Mobile Edge Computing (MEC) networks have attracted significant attention for enabling users to offload computation-intensive tasks to edge servers. Task offloading scheduling is a critical challenge, especially when complete information about tasks and edge servers is only partially accessible in practice. In general, each edge server can only obtain its own information but has no access to the complete real-time information of other edge servers, resulting in information incompleteness. To address this issue, this paper investigates the problem of Energy Minimization through Offloading with Incomplete Edge Information (EMO-IEI). Specifically, to address the uncertainty of real-time computing resources caused by incomplete edge information, we adopt the Exact Convex Regularization (ECR) method to estimate resource availability based on known expectations and variances. Utilizing these estimations, we reformulate the problem as a collapsing multi-knapsack problem and propose the GAP-ESM algorithm for efficient solution. Theoretical analysis validate that the GAP-ESM algorithm achieves an approximation ratio of (1 + κ/κ−ρκ−ρ ), where κ is system parameter associated with the energy requirements of computing tasks, and ρ is a tunable design parameter balancing approximation quality and computational complexity. Extensive simulations demonstrate that the proposed GAP-ESM algorithm outperforms baseline schemes in terms of overall energy consumption and task completion rate.

Trusted execution environments (TEEs), like TrustZone, are pervasively employed to protect security sensitive programs and data from various attacks issued by untrusted rich execution environments (REEs) while they execute compact TEE operating systems which implement minimum security-critical operations but have poor device driver support. In this paper, we propose a twin driver approach where a pair of TEE and REE drivers is generated and cooperate to enable secure and efficient TEE driver support. To begin with, we propose a driver data flow analysis framework named driver analyzer (DrvAna) to automatically analyze the shared states between the TEE and REE driver where a novel data structure named value-type tree is investigated to facilitate field-sensitive data flow analysis upon the driver state. Furthermore, in order to maintain a minimal trusted computing base, we propose a Linux driver runtime (LDR) inside the TEE, a sandbox environment that confines the TEE driver based on the ARM domain access control features and mediates the driver's interaction with the TEE. We implement a DrvAna prototype based on LLVM as well as an LDR prototype on an NXP IMX6Q SABRE-SD evaluation board, adapt 6 existing Linux drivers into LDR, and evaluate their performance. The experimental results show that the LDR drivers can achieve comparable performance with their Linux counterparts with negligible overheads.

Camouflaged object detection (COD) relies on multi-granularity structural information and fine-grained details to distinguish objects from highly similar backgrounds. Whereas low-resolution data lacks high-frequency cues such as textures and sharp edges, retaining only coarse structures. These not only weaken discriminative features but also introduce resolution-induced camouflage beyond natural blending. Existing COD methods assume high-resolution data and fail to address this dual-source ambiguity, resulting in significant performance degradation and underscoring the need for approaches that explicitly explore essential spatial priors under low-resolution constraints. Therefore, we propose KRNet, the first framework explicitly designed for COD in low-resolution settings. KRNet presents a Leader-Follower framework where the Leader extracts dual gold-standard distributions: conditional and hybrid, from supporting data to drive the Follower in rectifying knowledge learned from low-resolution data. The framework further benefits from a cross-consistency strategy, and a stronger time-prompt conditional encoder that improve the rectification of these distributions. Extensive experiments on benchmark datasets demonstrate that KRNet outperforms state-of-the-art COD methods and SR-assisted COD approaches, highlighting its effectiveness in tackling the challenges of low-resolution data in COD. Code: https://github.com/whyandbecause/KRNet/tree/main.

Recently, there has been an emerging radio frequency fingerprint identification (RFFI) technology that enhances fingerprint distinguishability by deliberately injecting an RF fingerprint into the device's Wi-Fi baseband signal. The current RF fingerprint injection methods are impractical, degrading the communication quality between Wi-Fi devices while offering limited improvements in distinguishability among a set of devices. To address these issues, we propose injecting I/Q imbalance into a short training field (STF) instead of the entire baseband signal. Our findings indicate that this method can effectively preserve the quality of the original wireless communication. Besides, a temperature-independent RF feature differential carrier frequency offset (DCFO) is proposed as an extended feature for the enhancement of fingerprint distinguishability. Building upon these, we introduce a fingerprinting scheme called PR-RFFI that generates distinguishable fingerprints for a set of devices by injecting appropriate I/Q imbalance and DCFO into the STF. Leveraging the short-term invariance of the channel, we design a practical I/Q imbalance extraction method based on the communication-quality preserving injection. Moreover, we design an optimal assignment method for I/Q imbalance and DCFO to maximize the distinguishability of RF fingerprints for all devices. Finally, we implement the PR-RFFI solution and conduct experiments in real-world and simulation scenarios. The experimental results demonstrate that PR-RFFI consistently maintains good communication quality, and achieves over 98% precision, recall, and F1-score.

Headphones have become ubiquitous in daily work and communication, leading users to assume a sense of privacy and security during confidential conversations while overlooking the potential risk of eavesdropping. In this paper, we present mmEar, an end-to-end eavesdropping system that demonstrates the feasibility of compromising headphones using a commercial off-the-shelf (COTS) mmWave radar. Unlike previous approaches that rely on relatively strong vibrations, mmEar targets extremely faint, low-SNR speech-induced vibrations on headphone surfaces. To address this challenge, we introduce a Faint Vibration Emphasis (FVE) technique that amplifies phase variations on the IQ plane, followed by a deep denoising network for enhanced signal quality. Furthermore, we design a diffusion-based generative model within a pretrain–finetune framework, leveraging large-scale synthetic data to significantly improve generalization and robustness across diverse scenarios. Extensive experiments on multiple headphone and earphone models validate the practicality and effectiveness of the proposed attack, revealing that most tested devices can be compromised to recover intelligible speech.

In recent years, the Internet of Things (IoT) has rapidly advanced, with applications ranging from smart homes to industrial manufacturing, often involving densely deployed nodes such as temperature and humidity sensors. Since these nodes have limited computation and energy, the use of stuffed Wi-Fi management frames for data transmission has emerged as a promising way to avoid the association overhead of the traditional transmission mode. However, this unassociated data transmission mode continues to encounter significant channel contention in dense deployments. To this end, we propose ODGMAC, an on-demand grouping-based MAC solution that dynamically groups transmission-awaiting nodes and allocates time slots on a per-group basis, thereby enabling intra-group contention to improve transmission efficiency and reduce node energy consumption. Firstly, we present a fuzzy control-based algorithm at the access point (AP) to dynamically identify nodes with transmission demands in the current beacon period. On this basis, we then propose a hierarchical group-based time slot allocation methodology. Specifically, the nodes are initially clustered according to their per-packet airtime requirements. Within each cluster, we evenly partition nodes into multiple groups and assign each group to a unique time slot for channel contention, where the optimal slot count is determined by a renewal-theory-based analytical model with a discrete search over candidate counts. Finally, we implement the ODGMAC testbed with one AP and 100 IoT nodes, and conduct real-world experiments in a dense environment. The experimental results show that our solution outperforms existing methods in terms of both data delivery rate and node power consumption. Specifically, under severe channel collision conditions, our solution achieves an average increase of 14.77% in data delivery rate and an average reduction of 8.21% in node power consumption, while maintaining excellent fairness. Moreover, extended simulations show that our solution scales to 1000 nodes and maintains excellent performance under node mobility.

Collaborative Edge Computing (CEC) enables effective load balancing by decomposing tasks across edge servers. However, due to limited computing and storage resources in CEC networks, eliminating computational redundancies becomes particularly important for improving overall efficiency and conserving resources. To address this, we propose a novel compute-storage cooperation framework that jointly optimizes task offloading and computation result caching to minimize system-wide delay and caching cost. The optimization problem is decomposed into two subproblems: reusable task scheduling and reusable data caching. Accordingly, the CoRe-S algorithm and the VaRe-C algorithm along with a proactive pre-caching mechanism are proposed to solve these subproblems, respectively. By leveraging temporal and spatial correlations among computational tasks, the proposed framework directly caches computation results to reduce redundant processing. In addition, the age of data is incorporated into the evaluation metric to better assess the value of cached results, thereby enhancing reuse efficiency. Theoretical analysis and extensive simulations are conducted to validate the effectiveness and superiority of the proposed algorithms. Compared with state-of-the-art baselines, our method reduces the total cost by up to 41.89% and achieves a cache hit rate of 53.1%.

Microcontroller units (MCUs) are widely used in embedded devices due to their low power consumption and cost-effectiveness. MCU firmware controls these devices and is vital to the security of embedded systems. However, performing dynamic security analyses for MCU firmware has remained challenging due to the lack of usable execution environments -- existing dynamic analyses cannot run on physical devices (e.g., insufficient computational resources), while building emulators is costly due to the massive amount of heterogeneous hardware, especially peripherals. Recent advances in automated peripheral emulation have made MCU emulation more scalable. However, these efforts only support limited peripherals and are hard to extend because they require ad-hoc adaptations.

Federated Learning (FL) enables decentralized model training on distributed data sources, aggregating information to enhance generalization while preserving privacy. With the growing demand for personalized AI models, Personalized Federated Learning (PFL) has become essential for adapting models to individual user preferences. However, achieving strong global generalization while maintaining effective local personalization presents a significant challenge, especially under non-i.i.d. data distributions. We propose APO-PFL (Aligned Proximal Policy Optimization Personalized Federated Learning), a novel PFL framework that leverages Proximal Policy Optimization (PPO) to align aggregation weights dynamically. APO-PFL optimizes the fusion of local models using reinforcement learning to enhance global model performance while striving to maintain effective personalized inferences. Experimental results on CNN and GPT-2 models demonstrate that APO-PFL achieves superior global generalization and outperforms baseline methods while delivering consistent personalized predictions across heterogeneous devices.

The rapid evolution of the 6G network infrastructure has position cloud-edge systems integrating giant AI models and IoT devices as key enablers for next-generation surveillance solutions. However, traditional centralized architectures for such solutions face challenges in efficient multimodal data transmission and processing due to the massive data generated by terminal devices and the high computational demands of giant AI models served at the cloud side. To address these limitations, we propose DEVOLUTION, a 6G-based symbiotic cloud-edge framework that optimizes multimodal data transmission and the operation of the giant AI model in surveillance solutions. DEVOLUTION employs a two-stage hierarchical index encoding mechanism to dynamically distribute computation loads between Elasticsearch cluster edge devices and cloud servers, mitigating bandwidth constraints while preserving data locality. Furthermore, DEVOLUTION introduce a distributed giant model adaptation strategy, where cloud servers fine-tune the Chinese CLIP model (CN-CLIP), while edge devices deploy a lightweight Chinese CLIP Self-attention and Crossattention (CN-CLIP-SA-CA) fusion model, enabling secure and efficient cross-modal data transformation and alignment. In the end, a symbiotic retrieval engine, optimized with a best-first beam search (BFBS) strategy for 6G environments, ensures highaccuracy, low-latency multimodal retrieval. Experimental studies are being conducted on real datasets and the results demonstrate that DEVOLUTION significantly reduces latency, enhances privacy through edge-localized processing, and outperforms traditional methods (BM25, IVF, HIVF, KNN). A very small number of top candidate sets require encryption and decryption and transmission to recall. The retrieval precision can reach 96%–100% of the accuracy of Elasticsearch KNN, while retrieval reduces the retrieval time by 1.2%-45.2% compared to several latest schemes in different datasets and scales.

This work studies the challenge of scheduling real-time control commands in Networked Control Systems (NCS), where control actions rely on the freshness of data collected from multiple sources. In dynamic environments, ensuring that control commands in an NCS are accurate and frequent is essential for maintaining the system responsiveness. For this aim, we introduce a new metric, Age of Result (AoR), which quantifies the time elapsed since the last control command was generated and executed. This metric reflects the system’s capability to adapt to real-time changes in the operational environment by considering both data freshness and control command frequency. We conduct a detailed analysis of AoR in NCS, paying special attention to the dependencies between sensing and computing phases. We first address computation-intensive and network-intensive scenarios, proposing random sampling (RS)-based approximate algorithms for each case. Subsequently, we develop another RS-based algorithm and a heuristic approach for the general model. Simulation results demonstrate that our approach can effectively minimize AoR and significantly enhance the system performance and real-time adaptability compared to existing strategies.

LoRa, as a typical representative of Low Power Wide Area Networks (LPWAN), has been widely used to connect massive IoT devices. However, in mobile applications, there is significant packet loss in LoRa transmission due to link performance degradation. Existing studies take little account of end-devices' movement, particularly when the movement pattern is unknown. We propose LMLoRa to enhance theLink Performance forMobile LoRa networks in general scenarios for both single-gateway and multi-gateway applications. The key observation is that, due to LoRa's unique feature, repeating the original packet content enables the use of smaller, more energy-saving transmission parameters, which not only enhances link performance but also reduces energy consumption. Technically, we propose a link performance estimation model based on packet content repetition for both single-gateway and multi-gateway mobile networks. Then, we propose the corresponding channel frequency selection model to avoid transmission collisions. Finally, we design low-overhead communication mechanisms to operate the system. To evaluate the performance of LMLoRa in various scenarios, we design and implement real-world testbeds and a simulation platform for both single-gateway and multi-gateway scenarios. Extensive results show that LMLoRa improves packet delivery ratio by an average of 33.4% to 69.2% compared with the state-of-the-art.

Unmanned aerial vehicles (UAVs) are being widely employed in wireless communication applications, e.g., collecting data from ground nodes (GNs). Minimizing UAV energy in these applications is crucial due to the limited energy supply onboard. Unlike previous studies that assume UAVs fly at a fixed altitude and simplify the energy consumption model of UAVs, we consider the impact of varying UAV altitudes on the ground-to-air communication and utilize a general communication model for GN. Furthermore, we conduct real-world flight tests and introduce a practical speed-related flight energy consumption model of UAVs. This paper focuses on the UAV altitude-speed scheduling and GN transmission switching (UASS-GTS) problem, specifically in scenarios where the UAV flies straight for monitoring applications such as power transmission lines, roads, and water/oil/gas pipes. However, minimizing energy consumption presents challenges due to the tight coupling of altitude scheduling and speed scheduling. To tackle this, first, we develop the looking before crossing algorithm for speed scheduling. We then extend this algorithm by integrating altitude scheduling to propose the Altitude-Speed Scheduling of UAV for Minimizing Energy (ASSUME) algorithm, using a dynamic programming method. The ASSUME algorithm is theoretically proven to be optimal. Additionally, based on ASSUME, we propose an offline-inspired online heuristic algorithm to handle agnostic situations where GN information is not available unless flies close. Simulations indicate that the ASSUME algorithm saves an average of 26.1%–62.7% energy compared to the baseline methods, and the performance gap between the online algorithm and the offline optimal algorithm ASSUME is 22.8%.

Unmanned aerial vehicles (UAVs) play a critical role in disaster response, swiftly gathering information from various points-of-interest (PoIs) across extensive areas. The freshness of this information is measured by the age of information (AoI), representing the time since the latest information acquisition of a specific PoI. However, devising AoI-minimizing routes for UAVs in obstructed post-disaster environments poses unique challenges that have yet to be fully overcome. Obstacles, like post-disaster barriers, can impede direct flight paths between PoIs, and limited battery life requires energy-conscious route planning. Additionally, existing solutions fail to universally minimize varying data freshness requirements. This research addresses the AoI-driven UAV travel problem, seeking to establish periodic routes that optimize AoI metrics while considering energy and general graph constraints. We develop a learning-based algorithm to enhance the current route iteratively, utilizing guidance from a deep reinforcement learning (DRL) agent and executing a series of operations to potentially decrease AoI while adhering to topological and energy constraints. The algorithm is validated on real post-disaster datasets, demonstrating significant improvements in various AoI metrics compared to other learning-based approaches. Furthermore, our algorithm outperforms approximation algorithms and can approach the global optimum when tailored to existing AoI-minimizing problems.

Mobile crowdsourcing has emerged as a popular approach for organizations to leverage the collective intelligence of a crowd of users to obtain services. Considering users’ costs for providing services, it is vital for the requester to design incentive mechanisms to encourage users’ participation in crowdsourcing under the budget constraint. This aligns with the concept of budget-feasible mechanism design. Existing budget-feasible mechanisms often assume immediate user reachability and willingness of joining the crowdsourcing, which is unrealistic. To address this issue, a promising approach is to have participating users diffuse auction information to potential users in the social network. However, this brings another challenge in that participating users can be strategic and therefore hesitant to invite more potential competitors to join the crowdsourcing platform. In this paper, we focus on developing diffusion mechanisms that incentivize strategic users to actively diffuse auction information through the social network. This helps to attract more informed users and ultimately increases the value of the procured services. Specifically, we propose optimal budget-feasible diffusion mechanisms that simultaneously guarantee individual rationality, budget-feasibility, strong budget-balance, incentive-compatibility (i.e., users report real costs and diffuse auction information to all their neighbors) and approximation. Experiment results under real datasets further demonstrate the efficiency of proposed mechanisms.

Low-altitude airspace in major cities across the world is increasingly congested with unmanned aerial vehicles (UAVs) and other aircraft. Emerging technologies, innovative business models, and supportive government policies are driving the growth of the low-altitude economy, where UAVs play a crucial role. Given the limited on-board energy of UAVs, this paper investigates the Joint UAV Speed and Altitude Scheduling (JUSAS) problem for data collection from sensors deployed along power transmission lines, bridges, highways, railways, water/gas/oil pipelines, or rivers/coasts. Distinct from existing work, the paper focuses on jointly optimizing UAV speed and altitude scheduling while determining the wireless sensor collection order. It accounts for the altitude-specific sensor transmission range model and the complexities of overlapping range relationships. We first propose theSlowest Segment First(SSF) policy to obtain an optimal UAV speed scheduling for fixed-altitude scenarios. Building upon this, we then reformulate JUSAS as a shortest-path-type problem using our novel flight scheduling graph, solved efficiently through theSSF-based Ant Colony Optimization(SSF-ACO) algorithm. To handle practical scenarios without prior sensor information along the path, we develop SSF-ACO-Online for real-time scheduling. Extensive simulations demonstrate that SSF-ACO significantly outperforms four other algorithms (i.e., SSF-Only, SSF-GA, SSF-PSO, and SSF-SA) in energy efficiency, and reduces 13.11% energy consumption on average. SSF-ACO-Online achieves comparable performance with energy consumption 1.24% higher than offline counterpart in average.

Due to the diversity of architectures and peripherals of Internet of Things (IoT) systems, blackbox fuzzing stands out as a prime option for discovering vulnerabilities of IoT devices. Existing blackbox fuzzing tools often rely on companion apps to generate valid fuzzing packets. However, existing methods encounter the challenges of bypassing the cloud server side validation when it comes to fuzz devices that rely on cloud-based communication. Moreover, they tend to concentrate their efforts on Java components within Android companion apps, limiting their effectiveness in assessing non-Java components such as JavaScript-based mini-apps. In this paper, we introduce a novel blackbox fuzzing method, named RIoTFuzzer, designed to remotely uncover vulnerabilities of IoT devices with the assistance of companion apps, particularly those powered by All-in-one Apps with the JavaScript-based mini-apps feature enabled. Our approach utilizes document-based control command extraction, hybrid analysis for mutation point identification and side-channel-guided fuzzing to effectively address the challenges of fuzzing IoT devices remotely. We apply RIoTFuzzer to 27 IoT devices on prominent platforms and discovered 11 vulnerabilities. All of them have been acknowledged by the corresponding vendors. 8 have been confirmed by the vendors and have been assigned 4 CVE IDs. Our experiment results also demonstrate that side-channel-guided fuzzing can significantly enhance the efficiency of fuzzing packets sent to IoT devices, with an average increase of 76.62% and a maximum increase of 362.62%.

LoRa, as a Low-Power Wide Area Networks (LP-WAN) technology, is extensively employed for connecting Internet of Things (IoT) applications. LoRa time-slotted networks have gained popularity due to their high channel utilization and robust anti-interference capability. However, the queueing latency of end-devices (EDs) in these networks is often overlooked in the time-slot-scheduled LoRa network, leading to data obsolescence and insufficient notification time. Existing research mainly focuses on reducing transmission delay and avoiding collisions in LoRa networks, while neglecting the importance of ensuring low queueing latency for EDs. In this paper, we propose a semidefinite relaxation (SDR)-based channel scheduler called Q-MAC to achieve low queueing latency in time-slotted LoRa networks. The core idea is to allocate time slots and channels effectively for packets while avoiding collisions. To accomplish this, we formulate an optimization model to minimize latency and packet collisions. This model is a multivariable-coupled non-convex integer problem, we transform the model into a Quadratically Constrained Quadratic Programming (QCQP) problem. Subsequently, we employ the SDR and heuristic algorithms to obtain feasible solutions. Simulation results demonstrate that Q-MAC can significantly reduce queueing latency, achieving an average improvement of 8.57 × compared to existing approaches.

The Industrial Edge Computing (IEC) network has recently received considerable attention, where industrial devices offload their computation-intensive and delay-sensitive tasks to servers located at the network edge. Task offloading scheduling is a fundamental problem in IEC networks to achieve satisfactory quality of service. Many prior efforts have been devoted to scheduling task offloading for networks with complete information, while the complete information is hard or even infeasible to acquire by the scheduler. Therefore, their performance degrades in IEC networks with incomplete information. Scheduling task offloading for IEC networks with incomplete information is urgent and presents great technical challenges. This paper proposes a group-centric task offloading framework tailored for IEC networks with incomplete information, and models the minimum delay scheduling problem as a Partially Observable Markov Decision Process. Then, the SGOS algorithm integrating the Long Short-Term Memory with Soft Actor-Critic networks in reinforcement learning is proposed to devise online task offloading schedules for IEC networks with incomplete information. Extensive experimental results verify that the SGOS algorithm can achieve the best performance compared with base-line schemes in terms of major metrics, including convergence, delay, and workload balance.

LoRa, as a typical representative of Low Power Wide Area Networks (LPWAN), has been widely used to connect massive IoT devices. However, in mobile applications, there is massive packet loss in LoRa transmission due to link performance degradation, especially when LoRa end-devices move far from the gateway or into obstructed areas. Existing studies take little account of end-device movement, particularly when the movement pattern is unknown. We propose LMLoRa to enhance the Link Performance for Mobile LoRa networks in general scenarios. The key observation is that repeating the original packet content enhances link performance and allows smaller and more energy-efficient transmission parameter selections. Technically, LMLoRa proposes a link performance estimation model for mobile LoRa networks based on packet content repetition. Second, we exploit key hardware features of LoRa to obtain much continuous RSSI information for link quality prediction. Additionally, LMLoRa develops a channel frequency allocation policy to mitigate transmission collisions. Finally, LMLoRa designs a communication mechanism to assist the estimation model and work the whole system with low communication overhead. We design and implement LMLoRa in complex realworld environments, results show that LMLoRa enhances packet reception rate by 33.4% and energy efficiency by 14.4% on average compared with the state-of-the-art.

LoRa Localization, a fundamental service in LoRa networks, has garnered significant attention due to its long-range capabilities and low power consumption. However, existing approaches for LoRa localization are either incompatible with commercial devices or highly susceptible to environmental factors. To tackle this challenge, we propose SyncLoc, a TDoA-based LoRa localization framework that integrates a dedicated node for multi-dimensional time-drift correction. Our proposal is built on two key observations: firstly, the nanosecond-level measurement of time differences between gateways, and secondly, the substantial impact of SNR on gateway time drift. To accomplish our objective, we present three progressively enhanced versions of SyncLoc, each intended to comprehensively analyze the factors influencing LoRa time synchronization accuracy across different deployment scenarios involving nodes, carrier frequencies, and spreading factors. In addition to improving accuracy, we identify inefficiencies in LoRa’s multi-node concurrent localization, and introduce SyncLoc-4, a multi-node localization scheduling mechanism that optimizes efficiency with a 2-approximation ratio. Extensive experiments utilizing commercial LoRa devices in real-world demonstrates a 2.44× improvement in accuracy. Furthermore, simulations of large-scale networks exhibit a 2.47× boost in localization scalability (i.e., the number of concurrently located nodes) when employing SyncLoc instead of LoRaWAN.

