Modern deep neural network (DNN) and large language model (LLM) applications integrate multiple models into inference pipelines with stringent latency requirements for customized tasks. To mitigate extensive request timeouts caused by accumulation, systems for inference pipelines commonly drop a subset of requests so the remaining ones can satisfy latency constraints. Since it is commonly believed that request dropping adversely affects goodput, existing systems only drop requests when they have to, which we call reactive dropping. However, this reactive policy can not maintain high goodput, as it neither makes timely dropping decisions nor identifies the proper set of requests to drop, leading to issues of dropping requests too late or dropping the wrong set of requests.

Ensuring atomic execution of cross-shard transactions is a fundamental challenge for sharded blockchains, particularly in scenarios demand coordination across multiple shards. However, existing solutions either rely on on-chain coordination, leading to high communication overhead, or leverage secure hardware for off-chain execution, imposing strong trust assumptions and reducing general applicability. To this end, we propose RollShard, a sharded blockchain that integrates stateless off-chain mechanism to efficiently process multi-shard transactions (MSTs). In RollShard, each MST is abstracted into a transaction DAG by the Sequencer Shard to ensure the authenticity of the transaction content and the correctness of its execution order. Batched MSTs are dispatched to off-chain executors, each of which simulates transaction logic using a virtual zero-state model integrate with a hierarchical state-delta tree (HSDT). The HSDT employs a Merkle Sum tree to precisely capture batched MSTs’ impact on per-shard account states. Based on the HSDT, the executor generates the zero-knowledge proof to attest the correctness of each shard’s state changes and global value conservation. The resulting net state deltas are then optimistically committed to the relevant shards without cross-shard coordination, reducing intra-shard coordination. We design a game-theoretic incentive mechanism to ensure rational behavior of off-chain executors, showing that honest execution forms a Nash equilibrium under collateral staking. Experimental results based on a prototype deployed in a local area network demonstrate that ROLLSHARDsignificantly outperforms two baseline coordination models proposed in ByShard, namely the Linear and Distributed designs. Specifically, under high workload, RollShard improves throughput by 44.9% and 158%, and reduces cross-shard latency by 38.9% and 42.1%, compared to the Linear and Distributed models, respectively.

Wi-Fi device-free tracking enables the inference of user behaviors without physical contact, which is crucial for intelligent indoor location-based services. Nevertheless, the practical implementation of current tracking systems is constrained by several critical limitations: 1) The low-quality sensing signals in complex scenarios lead to increased tracking errors; 2) Existing methods inadequately adjust to dynamic environments, necessitating additional data collection or retraining processes. To address these challenges, this paper introduces EOC-Tracking, a device-free Wi-Fi tracking system that dynamically incorporates environmental information. Our key innovation involves leveraging obstacles to correct illogical users' trajectories and facilitate adjustment to varying environments. This significantly improves the accuracy of the follow-up in complex and changing environments. The EOC-Tracking system is built upon three fundamental design principles: 1) A lightweight dual-branch neural network architecture that effectively fuses environmental data with Wi-Fi signal characteristics; 2) An autonomous map updating mechanism that facilitates real-time adaptation to environmental layout modifications without human intervention; 3) A sophisticated data-driven, phased training paradigm that optimizes the model's ability to learn and apply obstacle constraints. We implement EOC-Tracking using commercial Wi-Fi devices and deploy it on low-power embedded systems such as the MCU. Experimental results demonstrate that EOC-Tracking can reduce tracking errors by at most 49.48% compared to datadriven methods and 62.21% compared to model-based methods in various complex scenarios.

Many existing transport designs aim to deliver ultra-low latency and high bandwidth for applications in high-speed datacenter networks. However, almost all of them intertwine the control of small and large flows using the same control entity (e.g., sender or receiver) and congestion feedback signal (e.g., ECN or credit), thus bringing significant performance impairments. By contrast, we seek to decouple the rate control of small flows from that of large ones.

Multimodal Large Language Models (MLLMs), while powerful, pose significant privacy risks by memorizing and potentially exposing sensitive information linked to individuals' visual appearances. Existing machine unlearning techniques, developed primarily for text-based models, are ill-equipped to handle the deeply entangled nature of visual and semantic knowledge. To address this challenge, we introduce GAIA-UL, a novel three-stage framework that performs Surgical Unlearning of visual knowledge. Our approach first conducts a Causal Hotspot Diagnosis, using gradient-based analysis to precisely identify influential parameters within the visual-semantic pathway. Second, it performs a Targeted Adapter Intervention, surgically injecting lightweight, trainable adapters only at these hotspots while freezing the base model. Finally, it employs Semantically Orthogonal Fine-tuning, a novel objective that forces the model's internal representation of a target face to become orthogonal to embeddings of associated sensitive concepts, thereby erasing the link at a deep representational level. Extensive experiments on the MLLMU-Bench benchmark demonstrate that GAIA-UL significantly outperforms existing baselines, achieving superior visual knowledge ablation while robustly preserving general model utility and text-only knowledge.

In ocean environments, underwater sensor nodes (USNs) are susceptible to failure due to various factors, such as seawater corrosion, hardware failure, depleted battery, harsh deployment scenarios, and intentional sabotage. This article focuses on the topology restoration problem of disconnected subnetworks (TR-DSNs) caused by large-scale USN failures in underwater wireless sensor networks (UWSNs). The existing research cannot be well adapted to dynamic ocean environments because they ignore the effects of underwater communication channel and current movement on the cost and reliability of network restoration. It would consequently lead to high restoration cost and unreliable data transmission for UWSNs. To solve the mentioned problem, we first build a reliability evaluation model of topology restoration that considers the link quality, network connectivity, and data transmission of UWSNs in dynamic ocean environments. Then, a reliability-driven topology restoration strategy (called RDTRS) based on underwater relay node (URN) placement is designed. RDTRS comprises three key algorithms: 1) URN placement path generation; 2) URN location determination; and 3) URN location adjustment. By RDTRS, the number of URNs can be reduced on the premise of ensuring the restoration reliability of UWSNs. In the end, we validate the performance of RDTRS in terms of network restoration cost, packet delivery ratio, and transmission latency.

