieves a good capacity, reliability and high undetectability.

Truthful auctions make bidders reveal their true valuations for goods to maximize their utilities. Currently, almost all spectrum auction designs are required to be truthful. However, disclosure of one's true value causes numerous security vulnerabilities. Secure spectrum auctions are thus called for to address such information leakage. Previous secure auctions either did not achieve enough security, or were very slow due to heavy computation and communication overhead. In this paper, inspired by the idea of secret sharing, we design an information-theoretically secure framework (ITSEC) for truthful spectrum auctions. As a distinguished feature, ITSEC not only achieves information-theoretic security for spectrum auction protocols in the sense of cryptography, but also greatly reduces both computation and communication overhead by ensuring security without using any encryption/description algorithm. To our knowledge, ITSEC is the first information-theoretically secure framework for truthful spectrum auctions in the presence of semi-honest adversaries. We also design and implement circuits for both single-sided and double spectrum auctions under the ITSEC framework. Extensive experimental results demonstrate that ITSEC achieves comparable performance in terms of computation with respect to spectrum auction mechanisms without any security measure, and incurs only limited communication overhead.

Mobile crowdsensing is a new paradigm in which a crowd of mobile users exploit their carried smart devices to conduct complex computation and sensing tasks in mobile social networks (MSNs). In this paper, we focus on the task assignment problem in mobile crowdsensing. Unlike traditional task scheduling problems, the task assignment in mobile crowdsensing must follow the mobility model of users in MSNs. To solve this problem, we propose an oFfline Task Assignment (FTA) algorithm and an oNline Task Assignment (NTA) algorithm. Both FTA and NTA adopt a greedy task assignment strategy. Moreover, we prove that the FTA algorithm is an optimal offline task assignment algorithm, and give a competitive ratio of the NTA algorithm. In addition, we demonstrate the significant performance of our algorithms through extensive simulations, based on four real MSN traces and a synthetic MSN trace.

As more and more applications migrate into clouds, the placement of virtual machines for these applications has much impact on the performance of cloud systems. A number of virtual machine (VM) placement techniques have been proposed over recent years. However, most of the existing works on VM placement ignore the response latency of the requests from tenants. In this paper, we investigate the techniques of VM placement with stochastic requests from the tenants to minimize the total (average) response latency. We first model the requests for each application from the corresponding tenant as independent Poisson stream. Moreover, the VMs are modeled as simple M/M/1 queueing systems. Then, we define the problem of VM placement for minimizing the total response delay (VMMD) and show it is NP-hard. We propose three heuristic algorithms, namely, Greedy, Local Adjustment (LA) and Simulated Annealing (SA). We conduct abundant simulation experiments to evaluate the performance of our proposed algorithms. The simulation results show that the proposed algorithms are efficient in decreasing the total response latency of the requests from tenants. Especially, the SA heuristic, which decreases the total response latency about 68% at most, shows the best performance on minimizing the total response latency in cloud systems.

The popularity of mobile devices has far expanded the application scenarios of spatial crowdsensing, due to its ability to provide fine-grained multi dimensional sensor readings associated with location information. Privacy is one of the fundamental issues in crowdsensing, as these location-based sensor readings may reveal identities or activities of participants. In this paper, we adopts the state-of-art location privacy definition geo-indistinguishability, provide an efficient and effective privacy preserving histogram aggregation mechanism BFMM (Bit Flipping Matrix Mechanism) for fine-grained multi dimensional location-based data. Theoretical analyses and experimental results demonstrate the efficiency and effectiveness of our approach for fine-grained multidimensional location-based data. Specifically, the aggregation accuracy of our approach averagely outperforms existing methods by a factor of number of buckets in the histogram.

Wireless mesh networks (WMNs) are being used and deployed widely all around the world for various reasons such as public safety, environmental monitoring and city-wide wireless Internet services [4], [12]. Although faced with some difficulties, wireless mesh network is thought to be a preferred municipal Internet service provider [5] with huge potential due to its automatic connection, ease of installation, dynamic route discovery, flexibility and other advantages. Since plenty of cities such as Singapore and the city of Cambridge have put providing ubiquitous Internet access on the agenda [3], the research on providing QoS-guaranteed municipal WMNs is in urgent need.

Unlike traditional auction schemes that do not take security into account, a good secure auction scheme first has to satisfy the following two properties: 1) correctness: the auction result should be the same as the result using traditional correct schemes; 2) security: during the auction process, the final result is the only information revealed to the participants. Furthermore, the secure auction scheme should be efficient enough so that the final result can be determined within a reasonable time. Existing works mainly deal with single-sided spectrum auction, and are not totally secure though claimed to be secure. For example, in [2], the auctioneer can easily know the sums of bids for all the possible allocations, and in [3], the auctioneer can obtain the bids of all buyer groups and their ranking order in the auction. This kind of information is sensitive and should be kept secret. In a very timely recent study [4], the authors propose PS-TRUST, a solution for secure double spectrum auction based on TRUST [5]. To our best knowledge, this is the first work that achieves both correctness and security for secure spectrum auction. Unfortunately, the efficiency of this work is not satisfactory.

Performing efficient data aggregation while keeping the property of privacy preservation of user-related data is of high concern. Extensive research has been conducted to address this problem in multiple areas. As a fact, the security of a number of privacy-preserving protocols are threatened by collusion between participants. Therefore, security analysis plays a fundamental role in privacy-preserving data aggregation protocols. In this paper, we present a new kind of collusion attack strategy called Hamburger Attack. It is of logical simplicity, but has the advantage of being effective and efficient. We employ it to check the security of several existing privacy-preserving data aggregation schemes. We show that under hamburger attack, some prior privacy-preserving data aggregation protocols will disclose part, even all, of the private data that they intended to protect. On the other hand, the hamburger attack is beneficial to designing new privacy-preserving data aggregation schemes. It can assist them in avoiding this kind of collusion attack.

As the size of smartphone touchscreens becomes larger and larger in recent years, operability with single hand is getting worse especially for female users. We envision that user experience can be significantly improved if smartphones are able to detect the current operating hand and adjust the UI subsequently. In this paper, we propose a novel scheme that leverages user-generated touchscreen traces to recognize current operating hand accurately, with the help of a supervised classifier constructed from twelve different kinds of touchscreen trace features. As opposed to existing solutions that all require users to select the current operating hand or dominant hand manually, our scheme follows a more convenient and practical manner, and allows users to change operating hand frequently without any harm to user experience. We conduct a series of real-world experiments on Samsung Galaxy S4 smartphones, and evaluation results demonstrate that our proposed approach achieves 94.1% accuracy when deciding with a single trace only, and the false positive rate is as low as 2.6%.

In this paper, we propose differentially private protocols for Naive Bayes classification over distributed data. Compared with existing works, the privacy and security models in the proposed protocols are stronger: firstly, both the miner and parties can be arbitrarily malicious and can collude with each other to violate the remaining honest parties privacy; secondly, all communication channels between them can be assumed to be insecure. Specifically, we build a guarantee of differential privacy into the cryptographic construction so that the proposed protocols can tolerate collusions and resist eavesdropping attacks which are caused by insecure communication channels. Additionally, the proposed protocols can be implemented at lower computation and communication costs, and some extensions to our protocols (e.g. supporting parties dynamic joins or leaves) are also proposed in this paper. Both theoretical analysis and simulation results show that the proposed privacy-preserving protocols for Naive Bayes have strong security and better classification performance than the standard one.

A cyclic mobile social network (MSN) is a new type of delay tolerant network, in which mobile users periodically move around, and contact each other through their carried short-distance communication devices. In this paper, we introduce utility-based routing into cyclic MSNs, and propose a deadline-sensitive utility-based routing model. If a message is successfully delivered to its destination before a deadline, its source will receive a positive benefit as the reward. Otherwise, the source receives zero benefit. Also, each message delivery incurs a forwarding cost, no matter whether it succeeds or fails. The utility of a message delivery is defined as the benefit minus the forwarding cost. Under this model, we propose a deadline-sensitive opportunistic utility-based single-copy routing algorithm, DOUR. Each node first determines an optimal forwarding sequence, which is composed of a series of forwarding opportunities, in a distributed and greedy manner. Then, it forwards messages via these forwarding opportunities. Theoretical analysis and extensive simulations prove that DOUR can achieve the optimal utility for each message delivery. Moreover, we extend our algorithm to the case of multi-copy routing, and show that our proposed algorithms can inherently make a good tradeoff among the benefit, delay, and cost for each message delivery.