Recent years have witnessed a surge of headphones (including in-ear headphones) usage in works and communications. Because of the privacy-preserve property, people feel comfortable having confidential communication wearing headphones and pay little attention to speech leakage. In this paper, we present an end-to-end eavesdropping system, mmEar, which shows the feasibility of launching an eavesdropping attack on headphones leveraging a commercial mmWave radar. Different from previous works that realize eavesdropping by sensing speech-induced vibrations with reasonable amplitude, mmEar focuses on capturing the extremely faint vibrations with a low signal-to-noise ratio (SNR) on the surface of headphones. Toward this end, we propose a faint vibration emphasis (FVE) method that models and amplifies the mmWave responses to speech-induced vibrations on the In-phase and Quadrature (IQ) plane, followed by a deep denoising network to further improve the SNR. To achieve practical eavesdropping on various headphones and setups, we propose a cGAN model with a pretrain-finetune scheme, boosting the generalization ability and robustness of the attack by generating high-quality synthesis data. We evaluate mmEar with extensive experiments on different headphones and earphones and find that most of them can be compromised by the proposed attack for speech recovery.

Recently, there has been an emerging radio frequency fingerprint identification (RFFI) technology that enhances fingerprint distinguishability by deliberately injecting I/Q imbalance into the device’s Wi-Fi baseband signal. Due to the additional injection of I/Q imbalance, this approach inevitably impacts the communication quality between devices, as it reduces the accuracy of channel estimation. To address this issue, we propose injecting the I/Q imbalance into a short training field (STF) instead of the entire baseband signal. Our findings indicate that this method can effectively preserve the quality of the original wireless communication. Building upon this, we introduce a fingerprinting scheme called CQP-RFFI that generates distinguishable fingerprints for a set of devices by injecting appropriate I/Q imbalance into the STF. Leveraging the short-term invariance of the channel, we design a practical I/Q imbalance extraction method based on the communication-quality preserving injection. Moreover, we design an optimal assignment method for I/Q imbalance to maximize the distinguishability of RF fingerprints for all devices. Finally, we implement the CQP-RFFI solution and conduct experiments in real-world scenarios. The experimental results demonstrate that CQP-RFFI achieves 96% precision, recall, and F1-score, and can consistently maintain good communication quality.

Metaverse has been viewed as the next generation of human-computer interaction, which requires collecting information from both the physical and virtual world. One potential way is to employ virtual service providers (VSPs) to finish collection tasks by designing posted-pricing mechanisms via the crowdsensing platform. As VSPs’ costs and values are usually unknown, learning the optimal posted-pricing policy under uncertainty is undoubtedly critical to utilize the budget efficiently. However, existing posted-pricing learning algorithms assume that agents provide services without blocking and agents’ attributes follow an independent identical distribution, both of which are unrealistic in Metaverse, e.g., VSPs should continuously sense the physical world to make provided services realistic, which makes the long working VSP unavailable/blocked for a certain period of time. In this paper, we address the budgeted pricing problem under uncertainty by considering blocking constraints and unknown non-identical VSPs’ attributes. The problem is modeled as a Budgeted-pricing Blocking Bandit (B2-bandit) problem, which remains unaddressed even for the oracle case with known VSPs’ information. We thus first propose a pricing policy for the oracle case with an instance-dependent approximation ratio to the global optimum. For the general B2-bandit problem with unknown information, we propose an online learning algorithm satisfying blocking constraints and incurring an accumulated regret up to$O(MK\log B)$as compared to the oracle approximation algorithm, where$M,K,B$are the number of VSPs, candidate prices and the budget, respectively. Experiments on real datasets validate that the proposed algorithm improves more than 172% accumulated value compared to baseline pricing algorithms.

Energy harvesting is a promising technique to address the energy hunger problem for thousands of wireless devices. In Radio Frequency (RF) energy harvesting systems, a wireless device first harvests energy and then transmits data with this energy, hence the ‘harvest-then-transmit’ (HTT) principle is widely adopted. We must carefully design the HTT schedule, i.e., schedule the timing between harvesting and transmission, and decide the data transmission power such that the throughput can be maximized with the limited harvested energy. Distinct from existing work, we assume energy harvested from RF sources is time-varying, which is more practical but more difficult to handle. We first discover a surprising result that the optimal transmission power is independent of the transmission time, but solely depends on the RF harvesting power, for a simple case when the energy harvesting is stable. We then obtain an optimal offline HTT-scheduling for the general case that allows the RF harvesting power to vary with time. To the best of our knowledge, it is the first optimal HTT-scheduling algorithm that achieves maximum data throughput for time-varying RF powered systems. Finally, an efficient online heuristic algorithm is designed based on the offline optimality properties. Simulations show that the proposed online algorithm has superior performance, which achieves more than 90% of the offline maximum throughput in most cases.

This work studies the Age-of-Information (AoI) optimization problem in the information-gathering wireless network systems, where time-sensitive data updates are collected from multiple information sources, and each source is equipped with a battery and harvests energy from ambient energy, such as solar, wind, etc. The arrival of the harvested energy can be modeled as the stochastic process, and an information source can deliver its data update only when 1) there is energy in the battery, and 2) this source is selected to transmit its data update based on the transmission policy. This work analyzes how the energy arrival pattern of each source and the transmission policy jointly influence the average AoI among multiple sources. To the best of our knowledge, this is the first work that formally develops the closed-form expression of average AoI in the Stationary Randomized Sampling (SRS) policy space and proposes approximation schemes with constant ratios in multi-source systems under a stochastic energy harvesting model. More specifically, under the perfect wireless channel, the closed-form expression of AoI under the SRS policy space with arbitrary finite battery size is developed. Based on the result, we propose the Max Energy-Aware Weight (MEAW) policy, which is proven to achieve 2-approximation in the full policy space. Under the uncertain wireless channel, we develop the closed-form expression of Whittle’s index to address the target problem. Based on the result, we propose the Energy-aware Whittle’s index policy (EWIP) and prove its approximate performance by using the Lyapunov optimization techniques. Experimental results show that MEAW under the perfect channel setting and EWIP under the uncertain channel setting both perform close to the theoretical lower bound and outperform the state-of-the-art schemes.

Camouflaged objects often blend in with their surroundings, making the perception of a camouflaged object a more complex procedure. However, most neural-network-based methods that simulate the visual information processing pathway of creatures only roughly define the general process, which deficiently reproduces the process of identifying camouflaged objects. How to make modeled neural networks perceive camouflaged objects as effectively as creatures is a significant topic that deserves further consideration. After meticulous analysis of biological visual information processing, we propose an end-to-end prudent and comprehensive neural network, termed IdeNet, to model the critical information processing. Specifically, IdeNet divides the entire perception process into five stages: information collection, information augmentation, information filtering, information localization, and information correction and object identification. In addition, we design tailored visual information processing mechanisms for each stage, including the information augmentation module (IAM), the information filtering module (IFM), the information localization module (ILM), and the information correction module (ICM), to model the critical visual information processing and establish the inextricable association of biological behavior and visual information processing. The extensive experiments show that IdeNet outperforms state-of-the-art methods in all benchmarks, demonstrating the effectiveness of the five-stage partitioning of visual information processing pathway and the tailored visual information processing mechanisms for camouflaged object detection. Our code is publicly available at: https://github.com/whyandbecause/IdeNet.

Mobile devices have been increasingly deployed in large-scale cyber-physical systems (CPS) to traverse the field and retrieve various data measurements from designated physical entities with stringent performance requirements. This work studies the Availability-constrained real-time Fresh Data Retrieval problem in CPS with a Speed Adjustable mobile device (AFDR-SA). The goal is to maintain the temporal validity of the real-time data with different priorities to be retrieved in the system while meeting the data availability constraints imposed by the communication range between the mobile device and the physical entities. The general case of the AFDR-SA problem is proved to be NP-hard. A dynamic programming (DP)-based optimal algorithm is proposed for a special scenario where the retrieval times of individual data items with the same priority are of the same length. For the general case where data items can have arbitrary retrieval times and different priorities, another different DP-based scheme is proposed, which is proved to be optimal given the retrieval order. A fast heuristic with low complexity is also proposed for the general problem to improve the computational efficiency. The experimental results show that the proposed schemes for the general case outperform the state-of-the-art methods and have close performance compared to the optimal solution while incurring much less computational overhead.

In many IoT applications, information needs to be gathered from multiple heterogeneous sources to the base station for real-time processing and follow-up actions. Undoubtedly, information freshness, measured by age of information (AoI), is critical in taking responsive actions. Recent studies have taken AoI into the consideration of transmission scheduling over wireless channels. However, existing studies on guaranteeing AoI either assume error-free wireless channels or priorly known link reliability, which is unrealistic. In this paper, we tackle the AoI-guaranteed transmission scheduling problem over an unreliable channel with the aim of throughput maximization, which is modelled as an AoI-Guaranteed Multi-Armed Bandit (AG-MAB) problem. Since the problem has not been studied in the literature even for the oracle case with given link reliability, we first propose an optimal stationary randomized sampling (SRS) policy for the oracle case. For the AG-MAB problem with unknown link reliability, we propose learning algorithms that meet the AoI requirements with probability 1 and incur sublinear regret compared to Oracle SRS, which can also detect the unsatisfiability of the AoI constraint and switch to the fallback policy promptly with guaranteed accuracy. Numerical results show that our algorithm outperforms the AoI-constraint-aware baselines on throughput with per-source AoI requirement guaranteed.

As an emerging Low-Power Wide Area Networks (LPWAN) technology, LoRa is dedicated to providing long-range connections for pervasive Internet-of-Things devices. As LoRa operates in the unlicensed spectrum with an ALOHA-based MAC-layer protocol stack, transmissions from multiple LoRa end-devices inevitably collide with each other, leading to packet losses and increased transmission delay. Targeting at collisions caused by interferences under thesamespreading factor (SF) settings, researchers introduce multiple lines of techniques. Despite their efforts, these techniques commonly neglect the potential collisions caused by interferences underdifferentSF settings, resulting in imperfect orthogonality. Given the disparate transmission power configurations and diverse deployed locations, the collisions under different SFs commonly exist in practical networks and significantly limit the LoRa reliability. This paper presents X-MAC, the first scheduler aware of imperfect orthogonality. Technically, X-MAC detects the collisions under different SFs via tracking historical transmissions, and performs dynamic channel scheduling to avoid collisions caused by interferences under the same and different SFs. Extensive evaluations on testbed devices show that, compared with the state-of-the-art methods, X-MAC boosts the network scalability (number of concurrent end-devices) by 1.26× to 2.41× with packet reception rate requirement of > 95%.

Online rumor detection is crucial for a healthier online environment. Traditional methods mainly rely on content understanding. However, these contents can be easily adjusted to avoid such supervision and are insufficient to improve the detection result. Compared with the content, information propagation patterns are more informative to support further performance promotion. Unfortunately, learning the propagation patterns is difficult, since the retweeting tree is more topologically complicated than linear sequences or binary trees. In light of this, we propose a novel rumor detection framework based on structure-aware retweeting graph neural networks. To capture the propagation patterns, we first design a novel conversion method to transform the complex retweeting tree as more tractable binary tree without losing the reconstruction information. Then, we serialize the retweeting tree as a corpus of meta-tree paths, where each meta-tree can preserve a basic substructure. A deep neural network is then designed to integrate all meta-trees and to generate the global structural embeddings. Furthermore, we propose to integrate content, users, and propagation patterns to enhance more reliable performance. To this end, we propose a novel self-attention-based retweeting neural network to learn individual features from both content and users. We then fuse the node-level features with our global structural embeddings via a mutual attention unit. In this way, we can generate more comprehensive representations for rumor detection. Extensive evaluations on two real-world datasets show remarkable superiorities of our model compared with existing methods.

Radio frequency fingerprint identification (RFFI) is a promising technique for smartphone identification. However, we find that the temperature of the RF front end in smartphones can significantly impact the RF features, including the carrier frequency offset (CFO) and statistical RF features. The unstable RF features caused by temperature changes can negatively affect the performance of state-of-the-art RFFI approaches. To this end, we propose the RF-TESI solution for smartphone identification under temperature variation. First, we construct a dataset by extracting temperature and RF features. In the dataset, the extracted temperature values constitute a set of temperature values and each registered temperature value corresponds to a group of RF features. Next, we evaluate the distinctiveness of RF features across smartphones to select the most suitable RF fingerprint. Then, we train multiple random forest models, each tagged with a registered temperature. In addition, because there are still many temperatures out of the temperature set, we design an RF fingerprint estimation method to estimate RF fingerprints at unregistered temperatures. Finally, the experiments show RF-TESI demonstrates satisfactory performance under different scenarios, taking into account variations in temperature, time and position. Besides, our proposed approach is better than all state-of-the-art approaches in smartphone identification.

Federated learning (FL) has been proposed as a privacy-preserving distributed learning paradigm, which differs from traditional distributed learning in two main aspects: the systems heterogeneity, meaning that clients participating in training have significant differences in systems performance including CPU frequency, dataset size, and transmission power, and the statistical heterogeneity, indicating that the data distribution among clients exhibits Non-Independent Identical Distribution. Therefore, the random selection of clients will significantly reduce the training efficiency of FL. In this article, we propose a client selection mechanism considering both systems and statistical heterogeneity, which aims to improve the time-to-accuracy performance by trading off the impact of systems performance differences and data distribution differences among the clients on training efficiency. First, client selection is formulated as a combinatorial optimization problem that jointly optimizes systems and statistical performance. Then, we generalize it to a submodular maximization problem with knapsack constraint, and propose the Iterative Greedy with Partial Enumeration (IGPE) algorithm to greedily select the suitable clients. Then, the approximation ratio of IGPE is analyzed theoretically. Extensive experiments verify that the time-to-accuracy performance of the IGPE algorithm outperforms other compared algorithms in a variety of heterogeneous environments.

Traditional Chinese Medicine (TCM) is the crystallization of Chinese medical heritage for thousands of years and plays a huge role in human health. How to integrate TCM products with modern information intelligent production means to efficiently obtain standardized Chinese medicine services is a very worthy research direction. In this paper, the decocting service of TCM is taken as the research object, and combined with artificial intelligence, blockchain and other information technologies, an intelligent service system for trusted collaboration in the whole process of decocting TCM is innovatively proposed and designed. Under the intelligent collaboration of a variety of decocting devices, key links, such as automated prescription business management, intelligent dispensing and decocting, and blockchain-based quality monitoring, were studied, and collaborative decision-making optimization and information trusted traceability of the whole process of decocting Chinese medicine were realized. The service system was applied in a Chinese medicine piece processing company, realizing the ability to handle 880,000 prescriptions annually, 144 decocting stations with intelligent collaboration, and 35 indicators with full process traceability. The practice shows that the system can realize the trusted intelligent cooperation of the whole process of Chinese herbal decoction.

The onion service is the most important mechanism of the Tor network which enables service providers to publish anonymously various TCP services, such as web services. To access the target onion services, clients first know the 56-byte onion addresses. However, randomly generated onion addresses are difficult to memorize and can be easily used by attackers to generate phishing sites with similar onion addresses. In this paper, we propose a correlated onion address generation approach which is capable of generating a unique onion address via a customized string and a root onion address. This approach enables the generated onion addresses to be computed by clients using a human-memorable string, resulting in easier access to onion services. Based on this approach, we design and implement a Tor Domain Name System (TorDNS) that allows different service providers to register anonymously and clients to access anonymous services quickly through human-memorable pseudo-onion addresses. TorDNS is compatible with existing onion service mechanism and does not introduce additional privacy and security issues. In addition, similarity detection of pseudo-onion addresses can effectively reduce the risk of phishing sites on the Tor network.

Deep Neural Network (DNN) training on a large scale is extremely time-consuming and computationally intensive, which is accelerated by distributed training. In recent years, pipeline parallelism has been developed, which enables partitioning the model across several devices, e.g. GPU, and training efficiency is improved by dividing data batches into micro-batches, with each of them processed by a different stage of the model. Currently, parallel training assumes pipeline placement and partitioning are static, with parameters updating each iteration, without accounting for freezing. This results in computational resources not being fully utilized. In this paper, we propose FreezePipe, a novel method for optimizing deep learning training that combines the freezing mechanism with pipeline parallel training. In FreezePipe, a lightweight method for determining the freezing strategy based on gradient changes is employed. Considering that resources need to be released based on the frozen layer, a lightweight model partitioning algorithm was designed to determine the optimal strategy for pipeline partitioning. Experimental results show that FreezePipe can reduce the training time by 64.5% compared to Torchgpipe on CIFAR-10 dataset without compromising any model performance.

During the COVID-19 period, Lecture Recording System is of great significance to the remote and flexible education, and the key issue of the lecture recording automation is the detection of teacher and students. To achieve fast and accurate detection, We applied YOLO algorithm to the detection and adopted Eagleeye pruning method to reduce the amount of parameters and calculation of the YOLO model. We also designed and developed the Intelligent Lecture Recording System based on hisi Hi3531DV200. Experiments on the data set collected from Internet shows that the pruned model can achieve 94.8% precision and 31.25FPS speed on Hi3531DV200, which make the Intelligent Lecture Recording System fast, robust and accurate.

Node classification is a substantial problem in graph-based fraud detection. Many existing works adopt Graph Neural Networks (GNNs) to enhance fraud detectors. While promising, currently most GNN-based fraud detectors fail to generalize to the low homophily setting. Besides, label utilization has been proved to be significant factor for node classification problem. But we find they are less effective in fraud detection tasks due to the low homophily in graphs. In this work, we propose GAGA, a novel Group AGgregation enhanced TrAnsformer, to tackle the above challenges. Specifically, the group aggregation provides a portable method to cope with the low homophily issue. Such an aggregation explicitly integrates the label information to generate distinguishable neighborhood information. Along with group aggregation, an attempt towards end-to-end trainable group encoding is proposed which augments the original feature space with the class labels. Meanwhile, we devise two additional learnable encodings to recognize the structural and relational context. Then, we combine the group aggregation and the learnable encodings into a Transformer encoder to capture the semantic information. Experimental results clearly show that GAGA outperforms other competitive graph-based fraud detectors by up to 24.39% on two trending public datasets and a real-world industrial dataset from Baidu. Even more, the group aggregation is demonstrated to outperform other label utilization methods (e.g., C&S, BoT/UniMP) in the low homophily setting.

Summary With their high flexibility and versatility, unmanned aerial vehicles (UAVs) have maneuvered their way into many applications. Thanks to their ability to plan and coordinate, multiple UAVs complete tasks more effectively, which boosts their popularity in battlefield surveys, formation performances, and targeted searches. However, the risk of security threats also rises alongside their popularity. The UAV ad hoc network (UANET) has endeavored to contend with such risks through the optimized link state routing (OLSR) protocol. To test the security and strength of this effort, we present a sky black hole attack (SBHA) algorithm for OLSR, which is undetectable, based on the UANET's multi‐hop routing and the OLSR's known topology. This algorithm obtains the network's maximum profits by approaching and then replacing the calculated topology center and traffic center in UANET. Because of the ever‐changing topology, SBHA aims at UANET's single central node that cannot be detected in advance. This attack is difficult to detect by UANET and therefore difficult to defend. The simulation results show that SBHA can cause greater damage to UANET compared to a traditional black hole attack, and ordinary defense algorithms cannot reduce the negative impact of SBHA on UANET. In addition, SBHA also gains UANET control, and leads to drastic changes in UAVs' movement trajectory, which has more intuitive effects.

Wireless sensing has emerged as a powerful environmental sensing technology that is vulnerable to the impact of all kinds of ambient noises. LoRa is a novel interference-resilient technology of low-power wide-area networks (LPWAN), which has attracted wide attention from scientific and industrial communities. However, LoRa transmission suffers from serious latency in those complex wireless sensing environments requiring transmission reliability. In this article, we present CH-MAC, the first MAC-layer protocol based on the local corruption nature of packets and the time-varying nature of channels to reduce end-to-end transmission latency in LPWAN with reliable communication requirements. Specifically, CH-MAC employs Luby Transform code to divide and encode the payload into several blocks such that the receiver can retain part of the coded information in the corrupted packets. In addition, CH-MAC utilizes hopping to transmit different blocks of a packet with various channels to avoid sudden noise collision. Moreover, CH-MAC adopts a dynamic packet length adjustment mechanism to mitigate network congestion. Extensive evaluations on a real-world hardware testbed and a simulation platform show that CH-MAC can reduce end-to-end transmission latency by 2.63× with a communication success rate requirement of >95% compared with state-of-the-art methods.

Accurately predicting Origin-Destination (OD) passenger flow can help metro service quality and efficiency. Existing works have focused on predicting incoming and outgoing flows for individual stations, while little attention was paid to OD prediction in metro systems. The challenges are that OD flows 1) have high temporal dynamics and complex spatial correlations, 2) are affected by external factors, and 3) have sparse and incomplete data slices. In this paper, we propose an Adaptive Feature Fusion Network (AFFN) to a) adaptively fuse spatial dependencies from multiple knowledge-based graphs and even hidden correlations between stations and b) accurately capture the periodic patterns of passenger flows based on the auto-learned impact from external factors. To deal with the incompleteness and sparsity of OD matrices, we extend AFFN to multi-task AFFN to predict the inflow and outflow of each station as a side-task to further improve OD prediction accuracy. We conducted extensive experiments on two real-world metro trip datasets collected in Nanjing and Xi’an, China. Evaluation results show that our AFFN and multi-task AFFN outperform the state-of-the-art baseline techniques and AFFN variants in various accuracy metrics, demonstrating the effectiveness of AFFN and each of its key components in OD prediction.

In energy harvesting communication systems, it is possible for a transmitter to schedule the transmission by jointly scaling the rate and turning the transmitter ON/OFF adaptively. Such a joint rate and sleep schedule can greatly increase the throughput achieved by the transmitter with battery constraints. However, most existing works on joint rate and sleep scheduling assume the transition between different states does not have any cost, i.e., energy or time consumption. This is not realistic while the energy and time needed for booting a transmitter, i.e., turning a transmitter from OFF to ON, are not small enough to be ignored in most cases. In this paper, we investigate the joint rate and sleep scheduling on system throughput with more general booting consumption considered in energy harvesting communication systems. We first identify the structural properties of the optimal solution for the model with booting consumption considered. Inspired by these observations, we develop an optimal offline algorithm and an online heuristic algorithm to solve the problem. Experimental results from simulations and real tests show that the proposed algorithms can achieve much higher throughput on average in a realistic energy harvesting communication system, compared to those algorithms that only consider rate scheduling or ignore the booting consumption.

In a crowdsensing platform, users are invited to provide data services, and multiple requesters compete for desired services. Due to users' costs of providing services, it is critical to design incentive mechanisms to incentivize users with (monetary) rewards. Meanwhile, requesters may have individual budgets and compete for services with different procurement abilities. Such a setting falls into the budget-feasible mechanism design. However, most of the existing budget-feasible mechanisms focus on one-sided markets with a single requester rather than the two-sided markets with multiple requesters having different procurement abilities. Moreover, requesters and users can be selfish and strategic with their private information, which requires preventing information manipulation on both requesters' and users' sides. In this paper, we investigate budget-feasible mechanisms in two-sided crowdsensing markets where multiple strategic requesters come with private budgets to obtain services from the strategic users. We also consider the fairness on the requesters' side,i.e., a requester with more budget should obtain more service. We propose budget-feasible mechanisms for two models by distinguishing the types of services,i.e., the homogeneous or heterogeneous services. All proposed mechanisms satisfy fairness, budget feasibility, truthfulness on both users' and requesters' sides, and the constant approximation ratio. Numerical experiment results further demonstrate the efficiency of our proposed mechanisms.