Fingerprint localization methods typically require a substantial amount of manual effort to collect fingerprint data from various scenarios to construct an accurate radio map. While some existing research has attempted to use path planning strategies to save on labor costs, these approaches often suffer from being time-consuming and prone to locally optimal solutions. To address these shortcomings, our paper proposes a novel approach that utilizes imitation learning to construct and update a highly accurate radio map with minimal manual intervention in dynamic environments. Specifically, we employ a multivariate Gaussian process model to fit a rough standby fingerprint database with only a few pilot data points. We then utilize a state space model to calculate the variation range of the pilot data, which forms the CSI error band used to filter the rough radio map. Imitation learning and a confidence coefficient are utilized to predict and calibrate the global CSI data distribution. And we utilize the K-nearest neighbor algorithm to achieve the real-time localization function. Experimental results show that our proposed algorithm outperforms several state-of-the-art approaches in most test cases, exhibiting low computation complexity, lower localization error, and saving 73.3% of the manual workload.

The rise of Machine Learning as a Service (MLaaS) has driven the demand for complex and customized real-time inference tasks, often requiring cascading multiple deep neural network (DNN) models into inference pipelines. However, these pipelines pose significant challenges due to scheduling complexity, particularly in maintaining strict latency service level objectives (SLOs). Existing systems serve pipelines with model-independent scheduling policies, which ignore the unique workload characteristics introduced by model cascading in the inference pipeline, leading to SLO violations and resource inefficiencies. In this paper, we propose that the serving system should exploit the model-cascading nature and inter-model workload dependency of the inference pipeline to ensure strict latency SLO cost-effectively. Based on this, we design and implementSLOpt, a serving system optimized for real-time inference pipelines with a three-stage co-design of workload estimation, resource provisioning, and request execution.SLOptproposes cascade workload estimation and ahead-of-time tuning, which together address the challenge of cascade blocking and head-of-line blocking in workload estimation and resource provisioning.SLOptfurther implements an adaptive batch drop policy to mitigate latency amplification issues within the pipeline. These innovations enableSLOptto reduce the 99th percentile latency (P99 latency) by 1.4 to 2.5 times compared to the state of the arts while lowering serving costs by up to 29%. Moreover, to achieve comparable P99 latency,SLOptrequires up to 70% less cost than existing systems. Extensive evaluations on a 64-GPU cluster demonstrateSLOpt’s effectiveness in meeting strict P99 latency SLOs under diverse real-world workloads.

Visual perception, as a core component of Intelligent Transportation Systems (ITS), plays a key role in enhancing safety and efficiency in urban mobility. While single-task visual perception methods have applications in areas like pedestrian detection and traffic sign recognition, the complexity of real-world scenarios necessitates a shift toward multi-task approaches. This paper introduces the Edge-assisted Multi-task Visual Perception (EMVP) system, which is specifically designed to address the computational intensity and dynamic concurrency challenges inherent to multi-task processing in edge environments. EMVP adopts a collaborative architecture that strategically partitions computational tasks between vehicles and Road-Side Units (RSUs). By integrating Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), the system achieves a lightweight yet efficient multi-task model for resource-constrained environments. To adapt to the dynamic and concurrent nature of multi-vehicle scenarios, EMVP incorporates a content-aware adaptive inference mechanism based on reinforcement learning, enabling dynamic task scheduling to improve Quality of Service (QoS). Experimental results demonstrate that, compared to the single-task baseline model, the multi-task model of EMVP reduces the computational cost by 86.59% on average while achieving a 4.32% improvement in accuracy. Additionally, in dynamic multi-access environments, EMVP’s adaptive scheduling mechanism, which leverages spatiotemporal content awareness, achieves an average QoS improvement of 7.36% over the sub-optimal method.

Location-based services and identification hold promise for future smart home applications. Through them, we can provide customized services for specific users in current locations. Recent studies have demonstrated that Wi-Fi signals can be leveraged to achieve device-free tracking and gait recognition. Despite their good performance, these two technologies are not effectively integrated for the following reasons: First, the device-free tracking method might yield tracking results that conflict with human gait. Second, extracting gait features relies on knowing or accurately estimating the user's trajectory. Consequently, gait recognition and tracking are inherently linked, but there has been no effective approach to integrate these two techniques. In this paper, we present STAGR, a system capable ofSimultaneousTrackingAndGaitRecognition. The main contribution of our technique is that we establish a theoretical model that reveals how to transform path-dependent spectra into path-independent spectra directly. Specifically, we conduct a preliminary study to demonstrate the need for simultaneous tracking and gait recognition. Second, we propose a novel method to extract path-independent gait features, which can significantly save execution time compared with the learning-based method. Third, we design a polar-coordinate filtering method to retain the gait features while correcting the trajectory. We implement a prototype STAGR system and conduct extensive experiments to verify the proposed mechanism. The experimental results show that we can realize simultaneous tracking and gait recognition. The median tracking error is$ 0.45m$, while the recognition accuracy is 95.3% for 6 users.