Spectrum auction is an emerging economic scheme to stimulate both primary spectrum operators (POs) and secondary users (SUs) to be involved in spectrum sharing. Previous spectrum auction works mostly assume each PO can only have one type spectrum or each SU can only buy homogeneous spectrum bands from the same PO. However, in a ubiquitous network scenario, each PO possesses heterogeneous spectrum resources such as WiFi, 3G and each SU may request different types of spectrum bands from the same PO. Existing auction schemes cannot be used to effectively solve the problem. Therefore, the authors come out with a lightweight combinatorial double auction to tackle this challenge. Since spectrum combinatorial double auction problem is NP‐hard, the authors develop a general greedy algorithm G‐Greedy to solve the problem. Inspired by the recent group‐buying discounts, they also invent an enhanced scheme E‐Greedy to further optimise total utility. They theoretically prove the economy properties of the proposed schemes such as individual rationality, budget balance and truthfulness. Simulation results show that both of the two algorithms can yield higher utilities and are effective.

Abstract Covert channels are malicious conversations disguised in legitimate network communications, allowing information leak to the unauthorized or unknown receiver. Various network steganographic schemes that modify the header fields of transmission control protocol/Internet protocol (TCP/IP) have been proposed in recent years. People before conducted detection research based on the surface content of the header field and did not take into account the differences between the behavior characters of covert channels and the inherent behavior regularities of the header fields. Up to date, there is little comprehensive research on the steganalysis against the storage covert channels. In this paper, we focus on the detection of storage covert channels and introduce a novel comprehensive detection method based on the protocol behaviors. The protocol behavior characters are utilized to evaluate the regularities or correlations of header fields between adjacent packets according to the conventional use. First, the behavior features of the header fields in TCP/IP are extracted; a support vector machine is then applied to the behavior feature sets for discovering the existence of covert channels. Some recognized covert channel tools are detected in our detection experiment. Experimental results and discussion show that our detection method is of effectiveness. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract Certificateless signature and certificate‐based signature are two attractive replacements of regular signature and identity‐based signature because they can alleviate the vexing certificate management problem in regular signatures and can also eliminate the inherent key escrow problem in identity‐based signatures. Although a number of certificateless signatures and certificate‐based signatures from bilinear pairings have been proposed, their lattice‐based counterparts still remain unrealized. In this paper, we present the first constructions of certificateless signature and certificate‐based signature from lattices. Both constructions are proven to be secure in the random oracle model under conventional small integer solution assumption that is as hard as approximating several standard lattice problems. Copyright © 2014 John Wiley & Sons, Ltd.

Recently, Jung and Li [1] propose a highly efficient privacy-preserving product calculation scheme without requiring secure communication channels. Then, they present secure approaches to solve several application problems using the product calculation protocol. In this work, we observe several security flawsin their privacy-preserving product calculation scheme and some application protocols. We show the security vulnerabilities will result in the disclosure of private data. In two application protocols, almost all the private numbers will be revealed. We further suggest solutions to fix the security problems.

We study a spectrum auction problem where each request from new spectrum users has spatial, temporal, and spectral features. Our goal is to design truthful auction mechanisms that maximize either the overall social efficiency of new users (a.k.a buyers) or the revenue of the spectrum owner (a.k.a seller). Given that the optimal conflict-free spectrum allocation problem is NP-hard, this paper proposes a series of near-optimal auction mechanisms based on the following approximation techniques: linear programming (LP) relaxation, randomized rounding, derandomized rounding, monotone derandomization, and Lavi-Swamy method. Comparing with the prior art, we make two significant advances: First, our auction mechanisms are not only truthful but also provide theoretically-provable performance guarantee, an important feature that existing work under the same auction model does not have. Second, our auction mechanisms support both spatial and temporal spectral reuse, which makes the problem more challenging than existing work that deals with only spatial or temporal reuse. We perform extensive simulations to study the performance of the proposed mechanisms, and the simulation results corroborate our theoretical analysis.

Because data dissemination is crucial to wireless sensor networks (WSNs), its energy efficiency and reliability are of paramount importance. While achieving these two goals together is highly nontrivial, the situation is exacerbated if WSN nodes are duty-cycled (DC) and their transmission power is adjustable. In this paper, we study the problem of minimizing the expected total transmission power for reliable data dissemination (multicast/broadcast) in DC-WSNs. Due to the NP-hardness of the problem, we design efficient approximation algorithms with provable performance bounds for it. To facilitate our algorithm design, we propose the novel concept of Time-Reliability-Power (TRP) space as a general data structure for designing data dissemination algorithms in WSNs, and the performance ratios of our algorithms based on the TRP space are proven to be$ {\cal O}(\log \Delta \log k)$for both multicast and broadcast, where$\Delta $is the maximum node degree in the network and$k$is the number of source/destination nodes involved in a data dissemination session. We also conduct extensive simulations to firmly demonstrate the efficiency of our algorithms.

Many spectrum auction mechanisms have been proposed for spectrum allocation problem, and unfortunately, few of them protect the bid privacy of bidders and achieve good social efficiency. In this paper, we propose PPS, a Privacy Preserving Strategyproof spectrum auction framework. We design two schemes based on PPS separately for 1) the single-unit auction model (SUA), where only single channel will be sold in the spectrum market; and 2) the multi-unit auction model (MUA), where the primary user subleases multi-unit channels to the secondary users and each of the secondary users wants to access multi-unit channels either. Since the social efficiency maximization problem is NP-hard in both auction models, we present allocation mechanisms with approximation factors of (1 + ε) and 32 separately for SUA and MUA, and further judiciously design strategyproof auction mechanisms with privacy preserving based on them. Our extensive evaluations show that our mechanisms achieve good social efficiency and with low computation and communication overhead.

A mobile social network (MSN) is a special kind of delay tolerant network (DTN) composed of mobile nodes that move around and share information with each other through their carried short-distance wireless communication devices. A main characteristic of MSNs is that mobile nodes in the networks generally visit some locations (namely, community homes) frequently, while visiting other locations less frequently. In this paper, we propose a novel zero-knowledge multi-copy routing algorithm, homing spread (HS), for homogeneous MSNs, in which all mobile nodes share all community homes. HS is a distributed and localized algorithm. It mainly lets community homes spread messages with a higher priority. Theoretical analysis shows that HS can spread a given number of message copies in an optimal way when the inter-meeting time between any two nodes and between a node and a community home follows independent and identical exponential distributions, respectively. We also extend HS to the heterogeneous MSNs, where mobile nodes have different community homes. In addition, we calculate the expected delivery delay of HS, and conduct extensive simulations. Results show that community homes are important factors in message spreading. By using homes to spread messages faster, HS achieves a better performance than existing zero-knowledge MSN routing algorithms, including Epidemic (with a given number of copies), and Spray&Wait.

Utility-based routing is a routing scheme based on a special composite utility metric. The existing utility-based routing algorithms have not yet considered the delivery delay, so that they cannot work well in low-duty-cycle wireless sensor networks (WSNs). In this paper, we present a time-sensitive utility model. A successful end-to-end message delivery will obtain a positive benefit, which linearly decreases along with an increasing delivery delay; otherwise, a failed delivery will receive zero benefit. The utility is the benefit minus the total transmission costs, no matter if the message delivery succeeds or fails. Such a utility model is analogous to the postal service in the real world. Under this novel utility model, we design two optimal time-sensitive utility-based routing algorithms for the non-retransmission setting and the retransmission-allowed setting, respectively. In our designs, we derive an iterative formula to compute the expected utility of each message delivery, and we present a binary search method to determine the optimal retransmission times. As a result, the two algorithms can achieve the optimal expected utility for each message delivery, which is the optimal balance among the concerned factors, including benefit, reliability, delay, and cost. The simulation results also prove the significant performances of our proposed algorithms.

Virtual multi-input multi-output (MIMO) or vMIMO is becoming an attractive technology to achieve spatial diversity in wireless networks without using additional antennas, and to reduce power consumption by cooperation among multiple nodes. As data gathering is one of the most important operations in many sensor network applications, this paper studies energy-efficient data gathering in wireless sensor networks using vMIMO. We define the joint vMIMO and data gathering (vMDG) problem, which is NP-hard. We also propose a distributed method called D-vMDG as an approximation algorithm. This algorithm first constructs a tree-like topology by taking the unique features of vMIMO into account. Then, an energy-efficient routing protocol based on dynamic programming is proposed for each node on the constructed topology. Our theoretical analysis shows that D-vMDG can achieve an approximation ratio of O(1). Our simulations show that D-vMDG decreases the energy consumption by 81 and 36 percent compared to the well-known MDT [26] and MIMO-LEACH [19] algorithms respectively.