Unmanned aerial vehicles (UAVs) are being widely exploited for various applications,e.g., traversing to collect data from ground sensors, patrolling to monitor key facilities, moving to aid mobile edge computing. We summarize these UAV applications and formulate a problem, namely thegeneral waypoint-based PoI-visiting problem. Since energy is critical due to the limited onboard storage capacity, we aim at minimizing flight energy consumption. In our problem, we pay special attention to the energy consumption for turning and switching operations on flight planning, which are usually ignored in the literature but play an important role in practical UAV flights according to our real-world measurement experiments. We propose specially designed graph parts to model the turning and switching cost and thus transfer the problem into a classic graph problem,i.e., general traveling salesman problem, which can be efficiently solved. Theoretical analysis shows that such problem transformation has the graph redefinition approximation ratio upper bound,$max\lbrace \Theta /\delta ,2\rbrace$, where$\Theta$is related to the designed graph parts and$\delta$is a constant. Finally, we evaluate our proposed algorithm by simulations. The results show that it costs less than 107% of the optimal minimum energy consumption for small scale problems and costs only 50% as much energy as a naive algorithm for large scale problems.

Remote Direct Memory Access (RDMA) suffers from unfairness issues and performance degradation when multiple applications share RDMA network resources. Hence, an efficient resource scheduling mechanism is urged to optimally allocates RDMA resources among applications. However, traditional Network Utility Maximization (NUM) based solutions are inadequate for RDMA due to three challenges: 1) The standard NUM-oriented algorithm cannot deal with coupling variables introduced by multiple dependent RDMA operations; 2) The stringent constraint of RDMA on-board resources complicates the standard NUM by bringing extra optimization dimensions; 3) Naively applying traditional algorithms for NUM suffers from scalability issues in solving a large-scale RDMA resource scheduling problem. In this paper, we present how to optimally share the RDMA resources in large-scale data center networks with a distributed manner. First, we propose Distributed RDMA NUM (DRUM) to model the RDMA resource scheduling problem as a new variation of the NUM problem. Second, we present distributed algorithms to efficiently solve the large-scale, interdependent RDMA resource sharing problem for different RDMA use cases. Through theoretical analysis, the convergence and parallelism of proposed algorithms are guaranteed. Finally, we implement the algorithms as a kernel-level indirection module in the real-world RDMA environment, so as to provide end-to-end resource sharing and performance guarantee. Through extensive evaluations by large-scale simulations and testbed experiments, we show that our method significantly improves applications’ performance under resource contention, achieving$1.7-3.1\times $higher throughput, and in a dynamic context, the largest performance improvement reaches 98.1% and 64.1% in terms of latency and throughput, respectively.

Virtualized network has become the cornerstone of today's large-scale cloud data centers. In particular, the data plane of virtualized network, consisting of virtual switch, virtual router and other software network functionalities, performs all network packets processing of virtual machines (VMs). However, current virtualized data plane solutions incur drastic performance interference with co-resident VMs, and thus suffer from unpredictable network performance, especially in terms of tail latency. In this work, we show that the performance issue stems from the fact that CPU plays a dual role of both communication and computation in virtualized networks. A number of virtual network components and their complex packets processing create an undue burden on the hosts' CPUs and in turn cause the mutual performance interference among VMs and networks. To address this issue, we present a multipath data plane solution, where the traffic of VMs can be adaptively and seamlessly offloaded to the adjacent hosts. At the core of this design is to optimize the VM traffic allocation among multiple paths. We formulate the VM multipath traffic allocation problem with coupled variables of computing and network resources, which were only considered as mutually independent in prior researches. Then we present a distributed algorithm to efficiently solve the large-scale, interdependent global optimization problem, with convergence and optimality guarantees. Through extensive simulations and real-world testbed experiments, we show that our solution delivers consistent performance improvement (up to$6.7\times$improvement in aggregate throughput and$21.4\times$reduction in tail latency, respectively) in the dynamic cloud system.

Federated learning (FL) has been proposed to train a global model by distributed architecture, while keeping the training data local. Owing to the large scale of clients in FL, all clients to participate in training is not feasible. The heterogeneity of clients, including system and data heterogeneity, also poses huge challenge to the client selection problem. Traditional client selection mechanisms can’t handle these heterogeneities effectively, which lead to poor training efficiency. Hence, this paper comprehensively considers system and data heterogeneity to select clients and dynamically adjust the number of selected clients. For system heterogeneity, we build the latency model to predict the training time for selecting clients with best performance including CPU frequency, the size of dataset and transmission power. Besides, for data heterogeneity, the cluster model is established to cluster clients for alleviating the accuracy jitter owing to the non independent and identically distributed (Non-IID) dataset. We formulate the client selection problem aiming to minimize the overall training time on the premise of accuracy, and design the Federated Client Cluster and latency-Prediction Selection (FCCPS) algorithm to solve this problem. With extensive simulations, we show that the FCCPS algorithm can reduce the training time by up to 21% on Cifar-10 dataset and 13% on FashionMNIST dataset, as compared to FedAvg.

As an emerging Low-Power Wide Area Networks (LPWAN) technology, LoRa is dedicated to providing long-range connections for pervasive Internet-of-Things devices. As LoRa operates in the unlicensed spectrum, transmissions from multiple LoRa end-devices inevitably collide into each other, leading to packet losses and increased transmission delay. Targeting at collisions caused by interferences under the same spreading factor (SF) settings, researchers introduce multiple lines of techniques. Despite their efforts, these techniques commonly neglect the potential collisions caused by interferences under different SF settings, which are resulted by the imperfect orthogonality. Given the disparate transmission power configurations and diverse deployed locations, the collisions under different SFs commonly exist in practical networks, and significantly limit the LoRa reliability. In this paper, we present X-MAC, the first scheduler that is aware of imperfect orthogonality. Technically, X-MAC detects the collisions under different SFs via tracking historical transmissions, and further performs dynamic channel scheduling to avoid collisions caused by interferences both under the same and different SFs. Extensive evaluations on testbed devices show that, compared with the state-of-the-art methods, X-MAC boosts the network scalability (number of concurrent end-devices) by 2.41× with packet reception rate (PRR) requirement of > 95%.

MPTCP provides the basic multipath support for network applications to deliver high throughput and robust communication. However, the original MPTCP is designed with limited extensibility. Various research works have tried to extend MPTCP to attain better performance or richer functionalities. These existing approaches either modify the kernel implementation of MPTCP, which involve considerable engineering efforts and may accidentally introduce security issues, or control MPTCP via user-space tools, which suffer from restricted functionality support. To address this issue, we propose eMPTCP, an easy-to-use framework to fully extend MPTCP without security risks. Internally, eMPTCP has a modular and pluggable model which allows operators to specify a comprehensive MPTCP extension as a chain of sub-policies. eMPTCP further enforces the policies through packet header manipulations. To ensure safety, eMPTCP is implemented using eBPF. Despite the stringent constraints of eBPF, we show that it is possible to implement an elaborated framework for a fully extensible MPTCP. Through verifying MPTCP in a number of real-world cases and extensive experiments, we show that eMPTCP is able to support a wide range of MPTCP extensions, while the overhead of eMPTCP operations in the kernel is in the scale of nanosecond, and the extra processing time accounts for only about 0.63% of flows' transmission time.

Low-Power Wide Area Networks (LPWANs) are widely used to connect large-scale Internet of Things (IoT) applications. Long Range (LoRa) is a promising LPWAN technology sensitive to energy consumption, since LoRa nodes are generally battery-powered, and the battery life will influence the lifetime of the LoRa network. In practice, the battery life of LoRa nodes is short in many scenarios, due to the long transmission distance form the gateway leading to high energy consumption. Existing techniques for energy-efficient data transmission mainly focus on static gateways, and will consume huge energy of remote nodes. In this paper, we propose to integrate LoRa with mobility to minimize the energy consumption of nodes by effectively shortening the transmission distances, and design the first mobile LoRa data transmission system called LoRaDrone by leveraging the unmanned aerial vehicle (UAV) gateway flying close to nodes. Specifically, we present a low-power communication mechanism and a dynamic channel allocation policy to minimize the energy consumed in sensing and communicating with the UAV gateway, while considering the distinctive LoRa parallel reception and complex transmission collisions. Then, an optimal speed scheduling strategy is designed to ensure the reliability of data transmission, and minimize the energy consumption of the UAV. Evaluations on various scales verify the effectiveness of LoRaDrone under different nodes' distributions and UAV paths. Compared with the baselines, the energy consumption of nodes using LoRaDrone is at most reduced by$\mathbf{70.37}\times$at 5000 nodes.

In recent years, radio frequency (RF) fingerprinting has attracted more and more attention. Many different types of RF fingerprints have been proposed, such as carrier frequency offset (CFO), sampling frequency offset and error vector magnitude. Among them, the CFO fingerprint is recognized as a promising RF fingerprint. However, for commonly used smartphones, we find that its CFO fingerprint is unstable, because the temperature of crystal oscillator varies greatly and large fluctuations of temperature significantly affect its CFO fingerprint. Therefore, the solutions of CFO-based fingerprinting will no longer be effective for smartphones if the temperature of crystal oscillator is not involved. To this end, we propose a more reliable and applicable CFO-based fingerprinting approach called temperature-aware radio frequency fingerprinting (TeRFF). First, we construct a dataset by extracting crystal oscillator's temperature and the corresponding CFO value on multiple smartphones over a period. In the dataset, the extracted temperature values constitute a set of temperature values, and each registered temperature value corresponds to a group of CFO samples. On this basis, we train multiple Naive Bayes models, each tagged with a registered temperature value. Moreover, since there are many temperature values which are not in the temperature set, we design a CFO estimation method to estimate the CFO fingerprint at the unregistered temperature. Finally, the experimental results demonstrate that our proposed solution TeRFF makes the CFO fingerprinting still effective for smartphone identification, and its performance is better than other existing RF fingerprinting schemes.

Industrial Internet of Things (IIoT) has attracted increasing attention for improving the efficiency of manufacturing. Plenty of computation-intensive and latency-sensitive applications are required by IIoT networks, which pose significant challenges for the computation capacities of IIoT networks. To address these challenges, Edge-enabled Industrial Internet of Things (E-IIoT) emerges. Edge devices located at the edge of IIoT networks enlarge computation capacities of IIoT networks and improve their efficiency accordingly. How to schedule computation resources wisely is a major problem in E-IIoT networks. Since IIoT devices in an E-IIoT network monitor the industrial site collaboratively, tasks for processing sensory data collected by them are correlated accordingly. That means, scheduling highly correlated tasks to be processed at the same device can improve computation efficiency. Inspired by this fact, we propose a correlation aware scheduling (CAS) algorithm for E-IIoT networks in this article. In specific, computation model decision and processing order decision are made by considering computation resources of devices and correlations among tasks in the algorithm to minimize latency of E-IIoT networks. The NP-hardness of correlation aware latency minimization scheduling problem in E-IIoT networks is first proved. Theoretical analysis on approximation ratio of the CAS algorithm is provided, and simulation results demonstrate the effectiveness of the proposed algorithm in reducing latency.

Nowadays, not only wearable and portable devices but also aerial and ground robots can be made smaller, lighter, cheaper, and thus as large as hundreds of them may form a swarm to participate in a complicated cooperative application, such as searching, rescuing, mapping, and war-battling. Devices and robots in such a swarm have three important features, namely, large number, high mobility and short distance, hence they form a dynamic and dense wireless network. Successful swarm cooperative applications require low latency communications and real-time localization. This paper proposes to use ultra-wideband (UWB) radio technology to implement both functionalities, because UWB is very time-sensitive that an accurate distance can be calculated using the transmission and reception timestamps of data messages. A UWB swarm ranging protocol is designed to achieve simultaneously wireless data communication and swarm ranging that allows a device/robot to compute the distances to all the peer neighbors at the same time. This protocol is designed for dynamic and dense networks, meanwhile it can also be used in various wireless networks and implemented on various types of devices/robots including low-end ones. In our experiment, this protocol is implemented on Crazyflies, STM32 microcontroller powered micro drones, with onboard UWB wireless transceiver chips DW1000. Extensive real-world experiments are conducted to verify the proposed protocol on various performance aspects, with a total of 9 Crazyflie drones in a compact area. The implemented swarm ranging protocol is open-sourced athttps://github.com/SEU-NetSI/crazyflie-firmware

Because of the low-latency and high-throughput benefits of RDMA, an increasing number of collaborative applications in datacenters are re-designed with RDMA to boost the performance. Among various low-level hardware primitives provided by RDMA, exposed as parameters of APIs, the application designers select and hardcode them to exploit all the performance benefits of RDMA. However, with the dynamic nature of datacenter application, the hardcoded and fixed parameter selection fails to take full advantages of RDMA capabilities, which can cause up to 35% throughput performance loss. To address this issue, we present a tunable RDMA parameter selection framework, which allows parameter tuning at runtime, adaptive to the dynamic application and server status. To attain the native RDMA performance, we use a lightweight decision tree to reduce the overhead of RDMA parameter selection. Finally, we implement the tunable RDMA parameter selection framework with native RDMA API to provide a more abstract API. To demonstrate the effectiveness of our method, we implement a key-value service based on the abstract API. Experiment results show that our implementation has only a very small overhead compared with the native RDMA, while the optimized key-value service achieves 112% more throughput than Pilaf and 66% more throughput than FaRM.

Unmanned aerial vehicles (UAVs) are being widely exploited for various applications, e.g., traverse to collect data from ground sensors, patrol to monitor key facilities, move to aid mobile edge computing. We summarize these UAV applications and formulate an abstract problem, namely the general waypoint-based PoI-visiting problem, aiming at minimizing flight energy consumption, which is critical due to its limited onboard storage capacity. In our problem, we pay special attention to the energy consumption for turning and switching operations on flight planning, which is usually ignored in the literature but plays an important role in practical UAV flights. We propose a novel method that uses specially designed graph parts to model the turning and switching cost and thus transfer the problem into a classic graph problem, i.e., traveling salesman problem, which can be efficiently solved. Finally, we evaluate our proposed algorithm by simulations. The results show it costs less than 107% of the optimal minimum energy consumption for small scale problem and costs only half as much energy as a naive algorithm for large scale problem.

Java reflection technique is pervasively used in the Android system. To reduce the risk of reflection abuse, Android restricts the use of reflection at the Android Runtime (ART) to hide potentially dangerous methods/fields. We perform the first comprehensive study of the reflection restrictions and have discovered three novel approaches to bypass the reflection restrictions. Novel reflection-based attacks are also presented, including the password stealing attack. To mitigate the threats, we analyze these restriction bypassing approaches and find three techniques crucial to these approaches, i.e., double reflection, memory manipulation, and inline hook. We propose a defense mechanism that consists of classloader double checker, ART variable protector, and ART method protector, to prohibit the reflection restriction bypassing. Finally, we design and implement an automatic reflection detection framework and have discovered 5,531 reflection powered apps out of 100,000 downloaded apps, which are installed on our defense enabled Android system of a Google Pixel 2 to evaluate the effectiveness and efficiency of our defense mechanism. Extensive empirical experiment results demonstrate that our defense enabled system can accurately obstruct the malicious reflection attempts.

Nowadays, aerial and ground robots, wearable and portable devices are becoming smaller, lighter, cheaper, and thus popular. It is now possible to utilize tens and thousands of them to form a swarm to complete complicated cooperative tasks, such as searching, rescuing, mapping, and battling. A swarm usually contains a large number of robots or devices, which are in short distance to each other and may move dynamically. So this paper studies the dynamic and dense swarms. The ultra-wideband (UWB) technology is proposed to serve as the fundamental technique for both networking and localization, because UWB is so time sensitive that an accurate distance can be calculated using timestamps of the transmit and receive data packets. A UWB swarm ranging protocol is designed in this paper, with key features: simple yet efficient, adaptive and robust, scalable and supportive. This swarm ranging protocol is introduced part by part to uncover its support for each of these features. It is implemented on Crazyflie 2.1 drones, STM32 microcontrollers powered aerial robots, with onboard UWB wireless transceiver chips DW1000. Extensive real world experiments are conducted to verify the proposed protocol with a total of 9 Crazyflie drones in a compact area.

Despite the salient feature of cloud computing, the cloud provider still suffers from electricity bill, which in part comes from 1) the power consumption of running physical machines (PMs) to guarantee the resource/time requirements of virtual machines (VMs), and 2) the dynamically varying electricity price offered by smart grids. In the literature, there exist viable solutions adaptive to electricity price variation to reduce the electricity bill. However, they are not applicable to serving time-sensitive VM requests. In serving time-sensitive VM requests, it is potential for the cloud provider to apply proper consolidation strategies to further reduce the electricity bill. Few prior works have provided theoretical solutions of VM consolidation strategies that are adaptive to electricity price variations in serving time-sensitive VM requests. In this work, to address this challenge, we develop electricity-price-aware consolidation algorithms for both the offline and online scenarios. For the offline scenario, we first develop a consolidation algorithm with constant approximation, which always approaches the optimal solution within a constant factor of 5. For the online scenario, we propose an$O(\log (\frac{L_{max}}{L_{min}}))$-competitive algorithm that is able to approach the optimal offline solution within a logarithmic factor, where$\frac{L_{max}}{L_{min}}$is the ratio of the longest length of the processing time requirement of VMs to the shortest one. Our trace-driven simulation results further demonstrate that the average performance of the proposed algorithms produce near-optimal electricity bill.

Unmanned aerial vehicles (UAVs) are being widely used in wireless communication, e.g., collecting data from ground nodes (GNs), where energy is critical. Existing works combine speed scheduling, i.e., the controlling of speed, with trajectory design for UAVs, making it complicated to solve while loses focus on the fundamental nature of speed scheduling. We focus on speed scheduling by considering straight line flights, with applications in monitoring power transmission lines, roads, water/oil/gas pipes and rivers/coasts. By real-world flight tests, we disclose a speed-related flight energy consumption model, distinct from typical distance-related or duration-related models. Based on such a practical energy model, we develop the looking before crossing (virtual rooms) algorithm, where virtual rooms on the time-distance diagram represent the spatio-temporal constraint of GNs in wireless transmission. This algorithm is proved to be optimal in solving the offline problem, where all information is known before scheduling. For the online problem, i.e., GN information is not unavailable unless flies close, we propose an offline-inspired online heuristic. Simulation shows its performance is near the offline optimal. Our study on the practical flight energy model and speed scheduling sheds light on a new research direction on UAV-aided wireless communication.

Remote Direct Memory Access (RDMA) suffers from unfairness issues and performance degradation when multiple applications share RDMA network resources. Hence, an efficient resource scheduling mechanism is urged to optimally allocates RDMA resources among applications. However, traditional Network Utility Maximization (NUM) based solutions are inadequate for RDMA due to three challenges: 1) The standard NUM-oriented algorithm cannot deal with coupling variables introduced by multiple dependent RDMA operations; 2) The stringent constraint of RDMA on-board resources complicates the standard NUM by bringing extra optimization dimensions; 3) Naively applying traditional algorithms for NUM suffers from scalability and convergence issues in solving a large-scale RDMA resource scheduling problem.

Low Power Wide Area Networks (LPWAN) are an emerging well-adopted platform to connect the Internet-of-Things. With the growing demands for LPWAN in IoT, the number of supported end-devices cannot meet the IoT deployment requirements. The core problem is the transmission collisions when large-scale end-devices transmit concurrently. The previous research mainly includes transmission scheduling strategies, collision detection and avoidance mechanism. The use of these existing approaches to address the above limitations in LPWAN may introduce excessive communication overhead, end-devices cost, power consumption, or hardware complexity. In this paper, we present S-MAC, an adaptive MAC-layer scheduler for LPWAN. The key innovation of S-MAC is to take advantage of the periodic transmission characteristics of LPWAN applications and also the collision behaviour features of LoRa PHY-layer to enhance the scalability. Technically, S-MAC is capable of adaptively perceiving clock drift of end-devices, adaptively identifying the join and exit of end-devices, and adaptively performing the scheduling strategy dynamically. Meanwhile, it is compatible with native LoRaWAN, and adaptable to existing Class A, B and C devices. Extensive implementations and evaluations on commodity devices show that S-MAC increases the number of connected end-devices by 4.06× and improves network throughput by 4.01× with PRR requirement of > 95%.

Unmanned aerial vehicle (UAV) assisted data collection is a promising technology, where a base station (BS) is mounted on a UAV to collect data from ground sensors (GSs). However, it is very challenging to save the energy of UAV while completing the tasks of data collection. In this work, a novel energy consumption model of UAV is adopted, where the UAV flies at a proper speed is the most energy efficient, i.e., the UAV will cost more energy when it flies faster or slower. According to this model, we investigate the Energy-efficient Trajectory Planning and Speed Scheduling (ETPSS) problem, aiming at minimizing the total energy consumption of UAV by determining flight trajectory and speed of UAV while completing the task of data collection for each GS. To solve this problem, we decompose it into two sub-problems, i.e., trajectory design and speed scheduling, and propose a three-step scheme named Energy-efficient Trajectory and Speed optimization (ETSO). Moreover, the second step of ETSO optimally solves the speed scheduling sub-problem. Finally, we conduct simulation experiments, and the results demonstrate that the ETSO performs well on energy efficiency.

Cybercrime is significantly growing as the development of internet technology. To mitigate this issue, the law enforcement adopts network surveillance technology to track a suspect and derive the online profile. However, the traditional network surveillance using the single-device tracking method can only acquire part of a suspect’s online activities. With the emergence of different types of devices (e.g., personal computers, mobile phones, and smart wearable devices) in the mobile edge computing (MEC) environment, one suspect can employ multiple devices to launch a cybercrime. In this paper, we investigate a novel cross-device tracking approach which is able to correlate one suspect’s different devices so as to help the law enforcement monitor a suspect’s online activities more comprehensively. Our approach is based on the network traffic analysis of instant messaging (IM) applications, which are typical commercial service providers (CSPs) in the MEC environment. We notice a new habit of using IM applications, that is, one individual logs in the same account on multiple devices. This habit brings about devices’ receiving sync messages, which can be utilized to correlate devices. We choose five popular apps (i.e., WhatsApp, Facebook Messenger, WeChat, QQ, and Skype) to prove our approach’s effectiveness. The experimental results show that our approach can identify IM messages with high F1 -scores (e.g., QQ’s PC message is 0.966, and QQ’s phone message is 0.924) and achieve an average correlating accuracy of 89.58% of five apps in an 8-people experiment, with the fastest correlation speed achieved in 100 s.

MapReduce is a widely-used programming model in cloud environment for parallel processing large-scale data sets. The combination of the high-level language with a SQL-to-MapReduce translator allows programmers to code using SQL-like declarative language, so that each program can afterwards be complied into a MapReduce jobflow automatically. This way is helpful to narrow the gap between non-professional users and cloud platforms, and thus significantly improve the usability of the cloud. Although a number of translators have been developed, the auto-generated MapReduce programs still suffered from extremely inefficiency. In this paper, we present an efficient Cost-Aware SQL-to-MapReduce Translator (CAT). CAT has two notable features. First, it defines two intra-SQL correlations: Generalized Job Flow Correlation (GJFC) and Input Correlation (IC), based on which a set of looser merging rules are introduced. Thus, both Top-Down (TD) and Bottom-Up (BU) merging strategies are proposed and integrated into CAT simultaneously. Second, it adopts a cost estimation model for MapReduce jobflows to guide the selection of a more efficient MapReduce jobflows auto-generated by TD and BU merging strategies. Finally, comparative experiments on TPC-H benchmark demonstrate the effectiveness and scalability of CAT.