Autonomous driving systems are fundamentally composed of sequential modular tasks, i.e., perception, prediction, and planning. For connected autonomous vehicles (CAVs), cooperative perception offers a promising solution to surpass their perception limitations, such as occlusion, by sharing sensing data with each other through wireless communication. Existing works typically prioritize sharing data from potential object-containing areas to maximize object detection accuracy under constrained communication resources. However, such detection-oriented approaches ignore a crucial fact that more accurate detection does not equal safer planning. Sharing large amounts of sensing data for detection accuracy can lead to communication resource wastage and performance degradation of subsequent driving tasks. To address this, we introduce Plan2comm, a communication-efficient cooperative perception framework via planning-oriented feature sharing, which shares only sensing data around planned trajectories to enable safer planning rather than mere detection accuracy. Specifically, a planning-oriented communication mechanism is designed to select and transmit the most valuable features from the perspective of the planning task. Moreover, an uncertainty-aware spatial-temporal feature fusion strategy is proposed to enhance high-quality information aggregation. Comprehensive experiments demonstrate that Plan2comm outperforms all other cooperative perception methods on motion prediction performance, and is more communication-efficient.

Wi-Fi contact-free sensing systems have attracted widespread attention due to their ubiquity and convenience. The integrated sensing and communication (ISAC) technology utilizes off-the-shelf Wi-Fi communication signals for sensing, which further promotes the deployment of intelligent sensing applications. However, current Wi-Fi sensing systems often require prolonged and unnecessary communication between transceivers, and brief communication interruptions will lead to significant performance degradation. This paper proposes Baton, the first system capable of accurately tracking targets even under severe Wi-Fi feature deficiencies. To be specific, we explore the relevance of the Wi-Fi feature matrix from both horizontal and vertical dimensions. The horizontal dimension reveals feature correlation across different Wi-Fi links, while the vertical dimension reveals feature correlation among different time slots. Based on the above principle, we propose the Simultaneous Tracking And Predicting (STAP) algorithm, which enables the seamless transfer of Wi-Fi features over time and across different links, akin to passing a baton. We implement the system on commercial devices, and the experimental results show that our system outperforms existing solutions with a median tracking error of 0.46m, even when the communication duty cycle is as low as 20.00%. Compared with the state-of-the-art, our system reduces the tracking error by 79.19% in scenarios with severe Wi-Fi feature deficiencies.

In Underwater Acoustic Sensor Networks (UASNs), the robustness of network is greatly affected by complex marine environments when implementing multi-hop data transmission. Factors such as the underwater acoustic channel and dynamic topological changes induced by multi-layered oceanic vortices exacerbate this influence. However, there is currently a research gap in the specific area of robustness optimization for UASNs. Existing studies on robustness optimization are unsuitable for UASNs as they neglect the considerations of the marine environment and node characteristics (e.g., residual energy). In this work, we propose PRobust, a percolation-based robustness optimization model for UASNs. PRobust consists of two distinct phases: percolation modeling and bottleneck optimization. In the percolation modeling phase, we incorporate both node and edge features, considering the physical and topological properties, and introduce a novel approach for calculating link quality. In the bottleneck optimization phase, we devise a graph theory-based method to identify bottlenecks, leveraging the flow information recorded by nodes to improve the accuracy of bottleneck discovery. Moreover, we integrated time slots and a current movement model into the proposed model, allowing its applicability to dynamically changing UASNs. Extensive simulation results indicate that, compared to existing methods, PRobust significantly enhances network robustness and performance with the same overhead after bottleneck optimization.

Serverless computing typically relies on the third-party forwarding method to transmit data between functions. This method couples control flow and data flow together, resulting in significantly slow data transmission speeds. This challenge makes it difficult for the serverless computing paradigm to meet the low-latency requirements of web services.

Memory disaggregation, a promising technique allowing applications to use remote memory, is increasingly appealing in datacenters due to its high resource utilization. Operationally, the application’s host server constantly evicts unused data to remote to make room for memory allocation of new pages. Inefficient evictions allow memory usage to hit its limit, resulting in application blocking, which brings severe throughput degradation. However, most existing works neglect the importance of eviction. They offload the eviction to a background thread and set a fixed trigger timing, rendering a belated eviction. Worse still, they overlook the impact of network congestion on eviction efficiency, making their strategy flawed in large-scale scenarios. In this paper, we present Glitter, an adaptive, multi-level awareness eviction solution that accelerates applications by minimizing the overhead of application blocking from host and network aspects. For host, Glitter presents an adaptive eviction threshold adjustment to optimize the eviction timing, reducing the occurrence of application blocking. For network, Glitter adopts an eviction flow scheduling to address the hazards posed by flow contention at switches, decreasing the duration of each application blocking. Through comprehensive experiments, Glitter gives an average 1.4 throughput boost to Fastswap, a state-of-the-art disaggregated×memory system.

With the rapid growth of datacenter network link speed, multi-round matching based proactive solutions (e.g., dcPIM) has become increasingly attractive. Such solutions enable receivers to obtain as much global information as possible through multi-round matching, thereby facilitating them to make near-optimal decisions on bandwidth allocation. However, the matching phase before transmitting data introduces significant latency overhead. In this paper, we present Mild, a zero-wait solution that runs a second sender-driven control loop in parallel, leveraging in-network telemetry (INT) to detect and fill the spare bandwidth during the matching phase. Furthermore, we introduce a selective dropping mechanism to ensure that the packets from the second loop do not impact the data transmission of the primary loop. Additionally, we use the well-protected primary loop to perform loss recovery for the dropped packets efficiently. We integrate Mild into a representative proposal dcPIM and evaluate its performance through 100Gbps large-scale simulations. Compared to the state-of-the-art solution, Mild reduces the tail flow completion time (FCT) of short flows by up to 55% while achieving up to 57%/45% lower average FCT of medium/large flows.