The choice of relay nodes significantly affects the performance of wireless cooperative networks. Previous research mostly focused on dedicating one relay node to a source node in the network. However, fairness can be improved by sharing each relay node among more than one source node. This paper first defines the shared relay assignment for (max-min) fairness (SRAF) problem, and formalizes it using a mixed integer program. We then propose a heuristic algorithm (RRA) to solve this problem. The algorithm mainly uses the binary search and rounding mechanisms to implement the shared relay assignment, so that the minimum throughput of all source nodes is improved. The theoretical analysis proves that the proposed algorithm can reach the approximate performance of 2+ε, where ε is an arbitrarily small positive number. An improved version of RRA, called IRRA, can improve the minimum throughput while still preserving the worst-case performance. Our simulations show that the IRRA algorithm can achieve about 18% improvement over the best existing approach in the minimum throughout among the source nodes.

Truthful spectrum auctions have been extensively studied in recent years. Truthfulness makes bidders bid their true valuations, simplifying greatly the analysis of auctions. However, revealing one's true valuation causes severe privacy disclosure to the auctioneer and other bidders. To make things worse, previous work on secure spectrum auctions does not provide adequate security. In this paper, based on TRUST, we propose PS-TRUST, a provably secure solution for truthful double spectrum auctions. Besides maintaining the properties of truthfulness and special spectrum reuse of TRUST, PS-TRUST achieves provable security against semi-honest adversaries in the sense of cryptography. Specifically, PS-TRUST reveals nothing about the bids to anyone in the auction, except the auction result. To the best of our knowledge, PS-TRUST is the first provably secure solution for spectrum auctions. Furthermore, experimental results show that the computation and communication overhead of PS-TRUST is modest, and its practical applications are feasible.

Due to high mobility and frequent disconnections in a vehicular network, reliable and efficient vehicular communication is very challenging. Previous studies focus on predicting the trajectories of single vehicles. Due to many random factors, however, there is little regularity in the movements of a single vehicle in an urban area, and this motivates us to take a holistic network perspective. With this insight, we model the time varying regularities of road traffic flows in road segments and intersections by mining statistic trajectories of all vehicles in the network. Based on these regularities and local real-time traffic information, we propose a new method to calculate the expected transfer delay from a current position to a given destination. We also propose a method to collect updated destination information. By combining the above two methods, we design a routing algorithm for vehicle-to-vehicle data transmission in vehicular networks, and then prove that it is a linear-time algorithm. Finally, we evaluate our algorithm by using information of real taxi vehicles. The results show that the performance of our algorithm is significantly better than other solutions in terms of packet delay.

Due to the high volume and velocity of big data, it is an effective option to store big data in the cloud, because the cloud has capabilities of storing big data and processing high volume of user access requests. Attribute-Based Encryption (ABE) is a promising technique to ensure the end-to-end security of big data in the cloud. However, the policy updating has always been a challenging issue when ABE is used to construct access control schemes. A trivial implementation is to let data owners retrieve the data and re-encrypt it under the new access policy, and then send it back to the cloud. This method incurs a high communication overhead and heavy computation burden on data owners. In this paper, we propose a novel scheme that enabling efficient access control with dynamic policy updating for big data in the cloud. We focus on developing an outsourced policy updating method for ABE systems. Our method can avoid the transmission of encrypted data and minimize the computation work of data owners, by making use of the previously encrypted data with old access policies. Moreover, we also design policy updating algorithms for different types of access policies. The analysis show that our scheme is correct, complete, secure and efficient.

With the development of cloud computing, there is an increasing number of market-based mechanisms for cloud resource allocation. Inspired by the emerging group-buying Web sites, we advocate that group-buying can be applied to cloud resource allocation, and thus cloud providers can benefit from demand aggregation due to the advantage of group-buying in attracting customers, while cloud users can enjoy lower price. However, none of the existing allocation mechanisms is specifically designed for the scenario with group-buying, and it is a challenge for mechanism design to take full advantage of group-buying to maximize the total utility. In this paper, we fill this gap by proposing an innovative auction mechanism. The mechanism is designed based on a combinatorial double auction, in which the allocation algorithm and payment scheme are specifically designed to efficiently generate allocation and compute prices considering group-buying. We theoretically prove that the necessary economic properties in auction design, such as individual rationality, budget balance and truthfulness, are satisfied in our work. The experiments show that the proposed mechanism yields higher total utility, and has good scalability.

Content Delivery Network (CDN) is proposed for replicating data objects at multiple locations in the network and encounters vast potential for future development, as a result of which, a number of replica placement techniques have been proposed over the last decade. However, most of the existing works on replica placement (RP) ignore the statistical property of the demands and the restricted service rate of the servers. In this paper, we investigate the techniques of replica placement in CDNs with stochastic demands and M/M/1 servers to optimize the overall performance in the network. We first model the demands and the servers as independent Poisson streams and simple M/M/1 queueing systems, respectively. Then, a formal definition and formalization of RP problem will be given. We show that RP problem is NP-complete and propose two heuristic algorithms: Greedy Dropping (GD) and Tabu Search (TS). We conduct abundant simulation experiments to evaluate the performance of our proposed algorithms. According to our simulation results, both of the two algorithms are efficient in finding a feasible solution with high probability. Especially, the TS decreases the average delay of the demands about 50% on average.

Relay assignment significantly affects the performance of cooperative communications. Previous studies in this area have mostly focused on assigning a dedicated relay to each source-destination pair for one-to-one (121) traffic. On the other hand, many-to-one (M21) traffic, which is also common in many situations (for example, several users associate with one access point in a wireless access network such as a WLAN), hasn't been well studied. This paper addresses the shared relay assignment (SRA) problem for M21 traffic. We formulate two new optimization problems: one is to maximize the minimum throughput among all the sources (hereafter called M21-SRA-MMT), and the other is to maximize the total throughput over all the sources while maintaining some degree of fairness (hereafter called M21-SRA-MTT). As both of these problems are NP-hard, we propose two approximation algorithms whose performance factors are 5.828 and 3, respectively, based on the rounding mechanism. Extensive simulation results show that our algorithm for M21-SRA-MMT can significantly improve the minimum throughput compared with existing algorithms, while our algorithm for M21-SRA-MTT can achieve the close-to-optimal performance.

Certificateless cryptography is a new type of public key cryptography, which removes the certificate management problem in traditional public key cryptography and the key escrow problem in identity-based public key cryptography. Multi-proxy signature is an extension of proxy signature, which allows an original signer authorizing a group of proxy signers and only the cooperation of all proxy signers in the group can create valid proxy signatures on behalf of the original signer. Recently, Jin and Wen combined certificateless cryptography with multi-proxy signature, and proposed a model as well as a concrete scheme of certificateless multi-proxy signature. They claimed that their scheme is provably secure in their security model. Unfortunately, in this paper by giving two attacks, we will show that their certificateless multi-proxy signature scheme can be broken. The first attack indicates their security model is flawed and the second attack indicates their certificateless multi-proxy signature scheme is insecure. Possible improvements are also suggested to prevent these attacks.

ABSTRACT In this paper, we propose a novel distributed covert channel in HTTP. Different from traditional covert channels in HTTP, the channel deploys multiple HTTP clients to dilute steganographic features. In a proper multiple‐to‐multiple transmission model based on the modulation of URLs, the channel is efficient and reliable. With a Poisson request generator, simulating behaviors of normal HTTP visitors, the covert traffic has a legitimate HTTP appearance that helps it be undetectable. The transmission experiments prove that the channel is error free and has a high transmission rate. The results of the undetectability experiment show that the channel is adjustable. By adjusting a certain parameter, the channel can trade off two different features, the transmission rate and the undetectability, which can meet different demands in practical applications. Copyright © 2013 John Wiley & Sons, Ltd.

Mobile social networks (MSNs) are a kind of delay tolerant network that consists of lots of mobile nodes with social characteristics. Recently, many social-aware algorithms have been proposed to address routing problems in MSNs. However, these algorithms tend to forward messages to the nodes with locally optimal social characteristics, and thus cannot achieve the optimal performance. In this paper, we propose a distributed optimal Community-Aware Opportunistic Routing (CAOR) algorithm. Our main contributions are that we propose a home-aware community model, whereby we turn an MSN into a network that only includes community homes. We prove that, in the network of community homes, we can still compute the minimum expected delivery delays of nodes through a reverse Dijkstra algorithm and achieve the optimal opportunistic routing performance. Since the number of communities is far less than the number of nodes in magnitude, the computational cost and maintenance cost of contact information are greatly reduced. We demonstrate how our algorithm significantly outperforms the previous ones through extensive simulations, based on a real MSN trace and a synthetic MSN trace.