Cardinality estimation is the task of determining the number of distinct elements (or the cardinality) in a data stream, under a stringent constraint that the input data stream can be scanned by just one single pass. This is a fundamental problem with many practical applications, such as traffic monitoring of high-speed networks and query optimization of Internet-scale database. To solve the problem, we propose an algorithm named HLL-TailCut, which implements the estimation standard error 1.0/√m using the memory units of four or three bits each, whose cost is much smaller than the five-bit memory units used by HyperLogLog, the best previously known cardinality estimator. This makes it possible to reduce the memory cost of HyperLogLog by 20%~45%. For example, when the target estimation error is 1.1%, state-of-the-art HyperLogLog needs 5.6 kilobytes memory. By contrast, our new algorithm only needs 3 kilobytes memory consumption for attaining the same accuracy. Additionally, our algorithm is able to support the estimation of very large stream cardinalities, even on the Tera and Peta scale.

In cloud computing, it is crucial to maintain service continuity, while power outage is one of the most common and serious threats. To improve the resilience of cloud against power outage, a service provider usually deploys emergency energy supply (e.g., UPSs and generators) in a data center. When a power outage at a data center happens, the cloud service provider needs to make the operation decision on which subset of VMs to keep running and which servers to host such VMs to minimize its loss (or maximize its profit) using the emergency energy supply while the selected VMs are running in the affected data center until they are finished, migrated to other data centers, or normal power supply of the affected data center has been restored. No prior research has theoretically studied such a cloud service continuity problem under power outage. In this paper, we tackle this challenge and investigate the cloud service continuity problem. Specifically, we consider that a profit is associated with maintaining the continuity of a service, denoted as service continuity profit. Based on that we first formulate an optimization problem that aims to maximize the total profit subject to energy constrains. After showing the hardness of the problem, we focus on the design of approximation algorithms for solving the problem, where we consider two practical cases. In the first one with sufficient number of servers for re-provisioning, we develop a constant approximation algorithm of which the worst-case performance approaches the optimal solution within a constant factor (≈4.5-6.4). In the second one, we consider the general case with limited number of servers, and we develop an approximation algorithm with an approximation ratio of around 5.7-8. By combining these two algorithms together, we can achieve both good worst-case performance and average performance. Simulation results demonstrate the efficiency in terms of maximizing the service continuity profit of the proposed algorithms.

Recently researchers use skyline techniques to optimize service selection procedure, where they can filter those low-quality web services from the large amount of candidates and return a much smaller high-quality service set. The skycube concept is adopted for quickly responding to the skyline queries with different combinations of Quality of Web Service (QoWS) parameters. As the skycube computation is quite time-consuming, it is a compelling challenge to accelerate this procedure. However, the current solutions usually have a number of redundant computations which will significantly affect the efficiency. To address such drawbacks, after an in-depth analysis of skycube computation procedure, we introduce a partial skycube, which only consists of the skylines with frequently used combinations of QoWS. Then the computational relationships between the skyline on one subspace and its parent-space are studied. Based on the relationships, we develop ParCube algorithm to speedup partial skycube computation by reusing the intermediate comparison results. Meanwhile, at the execution phase, ParCube can be further optimized with parallel execution mode and optimized scheduling strategy. Finally, we evaluate the efficiency and scalability of ParCube on both single machine and cluster environment. The results show that ParCube can efficiently compute partial skycube and scale well in cluster environment.

Bandwidth allocation to virtual machines (VMs) has a significant impact on the performance of communication-intensive big data applications hosted in VMs. It is crucial to accurately determine how much bandwidth to be reserved for VMs and when to adjust it. Past approaches typically resort to predicting the long-term network demands of applications for bandwidth allocation. However, lacking of prediction accuracy, these methods lead to the unpredictable application performance. Recently, it is conceded that the network demands of applications can only be accurately derived right before each of their execution phases. Hence, it is challenging to timely allocate the bandwidth to VMs with limited information. In this paper, we design and implement AppBag, an Application-aware Bandwidth guarantee framework, which allocates the accurate bandwidth to VMs with one-step-ahead traffic information. We propose an algorithm to allocate the bandwidth to VMs and map them onto feasible hosts. To reduce the overhead when adjusting the allocation, an efficient Lazy Migration (LM) algorithm is proposed with bounded performance. We conduct extensive evaluations using real-world applications, showing that AppBag can handle the bandwidth requests at run-time, while reducing the execution time of applications by 47.3 percent and the global traffic by 36.7 percent, compared to the state-of-the-art methods.

Modern multi-queue data centers often use the standard Explicit Congestion Notification (ECN) scheme to achieve high network performance. However, one substantial drawback of this approach is that micro-burst traffic can cause the instantaneous queue length to exceed the ECN's threshold, resulting in numerous mismarkings. After enduring too many mismarkings, senders may overreact, leading to severe throughput loss. As a solution to this dilemma, we propose our own adaptationthe Micro-burst ECN (MBECN) scheme-to mitigate mismarking. MBECN finds a more appropriate threshold baseline for each queue to absorb micro-bursts, based on steady-state analysis and an ideal generalized processor sharing (GPS) model. By adopting a queue-occupation-based dynamically adjusting algorithm, MBECN effectively handles packet backlog without hurting latency. Through testbed experiments, we find that MBECN improves throughput by ~20% and reduces flow completion time (FCT) by ~40%. Using large scale simulations, we find that throughput can be improved by 1.5~2.4× with DCTCP and 1.26~1.35× with ECN*. We also measure network delay and find that latency only increases by 7.36%.

Data anonymization is widely used to preserve the utility of published datasets without compromising privacy. The state-of-the-art data anonymization approaches are mainly single-record-based algorithms. They group similar records together one by one, then form equivalence classes through generalization. However, these algorithms didn't utilize equivalence classes which exist in the raw dataset. In this paper, we propose a new concept named natural equivalent class. It refers to the record set with the same quasi-identifier values naturally existing in the raw dataset. We theoretically prove that the natural equivalent class can effectively reduce the computational complexity of clustering algorithms as well as information loss. Then, we propose a novel clustering-based anonymization algorithm, which tries to cluster records without separating any natural equivalent class. Extensive experiments on real world datasets show that our approach outperforms the previous clustering-based anonymization algorithms in terms of efficiency and data utility.

Packet loss is a common problem in data center networks. The factors causing packet loss are various. Among them, micro-burst is the most important reason. Some previous works have studied the causes and influence o f micro-burst in single queue data center. However, through simulations and experiments, we find that micro-burst could bring m ore serious performance degradation in multi-queue data centers. The micro-burst traffic could cause E CN marking ratio rising from 4% to 22%, and cause throughput loss by up to 40%. Through observing queue length, we find that the standard E CN, which adopts immutable threshold, is not suitable for micro-burst traffic because micro-burst could trigger spurious congestion signals frequently, especially in DCTCP. In this paper, we not only show how much influence the micro-burst brings, but also propose Queue-length Aware ECN (QA-ECN) scheme to mitigate micro-burst. Finally, the simulations and experiments show that QAECN could reduce ECN marking ratio to 2.5%. In addition, the throughput and flow completion time could be improved by up to 22.9% and 34.1%, respectively.

In Network Function Virtualization (NFV), multiple network functions cooperate to provide various network services. To reduce the end-to-end latency through a chain of network functions, research hotspots have turned to complete NF parallelism frameworks. However, several issues remain in them such as the manual dependency analysis on NFs and the excessive parallelism for NFs. Therefore, in this paper, we present ParaNF, an effective delay-balanced NF parallelism framework. ParaNF mainly consists of two logical components. First, the ParaNF orchestrator conducts a dynamic dependency analysis to find out which NFs can be parallelized and then conducts a delay-balanced NF parallelism optimization strategy. Second, the ParaNF infrastructure performs light-weight, dynamic packet copying and merging guided by an efficient label mechanism to support high-performance NF parallelism. We implement a ParaNF prototype with DPDK. Our evaluations show that ParaNF not only realizes the line-speed packet processing, but also achieves significant reduction in latency by up to 47% than the traditional SFC and 35% than OpenBox.

Diverse anonymous communication systems are widely deployed as they can provide the online privacy protection and Internet anti-censorship service. However, these systems are severely abused and a large amount of anonymous traffic is malicious. To mitigate this issue, we propose a novel and practical traceback technique to confirm the communication relationship between the suspicious server and the user. We leverage the software-defined network (SDN) switch at a destination server side to intercept target traffic towards the server and alter the advertised TCP window sizes so as to stealthily vary the traffic rate at the server. By carefully varying the traffic rate, we can successfully modulate a secret signal into the traffic. The traffic carrying the signal passes through the anonymous communication system and reaches the SDN switch at the user side. Then we can detect the modulated signal from the traffic so as to confirm the communication relationship between the server and the user. To validate the feasibility and effectiveness of our technique, extensive real-world experiments are performed using three popular anonymous communication systems, i.e., SSH tunnel, OpenVPN tunnel, and Tor. The results demonstrate that the detection rates approach 100% for SSH and Open VPN and 95% for Tor while the false positive rates are significantly low, approaching 0% for these three systems.

According to social psychology studies, social networks are strongly subject to group influence; that is, users' behaviors or sentiment are primarily influenced by their group environments. But only a few studies to date have examined group influence in online social networks (OSNs). In this paper, a Factor Graph-based Group Influence model (F2GI) is proposed to depict the influence of perceived groups on a user. First, we analyze individuals' group environments and propose definitions and detecting methods of two types of groups: following groups and interacting groups. Secondly, based on these groups, we quantity the groups' features and propose the F2GI model able to describe group influence. Finally, using the retweet behaviors as the primarily manifestations of group influence, we apply our model to predict individuals' retweet behaviors and to validate how much influence the group puts on individuals. The results show that our group influence model can depict group influence more effectively and predict the actions of users who are affected by perceived groups accurately with social psychology ideas than classical machine learning methods. We believe that, moreover, our model can also be applied to marketing strategies, advertisement strategies and the prediction of public opinion.

In recent years, 802.11ac wireless local area networks (WLANs) have been popular in campus and enterprise environments, and a large number of access points (APs) are densely deployed to meet the rapidly increasing clients' demand. In such networks, it is challenging to promote the performance of AP-client association since numerous clients need to find their respective optimal APs under the conditions of multiple capability-limited APs and a small number of available channels. Thus, the conventional association mechanism that only utilizes local information on the client side, such as received signal strength indicator (RSSI), may lead to poor network performance. In this context, we propose and implement an airtime-aware centralized association control system that deals with client requests in a centralized manner and makes AP-client association decisions according to the global information, that is, AP airtime utilization and client requests' RSSI. In particular, we design an AP airtime measurement method to obtain AP airtime utilization from the ath10k driver, and present an airtime-aware AP selection algorithm to implement AP selection policy. Furthermore, we develop an experimental testbed, and conduct the experiments to evaluate the performance of our system. The results demonstrate that our system can significantly improve network throughput and effectively guarantee clients' fairness.

Billions of lightweight Internet of Things (IoT) devices have been deployed for various applications nowadays. Most of them first collect interested data and then process them in some degree according to application requirements. Transparent computing (TC) is a promising technique that makes such lightweight devices suitable to process even large-size applications. The advantage of TC is to separate code storage from its execution, allowing IoT devices to load code blocks from nearby TC storage server on demand. Distinct from existing work, this paper allows the TC IoT devices to offload some tasks to servers, since wireless IoT devices are usually powered by batteries, having limited energy resources. If a task is offloaded, a challenging problem is that its input data collected by the IoT device must be transferred as well, which incurs additional transmission time and energy. This paper proposes a two-step approach aiming at minimizing the energy consumption of the IoT device while satisfies the delay constraint. This approach first studies the offloading decision problem that determines for each task whether to offload task data or load task code blocks, while loading code indicates code receiving and executing energy cost. Second, the transmission power scheduling problem is investigated to further reduce offloading energy for a given delay constrained offloading task set. Heuristic decision making algorithms and optimal power scheduling algorithm are proposed, respectively. Such two-step approach is shown by extensive simulation to be near optimal for the original problem thanks to the optimal design of the power scheduling algorithm.

Radiative wireless power transfer (WPT) is a promising technique to power wireless devices' transmission. In a resource-limited device, receiving energy and transmitting data cannot operate at the same time because they share the same spectrum or hardware. This paper studies the problem for a wireless device to decide when to harvest energy, when to deliver data, and what transmission rate to use. Distinct from the most existing works, we focus on delay minimization in transmitting a sequence of data packets over a point-to-point channel, which is critical for time-sensitive applications. Since the battery is capacitated, the device must repeatedly switch between harvesting energy and transmitting data. For the offline case where packet information is known before scheduling, a surprising result is discovered that for all (energy receiving and data transmitting) cycles, except the last one, the optimal transmission rate should be a constant which is called the wOPT rate. Based on this discovery, the offline delay minimization problem is optimally solved. For the online case where packets arrive dynamically without prior information, we propose a simple online algorithm: using the wOPT rate to transmit whenever both energy and data are ready. It is proved to be 1.16-competitive if the battery is initially empty, namely, its delay is less than 1.16 times the offline optimal delay for any given packet set. When the battery is with arbitrary initial energy, simulation results show that the performance is near optimal. The discovery of the wOPT rate reveals an essential property of WPT and is expected to be significant in solving other related problems.

Transmission is crucial for big data analysis and learning in industrial Internet of Things (IoT) systems. To transmit data with limited energy is a challenge. This paper studies the problem of data transmission in energy harvesting systems with capacitor to supply energy where the energy receiving rate varies over time. The energy receiving rate is slower when the capacitor receives more energy. Based on this characteristic, we study the problem of how to transmit more data when the energy receiving time is not continuous. Given many packets that arrive at different time instances, there is a tradeoff between transmitting the packet right now or saving the energy to transmit the future arriving packets. We formalize two types of problems. The first one is how to minimize the total completion time when there is enough energy to transmit all the packets. The second one is how to transmit as many packets as possible when the energy is not enough to transmit all the packets. For the first problem, we give a 1 + α approximation off line algorithm when all the information of the packets and the energy receiving periods is known in advance, and a max{2, β} competitive ratio online algorithm where the information is not known in advance. For the second problem, we study three cases and give a 6 + [h/(b/R)] approximation off line algorithm for the general situation. We also prove that there does not exit a constant competitive ratio online algorithm.

Radio frequency identification (RFID) technology has rich applications in cyber-physical systems, such as warehouse management and supply chain control. Often in practice, tags are attached to objects belonging to different groups, which may be different product types/manufacturers in a warehouse or different book categories in a library. As RFID technology evolves from single-group to multiple-group systems, there arise several interesting problems. One of them is to identify the popular groups, whose numbers of tags are above a pre-defined threshold. Another is to estimate arbitrary moments of the group size distribution, such as sum, variance, and entropy for the sizes of all groups. In this paper, we consider a new problem which is to estimate all these statistical metrics simultaneously in a time-efficient manner without collecting any tag IDs. We solve this problem by a protocol named generic moment estimator (GME), which allows the tradeoff between estimation accuracy and time cost. According to the results of our theoretical analysis and simulation studies, this GME protocol is several times or even orders of magnitude more efficient than a baseline protocol that takes a random sample of tag groups to estimate each group size.

Radio-frequency identification (RFID) technology has been widely adopted in various industries and people's daily lives. This paper studies a fundamental function of spatial-temporal joint cardinality estimation in distributed RFID systems. It allows a user to make queries over multiple tag sets that are present at different locations and times in a distributed tagged system. It estimates the joint cardinalities of those tag sets with bounded error. This function has many potential applications for tracking product flows in large warehouses and distributed logistics networks. The prior art is either limited to jointly analyzing only two tag sets or is designed for a relative accuracy model, which may cause unbounded time cost. Addressing these limitations, we propose a novel design of the joint cardinality estimation function with two major components. The first component is to record snapshots of the tag sets in a system at different locations and periodically, in a time-efficient way. The second component is to develop accurate estimators that extract the joint cardinalities of chosen tag sets based on their snapshots, with a bounded error that can be set arbitrarily small. We formally analyze the bias and variance of the estimators, and we develop a method for setting their optimal system parameters. The simulation results show that, under predefined accuracy requirements, our new solution reduces time cost by multiple folds when compared with the existing work.

Analyzing the popularity of microblogging services is of great significance in various applications. The number of posts provides novel insights into the popularity of microblogging services, and is critical to learn about the frequency of use. Existing approaches analyze this parameter by observing posts published by a large number of users, which may lead to underestimate the value since the sampled user may stop to use the service during the observing process. In this paper, we propose a novel method to estimate the number of posts in microblogging services. The basic idea behind this method is to make use of a common API provided by microblogging services, i.e., the public_timeline API. Posts sampled by this API may duplicate among multiple invocations, so the capture-recapture model can be used to estimate the total number of posts. Based on the traditional capture-recapture model, we propose an improved model to address challenges on low sampling probability and unequal sampling probability. We validate the proposed method using a real life Sina Weibo dataset, and the experimental results demonstrate the effectiveness and accuracy of our proposed method.

Edge Computing is a new computing paradigm that performs data processing at the edge of the network (i.e., edge servers) to lower data processing latency. Existing research works have paid lots of attention to how to offload computation tasks from terminals to edge servers, but most of them ignored how to store tasks' necessary data like pretrained models or databases on edge servers. Recently, the data-intensive tasks like deep learning and augmented reality are becoming common, which need large data storages and powerful computation resources. This leads to a cumbersome challenge, since many lightweight edge servers have limited resources. If an edge server does not have a task's necessary data, it needs to offload the task to cloud data centers or download the necessary data from the cloud. Both cases could increase the data processing latency. To address this problem, this paper proposes an edge-side collaborative storage framework (ECS). In ECS, the edge servers collaboratively store and process data-intensive tasks' necessary data. Particularly, if an edge server does not have the necessary data, it will forward the task to the nearest servers that contain the data. An effective iterative data placement algorithm is also proposed to improve ECS's performance. The experimental results show that ECS is 2× better than the traditional non-shared storage framework in terms of the cache hit rate.

With the expansion of the scale of Internet users and the increasing of network application types, the motive force of the network development has gradually changed from technologies to applications, and the network resources need be customized for different applications in order to guarantee different QoS. However, network resources are always scarce for application requirements, and the existing works only attach importance to guarantee QoS of network applications and ignore the promotion of the network resources utilization. So, an Application-Driven-based Network Resource Control Method (ADNRC) is presented in this paper. The thought of the separation of the transmission and the control based on SDN is introduced in this method, and the network slices for different applications are constructed to customize the network resources and guarantee QoS of network applications. At the same time, the multi-path routes between the sources and the destinations are generated during the procedure of constructing the network slices, and the network flows are scheduled in real time based on the multi-path routes and the network status to enhance the utilization of network resources. The experimental results show that the proposed method in this paper is better than existing methods in the aspect of both the QoS guarantee of network applications and the promotion of the network resources utilization.

Recently, personality detection based on texts from online social networks has attracted more and more attentions. However, most related models are based on letter, word or phrase, which is not sufficient to get good results. In this paper, we present our preliminary but interesting and useful research results to show that the structure of texts can be also an important feature in the study of personality detection from texts. We propose a model named 2CLSTM, which is a bidirectional LSTMs (Long Short Term Memory networks) concatenated with CNN (Convolutional Neural Network), to detect user's personality using structures of texts. Besides, a concept, Latent Sentence Group (LSG), is put forward to express the abstract feature combination based on closely connected sentences and we use our model to capture it. To the best of our knowledge, most related works only conducted their experiments on one data set, which may not well explain the versatility of their models. We implement our evaluations on two different kinds of datasets, containing long texts and short texts. Evaluations on both datasets have achieved better results, which demonstrate that our model can efficiently learn valid text structure features to accomplish the task.

Smart mobile devices have become an integral part of people's life and users often input sensitive information on these devices. However, various side channel attacks against mobile devices pose a plethora of serious threats against user security and privacy. To mitigate these attacks, we present a novel secure Back-of-Device (BoD) input system, SecTap, for mobile devices. To use SecTap, a user tilts her mobile device to move a cursor on the keyboard and tap the back of the device to secretly input data. We design a tap detection method by processing the stream of accelerometer readings to identify the user's taps in real time. The orientation sensor of the mobile device is used to control the direction and the speed of cursor movement. We also propose an obfuscation technique to randomly and effectively accelerate the cursor movement. This technique not only preserves the input performance but also keeps the adversary from inferring the tapped keys. Extensive empirical experiments were conducted on different smart phones to demonstrate the usability and security on both Android and iOS platforms.

Summary Edge computing is a new computing paradigm that performs data processing at the edge of the network (ie, edge servers) to lower data processing latency. Prior research significantly focused on offloading tasks from terminals to edge servers, yet most ignored how to store task's necessary data (such as databases and pretrained machine‐learning models) on edge servers. Today, as data‐intensive tasks such as deep learning and augmented reality become common, large data storage and powerful computation resources are needed. This is a cumbersome challenge, because many lightweight edge servers have limited resources. If an edge server does not have a task's necessary data, then it needs to offload the task to cloud datacenters or download the necessary data from the cloud. Either case could increase data processing latency. To address this problem, this paper proposes an edge‐side collaborative storage framework called Edge‐side Cooperative Storage (ECS). In ECS, edge servers collaboratively store and process data‐intensive tasks's necessary data. Here, we particularly focus on how to effectively place data on ECS, using an approach that differs from existing works (that model data placement problems as linear/integer programming problems). Our work models cooperative storage as a graph and solves the data placement problem by using a graph‐based iterative algorithm. This algorithm easily extends to a distributed version, so distributed ECSworks efficiently without a centralized scheduler. We also evaluate ECS's effectiveness and convergence through simulations. Simulation results show that ECSis 2× better than a traditional nonshared storage framework in terms of the cache hit rate.

Summary Enterprises increasingly use the Hadoop Distributed File System (HDFS) to manage and store big data for many applications. However, HDFS uses triple replication, leading to staggering data center storage costs. As big data increases in volume and its heat levels becomes more sensitive, there comes a point where storing so much cold data actually makes it less accessible and more expensive. Meanwhile, as data centers expand, the heterogeneity of nodes also becomes an issue. Rack‐aware data placement adopted by HDFS results in an unbalanced load and uneven resource allocation because it ignores the data nodes' heterogeneity. Here, we attempt to resolve these problems by proposing a hotness‐aware data placement strategy (named HaDaap). In HaDaap, the first step is to use a hotness‐aware data clustering algorithm to set the data's degree of heat. Then, cold data (with a redundancy of erasure code) are placed through a Double Sort Exchange algorithm to reduce storage costs and increase data availability. Finally, hot data are placed via a dynamic replication placement mechanism that comprehensively factors availability, load, and storage costs. Experimental results show that with these enhancements, HaDaap uses resources rationally and substantially reduces storage costs by considering the difference of data hotness in heterogeneous Hadoop clusters.

Wireless power transfer (WPT) is a recently developed technique to perfectly address the energy problem for smart city sensors that do not have a readily wired power supply. In a radio-frequency-powered wireless communication system, sensors first harvest energy via the radio-frequency WPT, and then, transmit sensed data to the receiver. The “harvest-then-transmit” protocol is used to coordinate the two operations, in which we wish to optimally decide when to harvest energy, when to transmit data, and what transmission rate should be used such that the data are maximally transmitted. Unlike existing works, we assumed that the wireless transferred power is dynamically changing, instead of being constant, which is more realistic in green smart city applications, e.g., Industry 4.0 workshop, smart transportation, and smart buildings, where environments are continuously changing, so the wireless transferred power is affected dynamically. In this paper, we present an optimal scheduling algorithm for the offline case where the varying WPT is known in advance. Based on the optimal principles learned from the offline case, we have designed an efficient online algorithm. Finally, we report our simulation results that demonstrate that our online scheduling algorithm can adaptively and efficiently achieve high data throughput.