The rapid development of real-time communications (RTC) has created many challenges for designing a proper transport control module, which determines how much media data can be sent in real time. Reinforcement learning (RL) -based transport control algorithms have shown great potential, but still face some unique challenges. For example, accurate bandwidth prediction is often necessary but it is difficult to guarantee accuracy due to bandwidth non-stationarity. In addition, how to alleviate the cold-start and overestimation problems of learning-based algorithms to achieve higher training efficiency is also a headache for researchers. In this work, we propose a new training framework that leverages the advanced Transformer model to capture the non-stationarity of the bandwidth sequence and improve the bandwidth prediction accuracy, while using knowledge distillation and transfer learning techniques to train the RL model efficiently and alleviate the cold-start problem of the model in the training environment. Besides, we employ the Double-Q learning mechanism to suppress the overestimation problem and further enhance the training efficiency. Based on this framework, we have trained a new RTC transport control algorithm NSAC and test it on our own platform. The experiments prove that NSAC adapts better to the unstable network environment than the state-of-the-art solutions. In conditions of weak network, the video throughput experiences a 14.11% increase, accompanied by reductions of 5.64%, 28.12%, and 25.86% in delay, loss rate, and stall rate, respectively. These improvements notably enhance the quality of user experience.

The ubiquity of acoustic devices and the fine-grained sensing of acoustic signals have made acoustic device tracking a popular option. We propose to expand the use of commercial devices with microphones as an extension of the audio system to support intelligent applications, such as VR/AR. This paper introduces a novel 6D acoustic pose estimation system. To realize device-based pose estimation, most existing systems deploy multiple speakers. However, due to limited inaudible bandwidth, concurrent transmissions with multiple speakers pose challenges in balancing resolution and frame rate. To address this problem, we design 2×Track, a band multiplexing signal model that doubles the availability of limited bandwidth by utilizing a unique encoding strategy for concurrent transmissions. We also propose solutions to enhance signal feature estimation and implement a 6DoF pose tracking scheme tailored for distributed systems. The prototype is deployed on a typical circular microphone array, and experimental results show that 2×Track achieves a median position and orientation error of 7.6mm and 4.1°, respectively, in a 4-speaker setup. Our extended applications on commercial devices also showcase the versatility of our system, particularly in face orientation detection, air mouse and drone tracking.

Cooperative perception is an emerging paradigm that expects to conquer the sensory limitations of individual vehicles by sharing sensor information with each other and significantly improve driving safety. However, achieving highly precise data sharing and low communication overhead remains a challenge for cooperative perception, especially when real-time communication is necessary in autonomous driving. As a result, it is essential to decrease the transmitted sensor data while maintaining the perception performance. For this purpose, we propose a communication-efficient raw-level cooperative perception system for connected autonomous vehicles (CAVs), which is able to significantly compress the raw sensor data each CAV shares with each other by only transmitting the most informative keypoints. Specifically, at the local level, a voxel-based instance-aware keypoints selection strategy is proposed to select the points that belong to regions of interest. To further supervise the local keypoints selection, we present a collaborative global-local learning strategy, enabling each vehicle to consider both the local scenario and the global context when selecting the transmitted data. Comprehensive evaluations indicate the superiority of the proposed system, which achieves more than 300× lower communication volume compared to the raw data, with a performance degradation of less than 1%.

Sleep posture recognition is practically important in various scenarios such as sleep healthcare, bedridden patient care, and chronic disease diagnosis. With concerns of user privacy preserving, we prefer the wireless sensing methods to computer vision methods when dealing with sleep posture recognition. However, the existing wireless sensing methods suffer from at least one of the following major limitations: (i) difficult to deploy in practice; (ii) few posture categories; (iii) insufficient accuracy; (iv) poor generalization ability. In this paper, we use commercial-off-the-shelf (COTS) mmWave radar to implement a sleep posture recognition system called PosMonitor. When designing the PosMonitor system, we need to address the following challenging issues. First, we propose an angle purification method based on multi-frame joint analysis to alleviate the sparsity and instability of the point cloud. Then, we endow the point cloud with respiratory features to enhance its representation of the sleep posture. Further, to make the system applicable to different users, we extract relative respiratory features by normalization to overcome individual differences. Extensive experimental results show that our PosMonitor system can achieve 98% accuracy on average in recognizing 6 typical sleep postures and has good reliability across different conditions.

Cooperative perception is an effective way for connected autonomous vehicles (CAVs) to surpass their sensing limitations, by sharing information like intermediate features extracted from images or point clouds with each other. To reduce bandwidth consumption, feature filtering is adopted by existing methods to share only the most valuable information. However, these methods assume that the features on the same channel across all spatial regions or those in the same spatial regions across all the channels are equally important. This assumption results in coarse-grained feature filtering, which greatly decreases the cooperative perception performance. To solve this problem, this paper proposes a fine-grained channel-spatial feature filtering scheme, named SwissCheese, for communication-efficient cooperative perception. The key idea of SwissCheese is to exploit the disparity in semantic information on features between different spatial regions on different channels. Specifically, a fine-grained collaborative attention module is developed to jointly learn fine-grained attention along the channel-spatial dimensions. Moreover, a dual-dimensional feature selection strategy that selects sparse features for transmission based on the current available bandwidth is designed to achieve optimal perception performance. Experiment results show that SwissCheese significantly reduces the transmission data size by 90% with a subtle loss in perception performance.

Due to the large volume of IoT data, conventional sensor network based and the cloud base IoT systems cannot handle latency-sensitive and resource-consuming IoT applications. Sensor networks do not have enough computation resources and also suffer from a limited network lifetime. On the other hand, the cloud based IoT system is far away from the users and the physical world, and cannot satisfy the real-time requirement of IoT applications. We adopt the IoT edge network to address these challenges and process IoT applications in modern IoT systems. The IoT edge network is an emerging computing architecture in the IoT. Compared to the sensor nodes in conventional sensor networks, the edge servers have more computation resources. Compared to the remote cloud, the edge servers are closer to the users and the physical world. However, processing IoT applications in the edge network still remains challenging. First, how to process concurrent IoT applications has not been fully investigated. Second, the inner relationship between the network resource and the application latency has not been deeply analyzed. Third, the function conflict problem in edge servers has not been taken seriously. To solve the above challenges, we propose the Energy and Latency Efficient Processing Plan for Concurrent IoT Applications Problem which aims to construct an application processing plan by jointly considering the concurrency, the energy-latency relationship, and the function conflict problems. We prove that such a problem is NP-Hard, and algorithms are proposed accordingly. Furthermore, we also estimate the performance of the proposed algorithms by numerical results.