The primary goal of breadcrumb trail sensor networks is to transmit in real-time users' physiological parameters that measure life-critical functions to an incident commander through reliable multihop communication. In applications using breadcrumb solutions, there are often many users working together, and this creates a well-known body shadowing effect (BSE). In this article, we first measure the characteristics of body shadowing for 2.4GHz sensor nodes. Our empirical results show that the body shadowing effect leads to severe packet loss and consequently very poor real-time performance. Then we develop a novel Intentional Forwarding solution. This solution accurately detects the shadowing mode and enables selected neighbors to forward data packets. Experimental results from a fully implemented testbed demonstrate that Intentional Forwarding is able to improve the end-to-end average packet delivery ratio (PDR) from 58% to 93% and worst-case PDR from 45% to 85%, and is able to meet soft real-time requirements even under severe body shadowing problems.

A mobile social network (MSN) is a special delay tolerant network (DTN) composed of mobile nodes with social characteristics. Mobile nodes in MSNs generally visit community homes frequently, while other locations are visited less frequently. We propose a novel zero-knowledge MSN routing algorithm, homing spread (HS). The community homes have a higher priority to spread messages into the network. Theoretical analysis shows that the proposed algorithm can spread a given number of message copies in an optimal way when the inter-meeting times between any two nodes and between a node and a community home follow exponential distributions. We also calculate the expected delivery delay of HS. In addition, extensive simulations are conducted. Results show that community homes are important factors in efficient message spreading. By using homes to spread messages faster, HS achieves a better performance than existing zero-knowledge MSN routing algorithms, including Epidemic, with a given number of copies, and Spray&Wait.

We consider a promising application in Vehicular Cyber-Physical Systems (VCPS) called On-road Ad Delivery (OAD), where targeted advertisements are delivered via roadside APs to attract commuters to nearby shops. Different from most existing works on VANETs which only focused on a single technical area, this work on OAD involves technical elements from human factors, cyber systems and transportation systems since a commuter's shopping decision depends on e.g. the attractiveness of the ads, the induced detour, and traffic conditions on different routes. In this paper, we address a new optimization problem in OAD whose goal is to schedule ad messages and allocate a limited amount of AP bandwidth so as to maximize the system-wide performance in terms of total realized utilities (TRU) of the delivered ads. A number of efficient heuristics are proposed to deal with ad message scheduling and AP bandwidth allocation. Besides largescale simulations, we also present a case study in a more realistic scenario utilizing real traces collected from taxis in the city of Shanghai. In addition, we use a commercial traffic simulator (PARAMICS) to show that our proposed solutions are also useful for traffic management in terms of balancing vehicular traffic and alleviating congestion.

In mobile ad hoc networks (MANETs), it is important to understand the throughput-delay trade-off (TD trade-off) problem in large-scale scenarios. In the literature, the TD tradeoff problem has been studied extensively and many of them are based on the independent and identically distributed (i.i.d.) mobility model, in which each node can randomly move to any place in the network, after every time slot. Although the i.i.d. model has been widely used, it cannot fully represent MANETs in which nodes change positions less frequently. To characterize such MANETs, in this paper, we propose a generalized i.i.d. (g.i.i.d.) mobility model, in which each node moves once after every 1/f (0 < f ≤ 1) time slots, and remains static between two moves. To investigate the TD trade-off under the g.i.i.d. model, we develop a novel multi-relay multi-hop (MRMH) scheme that exploits the opportunities of multi-hop transmissions when the network is static. Furthermore, to enable the multi-hop transmissions, we construct a new percolation highway system, which has not been used in the TD trade-off analysis for MANETs. Using the proposed MRMH scheme, we develop and prove constructive bounds for throughput and delay in MANETs with different scales of f. Our constructive bound is asymptotically optimal for f = 1 (i.e., the i.i.d. model).

In this paper, we propose a time-sensitive utility model for delay tolerant networks (DTNs), in which each message has an attached time-sensitive benefit that decays over time. The utility of a message is the benefit minus the transmission cost incurred by delivering the message. This model is analogous to the postal service in the real world, which inherently provides a good balance between delay and cost. Under this model, we propose a Time-sensitive Opportunistic Utility-based Routing (TOUR) algorithm. TOUR is a single-copy opportunistic routing algorithm, in which a time-sensitive forwarding set is maintained for each node by considering the probabilistic contacts in DTNs. By forwarding messages via nodes in these sets, TOUR can achieve the optimal expected utilities. We show the outstanding performance of TOUR through extensive simulations with several real DTN traces. To the best of our knowledge, TOUR is the first utility-based routing algorithm in DTNs.

Though there exist ready-made DSRC/WiFi/3G/4G cellular systems for roadway communications, there are common defects in these systems for roadway safety oriented applications and the corresponding challenges remain unsolved for years, i.e., WiFi cannot work well in vehicular networks due to the high probability of packet loss caused by burst communications, which is a common phenomenon in roadway networks; 3G/4G cannot well support real-time communications due to the nature of their designs; DSRC lacks the support to roadway safety oriented applications with hard realtime and reliability requirements [1]. To solve the conflict between the capability limitations of existing systems and the ever-growing demands of roadway safety oriented communication applications, we propose a novel system design and implementation for realtime reliable roadway communications, aiming at providing safety messages to users in a realtime and reliable manner. In our extensive experimental study, the latency is well controlled within the hard realtime requirement (100ms) for roadway safety applications given by NHTSA [2], and the reliability is proved to be improved by two orders of magnitude compared with existing experimental results [1]. Our experiments show that the proposed system for roadway safety communications can provide guaranteed highly reliable packet delivery ratio (PDR) of 99% within the hard realtime requirement 100ms under various scenarios, e.g., highways, city areas, rural areas, tunnels, bridges. Our design can be widely applied for roadway communications and facilitate the current research in both hardware and software design and further provide an opportunity to consolidate the existing work on a practical and easy-configurable low-cost roadway communication platform.

As the advancement of sensing and networking technologies, participatory sensing has raised more and more attention as it provides a promising way enabling public and professional users to gather and analyze private data to understand the world. However, in these participatory sensing applications both data at the individuals and analysis results obtained at the users are usually private and sensitive to be disclosed, e.g., locations, salaries, utility usage, consumptions, behaviors, etc. A natural question, also an important but challenging problem is how to keep both participants and users data privacy while still producing the best analysis to explain a phenomenon. In this paper, we have addressed this issue and proposed M-PERM, a mutual privacy preserving regression modeling approach. Particularly, we launch a series of data transformation and aggregation operations at the participatory nodes, the clusters, and the user. During regression model fitting, we provide a new way for model fitting without any need of the original private data or the exact knowledge of the model expression. To evaluate our approach, we conduct both theoretical analysis and simulation study. The evaluation results show that the proposed approach produces exactly the same best model as if the original private data were used without leakage of the fitted model to any participatory nodes, which is a significant advance compared with the existing approaches [1-5]. It is also shown that the data gathering design is able to reach maximum privacy protection under certain conditions and be robust against collusion attack. Furthermore, compared with existing works under the same context (e.g., [1-5]), to our best knowledge it is the first work showing that not only the model coefficients estimation but also a series of regression analysis and model selection methods are reachable in mutual privacy preserving data analysis scenarios such as participatory sensing.

Opportunistic routing (OR) takes advantage of the broadcast nature and spatial diversity of wireless transmission to improve the performance of wireless ad-hoc networks. Instead of using a predetermined path to send packets, OR postpones the choice of the next-hop to the receiver side, and lets the multiple receivers of a packet to coordinate and decide which one will be the forwarder. Existing OR protocols choose the next-hop forwarder based on a predefined candidate list, which is calculated using single network metrics. In this paper, we propose TLG - Topology and Link quality-aware Geographical opportunistic routing protocol. TLG uses multiple network metrics such as network topology, link quality, and geographic location to implement the coordination mechanism of OR. We compare TLG with well-known existing solutions and simulation results show that TLG outperforms others in terms of both QoS and QoE metrics.

Because data dissemination is crucial to Wireless Sensor Networks (WSNs), its energy-efficiency and reliability are of paramount importance. While achieving these two goals together is highly non-trivial, the situation is exacerbated if WSN nodes are duty-cycled (DC) and their transmission power is adjustable. In this paper, we study the problem of minimizing the expected total transmission power for reliable data dissemination (multicast/broadcast) in DC-WSNs. Due to the NP-hardness of the problem, we design efficient approximation algorithms with provable performance bounds for it.

As users drive through the vehicular networks with high speed, they frequently associate with different access points in succession to maintain connectivity. Without careful designed association control policy, it may lead to large number of handoffs and inefficient connections, which can potentially incur unacceptable delays and poor throughput. In this paper, we propose a new association control scheme with the goal of reducing the frequency of handoffs and improving throughput for all vehicular users. The defined problem HMTE (Handoff Minimization and Throughput Efficiency) is mathematically described as a min-max programming. Since the HMTE problem is NP-hard, we propose an alpha-approximation(alpha >= 2) algorithm CHMTE to resolve it. CHMTE employs a novel method to simplify the original problem to the Minimum cost Maximum flow problem, which is solved in polynomial time. Extensive evaluations show that the proposed algorithm outperforms the existing schemes in terms of the handoff frequency and network throughput.