Green wireless local area networks (WLANs) have captured the interests of academia and industry recently, because they save energy by scheduling an access point (AP) on/off according to traffic demands. However, it is very challenging to determine user association in a green WLAN while simultaneously considering several other factors, such as avoiding AP congestion and user migration constraints. Here, we study the energy‐efficient user association with congestion avoidance and migration constraint (EACM). First, we formulate the EACM problem as an integer linear programming (ILP) model, to minimize APs’ overall energy consumption within a time interval while satisfying the following constraints: traffic demand, AP utilization threshold, and maximum number of demand node (DN) migrations allowed. Then, we propose an efficient migration‐constrained user reassociation algorithm, consisting of two steps. The first step removeskAP‐DN associations to eliminate AP congestion and turn off as many idle APs as possible. The second step reassociates thesekDNs according to an energy efficiency strategy. Finally, we perform simulation experiments that validate our algorithm’s effectiveness and efficiency.

We investigate the problem of joint AP coverage adjustment and user association optimization for load balancing in multi-rate WLANs in this paper. We first divide the problem into two sub-problems and then formulate them as mixed integer linear programming models, which aim to minimize the AP utilization of the most congested AP while satisfying users' traffic demands. Then we design two corresponding heuristic algorithms that are performed in sequence to address the problem. Finally, we conduct extensive simulations to evaluate the performance of the proposed algorithms. The results not only show that the algorithms can balance the loads among APs effectively and efficiently, but also demonstrate that the solutions of joint AP coverage adjustment and user association optimization outperform that of AP coverage adjustment with low overhead.

Touch pattern based implicit authentication has been proposed to defend against diverse attacks against mobile devices that aim to obtain credentials, e.g., passwords, in the process of user authentication. However, this defense technique cannot obtain a complete user operation pattern by merely deriving user operation data via a touch-enabled screen, since user operations, including on-screen and in-air finger movements, are performed in a three-dimensional space. In this paper, we propose a novel three-dimensional magnetic finger motion pattern based implicit authentication technique, referred to as FingerAuth. To use FingerAuth, a user first wears a magnetic ring on her finger and uses this finger to operate her mobile device, e.g., typing messages and surfing websites. By using a built-in three-axis magnetometer on the mobile device, we can derive the three-dimension (3D) magnetic finger motion pattern that is used as a human behavioral feature to implicitly authenticate the user. We construct robust 3D magnetic finger motion pattern detection model using machine learning techniques. Real-world experiments were conducted to demonstrate that our approach achieves high accuracy of 96.38% as well as low false acceptance rate of 4.06% and low false rejection rate of 3.18%.

GPU-based clusters are widely chosen for accelerating a variety of scientific applications in high-end cloud environments. With their growing popularity, there is a necessity for improving the system throughput and decreasing the turnaround time for co-executing applications on the same GPU device. However, resource contention among multiple applications on a multi-tasked GPU leads to the performance degradation of applications. Previous works are not accurate enough to learn the characteristics of GPU application before execution, or cannot get such information timely, which may lead to misleading scheduling decisions. In this paper, we present GScheduler, a framework to detect and reduce interference for co-executing applications on the GPU-based cloud. The most important feature of GScheduler is to utilize GPU usage pattern extractor for detecting interference between applications. It is composed of key function-call graph extractor and key GPU resource usage vector extractor, the former is used to detect the similarity of GPU usage mode between applications, while the latter is used to calculate the similarity of GPU resource requirements in-between. In addition, an interference aware scheduler is proposed to minimize the interference. We evaluated our framework with 26 diverse, real-world CUDA applications. When compared with state-of the-art interference-oblivious schedulers, our framework improves system throughput by 36% on average, and achieves a 30.5% reduction of turnaround time on average.

Recent advances in big data analysis and applicationWith the rapid development of information and network technology in recent years, massive data from many different kinds of applications, such as social network, e-business, intelligent transportation and medical diagnosis [1], can be generated, collected, and aggregated under various circumstances and scenarios like WAN, LAN, mobile Internet, Internet of things, and so on [2].The potential value of big data can only be exploited and unleashed by means of efficient big data analysis and application.Meanwhile, big data-related security has also drawn much attention from both the academy and industry with its increasing importance.For these reasons, new methodologies and technologies need to be proposed and developed for advancing both big data analytics and applications.This special issue focuses on a new strategic research area that addresses "Recent Advances in Big Data Analysis and Application."From those submitted papers for the 3rd International Conference on Advanced Cloud and Big Data (CBD 2015) held in Yangzhou, Jiangsu, China, on October 30 to November 1, 2015, 9 papers are selected that target the following research issues in big data:• Big data processing and optimization mechanism, • big data security and privacy protection, and • big data analysis application.

Summary With the emerging of the large‐scale graph data,Pregel‐like graph parallel processing systems have been an essential tool to efficiently process the graph data. The first step to use thePregel‐like systems is to partition the graph into multiple blocks and distribute them on multiple machines. The partition strategy plays a significant role in determining the performance because a good partition could both ensure load balance and optimize network communication overhead, and vice versa. However, existing partition strategies fail to meet the requirements because they suffer from the following drawbacks: (1) they ignore the application features and (2) they ignore the multi‐application feature in productive environment. To overcome those drawbacks, we proposed thesuperblockpartition strategy, which utilizes theatomic blocksgenerated by pre‐processing of the original graph and could be constructed and re‐constructed dynamically according to the submitted applications in real time. The hash‐based and clustering‐based pre‐partition methods are covered in details. The application feature extraction method and heuristicsuperblockpartition algorithm are proposed to construct the superblocks. Experimental results show that thesuperblockpartition strategy could boost the graph processing performance and its partition efficiency also outperforms the hash‐based and topology optimal partition strategy. Copyright © 2016 John Wiley & Sons, Ltd.

Summary With the rapid development of information technology, enormous volumes of data are being generated by enterprises at all times. The management and storage of these large‐scale data have always been challenging enterprises. As these data are usually shared among users in a collaborative manner, secure data access and access performance are 2 key concerns for data storage of enterprises. However, current solutions fail to meet the requirements of enterprises since they suffer from the following drawbacks: (1) they do not support fine‐grained access control and cannot meet the strict secure data access requirements of enterprises, and (2) they suffer from the unpredictable access latency. Thus in this paper, we propose Frostor, an enterprise‐oriented cloud storage system, which addresses the secure data access issue through a user account and IP‐based fine‐grained access control mechanism, and guarantees the access performance via a two‐level performance optimization mechanism. We further implement Frostor and deploy it on the testbed environment in a real data center. Extensive evaluations have shown that Frostor implements fine‐grained access control, while achieving a significant reduction (≥60%) on access latency.

Summary Currently, people around the world daily use the Internet to access various services, such as e‐mail and online shopping. However, the behavior‐based tracking attacks have posed a considerable threat to users' privacy. Relying on characteristic patterns within the Internet activities, this attack can link a user's multiple sessions. In this paper, we investigate the behavior‐based tracking attack and propose some countermeasures to mitigate the threat. We preprocess the raw traffic data and then extract features ranging from lower layer network packets to high‐level application‐related traffic. Specifically, we focus on four types of application‐level traffic to infer users' habits, including HTTP, IM, e‐mail, and P2P. In addition, we extract the web queries entered into shopping websites and classify them to infer users' preferences. Then, we construct the preference models and propose an improved method. For evaluation, we collect traffic in the real‐world environment to construct a large‐scale dataset. Five hundred and nine users are selected in terms of the user's active degree. When the term frequency–inverse document frequency transformation is used, the improved method can identify an average of 93.79% instances correctly. Our extensive empirical experiments demonstrate the effectiveness and efficiency of our approaches. Finally, we discuss and evaluate several countermeasures. Copyright © 2016 John Wiley & Sons, Ltd.

With the rapid development of the Internet of Things, more and more small devices are connected into the Internet for monitoring and control purposes. One such type of devices, smart plugs, have been extensively deployed worldwide in millions of homes for home automation. These smart plugs, however, would pose serious security problems if their vulnerabilities were not carefully investigated. Indeed, we discovered that some popular smart home plugs have severe security vulnerabilities which could be fixed but unfortunately are left open. In this paper, we case study a smart plug system of a known brand by exploiting its communication protocols and successfully launching four attacks: 1) device scanning attack; 2) brute force attack; 3) spoofing attack; and 4) firmware attack. Our real-world experimental results show that we can obtain the authentication credentials from the users by performing these attacks. We also present guidelines for securing smart plugs.

In crowdsourcing markets, a requester announces a task and calls for contribution from potential participants. With strategic participants, the requester needs to reward the participants to introduce the incentives of participation. However, it is natural to ask whether it is worth introducing incentives if the total payment for eliciting incentives is too high. This paper addresses such a fundamental concern by designing a frugal mechanism with minimum payment used to procure the total amount of service contributions demanded. We design two mechanisms to provide the incentives of participation while minimizing the payment used by the requester. We first propose a frugal auction-based mechanism, which stimulates participants to truthfully report their information. We theoretically prove that the payment used is not more than the optimal cost (with no incentive considered) plus a bounded additive. We then design a Stackelberg-game-based mechanism, in which the requester fixes a certain total payment at the very beginning so as to encourage the participants to compete for it and participate in the task. We verify the existence of a unique Nash equilibrium (NE) and develop a novel algorithm to find the NE, as well as the optimal payment to extract the NE. Our simulation results show that the payment used in these mechanisms is close to the optimal solution with no incentive considered, while the extra payment caused by introducing truthfulness in auction-based mechanism is about twice that of the NE in Stakelberg-game-based mechanism.

Energy harvesting communication system enables energy to be dynamically harvested from natural resources and stored in capacitated batteries to be used for future data transmission. In such a system, the amount of future energy to harvest is uncertain and the battery capacity is limited. As a consequence, battery overflow and energy dropping may happen, causing energy underutilization. To maximize the data throughput by using the energy efficiently, a rate-adaptive transmission schedule must address the trade-off between a high-rate transmission which avoids energy overflow and a low-rate transmission which avoids energy shortage. In this paper, we study an online throughput maximization problem without knowing future information. To the best of our knowledge, this is the first work studying the fully-online transmission rate scheduling problem for battery-capacitated energy harvesting communication systems. We consider the problem under two models of the communication channel, a static channel model that assumes the channel status is stable, and a fading channel model that assumes the channel status varies. For the former, we develop an online algorithm that approximates the offline optimal solution within a constant factor for all possible inputs. For the latter, that the channel gains vary in range [hmin; hmax], we propose an online algorithm with a proven ⊖(log(hmax/ hmin))-competitive ratio. Our simulation results further validate the efficiency of the proposed online algorithms.

Radio frequency identification (RFID) technology has been widely used in Applications, such as inventory control, object tracking, and supply chain management. In this domain, an important research problem is called RFID cardinality estimation, which focuses on estimating the number of tags in a certain area covered by one or multiple readers. This paper extends the research in both temporal and spatial dimensions to provide much richer information about the dynamics of distributed RFID systems. Specifically, we focus on estimating the cardinalities of the intersection/differences/union of two arbitrary tag sets (called joint properties for short) that exist in different spatial or temporal domains. With many practical applications, there is, however, little prior work on this problem. We will propose a joint RFID estimation protocol that supports adaptive snapshot construction. Given the snapshots of any two tag sets, although their lengths may be very different depending on the sizes of tag sets they encode, we design a way to combine their information and more importantly, derive closed-form formulas to use the combined information and estimate the joint properties of the two tag sets, with an accuracy that can be arbitrarily set. By formal analysis, we also determine the optimal system parameters that minimize the execution time of taking snapshots, under the constraints of a given accuracy requirement. We have performed extensive simulations, and the results show that our protocol can reduce the execution time by multiple folds, as compared with the best alternative approach in literature.

For many practical applications, it is a fundamental problem to estimate the flow cardinalities over big network data consisting of numerous flows (especially a large quantity of mouse flows mixed with a small number of elephant flows, whose cardinalities follow a power-law distribution). Traditionally the research on this problem focused on using a small amount of memory to estimate each flow's cardinality from a large range (up to ${10}^{{9}}$ ). However, although the memory needed for each individual flow has been greatly compressed, when there is an extremely large number of flows, the overall memory demand can still be very high, exceeding the availability under some important scenarios, such as implementing online measurement modules in network processors using only on-chip cache memory. In this paper, instead of allocating a separated data structure (called estimator) for each flow, we take a different path by viewing all the flows together as a whole: Each flow is allocated with a virtual estimator, and these virtual estimators share a common memory space. We discover that sharing at the multi-bit register level is superior than sharing at the bit level. We propose a unified framework of virtual estimators that allows us to apply the idea of sharing to an array of cardinality estimation solutions, e.g., HyperLogLog and PCSA, achieving far better memory efficiency than the best existing work. Our experiment shows that the new solution can work in a tight memory space of less than 1 bit per flow or even one tenth of a bit per flow - a quest that has never been realized before.

Web-scale knowledge graphs containing billions of entities are common nowadays. Querying these graphs can be modeled as a subgraph matching problem. Since knowledge graphs are incomplete and noisy in nature, it is important to discover answers matching exactly as well as answers similar to queries. Existing graph matching algorithms usually use graph indices to accelerate query processing. For billion-node graphs, it may be infeasible to build the graph indices due to the amount of work and the memory/ storage required. In this paper, we propose an efficient algorithm for finding the best k answers for a given query without precomputing graph indices. An answer's quality is measured by a matching score that is computed online. To accelerate query processing, we propose a novel technique for bounding the matching scores during the computation. By using bounds, the low quality answers can be efficiently pruned. The bounding technique can be implemented in a distributed environment, allowing our approach to efficiently query web-scale knowledge graphs. We evaluate the effectiveness and the efficiency of our approach on real-world datasets. The result shows that our bounding technique can reduce the running time up to two orders of magnitude comparing to an approach without using bounds.

Although a variety of anonymization approaches have been proposed to achieve anonymity during data sharing, few of them can handle incomplete microdata, i.e. microdata with missing values. Directly applying existing approaches to incomplete microdata will incur extensive information loss, due to the existence of missing values. In this paper, we formulate this problem as missing value pollution, and analysis its influences on generalization based algorithms. Then we propose two top-down algorithms named Enhanced Mondrian and Semi-Partition, which achieve high data utility on incomplete microdata. Extensive experiments on real-world data show the effectiveness of our approach.

With the popularity of cloud computing technology, service hosting is used as a typical model to deploy different kinds of services on cloud platform. In recent years, how to effectively provide resources for service hosting has attracted more and more attention. However, most of the existing works only focused on how to effectively provide virtual machines for service hosting. They ignored how to efficiently place these virtual machines into physical servers, when considering multidimensional resource requirements. This may result in unreasonable virtual machine placement in servers, thereby causing the underutilization of resource. To address this problem, we propose a novel resource provisioning method including virtual machine provisioning for hosting service and virtual machine placement in servers. The proposed method decides how many virtual machines should be provided for each service by utilizing queuing theory. Then based on the virtual machines to be provided, the proposed method models the virtual machine placement problem as a variant of cutting stock problem, and decides how many servers should be provided by solving this problem. The proposed method is evaluated by simulations. Experimental results show the proposed method achieves a better performance than these baseline methods.

Crowdsourcing is often used to solicit contributions from an online community for ideas, evaluation and opinions. However, spamming can pollute such a system and manipulate the results of crowdsourcing. For detection of those spammers, the training data used in previous studies is often derived by experts labeling collected data and manually identifying spammers. The reliability of such training data is questionable. In this paper, we utilize two web based service providers Zhubajie (ZBJ) and Sandaha (SDH) and obtain reliable data about the spammers. We use such data to investigate the crowd-retweeting spam in Sina Weibo. We analyze profile features, social relationship and retweeting behavior of such spammers. We find that although these spammers are likely to connect more closely than legitimate users, the underlying social tie is different from the social relationship in other spam campaigns because of the unique retweeting features with the information cascade effect. Based on these findings, we propose retweeting-aware link based ranking algorithms to detect suspect spam accounts using seeds of identified spammers. Our evaluation shows that our algorithm is more effective than other link-based methods.

It is challenging to allocate the network bandwidth to virtual machines(VMs) hosting communication-intensive applications. Due to the temporal and spatial variability of the hosted applications, it is crucial how much bandwidth to be reserved for each VM and when to adjust it. Prior approaches typically resort to predicting the applications' network demands, according to which the VMs are placed once for all or periodically migrated. However, recent works conceded that the network demands of applications can only be accurately derived right before each execution phase. In this paper, we propose AppBag, an Application-aware Bandwidth guarantee framework which allocates the bandwidth to VMs using only one-stepahead information. An efficient VM migration algorithm is then proposed to adjust the bandwidth allocation and corresponding VM placement, subjected to the network demands variation in future execution phases. We further implement AppBag with OpenStack and deploy it on the testbed environment in our data center. Extensive evaluations using popular applications show that AppBag can handle the bandwidth requests at run-time while improving applications' performance and reducing the global traffic in the data center fabric.

Various attacks may disclose sensitive information such as passwords of mobile devices. Residue-based attacks exploit oily or heat residues on the touch screen, computer vision based attacks analyze the hand movement on a keyboard, and sensor based attacks measure a device's motion difference via motion sensors as different keys are tapped. A randomized soft keyboard may defeat these attacks. However, a randomized key layout is counter-intuitive and users may be reluctant to adopt it. In this paper, we introduce a novel and intuitive input system, secure finger mouse, which uses a mobile device's camera sensing the fingertip movement, moves an on-screen cursor and performs clicks by sensing click gestures. We design a randomized mouse acceleration algorithm so that the adversary cannot infer keys clicked on the soft keyboard by observing the finger movement. The secure finger mouse can defeat attacks including residue, computer vision and motion based attacks too. We perform both theoretical analysis and real-world experiments to demonstrate the security and usability of the secure fingertip mouse.

Wireless power transfer (WPT) technique enables wireless charging/recharging, thus is a promising way to power wireless devices' transmissions. Because current WPT technique requires a wireless device to stop transmitting data when receiving power, and also because the received power in this way is limited, careful scheduling is needed to decide when the device should receive power and when it should transmit such that data can be efficiently transmitted. This paper assumes the most fundamental point-to-point White Gaussian Noise channel is used for data transmission and attempts to obtain an optimal scheduling such that a sequence of data packets can be transmitted with the minimum delay. It is discovered that, for all (energy receiving, data transmitting) cycles, except the last one, the optimal transmission rate should be a constant which is called the wOPT rate. Based on this discovery, this paper optimally solves the offline delay minimization problem. Then, an online heuristic scheduling algorithm is proposed, which either receives energy or transmits at the wOPT rate. Simulations have demonstrated its efficiency. The discovery of the wOPT rate reveals an essential property of WPT, thus is expected to make significant impact in the field of WPT.

Android lacks fine-grained permission control for the external storage. Under the current coarse-grained mechanism, any application is able to access all the data on the external storage very easily. At the same time, many applications store sensitive data into the external storage, and some of these data are highly concerned with user privacy, which could bring severe security problems. In this paper, we propose a fine-grained permission control mechanism for external storage of Android. The mechanism is based on Filesystem in Userspace (FUSE) and offers the following features: protecting user private media files such as photos and videos; isolating the data of each application; providing access control settings for user. We implement this mechanism on the latest Android version, by introducing a new type of GID (ESDS-GID), extending the functionality of the emulated filesystem as well as the system services. The results of functional verification and performance benchmark show that with a reasonable performance overhead, this mechanism brings considerable enhancement for Android system security.

Distributed file system has the characteristics of large capacity, good scalability and high reliability, which make it widely used in many areas involving large-scale data storage. It offers simplified, highly-available services for users to access data. However, due to the non-metadata design, the performance of traversal operation on large directories in those non-metadata distributed file systems is poor. With the increasing amount of files, it severely affects the user experience. In this paper, we present a directory prefetching mechanism on the client side to reduce directory traversal operation latency in non-metadata distributed file system. The mechanism, combined with the client's cache, adopts the directory access history to predict future access pattern and fetches the content of the directory without user intervention. Our goal is to reduce the overall access latency in the non-metadata distributed file system in order to better satisfy the user experience.

In a participatory sensing system, data sensed from smartphone users are shared with the general public who requests data through submitting tasks. When multiple tasks request the data from a mobile user, the mobile user can make a transmission schedule to achieve the balance between the amount of data transmitted and energy consumption. Intuitively, reducing the amount of data transmitted by making use of data sharing between the tasks can save the energy consumption. However, due to the convexity of rate-power function for rate-adaptive transmitting devices, a schedule purely minimizing the amount of data transmitted may not always be the optimal one minimizing the energy consumption. Thus, there exists a tradeoff between the amount of data transmitted and energy consumption. This paper formulates the problem as a bi-objective optimization problem to simultaneously minimize the amount of data transmitted and the energy consumption. Two task models are studied, first-in-first-out (FIFO) task model and arbitrary deadline (AD) task model, respectively. We first provide optimal algorithms for the off-line case. We then study the online case where requests arrive dynamically without prior information. For FIFO tasks, we develop an online algorithm that is O(ln L)-competitive with respect to both the amount of data transmitted and energy consumption, where L is the longest length of the time duration of the tasks. For AD tasks, we devise an online algorithm that is O(ln2L)-competitive with respect to both the amount of data transmitted and energy consumption. Our simulation results validate the efficiency of our online algorithms.

Abstract Location‐related mobile social network services are popular nowadays, and their methods to obtain end users’ location information are based on people's self‐report location claims, using mobile devices to check positions and send them back to the service providers. However, this mechanism has a serious vulnerability that makes malicious users be able to access restricted resource by transmitting fake locations. Both academic and industrial researchers are recently aware of this problem's importance since the commercialized trend of location‐related mobile social network services. To address this issue, we propose mobile phone‐based physical‐social location, a mobile phone‐based location proof system to verify users’ location claims and defend various fake location information. Our core idea is that a user's location claim can be proved by a set of selective physical encountered people serving as “witnesses” who are co‐located with him/her in that area. The system is composed of proof generation and verification. In the proof generation phase, we leverage a certain number of co‐located people to generate certificates as location proofs during their encounters via bluetooth interface. In the verification phase, we propose an efficient verification scheme to make our system accurate and adaptive. We have implemented the MPSL system using real world Nokia N82 (Nokia, Espoo, Finland) phones. Our experimental results show that our mobile phone‐based system can achieve high verification accuracy and good performance. Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, we aim at the product promotion in O2O model and carry out the research of location-based influence maximization on the platform of LBSN. As offline consuming behavior exists under the O2O environment, the traditional online influence diffusion model could not describe the product acceptance accurately. Moreover, the existing researches of influence maximization tend to only concern on the online network of relationships but rarely take the offline part into consideration. This paper introduces the location property into the influence maximization to accord with the characteristic of O2O model. Firstly, we propose an improved influence diffusion model called TP Model which could accurately describe the process of accepting products under the O2O environment. Meanwhile, the definition of location-based influence maximization is presented. Then the user mobility pattern is analyzed and the calculation method of offline probability is designed. Considering the influence ability, a location-based influence maximization algorithm named TPH is proposed. Experiments prove TPH algorithm has general advantage. Finally, focusing on the performance of TPH algorithm under special circumstances, MR algorithm is designed as complement and experiments also verify its high effectiveness.

Website Fingerprinting (WF) attacks have posed a serious threat to users' privacy, which allow an adversary to infer the anonymous communication content by using traffic analysis. Recent studies have demonstrated the effectiveness of WF attacks through a large number of experiments. However, some researchers believe that the assumptions of WF attacks vastly simplify the problem and are critical in the practical scenarios. In this paper, we assess the threat model of WF and relax the assumptions about browsing behavior to improve the practical feasibility. To deal with the multi-tab browsing scenario, we propose a novel WF attack and identify webpages respectively. The main idea resides in the fact that the user visits the second page with a short delay after opening the first page due to the think time. We analyze the anonymous traffic transmitted in the delay and select fine-grained features to identify the first page. Furthermore, we exclude the first page's traffic and utilize coarse features to identify the second page. We deploy our attack in real word environment and the experiment lasts for two months. The Naive Bayes classifier is then applied on the collected datasets to classify the visited websites among 50 top ranked websites in Alexa. When the delay is set to 2 seconds, our attack can classify the first page with 75.9% accuracy, and the second page is 40.5%. The results show that the WF attack is still effective in the practical scenarios and we can't dismiss WF as a threat.