The smart speakers benefit from the ability to localize and identify users. Specifically, we can analyze the user's habits from the historical trajectory to provide better voice-based services. However, current voice localization method requires the user to actively issue voice commands, which makes smart speakers unable to track and identify silent users most of the time. This paper introducesWSTrack+, a system that combinesWi-Fi andSound to track human movement and recognize gait patterns. In particular, current smart speakers naturally support both Wi-Fi and acoustic functions. As a result, we are able to construct the system using just one router and a smart speaker, which is a more promising approach compared to existing systems that rely on multiple routers for sensing. To track and identify the silent user, our insights are twofold: 1) the smart speakers can hear the sound of the user's footstep, and then extract which direction the user is in; 2) we can extract the reflected path change rate from the Wi-Fi signals, and the acoustic signal can help us convert the path change rate into the actual user's velocity. Our implementation and evaluation on commodity devices demonstrate thatWSTrack+can realize simultaneous tracking and gait recognition, where the median tracking error is$0.34m$and the recognition accuracy is 88.6% for 12 users.

The evolution of Wi-Fi to next-generation 802.11bf demonstrates the potential of device-free Wi-Fi sensing applications, where we can remotely infer the behaviors of users without bringing into physical contact with them. Among these sensing applications, Wi-Fi tracking is critical to provide location based services. Recent Wi-Fi tracking systems can be cataloged into model-based and data-based approaches: (1) the model-based approach is to build the mathematical tracking model. However, this method is sensitive to environmental noise, and spends more execution time; (2) the data-based approach is to train a neural network. However, this method requires a lot of efforts to collect training dataset, and cannot handle all types of trajectories well. To resolve these issues, we propose theNNE-Tracking, a Neural Network Enhanced tracking framework. The core design principle ofNNE-Trackingis as follows: we improve the tracking accuracy based on the data-based approach, and utilize the model-based approach to supervise whether the neural network is already working well. Moreover, we also design a framework to estimate unknown parameters of the tracking model, so that the system can automatically generate the Wi-Fi map. We take the Wi-Fi passive tracking as a specific example to explain how to applyNNE-Trackingin practical applications. Experimental results demonstrate that our design can reduce 59.4% ∼ 85.3% tracking errors while significantly saving execution time. As for deployment costs, we can automatically infer the Wi-Fi map without manual calibration; As for stability, when we repeat the training process with different hidden layers and random seeds, the tracking standard deviation of these neural networks is only 1.4cm.

With the rise of artificial intelligence, various video analytics models have been applied in many fields. Numerous studies are preoccupied with expanding the size of the model to achieve greater accuracy, yet inference latency is unbearable when models are deployed to resource-constrained terminal devices. Edge computing ensures efficient and accurate inference by offloading video inference tasks to edge servers because of low network latency and high-performance hardware. However, edge-assisted video analytics systems encounter challenges due to the dynamic nature of video frames, fluctuating network signals, and the mismatch between arithmetic power and model size. To overcome these obstacles, we propose MDRL, an edge-assisted video analytics framework based on Multi-modal Deep Reinforcement Learning. MDRL adaptively determines the offloading strategy of video frames by observing multi-modal information from video frames and network signals and updates the parameters using Deep Reinforcement Learning (DRL) algorithms. We compare MDRL with various baselines and the experimental results show that MDRL has the highest overall optimization of latency, accuracy, and network bandwidth consumption in various experimental scenes.

Sharding technology has become crucial for enhancing the scalability of blockchains owing to the rapid extension of blockchain data. However, data migration and state reconstruction may cause a high overhead when a new shard is added. Existing solutions have high latency when expanding, and the balance of the state data between shards is poor after extension. To this end, this paper proposes an Effective and Balanced Storage Extension (EBSE) approach for sharding blockchain systems. EBSE can reduce the overhead and latency of the system extension, while ensuring a balance between shards after extension. When implementing the EBSE, we address the following three challenges. 1) To design a data structure that incorporates allocation principles, we designed a Jump Merkle Tree (JMT) based on the Merkle Tree prototype, incorporating node migration and orderliness. 2) To design additional rules that ensure the integrity of the state tree during shard addition, we designed a shard addition protocol to coordinate and standardize the behavior of each shard during the extension process. 3) To ensure the sustainability of the system after extension, we designed state tree addition and cleaning algorithms to remove the invalid information after the system extension. Extensive experiments are conducted to evaluate the performance of the proposed approach. The experimental results show that the EBSE outperforms the existing solutions in terms of balance and latency. Compared with the state-of-the-art sharding storage, EBSE effectively reduces the shard addition latency by 60% and achieves 4× superior shard data balance.

In recent years, Real-Time Communication (RTC) has been widely used in many scenarios, and Congestion Control (CC) is one of the important ways to improve the experience of such applications. Accurate bandwidth prediction is the key to CC schemes. However, designing an efficient congestion control scheme with accurate bandwidth prediction is challenging, largely because it is essentially a Partially Observable MDP (POMDP) problem, making it difficult to use traditional hand-crafted methods to solve. We propose a novel hybrid CC scheme LRCC, which combines attention-based Long Short-Term Memory (LSTM) and Reinforcement Learning (RL), realizing more accurate bandwidth prediction and congestion control by adding bandwidth memory information provided by the recurrent neural network to the RL decision-making process. Trace-driven experiments show that our proposed method can significantly reduce packet loss and improve bandwidth utilization in various network scenarios, outperforming baseline methods on overall QoE.