Stereoscopic videos have become very popular in recent years. Most of these videos are developed primarily for viewing on large screens located at some distance away from the viewer. If we watch these videos on a small screen located near to us, the depth range of the videos will be seriously reduced, which can significantly degrade the 3D effects of these videos. To address this problem, we propose a linear depth mapping method to adjust the depth range of a stereoscopic video according to the viewing configuration, including pixel density and distance to the screen. Our method tries to minimize the distortion of stereoscopic image contents after depth mapping, by preserving the relationship of neighboring features and preventing line and plane bending. It also considers the depth and motion coherences. While depth coherence ensures smooth changes of the depth field across frames, motion coherence ensures smooth content changes across frames. Our experimental results show that the proposed method can improve the stereoscopic effects while maintaining the quality of the output videos.

Web services (WSs) can cooperate with each other to provide more valuable WSs. Current approaches for WS cooperation have typically assumed that WSs are always willing to participate in some form of cooperation, and have undermined the fact that WSs are autonomous in this open environment. This assumption, however, becomes more problematic in community-based WS cooperation due to the dynamic nature of WS community. It is, therefore, important to devise a cooperation scheme respecting WS autonomy for community-based WS cooperation. In this paper, we model the community-based cooperation among autonomous WSs as a coalitional game in graph form. We show this game is non-cohesive and design a distributed coalition formation algorithm. We prove that the proposed algorithm can lead to an individually stable coalition partition, which indicates that every WS can maximize its benefit through cooperation without decreasing other WSs' benefit. We also conduct extensive simulations, and the results show that the proposed algorithm can greatly improve the average payoff per WS and average availability per coalition when compared with other cooperation schemes.

Spectrum auctions motivate existing spectrum owners (as sellers) to lease their selected idle channels to new spectrum users (as buyers) who need the spectrum desperately. The most significant requirement is how to make the auctions economic-robust (truthful in particular) while enabling spectrum reuse. Furthermore, in practice, both sellers and buyers would require to trade multiple channels at one time, while guaranteeing their individual profitability. Unfortunately, existing designs can not meet all these requirements simultaneously. We address these requirements by proposing True-MCSA, a framework for truthful double multi-channel spectrum auctions. True-MCSA introduces novel virtual buyer group (VBG) splitting and bidding algorithms, and applies a proper winner determination and pricing mechanism to achieve truthfulness and other economic properties, meanwhile successfully dealing with multi-channel requests from both buyers and sellers and improving spectrum utilization. Our experiments show that the auction efficiency is impacted by the economic factors with efficiency degradations within 30%, under different settings. Furthermore, the experimental results indicate that we can improve the auction efficiency by choosing a proper bidding algorithm and using a positive base bid. True-MCSA makes an important contribution on enabling spectrum reuse to improve auction efficiency in multi-channel cases.

Virtual multi-input-multi-output (or vMIMO) communication is a promising technology to improve the spatial diversity of wireless networks. Using this mechanism, multiple single-antenna nodes can coordinate their transmissions and receptions so as to reduce power consumption. This paper studies the problem of constructing an energy-efficient topology in wireless sensor networks using vMIMO communication. We first define the problem involving joint optimization of vMIMO, partner selection and topology control. As this problem is NP-Complete, a distributed and heuristic algorithm, called vMIMO topology control (VMTC), is proposed to solve this problem. The algorithm uses an improved binary searching method to obtain an initial power assignment. A local competition method is then adopted to implement the partner selection. At last, we reduce the power consumption of each node by using efficient vMIMO modes. Our theoretical analysis show that this algorithm can achieve an approximate performance of O(1). Our simulations show that VMTC helps to decrease the power consumptions by about 32% compared to the existing algorithms.

Reputation is useful for establishing trust between Web service (WS) providers and WS consumers. In the context of WS composition, a challenging issue of reputation management is to propagate a user's impression of a composite WS (i.e., the user's feedback rating) to its component WSs. In this paper, we propose a Shapley value based approach which can achieve fair impression propagation, that is, the reputation of a component WS is never awarded (or penalized) for the good (or bad) performances of the other peer component WSs in the same composite WS. The fairness of the proposed approach is validated through theoretical analysis and experimental results.

In most existing wireless networks, end users obtain data content from the wired network, typically, the Internet. In this manner, virtually all of their traffic must go through a few access points, which implies that the capacity of wireless network is limited by the aggregated transmission data rate of these access points. To fully exploit the capability of wireless network, we envision that future wireless networks shall be able to provide data content within themselves. In this paper, we address the behavior of such networks from a theoretical perspective. Specifically, we consider that multicast is used for distributed content delivery, and we investigate the asymptotic upper bound of the throughput capacity for distributed content delivery in large-scale wireless ad hoc networks (DCD-WANET). Our analysis shows how the upper bound of throughput capacity is affected by the geometric size of the network, the number of data items, the popularity of the data content, and the number of storage nodes that contain those data items. In particular, our theoretical results show that, if the number of storage nodes exceed a critical threshold, the upper bound grows with the number of storage nodes, according to a power-law where the scaling exponent depends on the popularity of data items. We also provide the data item placement strategy to achieve the upper bound of throughput capacity for DCD-WANET.

In this paper we investigate the dynamic traffic relationship characterized by a similarity value from one road point to another in vehicle networks. Due to the regularity of human mobility, traffic exhibits strong correlations in both temporal domain and spatial domain. By exploiting the similarity values, we derive application-specific message update rules for affinity propagation, based on which we propose an instant traffic clustering algorithm to partition the road points into time variant clusters, where the traffics within the same cluster are strongly spatially correlated. Online traffic clustering is also considered by clustering combination via evidence accumulation for further influence study. We also present a neural network based traffic prediction algorithm to predict the traffic conditions cluster by cluster for a future time based on the current and historical traffic data. Simulation study on real traffic data demonstrates that our proposed algorithms are able to identify the true influences among road points and provide accurate traffic predictions.

Though there exist ready-made DSRC/WiFi/3G/4G cellular systems for roadway communications, there are common defects in these systems for roadway safety oriented applications and the corresponding challenges remain unsolved for years, i.e., WiFi cannot work well in vehicular networks due to the high probability of packet loss caused by burst communications, which is a common phenomenon in roadway networks; 3G/4G cannot well support real-time communications due to the nature of their designs; DSRC lacks the support to roadway safety oriented applications with hard real-time and reliability requirements in the most recent study [1].

Utility-based routing is a special routing approach, which takes the reliability and transmission costs into account at the same time. However, the existing utility-based routing algorithms have not yet considered the delivery delay. Thus, they cannot work well in duty-cycle wireless sensor networks (WSNs) since delay is an important factor in such WSNs. In this paper, we propose a novel utility model time-sensitive utility model. Unlike previous work, the utility of a message delivery in our model is not only affected by the reliability and transmission costs but also by the delivery delay. Under the time-sensitive utility model, we derive an iterative formula to compute the time-varying utility of each message delivery. Based on the formula, we propose an optimal time-sensitive utility-based routing algorithm. The theoretical analysis and simulation results show that our proposed algorithm can maximize the average utility of message deliveries, which makes a good tradeoff among reliability, delay, and cost.

In order to save energy of sensors in the process of gathering data and transmitting information, Compressive Sensing (CS), as a novel and effective signal transform technology, has been used gradually in Wireless Sensor Networks (WSNs). In traditional usages of CS techniques in the previous literatures, the sparsities of the signals has to be known beforehand, which is much more importance for their recover results. However, it is difficult to realize precisely the structures of the signals actually in WSNs. Therefore, it is important to further exploit reasonable practicality availability in actual applications. In order to reduce energy of gathering and transmitting of sensors, this paper presents a model of optimized CS based on local regional data and design two corresponding algorithms, which could reconstruct the signals accurately and stably even if their sparsities could not be known in advance. Most important, our algorithms just need once extra transmission by sensors In the paper, we present two reasonable assumptions and then propose spatial-temporal correlation model for optimizing measure matrix of CS. Furthermore, two algorithms are designed in two kinds of situations that data satisfy random distribution or Gauss distribution, which is common in actual applications. According to experiments in the cases of both real data based on actual environments and two kinds of signals above based on simulation environments, our algorithm has been proved to be valuable for actual applications. Especially, when the amount of the sampling is only 15 with the dimension of the data is 256 and the sparsity is unknown, the relative error rate could be less than 6% in actual environments and 3.5% in simulation environments.