Researchers in service computing area introduce the service Skyline to optimize web service selection. It can eliminate those low-quality web services for large amounts of candidates and return a much smaller and high-quality set to the user. But there is one obvious limitation for these work that they can only compute Skyline on one combination of QoWS parameters. However, in practical, different users may be interested in different combinations of QoWS parameters, and existing work cannot afford such requirement for different QoWS preference. In this paper, we introduce the service Skyeube which consists of Skyline on all possible combinations of QoWS parameters. As it is computed previously in off-line manner, using Skyeube can speed up the response time in real-time web service selection. Unfortunately, the current Skycbue computation solutions suffer from the issue of dimension scalability. To overcome this problem, in this paper, the computational relationship between Skyline computation on one subspace and its super-space are studied. Then a novel computational model, which can compute Skyline on related subspaces by reusing the duplicate comparison results, is developed. Based on this model, a Column-sorting based Skyeube computation algorithm, called CSBSC, is proposed to compute Skyeube much more efficiently. The simulations demonstrate the efficiency and scalability of our CSBSC.

Increasing numbers of service providers have tended to use tens of geographically dispersed datacenters in recent years. Thus, a major unmet challenge is efficiently transferring data among multiple datacenters in different domains. Traditional single-path transfer mechanism based on BGP has limited reliability and low link utilization; therefore, multi-path inter-domain flow transfer mechanisms have become a research focus. However, the existing multi-path transfer mechanisms have some shortcomings, such as poor compatibility with existing network architecture and difficulty in selecting routes according to the demands of applications and the network status. This paper proposes a novel inter-domain multi-path flow transfer mechanism based on SDN and multi-domain cooperation. First, a hieratical iterative detection method is proposed to find diversified multi-paths based on the analysis of BGP notification and inter-domain collaboration. Second, an information exchange method is devised to exchange and maintain the network status (e.g., inter-domain topology updates, link load). Finally, a decision and deployment method is designed. The experimental results indicate that this mechanism has advantages in ensuring the success rate of data transfer task and improving network throughput.

In a wireless system, when multiple applications can share data transmitted by rate-adaptive wireless devices, there exists a trade-off between transmission redundancy and energy efficiency. This paper conducts the first theoretical analysis on such a trade-off. We formulate the problem as a bi-objective optimization problem to simultaneously minimize the transmission redundancy and the energy consumption. In the offline setting that the full information is known in advance, we provide optimal algorithms for the bi-objective optimization problem. In the online setting, we provide an online algorithm with proven performance bound to approximate the optimal solution without relying on any assumed distribution or future information. The proposed online algorithm is proved O(ln T)-competitive with respect to transmission redundancy and also O(ln T)-competitive with respect to energy consumption, where T is the number of time slots. That is, the output of the algorithm always approximates the optimal solution within a logarithmic factor over all possible inputs. Our simulation results further validate the efficiency of our online algorithm.

Myopia becomes a more and more serious worldwide problem as the number of myopic people (especially young people) grows rapidly. Efficient methods are required to monitoring the deterioration of nearsightness so as to take further treatment. This demo realizes a noval nearsightness monitoring system, called iSee, which utilizes the widely used smartphones to detect the deterioration of nearsightness by monitoring and analysing the the distance between the eyes and the smartphone screen. A prototype of iSee has been developed to evaluated the effectiveness under different environmental conditions.

Multi-radio WLAN mesh networks (WMNs) are intended to provide a wireless access infrastructure with high throughput and reliable transmission. With the increasing demand for ubiquitous Internet access, access networks tend to be large-scale, complex and dense, and energy consumption has become a critical concern. Since the networks are designed to support peak traffic demand but does not serve peak traffic demand all the time, which leads to significant energy wastage in off-peak conditions. This means that energy consumption can be effectively reduced through scheduling nodes on/off according to the current traffic demand. In multi-radio WMNs, each node is equipped with multiple radios, and radios can also be switched on/off for further energy saving. Therefore, we investigate the problem of joint node scheduling and radio switching for energy efficiency in this paper, which is proven to be NP-hard. We first formulate the problem as an integer linear programming model, which aims to minimize the power consumption of the network while satisfying node-radio-link, traffic demand and routing constraints. Then, we propose an efficient heuristic algorithm, which iteratively finds routing paths for all mesh access points (MAPs) while satisfying their traffic demand. In each iteration, exact one MAP is selected, and the nodes on its routing path are scheduled on and the corresponding radios are switched on. Finally, extensive simulations are conducted to evaluate the performance of the proposed algorithm in terms of the number of active nodes and radios. The results not only show that the algorithm can achieve energy saving efficiently and effectively, but also demonstrate that the joint optimization of node scheduling and radio switching can obtain better performance on energy efficiency.

With the rapid development and popularity of cloud computing technology, more and more Collaborative Virtual Environment (CVE) systems are migrated to cloud computing environment to improve the effectiveness of resource usage. Virtual Machine (VM) placement in cloud data center is a key issue of providing high-efficient cloud platform for CVE system. However, most existing VM placement algorithms ignore the following characteristics of actual cloud environment: (1) VMs deployment requests arrive and leave dynamically, (2) Cloud data center usually consists of many heterogeneous Physical Machines (PMs). Ignoring these two characteristics result in an inefficient and unbalanced use of multiple resources of PMs. Thus using these algorithms directly will lead to a poor resource utilization. In this article, we propose a two-phase online VM placement algorithm, which helps the cloud data center to minimize different resource usages and aims at a more efficient use of multiple resources. Our algorithm selects the most suitable PM type for VM based on Cosine Similarity, and adaptively maps VMs to PMs by using an approximation algorithm. The proposed algorithm is evaluated by simulations. Experimental results show our proposed algorithm ensures a more efficient use of multiple resources over the existing approaches.

In multi-radio wireless networks, it is vital to efficiently utilize the resource of non-overlapping channels, and thus the problem of channel assignment has been widely studied. Most proposed channel assignment approaches focus on reducing interference or maximizing throughput, assuming that all the radios on each node are keeping active. However, it is a significant waste of energy when network traffic is low, since the network is designed for peak traffic demands but does not serve peak traffic demands all the time. Even if the number of active radios is dynamically adaptive to time-varying network traffic, some radios will inevitably switch their channels, which significantly increase network delay. Therefore, we investigate the energy efficient channel assignment problem with switching optimization in this paper. To address this problem, we first define the optimization objective to minimize the number of active radios and channel switchings, and then propose several constraints, such as channel switching, radio amount, transmission existence, link rate, link interference, traffic demand and routing constraints. On this basis, we present an MILP model called EECA-SO, and evaluate its performance through numerical analysis. The results show that the proposed EECA-SO model can not only achieve energy saving effectively, but also obtain the trade off between energy efficiency and switching optimization.

Web-scale information networks containing billions of entities are common nowadays. Querying these networks can be modeled as a subgraph matching problem. Since information networks are incomplete and noisy in nature, it is important to discover answers that match exactly as well as answers that are similar to queries. Existing graph matching algorithms usually use graph indices to improve the efficiency of query processing. For web-scale information networks, it may not be feasible to build the graph indices due to the amount of work and the memory/storage required. In this paper, we propose an efficient algorithm for finding the best k answers for a given query without precomputing graph indices. The quality of an answer is measured by a matching score that is computed online. To speed up query processing, we propose a novel technique for bounding the matching scores during the computation. By using bounds, we can efficiently prune the answers that have low qualities without having to evaluate all possible answers. The bounding technique can be implemented in a distributed environment, allowing our approach to efficiently answer the queries on web-scale information networks. We demonstrate the effectiveness and the efficiency of our approach through a series of experiments on real-world information networks. The result shows that our bounding technique can reduce the running time up to two orders of magnitude comparing to an approach that does not use bounds.

Summary Tor is a famous anonymous communication system for preserving users' online privacy. It supports TCP applications and packs upper‐layer application data into encrypted equal‐sized cells with onion routing to hide private information of users. However, we note that the current Tor design cannot conceal certain application behaviors. For example, P2P applications usually upload and download files simultaneously, and this behavioral feature is also kept in Tor traffic. Motivated by this observation, we investigate a new attack against Tor, application classification attack, which can recognize application types from Tor traffic. An attacker first carefully selects some flow features such asburst volumesanddirectionsto represent the application behaviors and takes advantage of some efficient machine‐learning algorithm (e.g., Profile Hidden Markov Model) to model different types of applications. Then he or she can use these established models to classify target's Tor traffic and infer its application type. We have implemented the application classification attack on Tor using parallel computing, and our experiments validate the feasibility and effectiveness of the attack. We argue that the disclosure of application type information is a serious threat to Tor users' anonymity because it can be used to reduce the anonymity set and facilitate other attacks. We also present guidelines to defend against application classification attack. Copyright © 2015 John Wiley & Sons, Ltd.

Summary The use of Big Data and the development of cloud computing have led to greater dependence on data‐intensive services. Each service may actually request or create a large amount of data sets. The scope, number, and complexity of data‐intensive services are all set to soar in the future. To compose these services will be more challenging. Issues of autonomy, scalability, adaptability, and robustness, become difficult to resolve. Bio‐inspired algorithms can overcome the new challenging requirements of data‐intensive service provision. It is useful for the provision of data‐intensive services to explore key features and mechanisms of biological systems and accordingly to add biological mechanisms to services. In this paper, we will discuss single‐objective and multi‐objective data‐intensive service provision problems based on bio‐inspired algorithms. Further, we will propose an ant‐inspired negotiation approach. Finally, this paper points out future research topics. Copyright © 2015 John Wiley & Sons, Ltd.

Summary In the big data era, scientific workflow exhibits the characteristics of data intensity and becomes increasingly popular in scientific domains. Efficient scheduling of data‐intensive scientific workflow in a multiple datacenter (DC) environment has been a long‐standing challenge. Most of previous work on data‐intensive scientific workflow scheduling primarily focused on the optimization of reducing the volumes of data transfer between workflow tasks. In this paper, novel scheduling strategies for the execution of data‐intensive scientific workflow in multi‐DC environment are proposed aiming at the optimization of the overall data transfer time. A novel DC selection approach is proposed to minimize the number of DCs having enough storage capacity for the execution of scientific workflow as well as optimized inter‐DC network bandwidth for efficient data transfer between workflow tasks. A k‐means clustering‐based data placement strategy is adopted to intelligently place the initial data of scientific workflow thereby reducing the volume of initial data transfer between different DCs. A multilevel task replication scheduling strategy is invented to reduce the volumes of intermediate data transfer between DCs during the runtime of the scientific workflow. Simulations spanning a broad range of scientific workflow and multi‐DC settings are performed in order to verify the proposed approaches. The numerical results show that our combined scheduling strategy significantly reduces the overall data transfer time and data transfer volume when scientific workflow is scheduled in multi‐DC environment. Copyright © 2015 John Wiley & Sons, Ltd.

This paper presents an optimal rate scheduling algorithm called Truncation for an energy-harvesting enabled wireless transmitter to transmit a set of dynamically arrived packets with minimum transmission energy. Distinct from existing works, we allow packets to have individual delay constraints, which is the most general model ever assumed but is very much desired to guarantee per-application quality-of-service (QoS). Moreover, we restrict the allowable rates to a set of discrete values, which is more practical and required in many real applications. As the first achievement, we obtain an optimal offline algorithm, which assumes the rate is continuously adjustable. Then, we propose a general framework that transforms any algorithm using the continuous-rate model into an algorithm using only discrete-rates, while preserving the optimality as long as the optimality holds for convex rate-power functions. It is possible that the harvested energy is insufficient to guarantee all packets to meet their deadlines. Should this occur, maximizing throughput with the limited available energy becomes the goal to achieve. Our Truncation algorithm is able to identify this case and produces a schedule that guarantees maximum throughput, if packets share a common deadline. Furthermore, based on the optimal offline algorithms, an efficient online algorithm is designed which has been shown by simulations to produce near optimal results.

Deadline guaranteed packet scheduling for switches is a fundamental issue for providing guaranteed QoS in digital networks. It is a historically difficult NP-hard problem if three or more deadlines are involved. All existing algorithms have too low throughput to be used in practice. A key reason is they use packet deadlines as default priorities to decide which packets to drop whenever conflicts occur. Although such a priority structure can ease the scheduling by focusing on one deadline at a time, it hurts the throughput greatly. Since deadlines do not necessarily represent the actual importance of packets, we can greatly improve the throughput if deadline induced priority is not enforced. This paper first presents an algorithm that guarantees the maximum throughput for the case where only two different deadlines are allowed. Then, an algorithm called iterative scheduling with no priority (ISNOP) is proposed forthe general case where k > 2 different deadlines may occur. Not only does this algorithm have dramatically better average performance than all existing algorithms, but also guarantees approximation ratio of 2. ISNOP would provide a good practical solution for the historically difficult packet scheduling problem.

Tor is a popular low-latency anonymous communication system. It is, however, currently abused in various ways. Tor exit routers are frequently troubled by administrative and legal complaints. To gain an insight into such abuse, we designed and implemented a novel system, TorWard, for the discovery and the systematic study of malicious traffic over Tor. The system can avoid legal and administrative complaints, and allows the investigation to be performed in a sensitive environment such as a university campus. An intrusion detection system (IDS) is used to discover and classify malicious traffic. We performed comprehensive analysis and extensive real-world experiments to validate the feasibility and the effectiveness of TorWard. Our results show that around 10% Tor traffic can trigger IDS alerts. Malicious traffic includes P2P traffic, malware traffic (e.g., botnet traffic), denial-of-service attack traffic, spam, and others. Around 200 known malwares have been identified. To mitigate the abuse of Tor, we implemented a defense system, which processes IDS alerts, tears down, and blocks suspect connections. To facilitate forensic traceback of malicious traffic, we implemented a dual-tone multi-frequency signaling-based approach to correlate botnet traffic at Tor entry routers and that at exit routers. We carried out theoretical analysis and extensive real-world experiments to validate the feasibility and the effectiveness of TorWard for discovery, blocking, and traceback of malicious traffic.

Tor is a well-known low-latency anonymous communication system that is able to bypass the Internet censorship. However, publicly announced Tor routers are being blocked by various parties. To counter the censorship blocking, Tor introduced non-public bridges as the first-hop relay into its core network. In this paper, we investigated the effectiveness of two categories of bridge-discovery approaches: 1) enumerating bridges from bridge HTTPS and email servers, and 2) inferring bridges by malicious Tor middle routers. Large-scale real-world experiments were conducted and validated our theoretic findings. We discovered 2365 Tor bridges through the two enumeration approaches and 2369 bridges by only one Tor middle router in 14 days. Our study shows that the bridge discovery based on malicious middle routers is simple, efficient, and effective to discover bridges with little overhead. We also discussed issues related to bridge discovery and mechanisms to counter the malicious bridge discovery.

Tor is a popular anonymizing network and the existing work shows that it can preserve users' privacy from website fingerprinting attacks well. However, based on our extensive analysis, we find it is the overlap of web objects in returned web pages that make the traffic features obfuscated, thus degrading the attack detection rate. In this paper, we propose a novel active website fingerprinting attack under Tor's local adversary model. The main idea resides in the fact that the attacker can delay HTTP requests originated from users for a certain period to isolate responding traffic segments containing different web objects. We deployed our attack in PlanetLab and the experiment lasted for one month. The SVM multi-classification algorithm was then applied on the collected datasets with the introduced features to identify the visited website among 100 top ranked websites in Alexa. Compared to the stat-of-the-art work, the classification result is improved from 48.5% to 65% by delaying at most 10 requests. We also analyzed the timing characteristics of Tor traffic to prove the stealth of our attack. The research results show that anonymity in Tor is not as strong as expected and should be enhanced in the future.

Currently in large-scale scientific experiments, scientists often submit scientific workflow jobs at different time. From the view of system, the entire workload is a stream of jobs submitted at an unpredictable time and different job has different priority and deadline. Moreover the cost of performing these jobs cannot exceed a certain budget constraint. Therefore how to perform scientific workflow applications efficiently in cloud has become the urgent problem. However most of existing work didn't consider unpredictable submission time of jobs, as well as budget and deadline constrains. In this paper, we design an elastic resource provisioning and task scheduling mechanism to perform scientific workflows in cloud. Our goal is to complete as many high-priority workflows as possible under budget and deadline constrains. This mechanism consists of three phases: workflow preprocessing, elastic resource provisioning and task scheduling. We perform evaluation with real AMS experiment scientific computing data under different budget constraints. We also consider inaccurate task execution time, VM provisioning delays and task failures in evaluation. The results show that our mechanism achieves a better performance than these reference mechanisms. In addition, the inaccurate task execution time, VM provisioning delays, and task failures do not bring significant impact to mechanism's performance.

Massive information networks, such as the knowledge graph by Google, contain billions of labeled entities. Star queries, which aim to identify an entity, given a set of related entities, are common on such networks. Answering star queries can be modeled as a graph pattern matching problem. Traditional approaches apply graph indices to accelerate the query processing. Unfortunately, it is so costly that it is nearly infeasible to build indices on billion node graphs since the time or storage complexity of most indexing techniques is super-linear to the graph size. In this paper, we propose an algorithm to identify the top-k best answers for a star query. Instead of using expensive indices, our algorithm utilizes novel bounding techniques to derive the top-k best answers efficiently. Further, the algorithm can be implemented in a distributed manner scaling to billions of entities and hundreds of machines. We demonstrate the effectiveness and the efficiency of our approach through a series of experiments on real-world information networks.

Sampling-based approximate query processing method provides the way, in which the users can save their time and resources for 'Big Data' analytical applications, if the estimated results can satisfy the accuracy expectation earlier before a long wait for the final accurate results. Online aggregation (OLA) is such an attractive technology to respond aggregation queries by calculating approximate results with the confidence interval getting tighter over time. It has been built into the MapReuduce-based cloud system for big data analytics, which allows users to monitor the query progress and save money by killing the computation earlier once sufficient accuracy has been obtained. Unfortunately, there exists a major obstacle that is the estimation failure of OLA affects the OLA performance, which is resulted from the biased sample set that violates the unbiased assumption of OLA sampling. To handle this problem, we first propose a hybrid approximate query processing model to improve the overall OLA performance, where a dynamic scheme switching mechanism is deliberately designed to switch unpromising OLA queries into the bootstrap scheme for further processing, avoiding the whole dataset scanning resulted from the OLA estimation failure. In addition, we also present a progressive estimation method to reduce the false positive ratio of our dynamic scheme switching mechanism. Moreover, we have implemented our hybrid approximate query processing system in Hadoop, and conducted extensive experiments on the TPC-H benchmark for skewed data distribution. Our results demonstrate that our hybrid system can produce acceptable approximate results within a time period one order of magnitude shorter compared to the original OLA over Hadoop.

In the provision of dynamic data-intensive services, the cost and response time of data sets as well as the states of services may change over time. An ant colony system for this problem is studied in this paper. Specifically, we consider changing the QoS attributes of services and replacing a certain number of services with new ones at different frequencies. In order to adapt the ant colony system to handle the dynamic scenarios, several pheromone modification strategies in reaction to changes of the optimization scenarios are investigated. The aim of the strategies is to find a balance between preserving enough old pheromone information to speed up the search process, and resetting enough new pheromone information to facilitate the ants to find a new solution for the changed scenarios. The strategies differ in their degree of reinitialized pheromone values with respect to the information that has been used to decide the amount of pheromone values. Moreover, the behaviors of different strategies for modifying pheromone information are compared.

Tor is a popular low-latency anonymous communication system. However, it is currently abused in various ways. Tor exit routers are frequently troubled by administrative and legal complaints. To gain an insight into such abuse, we design and implement a novel system, TorWard, for the discovery and systematic study of malicious traffic over Tor. The system can avoid legal and administrative complaints and allows the investigation to be performed in a sensitive environment such as a university campus. An IDS (Intrusion Detection System) is used to discover and classify malicious traffic. We performed comprehensive analysis and extensive real-world experiments to validate the feasibility and effectiveness of TorWard. Our data shows that around 10% Tor traffic can trigger IDS alerts. Malicious traffic includes P2P traffic, malware traffic (e.g., botnet traffic), DoS (Denial-of-Service) attack traffic, spam, and others. Around 200 known malware have been identified. To the best of our knowledge, we are the first to perform malicious traffic categorization over Tor.

Virtualization based cloud and big data applications have been widely adopted in various fields. Because deploying the big data applications on the cloud will cause obvious performance degradation, the cloud and big data applications are provided with fixed resource separately. However, the traditional fixed resource allocation mechanism has two drawbacks: (1) low resource utility and (2) unresponsiveness to the performance degradation. To address these drawbacks, the cloud and big data hybrid environment is designed, where fair resource allocation is used to ensure fairness between cloud and big data applications while virtual machine migration is used to make each virtual machine in cloud application reach its own satisfactory. Herein, game theory is used to model the conflict and negotiation between cloud and big data applications. Firstly, the Nash Equilibrium is used to discover the best strategy for both applications. Secondly, as for virtual machine migration, we use Nash Bargaining game to present the situation where virtual machines compete for more resources allocation while their minimal demand is ensured. Finally, experiments are carried out to prove that the hybrid environment outperforms the traditional method both in resource utility and application performance.

Enhancing teamwork performance is a significant issue in mobile cloud-based learning. We introduce a service oriented system, Teamwork as a Service (TaaS), to realize a new approach for enhancing teamwork performance in the mobile cloud environment. To coordinate most learners' talents and give them more motivation, an appropriate task allocation is necessary. Utilizing the Kolb's learning style (KLS) to refine learner's capabilities, and combining their preferences and tasks' difficulties, we formally describe this problem as a constraint optimization model. Two heuristic algorithms, namely genetic algorithm (GA) and simulated annealing (SA), are employed to tackle the teamwork-enhanced task allocation, and their performances are compared respectively. Having faster running speed, the SA is recommended to be adopted in the real implementation of TaaS and future development.

Due to the limited number of orthogonal channels in a multi-radio multi-channel wireless mesh network (MR-WMN), overlapping channel assignment (CA) is one of the main factors that greatly affect the network capacity. In this paper, we first propose a model for measuring achieved network capacity in MR-WMNs. Then we prove that finding an optimal overlapping CA in a given MR-WMN with odd number of channels, is equivalent to finding an optimal assignment by only using its orthogonal channels. This theory allows us to use fewer channels to solve complicated CA problems. Third, we prove that in 802.11b/g-based MR-WMN, the simplified optimization problem is a Max-3-Cut problem. Although this problem is NP-hard, it has an efficient approximation algorithm that achieves approximation ratio of 1.19616 probabilistically by using the algorithm for Max-Cut whose approximation ratio is 1.1383 probabilistically. Based on the algorithm for Max-Cut, this paper proposes Max-Cut-based channel assignment (MCCA) which uses a heuristic method to adjust the result produced by the Max-Cut algorithm to achieve an even better result. Finally, we perform extensive simulations to compare the MCCA with a state-of-the-art Tabu-Search based algorithm. The results show that the Max-Cut-based overlapping CA algorithm effectively improves on the network capacity.