Serverless computing commonly adopts strong isolation mechanisms for deploying functions, which may bring significant performance overhead because each function needs to run in a completely new environment (i.e., the "one-to-one" model). To accelerate the function computation, prior work has proposed using sandbox sharing to reduce the overhead, i.e., the "many-to-one" model. Nonetheless, either process-based true parallelism or thread-based pseudo-parallelism still causes high latency, preventing its adaptation for latency-sensitive web services.

Byzantine fault-tolerant (BFT) consensus ensures system consistency in the presence of malicious replicas and is widely adopted in blockchain systems. To enhance scalability and throughput, recent advancements incorporate multiple leaders into BFT consensus. However, employing multiple leaders results in significant resource wastage in terms of storage, bandwidth, and CPU usage, attributable to transaction redundancy. Conversely, to eliminate duplication, the resilience in Byzantine settings is compromised. To bridge this gap, we propose PeterHofe, a novel ring-based collaborative transaction packaging method, aiming to maintain resource efficiency and minimize Byzantine leader influence, thereby reducing transaction latency and improving system robustness. PeterHofe extends the concept of partitioning transaction hash space into buckets, establishing many-to-many mappings between replicas and buckets to diminish Byzantine replica control. When implementing PeterHofe, we address the following two challenges. 1) To improve resistance to Byzantine censorship, we design a permutation-based ring structure with accompanying correctness proofs and mathematical analyses; 2) To further reduce transaction duplication, we introduce a Prophecy-Implementation mechanism with analyzed malicious behaviors. We implement PeterHofe on top of the latest and representative work, Narwhal and Tusk. Experimental results demonstrate that PeterHofe can achieve low resource waste and high system robustness simultaneously. Specifically, PeterHofe's resource waste rate is near 5~17% in general cases, which is a 20-fold reduction compared to the Random-based Strategy; compared with the state-of-the-art Hash-based Partitioning Strategy, the proportion of maliciously controlled transactions is reduced by at least 66%, leading to a latency decrease of up to 75%.

Device-free Wi-Fi tracking has become essential for ubiquitous wireless sensing. However, current device-free Wi-Fi tracking systems suffer from two limitations: First, abnormal signals interfere with tracking performance when the user walks across the direct link of the transceivers and second, the tracking error based on the velocity integral accumulates over time. This article proposes CrossTrack, the first device-free cross-link tracking system with commodity Wi-Fi. Our inspiration is to regard the cross-link behavior as an opportunity to correct the trajectory instead of disturbing noise like previous work. Our approach involves three main steps. First, we devise a metric that is capable of detecting cross-link behavior. Second, we propose a new theoretical model that identifies the cross-link position as a landmark. Third, we develop a path revision technique that utilizes this landmark to optimize the trajectory. The technique innovation of this article is to reveal the theoretical approach to transform cross-link interference into optimization in device-free tracking for the first time. We implement CrossTrack based on commercial Wi-Fi devices and conduct comprehensive experiments. Our results show that CrossTrack can reduce tracking errors by 48.75%, and the median tracking error is 0.41 m.

Wi-Fi CSI-based indoor localization systems can realize decimeter-level localization accuracy. However, these systems require that the location and antenna array orientation of Wi-Fi Access Point (AP) are known in advance, which makes it impractical for large-scale deployment. In this paper, we present MapFi, which can realize autonomous mapping of Wi-Fi infrastructure without labor-intensive site survey. To this end, we focus on addressing three problems. First, as there will be diverse layouts of devices and antennas with respective to numerous and heterogeneous Wi-Fi APs, we propose a general method to estimate AoA and generate the Wi-Fi map. Second, while the existing systems can provide a promising median localization accuracy, tail performance is usually far worse. Consequently, we develop a revision method to reduce tail errors. Third, when deployed in large-scale indoor environment, obstacles and long-distance communication might incur failed CSI collection. Therefore, we segment Wi-Fi APs into groups and finally merge these groups to generate the global Wi-Fi map. We conduct experiments in different scenarios to verify the proposed methods. The experimental results show that we can realize the$80\%$localization error within$1.15m$and$0.74m$in office room and open space respectively, which is as accurate as localization systems requiring known Wi-Fi map.

With the rapid growth in the demand for location-based services in indoor environments, wireless fingerprint localization has attracted increasing attention because of its high precision and easy implementation. However, an effective method does not exist owing to the problems of data loss, noise interference in the fingerprint database, and being time-consuming during the offline training phase. Therefore, this paper presents a novel indoor wireless fingerprint localization algorithm, termed BLS-Location, based on a broad learning system (BLS) that utilizes channel state information (CSI) to overcome the aforementioned problems. It includes an offline training phase and an online localization phase. In the offline training phase, the Kalman filter and the expectation-maximization (EM) algorithm are utilized for completing and denoising the data. Moreover, principal component analysis (PCA) is used to reconstruct the CSI data to reduce complexity and train the weights by BLS. In the online localization phase, we employ a novel probabilistic method based on the regression results of BLS to obtain the estimated location. The experimental results show that BLS-Location can significantly reduce the training time with a high accuracy, compared to several machine learning algorithms and four existing methods in two representative indoor environments.

With the growing demand of Location-Based Service, the fingerprint localization based on Channel State Information (CSI) has become a vital positioning technology because it has easy implementation, low device cost and adequate accuracy which benefits from fine-grained information provided by CSI. However, the main drawback is that the approach has to construct the fingerprint map manually during the off-line stage, which is tedious and time-consuming. In this paper, we propose a novel data collection strategy for path planning based on reinforcement learning, namely Asynchronous Advantage Actor-Critic (A3C). Given the limited exploration step length, it needs to maximize the informative CSI data for reducing manual cost. We collect a small amount of real data in advance to predict the rewards of all sampling points by multivariate Gaussian process and mutual information. Then the optimization problem is transformed into a sequential decision process, which can exploit the informative path by A3C. We complete the proposed algorithm in two real-world dynamic environments and extensive experiments verify its performance. Compared to coverage path planning and several existing algorithms, our system not only can achieve similar indoor localization accuracy, but also reduce the CSI collection task.