Cooperative communication is becoming an attractive technology as it can greatly improve the spatial diversity without additional antennas. This novel communication paradigm can effectively reduce power consumption via multi-node cooperation and resource allocation. This paper studies the energy-efficient node-disjoint multi-path routing for a given source-destination pair by joint route construction, relay assignment and power allocation methods. We first define a new bandwidth-power aware cooperative multi-path routing (BP-CMPR) problem, and formally prove its NP-hardness. The paper then presents a polynomial-time heuristic algorithm CMPR to solve the above problem. The algorithm adopts the Suurballe's method to find k minimal-weight node-disjoint paths from source to destination on a weighted graph. Then, dynamic programming is used to implement relay assignment and power allocation. The theoretical analysis shows that CMPR can reach approximation factors of 2 and \frac{4}{3} for BP-CMPR under the amplify-and-forward and decode-and-forward schemes respectively. The distributed version of the algorithm DCMPR is also presented for this problem. We also prove that both CMPR and DCMPR construct the same cooperative multi-path routing, and show via simulations that the performance of the proposed scheme is more than 15% better than that of a traditional multi-path routing scheme, and close to the optimal result for BP-CMPR in variety of situations.

A Key Establishment Protocol for Anonymous Wireless Roaming (KEP-AWR) allows a wireless user such as a Wi-Fi/WiMAX enabled laptop or smartphone to establish a session key with a foreign server and also roam from one foreign network to another in an anonymous way such that besides the involving foreign server and the user's home server, no one can find out who the user is. Most existing KEP-AWR protocols involve all the three parties, namely, the roaming user, the foreign server and the home server. Some recent protocols require the user and the foreign server only, and hence improving the efficiency as they incur fewer message flows. Recently, a one-pass KEP-AWR was proposed by Wang, Wong and Huang (WWH in short) which achieves key establishment and anonymity by just sending one message from the user to the foreign server, and it is the first KEP-AWR achieving the one-pass communication efficiency. However, the WWH protocol neither prevents the home server from knowing the session key nor supports Perfect Forward Secrecy (PFS). In this paper, we propose a new one-pass KEP-AWR which solves these two problems with an even lower computational complexity. The new protocol also achieves perfect Key-Compromise Impersonation (KCI) security.

Wireless Sensor Networks (WSNs) are increasingly available for mission-critical applications such as emergency management and health care. To meet the stringent requirements on communication performance, it is crucial to understand the complex wireless interference among sensor nodes. Recent empirical studies suggest that the packet-level interference model, also referred to as the packet reception ratio (PRR) versus SINR model or PRR-SINR model, offers significantly improved realism than other simplistic models such as the disc model. However, as shown in our experimental results, the PRR-SINR model yields considerable spatial and temporal variations in reality, which poses a major challenge for accurate measurement at run time. This paper presents a novel accuracy-aware approach to interference modeling and measurement for WSNs. First, we propose a new regression-based PRR-SINR model and analytically characterize its accuracy based on statistics theory. Second, we develop a novel protocol called accuracy-aware interference measurement (AIM) for measuring the proposed PRR-SINR model with assured accuracy at run time. AIM also adopts new clock calibration and in-network aggregation techniques to reduce the overhead of interference measurement. Our extensive experiments on a 17-node testbed of TelosB motes show that AIM achieves high accuracy of PRR-SINR modeling with significantly lower overhead than state of the art approaches.

As Radio Access Technology (RAT) being greatly developed nowadays, Call Admission Control (CAC) is also evolving to provide inter-network connectivity since mobile terminals are able to access multiple networks. In this paper, we propose a pricing-based resource allocation scheme in a heterogeneous wireless environment consisting of several different Radio Access Networks (RANs). We suppose that each RAN caters two kinds of customers, primary and secondary users. Secondary users (SUs) are charged once based on current system state by service provider to join a RAN. We integrate stochastic call admission control (SCAC) with dynamic pricing and formulate them as an infinite horizon average reward problem to maximize the expectation of system revenue in the long run. The optimal pricing policy characteristics are analyzed based on series of computation results under various system conditions. Assessments carried out for a small network model show that a system with relatively small PU blocking punishment gains more profit and the income could be improved by nearly 60% in particular cases.

In CRAHNs (Cognitive Radio Ad Hoc Networks), cognitive users coexist with primary users who have priority in accessing a set of licensed bands. Cognitive users can use those bands only when the primary users don't occupied them. And also, these occupied bands should be vacated immediately as soon as corresponding primary users wake up. Therefore, the more frequently primary user changes its activities, the less stable spectrum availability the cognitive user has. This makes route stability become one of the most concerned problems in CRAHNs. Some works have considered such kind of stability, but the overall performance of the network hasn't been taken into account when multiple cognitive user pairs (also called multi-sessions in the following) have routing requirements. Thus, in this paper, we investigate a joint routing and spectrum scheduling problem, in which multi-sessions exist, and the route stability is required. To solve this problem, a greedy strategy named Distributed Stability Algorithm (DSA) is proposed, which aims to maximize the route stability of the session, whose route stability is the minimal. The message complexity of our algorithm DSA is O((L+2)·Tmax·κ). Here, κ represents the number of transmission pairs, L and Tmaxare both two constants. Numerical results have shown that in a 100-node networks, compared with SAMER, our scheme can increase the route stability by 48.5% on average.

Service recommendation in a Web of services with uncertain QoS is a challenging problem. In this paper, we propose a reputation-based service recommendation framework. We formally define a service reputation model that analyzes the relations between uncertain QoS and reputation. We also devise a two-phase planning approach to constructing a composite service as the recommendation when none of existing services can fulfill the user's requirements alone. Furthermore, we design a utility difference based approach that can fairly distribute the overall rating of a composite service to its component services and theoretically prove its fairness. We evaluate the efficiency and fairness of our framework on a publicly available dataset: ICEBE05.

Recently, cooperative communication is shown to be a promising technology to achieve spatial diversity by keeping each node with only one antenna and having a node exploit a relay node's antenna. Most previous works assume that there are multiple source-destination pairs and multiple relay nodes in a network where each node only serves as one role: either transmission node or relay node. However, this assumption will not always hold in practice where each node has its own information to transmit. This paper studies the maximal lifetime scheduling problem for cooperative communication with guaranteed throughput service in a network environment where there are multiple source-destination pairs and each node can serve as both two roles: transmission node and "relay" node. We first analyze the power allocation of optimal lifetime for a single source-destination pair with a bounded throughput in different communication modes, and then design an optimal polynomial-time algorithm to maximize the network lifetime. This algorithm is based on iteration and tries to find a better solution than that before in each iteration. After that, the optimality of this algorithm has been proved formally. The simulation results show that the algorithm can prolong about 199% lifetime than that of direct transmission.

Interference modeling is crucial for the performance of numerous WSN protocols such as congestion control, link/channel scheduling, and reliable routing. In particular, understanding and mitigating interference becomes increasingly important for Wireless Sensor Networks (WSNs) as they are being deployed for many data-intensive applications such as structural health monitoring. However, previous works have widely adopted simplistic interference models that fail to capture the wireless realities such as probabilistic packet reception performance. Recent studies suggested that the physical interference model (i.e., PRR-SINR model) is significantly more accurate than existing interference models. However, existing approaches to physical interference modeling exclusively rely on the use of active measurement packets, which imposes prohibitively high overhead to bandwidth-limited WSNs. In this paper, we propose the passive interference measurement (PIM) approach to tackle the complexity of accurate physical interference characterization. PIM exploits the spatiotemporal diversity of data traffic for radio performance profiling and only needs to gather a small amount of statistics about the network. We evaluate the efficiency of PIM through extensive experiments on both a 13-node and a 40-node testbeds of TelosB motes. Our results show that PIM can achieve high accuracy of PRR-SINR modeling with significantly lower overhead compared with the active measurement approach.

Cooperative communication is shown to be a promising technology to enhance the capacity of wireless transmission without additional equipments. Under this communication paradigm, both relay selection and power allocation significantly affect the overall system performance. Thus, this paper studies the joint power allocation and relay assignment problem to optimize the Max-Min fairness of cooperative network. We first analyze the trade-off between the bandwidth provision and power consumption for different communication schemes. According to the analytic results, a heuristic algorithm PRF is designed to determine the fairness factor with the binary searching. The analyses show that PRF algorithm can obtain the approximate ratio 1- ξ, where ξ is an arbitrarily small positive constant. Meanwhile, this paper also presents another heuristic algorithm JFAP, which uses the offer-and-gain mechanism to reach the Max-Min fairness. The simulation results show that PRF and JFAP algorithms can improve the fairness factor at least 36% and 32% compared with the direct transmission scheme in many situations.

Scalar product protocol and privacy preserving add to multiply protocol (PPAtMP) are two significant basic secure multiparty computation protocols. In this paper, we claim that the two protocols are equivalent to each other and we can achieve one based on the other with the same communication and computation complexity. Then, we propose Secure Two-party Mean Protocol, Secure Shared x ln x Protocol and Secure Shared Generic Polynomial Protocol based on scalar product protocol and PPAtMP. Additionally, we analyze the correctness, security, communication overheads and computation complexity of each protocol proposed in this paper.