Workflow applications structured as Parallel Task Graphs (PTG) exhibit both data and task parallelism and arise in scientific and industrial domains. Most of previous works regarding PTG scheduling only target dedicated multicluster platform. In this paper we develop a scheduling algorithm, MTD (Moldable Task Duplication with forward migration of duplicated predecessors), which applies to non-dedicated heterogeneous multicluster platforms. Our novel contribution is that in MTD, dynamic critical task determination accounts for the heterogeneity and fluctuations of multicluster platform within the hypothetical deadline, and the strategy of moldable task duplication with forward migrations of duplicated predecessors is invented to fully exploit the flexibility of data-parallel tasks. Simulations show that our approach can achieve better average PTG makespan than its competitors.

In the composite service which runs for a long time under the heterogeneous and loose-coupled circumstance, the failure of service tends to occur. The transaction and recovery mechanism is urgently needed in order to guarantee the end-to-end QoS of the workflow and satisfy the user requirement. In this paper we address the composite service recovery issue in the way of substitution with the consideration of QoS constraint, based on our previous research work of the transactional construction and processing rules and the global optimization service selection algorithm, TSSA. Firstly, the service execution graph (SEG) is introduced and a service execution solution selection algorithm is proposed to choose one from the solution set of TSSA which has the highest success rate of recovery. Then, the concepts of execution backup path and switch cost are introduced and a search algorithm is presented to search for the optimal backup path when current service failure occurs. Meanwhile, a local induction algorithm based on positive feedback is described which could rapidly construct an transactional execution path when no backup execution path can be found in SEG and also guarantees the transactional constraint of composite service. Finally, experimental results show the recovery algorithm proposed in this paper is efficient and has high reliability.

Tor hidden services are commonly used to provide a TCP based service to users without exposing the hidden server's IP address in order to achieve anonymity and anti-censorship. However, hidden services are currently abused in various ways. Illegal content such as child pornography has been discovered on various Tor hidden servers. In this paper, we propose a protocollevel hidden server discovery approach to locate the Tor hidden server that hosts the illegal website. We investigate the Tor hidden server protocol and develop a hidden server discovery system, which consists of a Tor client, a Tor rendezvous point, and several Tor entry onion routers. We manipulate Tor cells, the basic transmission unit over Tor, at the Tor rendezvous point to generate a protocol-level feature at the entry onion routers. Once our controlled entry onion routers detect such a feature, we can confirm the IP address of the hidden server. We conduct extensive analysis and experiments to demonstrate the feasibility and effectiveness of our approach.

Personalized recommendation technology in E-commerce is widespread to solve the problem of product information overload. However, with the further growth of the number of E-commerce users and products, the original recommendation algorithms and systems will face several new challenges: (1) to model user's interests more accurately, (2) to provide more diverse recommendation modes, and (3) to support large-scale expansion. To address these challenges, from the actual demands of E-commerce applications (as Made-in-China website), a personalized hybrid recommendation system, which can support massive data set, is designed and implemented in this paper by using Cloud technology. Hereinto, the recommendation algorithms are designed based on a novel user interesting model for different scenarios, and the massive data parallel processing techniques in Cloud computing is utilized to realize the effective execution of recommendation algorithms. Finally, several experiments are presented to highlight the system performance.

In this paper, we present a study on the anonymity of Anonymizer, a well-known commercial anonymous communication system. We discovered the architecture of Anonymizer and found that the size of web packets in the Anonymizer network can be very dynamic at the client. Motivated by this finding, we investigated a class of novel packet size-based covert channel attacks against Anonymizer. The attacker between a website and the Anonymizer server can manipulate the web packet size and embed secret signal symbols into the target traffic. An accomplice at the user side can sniff the traffic and recognize the secret signal. In this way, the anonymity provided by Anonymizer is compromised. We developed intelligent and robust algorithms to cope with the packet size distortion incurred by Anonymizer and Internet. We developed techniques to make the attack harder to detect: 1) We pick up right packets of web objects to manipulate to preserve the regularity of the TCP packet size dynamics, which can be measured by the Hurst parameter; 2) We adopt the Monte Carlo sampling technique to preserve the distribution of the web packet size despite manipulation. We have implemented the attack over Anonymizer and conducted extensive analytical and experimental evaluations. It is observed that the attack is highly efficient and requires only tens of packets to compromise the anonymous web surfing via Anonymizer. The experimental results are consistent with our theoretical analysis.

Autonomous Systems (ASes) discover routing paths to the destination AS via BGP announcements advertised by neighbor ASes. However, the actual packets forwarding path may be inconsistent with the announced routing path. The inconsistency could cheat rational ASes to bring a massive commercial benefit to malicious ASes, and cause a great damage on the stability of Internet. Prior work on this issue could be summarized as path verification in the control plane and path probing in the data plane. These countermeasures could not discover the problem in time, and have a lot of overhead. In this paper, we design a Routing Collaboration Accountability Mechanism that enables the source AS of the path to discover the inconsistency. The source and destination AS collect analysis results of forwarded packets in a certain time interval to generate routing evidence. Making use of routing evidence, they collaborate with each other to verify the announced path. It is a lightweight mechanism without packets modification and be suitable in high packet rate network. The evaluation results show that it has less overhead than other methods.

Watermarking schemes for tracing network attack flows have been proposed to detect stepping-stone intrusion and fight against the abuse of anonymity. However, most existing network flow watermark detection techniques focus on fixed sample size of network data, thus resulting in not only unguaranteed rates of detection errors but also low efficiency of watermark detection. We herein propose an efficient sequential watermark detection (ESWD) model for tracing network attack flows. Based on the ESWD model, a statistical analysis of sequential detectors, with no assumptions or limitations concerning the distribution of the timing of packets, proves their effectiveness despite traffic timing perturbations. The experiments using a large number of synthetically-generated SSH traffic flows demonstrate that there is a significant advantage in using the ESWD model over the existing fixed sample size (FSS) detector, where the optimal sequential watermark detector (OSWD) based on the ESWD model results in almost 28% savings in the average number of packets compared to the FSS watermark detector. Furthermore, the nonparametric sequential sign watermark detector (SSWD) can also reduce the average packet number, given the required probability of detection errors.

Tor is a well-known low-latency anonymous communication system that is able to bypass Internet censorship. However, publicly announced Tor routers are being blocked by various parties. To counter the censorship blocking, Tor introduced nonpublic bridges as the first-hop relay into its core network. In this paper, we analyzed the effectiveness of two categories of bridge-discovery approaches: (i) enumerating bridges from bridge https and email servers, and (ii) inferring bridges by malicious Tor middle routers. Large-scale experiments were conducted and validated our theoretic findings. We discovered 2365 Tor bridges through the two enumeration approaches and 2369 bridges by only one Tor middle router in 14 days. Our study shows that the bridge discovery based on malicious middle routers is simple, efficient and effective to discover bridges with little overhead. We also discussed the mechanisms to counter the malicious bridge discovery.

Information leakage via side channels has become a primary security threat to encrypted web traffic. Existing side channel attacks and corresponding countermeasures focus primarily on packet length, packet timing, web object size and web flow size. However, we found that encrypted web traffic can also leak information via network delay between a user and the web sites that she visits. Motivated by this observation, we investigate a novel network-delay based side-channel attack to infer web sites visited by a user. The adversary can utilize pattern recognition techniques to differentiate web sites by measuring sample mean and sample variance of the round-trip time (RTT) between a victim user and web sites. We theoretically analyzed the damage caused by such an adversary and derived closed-form formulae for detection rate, the probability that the adversary correctly recognizes a web site. To defeat this side-channel attack, we proposed several countermeasures. The basic idea is to shape traffic from different web sites so that they have similar RTT statistics. We proposed the strategies based on the k-means clustering and K-Anonymity to ensure that traffic shaping will not cause excessive delay while providing a predictable degree of anonymity. We conducted extensive experiments and our empirical results match our theory very well.

In multi-channel wireless mesh networks (WMNs), it is significantly important to achieve efficient channel utilization. The channel assignment problem, which investigates how to seek a proper mapping between available channels and network interface cards (NICs) on mesh routers (MRs), is attracting more and more attention from the research community. However, most proposed channel assignment approaches assume that NICs are evenly placed on the MRs, and its amount is also pre-determined. Due to the non-uniform distribution of WMN traffic, the equal interface placement will inevitably cause that the bottleneck MRs suffer from NIC shortage, and MRs with less load only have a low utilization of NICs. Hence, in order to improve network performance and reduce network cost, interface placement should be considered carefully in the process of planning a WMN. In this paper, we investigate the joint interface placement and channel assignment problem, i.e., how to appropriately place NICs on MRs and assign channels to them. We first formulate the problem as a mixed integer linear programming (MILP) model, which aims to minimize the total number of NICs while satisfying logical link, link bandwidth, link interference and traffic demand constraints. Then, we propose an MILP-based heuristic algorithm, using iterated local search to obtain sub-optimal solutions efficiently. Finally, we conduct simulation experiments to compare the heuristic solutions with the optimal solutions, and the results show that our proposed heuristic algorithm can achieve good sub-optimal solutions. Moreover, the simulation results also demonstrate that the algorithm can be well applied in large-scale WMNs where the optimal solutions cannot be obtained, and can perform well under different traffic demands.

Cloud computing, as a popular technique to support and achieve CSCW, is gaining increasing importance in recent years, where the virtualization becomes the key technique. However, although utilizing virtualization can implement more efficient and flexible resource allocation, it may also come at the cost of increased system complexity and dynamics. In order to effectively adapt to performance fluctuations for ensuring high-performance, a generic approach to predict the performance influences of cloud platforms is highly desirable. To address this request, in this paper, the major factors that affect the performance of cloud and the relevant variation discipline are evaluated and analyzed thoroughly using a series of benchmarks in SEU (Southeast University) Cloud Computing Platform, where not only a general methodology on quantifying the performance influence but also the most important impact factors are proposed. Moreover, we use a real world application, as AMS experiment, to further evaluate the relevant performance.

Various low-latency anonymous communication systems such as Tor and Anonymizer have been designed to provide anonymity service for users. In order to hide the communication of users, most of the anonymity systems pack the application data into equal-sized cells (e.g., 512 B for Tor, a known real-world, circuit-based, low-latency anonymous communication network). Via extensive experiments on Tor, we found that the size of IP packets in the Tor network can be very dynamic because a cell is an application concept and the IP layer may repack cells. Based on this finding, we investigate a new cell-counting-based attack against Tor, which allows the attacker to confirm anonymous communication relationship among users very quickly. In this attack, by marginally varying the number of cells in the target traffic at the malicious exit onion router, the attacker can embed a secret signal into the variation of cell counter of the target traffic. The embedded signal will be carried along with the target traffic and arrive at the malicious entry onion router. Then, an accomplice of the attacker at the malicious entry onion router will detect the embedded signal based on the received cells and confirm the communication relationship among users. We have implemented this attack against Tor, and our experimental data validate its feasibility and effectiveness. There are several unique features of this attack. First, this attack is highly efficient and can confirm very short communication sessions with only tens of cells. Second, this attack is effective, and its detection rate approaches 100% with a very low false positive rate. Third, it is possible to implement the attack in a way that appears to be very difficult for honest participants to detect (e.g., using our hopping-based signal embedding).

Abstract Sensor networks have been receiving significant attention due to their potential applications in environmental monitoring and surveillance domains. In this paper, we consider the design issue of sensor networks by placing a few powerful aggregate nodes into a dense sensor network such that the network lifetime is significantly prolonged when performing data gathering. Specifically, givenKaggregate nodes and a dense sensor network consisting ofnsensors withK≪n, the problem is to place theKaggregate nodes into the network such that the lifetime of the resulting network is maximized, subject to the distortion constraints that both the maximum transmission range of an aggregate node and the maximum transmission delay between an aggregate node and its covered sensor are met. This problem is a joint optimization problem of aggregate node placement and the communication structure, which is NP‐hard. In this paper, we first give a non‐linear programming solution for it. We then devise a novel heuristic algorithm. We finally conduct experiments by simulation to evaluate the performance of the proposed algorithm in terms of network lifetime. The experimental results show that the proposed algorithm outperforms a commonly used uniform placement schema — equal distance placement schema significantly. Copyright © 2010 John Wiley & Sons, Ltd.

Large scale data processing is increasingly common in cloud computing systems like MapReduce, Hadoop, and Dryad in recent years. In these systems, files are split into many small blocks and all blocks are replicated over several servers. To process files efficiently, each job is divided into many tasks and each task is allocated to a server to deals with a file block. Because network bandwidth is a scarce resource in these systems, enhancing task data locality(placing tasks on servers that contain their input blocks) is crucial for the job completion time. Although there have been many approaches on improving data locality, most of them either are greedy and ignore global optimization, or suffer from high computation complexity. To address these problems, we propose a heuristic task scheduling algorithm called Balance-Reduce(BAR), in which an initial task allocation will be produced at first, then the job completion time can be reduced gradually by tuning the initial task allocation. By taking a global view, BAR can adjust data locality dynamically according to network state and cluster workload. The simulation results show that BAR is able to deal with large problem instances in a few seconds and outperforms previous related algorithms in term of the job completion time.

In order to integrate the massive distributed resources to accomplish the complex engineering applications cooperatively, workflow scheduling is an important aspect. However, as the available computing power of Grid resources is changing dynamically, static scheduling scheme will lead to low performance in real Grid environment. Therefore, the rescheduling mechanism should be taken into consideration. Although a few relevant mechanisms have been proposed in recent year, as they do not consider the essence of dynamic feature and the relevant algorithms are too simple, they can not obtain a good enough result yet. To address these problems, a load-aware based adaptive rescheduling mechanism for DAG application called LAR is proposed. Therein, during application running, the load exception will be detected and the execution state of application will be judged to decide whether the rescheduling process should be triggered. And in rescheduling stage, an effective rescheduling algorithm which utilizes the latest prediction information is present. The simulation results show that our mechanism can outperform the relevant algorithms in NRSL, and can effectively solve the performance decreasing problem in real Grid environment.

In a shared multicluster grid where mixed-parallel application workload and background workload co-exist, available processors for executing mixed-parallel application workload are those time-varying residual processors after reservations for background workloads. The grid scheduler aims to minimize the makespan of the mixed-parallel application and allocate processors to tasks belong to the mixed-parallel application with a coordinated manner, whereas advance reservation capability from Local Resource Manager(LRM) are fully exploited. We develop a heuristic algorithm MHEFT-RSV (ReSerVation) adapted from MHEFT (Mixed-Heterogeneous Earliest Finish Time) to multicluster grid with background workloads. Based on MHEFT-RSV, we further propose an exact branch-and-cut scheduling algorithm, which exploits the intertask precedence and resource constraints as much as possible, to accelerate the process of obtaining the schedule with minimized makespan. From the detailed simulation experiment we find that on average the exact branch-and-cut algorithm obtains shorter or equal makespan as MHEFT-RSV while MHEFT-RSV achieves better tradeoff between makespan and computation time.

Tor is a well-known low-latency anonymous communication system. To prevent the traffic analysis attack, Tor packs application data into equal-sized cells. However, we found that equal-sized cells at the application layer do not necessarily produce equal-sized packets at the network layer. Therefore, we introduced a packet size based attack that compromises Tor's communication anonymity with no need of controlling Tor routers. An attacker can manipulate size of packets between a web site and an exit onion router and embeds a signal into the target traffic. An accomplice at the user side can sniff the traffic and recognize this signal. To cope with the signal distortion incurred by Tor and Internet, we developed an effective signal recovery mechanism. Our real-world experiments validate the effectiveness of our attack against Tor. Our work demonstrates the need for re-considering the issue of padding anonymous communication data into equal size.

Data replication is an important technique to reduce access latency and bandwidth consumption in Grid environment. As one of the major functions of data replication, replica selection determines the best replica according to some specific criteria in Data Grid environment, where the data resources are limited and Grid users compete for these resources. In this paper, we focus mainly on a novel QoS preference-aware replica selection strategy which will meet individual QoS sensitivity (IQS) constraints for different users/applications. We first present a framework that characterize QoS properties of replica services and establish its mathematical model by introducing quantification methods. In order to deal with the IQS constraints and to perceive Grid users' QoS preferences accurately, we propose a QoS preference acquisition algorithm based on Analytic Hierarchy Process (AHP). We then design and implement a novel effective and efficient parallel genetic algorithm (PGA) based on Map Reduce paradigm for optimizing the objective function which corresponds to the optimal replica. Simulation results show that our strategy has a better performance in validity as well as scalability, and the optimal replica can always be obtained for Grid users with different IQS constraints under Data Grid environments that vary in system loads, scheduling strategies and user types.

Centralized control can improve the consistence of the network and reduce the load of routers. Trustworthy and Controllable Network takes centralized control as one of the basic control mechanisms and requires to build reliable centralized intra-domain routing. In this paper, we solved the problem of building reliable centralized intra-domain routing by finding the routing configuration which maximizes the disjoint paths of each ingress to all egresses. The problem is transformed to finding K paths for each ingress to the egresses. It is proved to be NP- hard to find the optimal solution when K≥2 if ingresses do not cross each other and when K≥3 if the ingresses cross each other. To solve the problem efficiently, a heuristic algorithm based on network flow theory called HANE is proposed and evaluated on different types of topologies. The experimental results show that HANE can achieve good performance.

The six papers included in this special issue focus on systems integration and collaboration in design, manufacturing, and services.

In recent years, as a popular technique to support CSCW, Grid computing is becoming more and more attractive. Hereinto, as the CPU load information can guide task scheduling process greatly, the long-term CPU load prediction becomes a very hot research field and has been widely studied. However, as the prediction errors will be accumulated gradually and meanwhile the relevant parameters' optimal values may change dynamically with the variance of load series, the previous prediction algorithms usually can not obtain good prediction accuracy when the length of prediction interval is quite large. To address these feature, a Data Aggregation based Adaptive Long term load Prediction mechanism called DA2LP is proposed in this paper. Therein, in order to reduce the number of prediction step and increase the amount of useful input load information, the data aggregation concept is introduced to integrate with AR model. Meanwhile, with the observation and analysis of the relevant parameters' impact on prediction accuracy in our prediction model, an adaptive parameter selection mechanism is proposed, where the optimal relevant parameters can be adapted automatically to enhance prediction accuracy during the prediction process. The experiments show that our proposed mechanism can outperform significantly the previous prediction methods in mean square error (MSE) for long term load prediction.

Network flow watermarks exploiting active traffic analysis approaches have been proposed for tracing attacks implemented through stepping stones or anonymized channels. Existing watermarking schemes either require longer observation duration leading to low efficiency of traceback or introduce large delays to the target flows, making themselves vulnerable to attacks. Especially, they are unsuitable for tracing multiple flows simultaneously due to their interference with each other. We herein propose a novel efficient and secret Double Interval Centroid-Based Watermark (DICBW) for network flow traceback by modulating the packet timing within each pair of adjacent intervals collaboratively and efficiently. By combining DICBW with spread spectrum (SS) coding techniques, we further present a general hybrid watermarking framework for efficient multi-flow traceback. Comparing with existing Interval Centroid-Based Watermark (ICBW), both statistical analysis of DICBW and the experiments using a series of synthetically generated SSH traffic flows demonstrate that DICBW can trace network flows more efficiently and the DICBW based hybrid watermarking framework can trace multiple flows simultaneously by exploiting the low cross-correlation of SS coding techniques.

Resource co-allocation is a crucial but challenging problem for Grid Computing. With the emergence of WS-Resource Framework and Open Grid Services Architecture, resource co-allocation is commonly associated with a service level agreement (SLA) to determine the achieved QoS level. In this paper, we present an approach for resource co-allocation in Multi-cluster Grid that maximizes the user satisfaction degree while satisfying the QoS requirements defined in a SLA. The QoS metrics considered in this paper include deadline, cluster availability, service reliability and budget. Experimental results are presented to show the effectiveness of our approach.

The dynamic feature is one of the most important differences between Grid and traditional heterogeneous distributed systems, thus the most significant challenge for task scheduling in Grid environment is how to relieve the resource performance dynamism effectively. However, the existing schedule algorithms usually suppose that computation or communication times are deterministic and static, thus they will lead to bad performance in the practical Grid environment. To address this problem, a mechanism which is used to estimate the probability distribution of task execution time based on resource load is proposed. And then a Resource Load based Stochastic DAGs Scheduling algorithm for Grid environments is introduced. The simulation results show that our mechanism can achieve a significant improvement in several metrics (such as normalized real schedule length) and can relieve the influence brought by the dynamic nature of Grid effectively.

Service composition with Quality of Service (QoS) is widely studied nowadays and there are some approaches effective for the composition problem in ideal conditions but no feasible solution for dynamic and uncertain QoS constraints environment. Therefore, a novel service negotiation mechanism is proposed in this paper. The mechanism is composed by three parts for negotiation: model, protocol and strategy. The model creates a new hierarchy architecture based on negotiation agent to improve the efficiency of tasks execution of each agent. The protocol dynamically updates solution search line, on which proposals are made by trading partners, and approximate Pareto-optimal solution is achieved through the neutral mediator. The strategy enhances the negotiation sensibility by considering three factors: time, opponent actions and global negotiation states. The negotiation process is coordinated by Manger Agent (MA), which provides suggestions for the next negotiation round based on the overall negotiation context. Experimental results show that the proposed approach is effective to find a feasible solution in the dynamically changeable composition environment.

Service composition is the current research focus in the field of Service-Oriented Computing. However, the service discovery and selection mechanisms are static and not flexible in existing approaches on service composition, and the end-to-end QoS of a composite service can not also be ensured. In this paper, Multi-agent based QoS-aware Service Composition solution (MQSC) is presented, in which the concept of user satisfaction degree is introduced to depict the QoS of the service and the composite service execution mechanism based on Directed Acyclic Graph (DAG) is presented in detail. Through the collaboration between agents, MQSC not only can provide a mechanism for the dynamic service composition but also can ensure the end-to-end QoS of the composite service.

Wireless mesh networks (WMNs), as a promising technology to provide broadband Internet access, is attracting more and more attention from research community. In the research on WMN design, gateway placement is one of the most important and challenging aspects, that is, finding the optimal number and locations of gateways. Although several gateway placement approaches have been proposed, few of them consider the effect of link interference and gateway failure on network performance. In this paper, we address this issue to further optimize network performance. First of all, a tri-state interference model is defined, and on that basis, a new metric for gateway interference is proposed. Next, the gateway placement problem, which involves reducing link interference and assuring fault tolerance, is formulated as a multi-objective integer linear program issue. Then, an interference-aware and K-coverage gateway placement algorithm (IKGPA) is proposed, and a distributed fault tolerance routing mechanism is presented. Finally, the performance of our algorithm IKGPA is evaluated. Simulation results not only show the effectiveness of our algorithm, but also demonstrate that our algorithm achieves fault tolerance assurance with placing only a few additional gateways.

Few e-Learning systems in pervasive environments take learning activities as a part of the learning process and give much flexibility to instructors to define a structural course which is helpful for students to navigate distributed learning resources. In this paper, we propose a process control model in Web-based e-Learning, and illustrate how to implement process control through learning path with workflow technology. This model improves the learning efficiency by providing instructors tools to design courses with learning paths and select the popular learning objects. It also enables students to achieve an orderly learning experience through defined learning path.