This paper focuses on the anti-interference transmission strategy for underwater acoustic sensor networks(UASNs). The interference existing between nodes communicating in a shared channel may significantly decrease the communication quality and increase the energy consumption of nodes. However, existing researches on transmission strategies for UASNs either do not consider inter-node interference or ignore the effect of acoustic channels. To solve the above problems, we propose to characterize the interference communication problem of nodes as an ordinal potential game model. Furthermore, a deep reinforcement learning (DRL)- based algorithm is designed to solve the problem, which selects the transmission power for nodes by learning historical information of signal-to-interference-plus-noise ratio(SINR) to minimize network interference. Finally, we verify that the DRL-based anti-interference transmission strategy proposed in this paper can obtain the optimal transmission strategy from variable underwater environment through extensive simulations, and show the feasibility of the algorithm under large-scale networks.

In underwater wireless sensor networks (USWNs), autonomous underwater vehicle (AUV)-assisted data collection has received significant attention for its characteristics of low energy consumption and long network lifetime. However, the existing AUV-assisted data collection schemes ignore the communication range of sensor nodes and the kinematics of AUVs. In this way, the path planned is inefficient and difficult to be applied in practice. To address the problems mentioned above, this paper proposes an AUV-assisted data collection scheme based on Fermat’s spiral (FS-PPS), which considers path planning within and outside the communication range of sensor nodes. Firstly, an improved firefly algorithm is used to determine the traversal order of nodes. Based on the determined sequence, we plan the path by adjusting the turning point and the steering angle of Fermat’s spiral. It can shorten the data collection path length. significantly while meeting the requirements. In the simulation, compared with two other schemes, FS-PPS can reduce data collection time by 17% and 6% on average.

Short-lived TCP connections are important for modern Internet applications and call for efficient network stack support. Traditional Linux TCP stack is inefficient because of the inherent kernel overheads like context switching and memory copy. A promising alternative, called user-level TCP stack, is thus becoming attractive in this context. However, existing user-level stacks mainly focus on scalability issues when handling shortlived connections and fall short in optimizing request-response latency. To fill this gap, this paper presents Pallas, a built-in packet scheduler in the user-space mTCP stack, to prioritize the transmissions of short connections over long connections to reduce the request-response latency. Pallas schedules at packetlevel first to complete 1-packet short connections as quickly as possible and then smoothly switches to connection-level to defer the transmissions of long connections. We have implemented a Pallas prototype and evaluated it on a 4-core machine. The results show that Pallas-enhanced mTCP delivers significant performance. For example, it speeds up the overall request response time of mTCP by 1.49 times.

Load balancing is essential for datacenter networks. However, prior solutions have significant limitations: they either are oblivious to congestion or involve a daunting and time-consuming parameter-tunning task over their heuristics for achieving good performance. Thus, we ask: is it possible to learn to balance datacenter traffic? While deep reinforcement learning (DRL) sounds like a good answer, we observe that it is too heavyweight due to the long decision-making latency. Therefore, we introduce BULB, a lightweight and automated datacenter load balancer. BULB learns link weights to guide the end-hosts to spread traffic, so as to free the central agent from quick flow-level decision-making. BULB offline trains a DRL agent for optimizing link weights but employs an imitation learning based approach to faithfully translate this agent’s DNN to a decision tree for online deployment. We implement a BULB prototype with a popular machine learning framework and evaluate it extensively in ns-3. The results show that BULB achieves up to 36.6%/56.4%, 19.9%/42.5%, 35.9%/54.8%, and 45.1%/67.7% better average/tail flow completion time than ECMP, CONGA, LetFlow, and Hermes, respectively. Moreover, BULB reduces the decision latency by 175 times while incurring only 2% performance loss after converting the DNN into a decision tree.

Radio-frequency identification (RFID) electronic license plate (RF-ELP) has been widely used to enable various automatic vehicle identification applications. Endowing RF-ELP with mobile vehicle sensing capabilities, such as localization and speed measurement is of practical importance, yet there is no solution on the shelf. Moreover, the position information is essential for accurate speed measurement, while the related RFID-based vehicular localization and indoor mobile localization methods suffer from at least one of the following major limitations: 1) difficult to deploy in practice; 2) requiring moving speed in advance; 3) only working for indoor-speed vehicles; and 4) not well compatible to frequency-hopping mechanism. To overcome the above limitations, this article proposes an RF-ELP-based mobile vehicle sensing (RESensing) system. RESensing conducts a new signal phase collection strategy to ensure the phase coupling in road-speed cases and converts phases of each interrogation to the relative speed to make it immune to frequency hopping and interinterrogation phase fluctuation. Then, the speed measurement and longitudinal localization are simultaneously performed by solving a nonlinear optimization model. Furthermore, the propagation model and antenna radiation pattern are investigated to facilitate the received signal strength index (RSSI)-based accurate lane-level lateral localization. To our knowledge, RESensing is the first RF-ELP-based speed measurement and localization system for mobile vehicles. The performance of RESensing is evaluated by real experiments under specifications of GB/T 37987 and EPC C1G2, which shows that RESensing achieves the mean speed error ratio of 4.34%, the longitudinal localization error of submeter level, and the lane estimation accuracy of nearly 100%.