This demonstration presents a mobile surveillance system that is developed at the University of Science and Technology of China and undergoing a technology transition. The goal of this project is to develop a low-cost, promptly-deployable, mobility manageable, location traceable, self-organizing wireless communication and mobile surveillance system, for real-time video surveillance and on-site guard for public security purposes, e.g., public security against terrorism in large-scale gatherings and events.

Recent years, researchers have proposed numerous positioning algorithms for wireless sensor networks, in which range-free schemes are more attractive because of their low-cost advantage. However, in practical circumstance, the raw range-free information (neighboring set and hop-counts et al.) is usually unreliable due to combined factors such as irregular signal patterns, environment noise and so on. To make matters worse, the unreliable localization information will lead to the reduction of localization accuracy. In order to tackle this problem, we propose a Reputation-based Revising Scheme (RRS) to preprocess the raw localization information before applying any positioning algorithm. In RRS, we reevaluate the reliability of raw information by utilizing the neighboring relationship. After being processed through RRS, the raw information is close to the information collected in ideal environment. According to our detailed simulations, we demonstrate that the revised localization information has 27% lower average error rate than the raw one. Based on the revised information, the traditional range-free localization algorithms are capable to achieve higher accuracy.

Along with the standardization of Web services composition language and the widespread acceptance of composition technologies, Web services composition is becoming an efficient and cost-effective way to develop modern business applications. As Web services are inherently unreliable, how to deliver reliable Web services composition over unreliable Web services is a significant and challenging problem. In this paper, we propose FACTS, a framework for fault-tolerant composition of transactional Web services. We identify a set of high-level exception handling strategies and a new taxonomy of transactional Web services to devise a fault-tolerant mechanism that combines exception handling and transaction techniques. We also devise a specification module and a verification module to assist service designers to construct fault-handling logic conveniently and correctly. Furthermore, we design an implementation module to automatically implement fault-handling logic in WS-BPEL. A case study demonstrates the viability of our framework and experimental results show that FACTS can improve fault tolerance of composite services with acceptable overheads.

Because of rapid channel attenuation in lossy environment, there is a severe packet drop loss problem in wireless ad hoc networks. Though MIMO (Multi-Input Multi-Output) technology can alleviate this effect greatly, it will result in higher transmission energy depletion as using more transmit antennas. Lots of schemes have been proposed to reduce such energy cost by transmit antenna selection. However, most of those only consider QoS requirements between neighbor nodes while ignoring QoS requirements for entire network. In this paper, we investigate an energy-efficient transmit antenna selection problem for many-to-one communications, while guaranteeing a certain packet drop loss ratio from any node to the sink. We prove that this problem (Transmit Antenna Selection Problem, TASP) is NP-hard. Thus, a greedy algorithm TASA (Transmit Antenna Selection Algorithm) is proposed to solve this problem. This paper also prove the correctness of the proposed algorithm and the time complexity of TASA algorithm is O(|E|+|V|-MT), where |V| represents the number of nodes, |E| indicates the number of links and MTis the number of transmit antennas on each node, respectively. Our numerical results show that in a 100 node network, TASA can reduce transmission energy cost by 44.3% on average.

Wireless sensor and actor networks (WSANs) have attracted much attention in the recent years. In WSANs, sensor nodes are in charge of the sensing task, while the actor nodes take actions in response to the sensed phenomena or the detected event. In order to execute the correct operations, the applications typically have to process the data or messages from the different sensors in the same order. This paper presents a Consistent Message Ordering (CMO) algorithm, which ensures that messages from the different sensors arrive at the different actors in a consistent order. The significance of the algorithm is fully localized, asynchronous and energy-efficient. We prove the correctness of the algorithm, and analyze the theoretical message complexity. And the experimental results show that CMO algorithm improves the performance at least 60% over TMOS algorithm in many situations.

Recently, mobility-assisted data collection has been proposed to prolong network lifetime. However, the upper bound of throughput capacity in such mobility-assisted data collection models has not been studied. In this work, we first derive the upper bound of throughput capacity when a mobile sink is available for data collection. Given the traveling speed of the mobile sink and the required delay deadline, we analyze the necessary conditions (e.g., optimal number of clusters, minimum buffer size at each cache, and minimum traveling distance) to achieve the maximum throughput capacity. Our analysis shows that 3W/4n per-node throughput capacity can be achieved at a low traveling speed, which is 3 times of the throughput capacity in a static WSN. We further extend the analysis to the case where multiple mobile devices can assist in data collection. We show that 4 mobile relays are enough to achieve the upper bound of throughput capacity O(W/n). Finally, we derive the throughput capacity under existing mobility-assisted data collection models.

Relay node placement is an important issue because it greatly affects the performance of wireless ad hoc sensor networks. The pervious works mainly pay attention on placing the minimum number of relay nodes to connect all the sensor nodes. But we find that this placement scheme may result in QoS reduction of the network, such as transmission delay, energy cost, etc. Therefore, this work studies the minimum relay node placement problem to guarantee both the connectivity and geometric spanner properties. We first show that there is no algorithm with the performance O(opt) for this problem, where opt is the minimum number of relay nodes to connect the sensor nodes. Thus, this paper presents an efficient placement algorithm MSGP with O(max{opt, n}) relay nodes, where n is the number of sensor nodes. Specially, if any two sensor nodes cannot communicate directly, the algorithm obtains O(1)-approximation bound. Then, as data gathering is one of the most important operations in sensor networks, we also study the spanner-aware placement problem for this communication scheme. The simulation results show the efficiency of the proposed algorithm. For example, MSGP algorithm can reach the spanner factor 3 with the additional 30% relay nodes compared with MST-based placement algorithm.

In recent years, many localization algorithms have been proposed for wireless sensor networks, in which the hop-count based localization schemes are attractive due to the advantage of low cost. However, these approaches usually utilize discrete integers to calculate the hop-counts between nodes. Such coarse-grained hop-counts make no distinction among one-hop nodes. More seriously, as the hop-counts between nodes increase, the cumulative deviation of hop-counts would become unacceptable. In order to solve this problem, we propose the concept of fine-grained hop-count. It is a kind of float-type hop-count, which refines the coarse-grained one close to the actual distance between nodes. Based on this idea, we propose a fine-grained localization algorithm (AFLA). In AFLA, we first refine the hop-count information to obtain fine-grained hop-counts, then use the Apollonius circle method to achieve initial position estimations, and finally further improve the localization precision through confidence spring model (CSM). We conduct the comprehensive simulations to demonstrate that AFLA can achieve 30% higher average accuracy than the existing hop-count based algorithm in most scenarios and converge much faster than the traditional mass-spring model based scheme. Furthermore, AFLA is robust to achieve an approximate 35% accuracy even in noisy environment with a DOI of 0.4.

Secure Multi-party Computation (SMC) has been a research focus in international cryptography community in recent years. SMC deals with the following situation: Two (or many) parties want to jointly perform a computation without disclosing their private inputs. Privacy-preserving convex hulls problem is a special case of SMC and it can be applied in many fields such as military and commercial fields. In this paper, we first present two privacy-preserving protocols to solve the convex hulls problem by using Yao 's millionaire protocol. We also discuss the security, correctness and performance of the two protocols. Based on the Euclid-distance Measure Protocol, an approximate solution to the convex hulls problem is proposed for fairness, which conceals more private information.

In this paper, a novel statistical algorithm for linguistic steganography detection, which takes advantage of distribution of words in the text segment detected, is presented. Linguistic steganography is the art of using written natural language to hide the very presence of secret messages. Using the text data, which is the foundational media in Internet communications, as its carrier, linguistic steganography plays an important part in Information Hiding (IH) area. The previous work was mainly focused on linguistic steganography and there were few researches on linguistic steganalisys. We attempt to do something to help to fix this gap. In our experiment of detecting the three different linguistic steganography methods: NICETEXT, TEXTO and Markov-chain-Based, the total accuracies on discovering stego-text segments and normal text segments are found to be 87.39% 95.51%, 98.50%, 99.15% and 99.57% respectively when the segment size is 5 kB, WkB, 20 kB, 30 kB and 40 kB. Our research shows that the linguistic steganalysis based on distribution of words is promising.

Service composition is becoming a prevalent way to building service-oriented business applications (SOBAs). In an open service environment, the profit of composition (PoC) is a primary concern of building such applications. How to improve the PoC is a significant issue in developing SOBAs but was largely overlooked by current research. Particularly, the modeling and prediction of PoC should play a key role to drive the composition process. In this paper, we focus on how to model and predict PoC in SOBAs. We regard the PoC of a composite service as a function of the quality of service (QoS) attributes, defined in the service level agreement (SLA) between the service and its external partners. Based on the PoC prediction approach, we further propose a profit-driven composition methodology to assist enterprises to make more profit in their SOBAs.