Abstract It is an effective approach to adopt software agent in pervasive computing (Per‐Com) environment. Meanwhile, task execution is the key problem which is affected directly by task representation method in Per‐Com environment. The task of agent in Per‐Com environment is dynamic and changeful, so the task representation should be flexible and agent‐oriented. However, most of the existing task representation methods cannot present task relationship clearly and lack unified format, which leads to a drawback that the corresponding task processing efficiency is not satisfying. In this paper, firstly, an agent‐based Per‐Com architecture is presented in which the agent is endowed with certain role. Secondly, through analyzing task relationship, an agent role based task relationship tree (TRT) model and an XML‐based agent task representation method are proposed. Thirdly, based on TRT, an agent task decomposing and online multi‐agent scheduling algorithm are presented to solve time‐optimal problems. Meanwhile, a load balancing strategy based on probability is proposed to increase agent utilization. Finally, a prototype of an application on traffic monitoring is presented. The results of evaluation indicate that the architecture is feasible and the simulation tests of time performance indicate that the proposed TRT‐based task processing methods have a better performance than others. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract In order to effectively utilize massive heterogeneous resources and provide transparent computing capability to upper applications, task scheduling as the key issue of pervasive computing system becomes significantly important. Previous proposed priority and duplication based task scheduling algorithms, which can be applied in pervasive computing environment, usually have following limitations: critical path cannot be calculated accurately while neglecting the effect of resource availability in scheduling; in duplication based resource allocation stage, duplications without restriction would lead to some negative effects on final schedule length (SL). For the purpose of solving these problems, a novel task scheduling algorithm based on dynamic critical path (DCP) and effective duplication, called DCPED, is presented in this paper. In DCPED, a more accurate DCP calculation method which takes resource availability into account is introduced. Meanwhile an effective task duplication strategy is proposed to eliminate ineffective duplications and make an optimized schedule result by using space compression technique and dynamic critical path length (DCPL) based evaluation technique respectively. Finally, simulation results show that DCPED can outperform previous algorithms significantly in NSL and speedup rate metrics. Especially, it is very effective for utilizing computing resources and scheduling the fine‐grain and large‐scale workflow applications in pervasive computing system. Copyright © 2008 John Wiley & Sons, Ltd.

Various low-latency anonymous communication systems such as Tor and Anoymizer have been designed to provide anonymity service for users. In order to hide the communication of users, many anonymity systems pack the application data into equal-sized cells (e.g., 512 bytes for Tor, a known real-world, circuit-based low-latency anonymous communication network). In this paper, we investigate a new cell counter based attack against Tor, which allows the attacker to confirm anonymous communication relationship among users very quickly. In this attack, by marginally varying the counter of cells in the target traffic at the malicious exit onion router, the attacker can embed a secret signal into the variation of cell counter of the target traffic. The embedded signal will be carried along with the target traffic and arrive at the malicious entry onion router. Then an accomplice of the attacker at the malicious entry onion router will detect the embedded signal based on the received cells and confirm the communication relationship among users. We have implemented this attack against Tor and our experimental data validate its feasibility and effectiveness. There are several unique features of this attack. First, this attack is highly efficient and can confirm very short communication sessions with only tens of cells. Second, this attack is effective and its detection rate approaches 100% with a very low false positive rate. Third, it is possible to implement the attack in a way that appears to be very difficult for honest participants to detect (e.g. using our hopping-based signal embedding).

Tor has become one of the most popular overlay networks for anonymizing TCP traffic. A novel and effective flow multiplication attack against Tor is proposed in this paper, which exploits the fundamental vulnerability of anonymous web browsing by using a man-in-the-middle attack on client's HTTP flow. In the flow multiplication attack, whenever a malicious exit onion router detects a web request to a target server, it responds with a malicious page embedded with specified number of image tags, which will cause the browser to initiate deterministic number of web connections on the same circuit to fetch those images. The entry onion router on the circuit can then find such traffic pattern and the communication relationship between the client and the web server will be discovered. Even if all active content systems such as JavaScript in the browser are disabled, our attack can still compromise the anonymity of Tor while achieving invisibility by keeping client's communication running continuously. The experiment results on Tor validate the feasibility and effectiveness of our attack.

Wireless mesh networks (WMNs) have recently evoked much research attention as a novel technology for last-mile broadband Internet access. When designing a WMN, gateway placement is significant for it determines the total network throughput. To address this problem, a novel gateway placement approach is proposed in this paper, in which three objectives are optimized, i.e. the number of gateways, the average MR (mesh router)-GW (gateway) hop count and the variance of gateway load. The gateway placement problem is modeled as a multiple objective linear program first, and then a two-stage load balanced gateway placement algorithm is proposed. The first stage is weight-based greedy gateway selection, and the second stage is load balanced MR attachment. Simulation results show that the algorithm behaves similarly compared with existing approaches in both number of gateways and average MR-GW hop count, while achieving better load balance.

A fundamental problem that confronts QoS-aware service selection and composition is the efficient and timely QoS information obtainment. Current research on this problem usually involves query-based or monitoring-based methods. However, in a dynamic and volatile service oriented Computing (SOC) environment, these solutions suffer some or all of the limitations, such as cost, timeliness guarantee and salability. This paper presents Pat, a P2P based publish/subscribe system to disseminate new revised QoS information. Pat aims at reliable and efficient QoS information dissemination in large-scale SOC environments. It exploits specialized rendezvous points (RP) and a replicas mechanism to reduce the risk of subscriptions loss and consequently improve reliability. A reverse RP ring is designed to quicken subscription delivery and QoS information publication. In addition, an optimization mechanism for composite services is built into Pat, which helps to reduce notification traffic. Simulation results show that Pat is reliable, efficient and scalable.

Traditional networks are surprisingly fragile and difficult to manage. The problem can partly be attributed to the exposition of too many details of the controlled objects leading to the deluge of complexity in control plane, and the absence of network-wide views leading to the blindness of network management. With these problems, this paper decomposes the necessary network management information into three parts: the basic information, the cross-layer association, and global information. And a new controlled object description model is presented in the trustworthy and controllable network control architecture which separates the functionality of control and management of network form the data plane of IP network, and constructs the formal control and management plane of IP network. The new model identifies and abstracts the controlled objects with object-oriented approach. Based on this model, a cross-layer database is built to store the different layer control objects and to present cross-layer association view, a processing mechanism to process the original information is presented for global network state view, and a control plane is constructed to realize network control. The control information description model restricts the complexity of the controlled objects to their own implementation by abstraction, and alleviates the difficulty of network management. The cross-layer association view and the global network state view composes the network-wide views. The network-wide views realize the visibility and improve the manageability of network. Finally, we present 3 examples to indicate that the model alleviates the complexity of configuration management.

Intelligent Network Management system based on Agent becomes increasingly important autonomous network management research topic. Previous work paid little attention to its formal specification and analysis. Here, an improved Color Petri Net named as Adaptive Colored Petri Net is presented to do such work. Main modifications of the Colored Petri Net lie in two points. Firstly, weight defined in it is adaptively self- selected. Secondly, transition in it is divided into two types: and-t and or-t to simulate practical manage actions. Elements of Intelligent Network Management system such as agent, manage task or executive scheme are packed as color tokens and firing rules are modified to adjust to practical management request. Several characters are shown with property analysis of Adaptive Colored Petri Net. Endeavor we done aims to be good for Intelligent Network Manage system designing and verifying.

Network flow watermarking schemes have been proposed to trace attackers in the presence of stepping stones or anonymized channels. Most existing interval-based watermarking schemes are ineffective at tracing multiple network flows in parallel due to their interference with each other, while recently proposed direct sequence spread spectrum (DSSS) watermarking technique is unsuitable for tracing low data rate traffic. By combining interval centroid based watermarking (ICBW) modulation approaches with spread spectrum (SS) based watermarking coding techniques, we herein propose an interval centroid based spread spectrum watermarking scheme (ICBSSW) for efficiently tracing multiple network flows in parallel. Based on our proposed theoretical model, a statistical analysis of ICBSSW, with no assumptions or limitations concerning the distribution of packet times, proves its effectiveness despite traffic timing perturbation and robustness against multi-flow attacks. The experiments using a large number of synthetically generated SSH traffic flows demonstrate that ICBSSW can efficiently trace multiple flows simultaneously and achieve high secrecy by utilizing multiple PN codes as random seeds for randomizing the location of the embedded watermark across multiple flows.

A fundamental problem that confronts SOA-based CSCW applications is the efficient and timely QoS information obtainment of component services. However, this issue has largely been overlooked. This paper presents a P2P based publish/subscribe service to disseminate new revised QoS information reliably and efficiently. Specialized rendezvous points and a replica mechanism are introduced to reduce the risk of subscriptions loss and consequently improve reliability. A message buffering and packaged delivery mechanism helps to reduce notification traffic. A reverse Chord ring, called RP ring, is designed to quicken subscription delivery and QoS information publication. Node partitioning technique is suggested to tackle the load balancing issue. Simulation results show that the service is reliable, efficient and scalable.

The increasing diversity of Internet application requirements has spurred recent interest in transport protocol for high speed delay product connectivity. Addressing the deficiency of previous protocols, this paper presents a spectrum of time based odd function congestion control protocols for deployment in high speed and long distance networks. Through extensive analysis we focus on SQUARE, one of the protocols in odd function congestion control. Without the need to revise the current end-to-end architecture, SQUARE is shown to be efficient when there is bandwidth available, to be fair when many flows compete with each other for the same bottleneck, to be friendly when deployed with the conventional TCP, to be robust when there are oscillations in the network.

Node churn is one hurdle to using peer-to-peer (P2P) networks as anonymous networks, which makes the anonymous path fragile and results in message loss and communication failures. A collaboration scheme including friendly neighbor-based incentive (FNI) and re-encryption mechanism is proposed to deal with the high node churn (changes in system membership) characteristic of unstructured P2P networks. The simple FNI mechanism is presented to encourage peers to forward other peers' queries, and establish more connections to improve the performance of P2P overlay network where only stable and well-behaved nodes can be chosen as relay nodes to prolong single path durability. The re-encryption mechanism is designed to replace those failed relay nodes and achieve routing resilience upon different node availabilities in real- world systems. The results from our security analysis and simulation show that the collaboration scheme greatly improves routing resilience and maintains low latencies and low communication overhead.

In a dynamic service oriented environment like grid, automated negotiation between service providers and consumers becomes a crucial issue that aims to minimize manual intervening for grid environments. Currently the foundation of automatic negotiation framework is missing in grid, as well as a widely adopted grid negotiation model. In this paper, a multi-agent based automated negotiation framework is introduced into the grid environment to facilitate automated negotiation, with the advantage of its independence from concrete grid negotiation models. A refined negotiation model for grid environment is proposed as a substantial negotiation implementation based on the proposed framework and the result suggests that agent based grid automated negotiation bring remarkable flexibility.

Grid service composition has been recognized as a flexible way for resource sharing and application integration since appearance of service-oriented architecture. Approaches are needed to select service candidates with various Quality of Service (QoS) levels according to use’s performance requirements. In this paper We model this problem as the Multi-Constrained Optimal Path Selection Problem (MCOP), due to the dynamic property of grid service, adaptive mechanism is introduced to ensure the whole QoS when some service candidates fail. An algorithm QAGSS is proposed. Simulation results show that QAGSS has greater success rate and lower cost than previous algorithms.

Fundamental problems that confront traditional centralized Grid information services are low query efficiency and single points of failure. This paper presents an improved DHT-based Grid information services architecture, that resolves these problems well. The architecture assigns each resource node and information server an m-bit identifier by hashing attribute values. Every node makes connection with successor information server according to their identifiers to organize Virtual Organization(VO). Resource query is highly efficient with locating identifier. Information servers are fully decentralized, solving single points of failure. Range query is a difficult problem in DHT but an indispensable part in Grid information services. We improve our architecture with tree data structure and Path Caching Schemes to support range query. Results from theory analysis and simulations show that DHT-based architecture is highly efficient, robust, load balanced and scalable.

In this paper, a super-media courseware system infrastructure based on the knowledge description is propose which features in separation of knowledge description from knowledge entity. Using agent technology, a personalized learning environment is developed with dynamical, real-time user information matching. The courseware system infrastructure is not only capable of balancing network load, but also proves self-adaptive to the users' circumstances. The simulation tests are run to demonstrate the advantage in performance.

With more and more research carried on in the QoS of Grid, QoS-based Grid task scheduling algorithm has become a hot research aspect. In this paper, various existing QoS-based Grid scheduling algorithms are analyzed firstly. And by introducing the conception of QoS matching offset between tasks and resources in Grid scheduling, resources and tasks can be clustering upon their offset in order that the resources in scheduling are able to be allocated on demand. Meanwhile, we take into consideration some parameters in the scheduling like QoS benefit value obtained by the task and some restricted condition: deadline of the task. The simulation results show that the algorithm's performance is better than most of the proposed algorithms in the aspects of effective resource utility, resource load balance, task acceptance rate and average QoS benefit value.

The grid security focuses on implementation of safe access for resource of different domains in dynamic grid environment. Trust as an important factor in grid security is increasingly applied to management of security. But the research of application with trust to access control is rare and coarse. In this paper, we propose the concept of trust degree which is the measurement of trust and combine it with access control framework. A fine-granularity access control model has been realized in a single domain and the trust degree based access control framework accomplishes the work to access resource across multi-domains. Conversion between domains is correct and effective. Simulation results present that the access control model is practicable and credible.

To access large and widely distributed data on data grid quickly and efficiently is an important goal of the implementation of data grid. Due to high latency of the Internet, large amounts of data need to be replicated in multiple copies at several distributed sites. However, the storage capacity is limited. So a good replacement algorithm is important to the efficiency of the access to the replicas. In this paper, we propose a prediction-based two-stage replica replacement algorithm. This algorithm achieves a good balance between value and cost by predicting replica value to make sure which replica will be replaced, and predicting the replacement cost to make it as low as possible. Simulation results show that compared with traditional replacement algorithms our prediction-based two-stage replica replacement algorithm shows better performance and efficiency of the data access on data grids.

How to guarantee user's QoS (Quality of Service) demands becomes increasingly important in service-oriented grid environment. Current research on grid resource advance reservation, a well-known and effective mechanism to guarantee QoS, forces on proposing theoretic architecture to support advance reservation. Detailed research on advance reservation is scarce. For that, SNAP (Service Negotiation and Acquisition Protocol) is extended to support QoS description in fine granularity and new calculation method of QoS deviation distance is proposed. Then, advance reservation state switching is analyzed and QoS deviation distance based negotiation algorithm is addressed. Preliminary results show that proposed negotiation algorithm that considers QoS deviation distance can produce remarkable improvement in user satisfaction and resource utilization.

To balance resource loads and minimize makespan are two vital goals in grid scheduling. However, it comes to be difficult for the dynamicity of grid resources, especially with meeting the QoS requirements of tasks considered. In this paper we propose a scheduling algorithm called the QoS group guided grid scheduling algorithm with task replicas (QGTR). The proposed algorithm makes scheduling decision that bases on recent QoS status feedback of resources, dividing tasks into groups according to the feedback QoS status of resources and then scheduling tasks in different groups to resources accordingly. In addition task replica is adopted to improve the resource utilization and gain a better schedule result. The simulation results show QGTR can effectively reduce the makespan and enhance the resource utilization in addition to meeting the QoS requirement of tasks with its best efforts.

This paper proposes an improved free-roaming mobile agent security protocol. The scheme uses "one hop backwards and two hops forwards" chain relation as the protocol core to implement the generally accepted mobile agent security properties. This scheme defends most known attacks, especially colluded truncation attacks and several special cases

Agent negotiation technology is becoming hotspot in the distributed resource allocation and the game theory is introduced in this research. There are some flaws in traditional game theory. In this paper, a cooperative game model for network resource negotiation carried on by agent in network is presented. The model is based on the idea of the asymmetric NBS (Nash bargaining solution) and coalitional game from cooperative game theory. The model not only provides the allocation of network resource that is steady, efficient, fair from the view of the agent, but present a algorithm is easy to be implemented under distributed conditions. The comparison analysis results demonstrate the advantage of the model

Task allocation algorithm has great influence on the efficiency multi-agent task system. The performance of the existing allocation algorithms will decline with the increasing of task complexity. So a task allocation framework is presented and a semi-online multi-agent task allocation algorithm(SOAL) based on dependences of sub-tasks is proposed, the relationship of dependences among sub-tasks is the partial knowledge for SOAL. In contrast to the existing approaches, the performance of SOAL is more close to the optimal offline algorithm. The competitive analysis results and the tests of time performance demonstrate the advantage of SOAL

As QoS-based resource management is becoming increasingly hot, the research on grid QoS plays a fundamental role in service-oriented grid environment. Grid QoS is classified into five categories in virtual organization layer in our early research. In this paper, we propose a kind of scheme to measure grid QoS based on this classification. In the scheme, the grid QoS parameters are quantified respectively. And QoS of grid application is put forward and the measurement model is applied to scheduling heuristics, so that grid QoS can be used as a critical part of a scheduling heuristic's objective function and to implement, analyze and compare resource management systems. Finally, we make a simple simulation and results indicate that applying the measuring model to scheduling heuristic as objective function can enhancing the performance of grid environments drastically

The trust in grid is becoming increasingly more and more popular. Trust has been recognized as an important factor for grid security. In the SEUGrid, the security strategies are almost static, contrary to the dynamicity of the grid environment. Considering this problem, this paper proposes a trust model imitating the trust system in real world. The primary application goal of SEUGrid is to run AMS-02 Monte Carlo (MC) simulation through the cooperative work of the resources in the Southeast University. The alternative sides of MC simulation: computing and storing, lay different emphasis on the capability of node. So the trust model presents different computing methods of trust value in the two contexts based on the updating strategy. The model also computes indirectly the trust value between grid entities via VO. Finally, the simulation results present that the trust model can make the SEUGrid more secure and flexible

The rapid development of Internet makes network management on large-scale network a critical issue. But with the management task of large-scale network becoming more complicated, neither centralized network management nor agent based network management can satisfy the increasing demands. This paper presents a network management framework to support dynamic scheduling decisions. In this framework, some algorithms are proposed to decompose the whole network management task into several groups of sub-tasks. During the course of decomposition, different priorities are assigned to sub-tasks. Then based on the priorities of these sub-tasks, the strategies of agent scheduling are established. Priority-ranked sub-tasks are grouped according to their inter-dependences. Sub-tasks with the same priority are put into the same group and they can be performed in parallel manner, while different groups of sub-tasks with different priorities must be implemented according to the order of their priorities. An experiment has been done with the algorithms, the results of which demonstrate the advantage of the algorithms.

For the high latencies of the Internet, it becomes a big challenge to access such large and widely distributed data fast and efficiently on data grids. To address this challenge, large amounts of data need to be replicated in multiple copies at several distributed sites. However, the number and size of storage are limited. So a good replacement algorithm is important to the performance and efficiency of replication technologies. In this paper, we introduce a new replica replacement algorithm that combines prediction factors and replacement cost factors together. Through predicting the popularity of replica in future time windows, hot spot replica is kept to improve mean job time. Cost factors are most concerned about replica replacement cost such as network latency and bandwidth, replica size and system reliability. We present a PC-based replacement algorithm that achieves a good balance between mean job time and bandwidth resource consumption. By using OptorSim simulator to compare our PC-based algorithm with traditional replacement algorithm, we find the results show that our PC-based algorithm improves performance and efficiency of the data access within the overall data grid.

Computer network system has become a large-scale distributed system. But various kinds of existing network management solutions cannot manage it efficiently and network management is facing the new challenges. Applying the principles of CSCW to network management by multi-agent system is a novel train of thought of constructing the new generation network management system. Distributed network self-management model (DNSM), which is based on multi-agent system, adopts the management policies based on management domain and provides users with Web-based management way, was put forward in this paper. This model not only provides network management with more intelligence but also avoids the usage of amounts of network bandwidth. The experimental results show that DNSM is better than the existing network management solutions on performance for the large-scale computer network.

Grid computing is appropriate for supporting cooperative work Many designers and engineers from different companies or institutions can dynamically form a virtual organization for a given design task. In order to protect each company's sensitive data and services, access control is therefore necessary and important. In this paper, we present a new approach to authorize and administrate access requests, in which the requests are obliged to negotiate with a policy enforcement point in order to gain access to the target grid service. The new access control model will exploit semantic Web technology, and use machine reasoning about the messages and policies at a semantic level.

E-learning has been a topic of increasing interest in recent years, meanwhile, the integration of e-learning resource has become a challenge. A robust, manageable, distributed infrastructure is absolutely necessary for e-learning resource integration. This infrastructure at least provides the capability of supporting message-oriented middleware, and event-driven integration, as well as the capability of supporting service routing and substitution, protocol transformations, and other message processing. These requirements are consistent with the principles of SOA. This paper tries to provide a novel SOA-oriented e-learning resource integration infrastructure. In particular, we introduce a novel service discovery model in the context of this e-learning resource integration infrastructure. The initial simulation results show that the model can effectively aggregate the service information and avoid the overload caused by frequent dynamic updating.

Web services provide an essential deploy environment to realize dynamic systems by facilitating application-to-application interaction. To improve the automation of Web services interoperation, a lot of technologies are recommended, such as Semantic Web services and agent. Moreover, QoS is key to dynamically selecting the services that best meet our needs. Based on Semantic Web services, agent, and combined QoS, we propose a model for automatically searching Web services in this paper. First the model gets the information of the required Web services from UDDI. Then according to the QoS requirements, appropriate Web services are selected and ranked so that consumers can make a choice by order priority. This approach presents an experimental solution for intelligent Web service searching. Finally, we give an example to illustrate how the model works.

With the continuing increase in density of VLSI, limited buffer can be placed inside the crossbar and this combined input-crosspoint-queued (CICQ) switch structure decouples the inputs and outputs matching. In this paper, an analysis of the performance of Round-Robin scheduling algorithm for CICQ switch has been made proves that the Round-Robin algorithm can achieve 100% throughput under uniform traffic but not stable under non-uniform traffic. We propose the DRR algorithm, which can achieve 100% throughput under arbitrary traffic even buffered only one cell in crosspoints in CICQ switch DRR algorithm is feasible for fast hardware implementation and its time complexity is O(1)

Multicasting is an efficient and effective approach for supporting content distribution based on the current Internet infrastructure. In this paper, we have proposed a source-initiated CRMA (cycle-breaking rollback multicast routing algorithm), which is a congestion-aware QoS (quality of service) based multicast routing algorithm. CRMA attempts to achieve the objective of minimizing network congestion conditions while satisfying bandwidth and end-to-end path delay requirements from users. Our proposed approach has several features: (1) it constructs a shared symmetric distribution tree for multicast source(s) and its members rather than the conventional approach of constructing multicast tree; (2) CRMA combines the multicast path computation with resource allocation through introducing the WFQ (weighted fair queuing) packet scheduling into path computation; (3) CRMA can be easily implemented and integrated into the common shortest path router architecture without a thorough adjustment. Through extensive simulations, we demonstrate that our CRMA can effectively reduce the cost, which is the reflection of network congestion conditions, of the computed multicast tree, compared to other proposed multicast routing schemes.

We introduce a peer-to-peer computing model, JP2P, which can be used in network computing and collaborative systems. The basic theory and logic structure of a network computing system are analyzed in detail, and the relevant protocols are discussed thoroughly. According to the discussion, we propose the general conceptions of the new distributed computing model and our views on how to deploy and apply the network computing system. A simple example is given to illustrate the application development process on the top of the JP2P architecture.

With the rapid development of network education, it has become very important to settle all costs of network education and to realize electronic payment. The settlement system for an e-learning system is a new-style electronic commerce just based on this problem. By analyzing and researching the current network education's settlement state, this paper proposes the business flow of the settlement system and gives the functions and modules of the system and details of the solution.

The cooperative mechanism is the most important portion in the study of computer supported cooperative work (CSCW). Based on the virtual background of teaching, the article describes the construction of a Petri net model based on a cooperative mechanism within CSCW by analyzing the cooperative relation between teacher and students in a real time long-distance teaching system. Thereafter, a cooperative controller to be realized in software is designed, and applications of the controller are discussed.

The multimedia-board is a CSCW application in long-distance education and a key technology to realize long-distance classrooms and real time multimedia answering systems. In order to meet the requirements of large-scale users' cooperation with real time multimedia in an IP network, the paper presents the concept of multimedia-board and the definition of a multimedia-board system. It goes on to describe a conversation model with a Petri net and provides a concurrent control mechanism and algorithm which may be applied to the multimedia-board and other CSCW systems. Finally, the authors present the design of a multimedia-board with B/S structure and its perspective in practice.