Topology control problem has been deeply studied in wireless sensor networks. Some methods try to minimize the maximal or average link interference, which may result in larger accumulative interference than the optimum for some node pairs. In this paper, we define the concept of path interference formally. And the path interference of the topology constructed by LIFE (Low Interference Forest Establisher) algorithm is even about O(D) factor of the optimum, where D is the network diameter. Thus, a localized algorithm, called LDPA, is presented to construct an efficient topology which guarantees the minimal path interference among any node pair. Moreover, we study the trade-off between the path interference and energy cost, thus define the problem of ε -interference. And an efficient algorithm, called ε -PI, is presented to solve it. The paper also theoretically proves that the proposed algorithms can satisfy the certain properties, such as minimal path interference or approximate path interference as well. And the experimental results show that the average path interference of LDPA is less than 1/2 of that of LIFE algorithm.

It is significant to deploy a mobile sensor network periodically sweeping from the upriver to the downriver to monitor the water quality of the third large river in China. An important problem in this network is how to maximize the dynamic coverage efficiency while maintain the connectivity of network. To solve this problem, we borrow the idea of flocking-birds model in artificial life field and propose a distributed coverage and connectivity control algorithm DC3. The simulation result shows that the dynamic coverage efficiency of this algorithm is significantly better than that of the random mobility model.

Although topology control has been extensively studied for stationary sensor networks, few theoretical results on topology control for mobile sensor networks (MSN) have been reported yet. In this paper, we propose two O(n3) time topology control algorithms, centralized and distributed versions respectively, under a variant rate mobile network model (VRMN). In VRMN each sensor node may move at a variant speed and associated with each node is its starting and ending positions in the unit time interval. Our algorithms are proactive such that it can guarantee the topology of a given instance of MSN can continuously connected in the unit time interval, regardless of how much the moving speeds of the sensor nodes are. Extensive simulations have been conducted and the simulation results reveal that the resultant topology after applying our distributed topology control algorithm shows good performances in terms of average transmission radius and connectivity.

Mobile wireless sensor network (M-WSN) is wireless sensor network without infrastructure, and includes mobile nodes. According to certain mobility models, mobile nodes move around in the network, and change their locations continually. Because the paths between nodes are not fixed any more, it normally takes nodes longer time to communicate each other. To solve this problem in the M-WSN, this paper presents a new ant-colony routing protocol. The new protocol uses the tracking range of mobile nodes to split the path between source node and destination node into two parts: indefinite path and definite path. The indefinite path is almost the same as the path of probabilistic search in the traditional ant-colony routing protocols. The definite path is the path in the tracking range of mobile nodes. The message from source node is first sent through indefinite path until the tracking range of mobile destination node is reached. Then the message is sent through definite path to reach the destination node. Simulation results showed that our protocol speeded the procedure of message delivery.

This paper studies delay-constraint topology control (DTC) in wireless sensor networks. That is, the delay of transmission is guaranteed through topology control. This problem has been proved to be NP-Completeness, and the previous works are in-adequate and lack of the theoretical analysis. In this

Word shift is a fundamental format based text steganography. It embeds secret information in text by shifting words slightly. Compared with study on steganography, research on its steganalysis is still in its infancy. In this paper, we present a blind steganalysis method to detect word shift in PDF document. Our method is to find features sensitive to word shift and use classifier to learn and remember feature differences between natural document and stego one. In order to design sensitive features, we propose two concepts "neighbor difference" and "environment equal", which reveal the spaces' statistical property. Then, we divided the PDF document into two types to make our method work efficiently. At last, we design a series of experiments to demonstrate performance of our method. The detection accuracy of our method can be up to 93.3%. Our initial results shows that our proposed concepts are very useful in text steganalysis and offer help for other related works.

QoS-aware Web services composition has recently received much attention. While most work focused on service selection, we study QoS in another stage of the life cycle of composite services, namely, scheduling. An interesting problem is whether we can obtain better QoS via scheduling even when the component services have been fixed. In this paper, we propose an approach to find an optimal (near-optimal) schedule with the least cancellation cost, which can further improve the overall QoS of composite services. An approach to analyze the expected cancellation cost of a schedule of a composite service is proposed and QoS-Aware service scheduling is formalized as a Constraint Satisfaction Optimization Problem (CoSOP). Two algorithms - heuristic back tracking and genetic algorithm - are presented to find an optimal (near-optimal) schedule, and their performance is studied by simulations. Preliminary experimental results show that our approach is effective.

The recent years, achievements in micro-sensor technology and low-power electronics make wireless sensor networks become into realities in applications.In this paper, we basically describe the implementation of the intensive irrigated agriculture monitoring system based on wireless sensor networks.we give some key technologies of wireless sensor networks according as the system performance requirements for this application, longevity and latency.

The IEEE 802.16a standard defines WiMax mesh network, using the base station (BS) as a coordinator for the centralized scheduling. This paper proposes a centralized scheduling algorithm for WiMax mesh networks. In our scheme, each node has one transceiver and can be tuned between multiple channels, intending to eliminate the secondary interference for reducing the length of scheduling. We first study the problem when sufficient channels are supported, then extend our solution to the case with insufficient number of channels. Both the scheduling algorithm and the channel assignment strategies are included. The simulation results show that the multi-channel single-transceiver MAC can reduce the length of scheduling substantially as compared with the single channel system, and double channel may provide a performance similar to the multiple channels.

When data mining occurs on distributed data, privacy of parties becomes great concerns. This paper considers the problem of mining quantitative association rules without revealing the private information of parties who compute jointly and share distributed data. The issue is an area of privacy preserving data mining (PPDM) research. Some researchers have considered the case of mining Boolean association rules; however, this method cannot be easily applied to quantitative rules mining. A new secure set union algorithm is proposed in this paper, which unifies the input sets of parties without revealing any element's owner and has lower time cost than existing algorithms. The new algorithm takes the advantages of both in privacy-preserving Boolean association rules mining and in privacy-preserving quantitative association mining. This paper also presents an algorithm for privacy-preserving quantitative association rules mining over horizontally portioned data, based on CF tree and secure sum algorithm. Besides, the analysis of the correctness, the security and the complexity of our algorithms are provided

This paper proposes an efficient algorithm for P2P file sharing systems based on WiMAX mesh mode and Wi-Fi technologies. Wireless networks in our system are hierarchically divided into three layers: Wi-Fi based wireless local area networks under a subscriber station; the mesh network of subscriber stations under a base station; the network of base stations. File lookup procedure may go through three steps: the requesting end host firstly looks up for the requested file within it's neighbor end hosts under the same subscribe station; if fails, then it sends lookup messages to the nearest subscriber stations in the mesh network following to a changed dynamic source routing protocol; if both the steps fail, then the requesting end host searches the requested file through the chord-based base stations' overlay. By statistical analysis and simulations, we prove that our layered P2P file sharing system can reduce the number of lookup messages in physical networks and prevent over expenses of precious bandwidth in wireless metropolitan area networks

In this paper, we address present an optimal solution for the problem of multimedia object placement for hybrid transparent data replication. The performance objective is to minimize the total access cost by considering both transmission cost and transcoding cost. The performance of the proposed solution is evaluated with a set of carefully designed simulation experiments for various performance metrics over a wide range of system parameters. The simulation results show that our solution consistently and significantly outperforms comparison solutions in terms of all the performance metrics considered.

In multiple-precision algorithms, the design and implementation of division is the most complicated. On the basis of some classical algorithms, this paper introduces an efficient improved algorithm. This algorithm omits the most majority of normalization of classical algorithms and uses integer arithmetic instead of floating-point data. By analyzing the algorithm and comparing the arithmetic cost, we conclude that this algorithm is at least three times faster than the most efficient previous solution. Key words: multiple-precision, algorithm, division.

For the problem of decision tree classification with privacy concerns, we propose several efficient secure multi-party computation protocols to construct a privacy preserving ID3 algorithm over horizontally partitioned data among multiple parties. Our algorithm presents the first solution to privacy preserving decision tree classification among more than two parties. We also make a performance comparison with the existing solution, which is only applicable to the twoparty case. The result shows that our solution has a significantly better performance.

Wireless sensor networks have posed a number of challenging problems such as localization, deployment and tracking, etc. One of the interesting problems is the calculation of the coverage path for sensor networks. In this paper, we design a localized algorithm to solve the worst coverage problem first introduced by Meguerdichian et al. All nodes cooperate to construct the worst coverage path with their one-hop neighbors’ information. Also, the correctness of the algorithm is proved under the diminishing model formally.