Indoor positioning technology based on Wi-Fi fingerprint recognition has been widely studied owing to the pervasiveness of hardware facilities and the ease of implementation of software technology. However, the similarity-based method is not sufficiently accurate, whereas the offline training of the neural network-based method is overly time consuming. An efficient model with high positioning accuracy is therefore not yet available. We propose a stacking ensemble broad learning localization system using channel state information as a fingerprint, which is termed EnsemLoca. A bootstrapping method is used to build the training set, which enables the EnsemLoca system to build the base learner in parallel by using bagging. The broad learning system (BLS), which is a novel neural network model, as a base learner, not only has the advantage of time complexity but also offers a sparse representation in which the features are filtered. A unique base learner is constructed by randomly selecting the samples and features, and they are combined by stack generalization. The experimental results show that the EnsemLoca system achieves higher accuracy than several machine-learning algorithms in both line-of-sight (LOS) and non-LOS environments, and is even stronger than deep neural networks characterized by accuracy. At the same time, it has the same theoretical complexity as BLS, which greatly reduces the offline training time.

Cooperative perception is an effective way for connected autonomous vehicles to extend sensing range, improve detection precision, and thus enhance perception ability by combining their own sensing information with that of other vehicles. The existing cooperation perception schemes share only raw-, feature-, or object-level data, thus lacking the flexibility to adapt to highly dynamic vehicular network conditions, which leads to either bandwidth saturation or bandwidth underutilization, degrading the detection precision in the long run. In this article, we propose ML-Cooper, a multilevel cooperative perception framework, to fully utilize the bandwidth and hence improve detection precision. The key idea of ML-Cooper is to divide each frame of sensing data of the sender vehicle into three parts, and the corresponding raw data, feature data, and object data are transmitted to and fused at the receiver vehicle. We also develop a soft actor–critic (SAC) deep reinforcement learning algorithm to adaptively adjust the proportion of the three parts according to the channel state information of the Vehicle-to-Vehicle (V2V) link. The experimental results on KITTI and our collected data sets on two real vehicles show that ML-Cooper can achieve the highest average detection precision compared to existing single-level cooperative perception schemes.

In software defined networking, theflatdesign of distributed control plane enables the management of multi-domain networks that are incapable of deploying a root controller. However, it is very difficult to avoid policy conflicts between independent local controllers due to the lack of centralized arbitration. Moreover, domains without trust may not be always cooperative and could even cheat to maximize their own interests. In this article, we first consider the cooperative scenario and address the problem of traffic engineering in a flat distributed control plane. We propose a fully distributed algorithm, calledDisTE, which can provide max-min fair bandwidth allocation for flows and maximize resource utilization.DisTEalso preserves the local topology of each domain and achieves policy consistency by multiple rounds of synchronization. We then consider the non-cooperative scenario, where selfish domains may discriminate bandwidth requests from other domains or overstate theirs owns to squeeze more bandwidths.

Mobile social networks include a large number of social members who forward messages cooperatively. However, spammers post links to viruses and advertisements, or follow a large number of users, which produces many misleading messages in mobile social networks. In this paper, we propose an adaptive social spammer detection (ASSD) model. We build a spammer classifier by using a small number of labeled patterns and some unlabeled patterns. The prediction accuracy is high compared with some conventional supervised learning methods. Moreover, the time and energy required to label the identity of social members are reduced by applying ASSD. Because social spammers frequently change their behavior to deceive the spammer detection model, an incremental learning method is designed to update the spammer detection model adaptively, without retraining. We evaluate ASSD by comparing it with other supervised and semi-supervised machine learning methods using the Social Honeypot Dataset. Experimental results show that the proposed model outperforms the baseline methods in terms of recall and precision. Additionally, ASSD maintains a high detection accuracy by adaptively updating the model with newly generated social media data.

In underwater monitoring systems, it is important to guarantee high availability of the data collection service. An effective approach is the use of autonomous underwater vehicles (AUVs) to gather data from the sensor nodes. In mobile underwater wireless sensor networks, node locations change continuously, which increases the difficulty of data collection. In this paper, a dynamic data collection algorithm based on mobile nodes (DDCA) is proposed to collect underwater data. AUVs can move directly to the predicted node location to shorten the time of data collection. The algorithm is divided into two parts: mobility prediction and data collection. The locations of the mobility sensor nodes are predicted, and then the trajectory of the AUV is planned according to the predicted locations of sensor nodes to achieve reliable data collection. Furthermore, a region partitioning strategy is proposed to reduce the time difference of each AUV completing the data collection. The simulation results demonstrate that the DDCA effectively reduces the time of data collection and shortens the time difference of AUVs returning to the sink node.

As the scale and complexity of deep learning models continues to grow, model training is becoming an expensive job and only a small number of well-financed organizations can afford. Are the resources in commodity clusters well utilized for training? or how much potential space are still there for further improving the training efficiency in commodity clusters? is an urgent question to answer.

In mobile social networks (MSNs), mobile users communicate with each other via mobile devices, such as smartphones and tablets, transmitting data through intermittent connections. Mobile users have high mobility, which creates higher requirements for efficient data forwarding in MSNs. Therefore, forwarding data efficiently and quickly becomes a key problem. To tackle this problem, this article proposes a routing method based on a dynamic transient social community (DTSC) to optimize the routing and forwarding performance in MSNs. In this process, combined with the duration of intensive contact between nodes and the social relations of mobile users, the similarity of each pair of contact nodes is calculated, and community detection is carried out. Then, by analyzing the emergence mode of the DTSC, the measurement value and corresponding routing algorithm of the community’s ability to deliver messages are designed. Our algorithm fully considers the duration of the node’s direct encounter and the social connection of the indirect contact to ensure that the node can deliver successfully in a short time. The experimental results show that the DTSC has an excellent performance in data forwarding.

Distributed streaming applications require the underlying network flows to transmit packets continuously to keep their output results fresh. These results will become stale if no updates come, and their staleness is determined by the slowest flow. At this point, coflows can be semantically comprised. Hence, efficient coflow transmission is critical for streaming applications. However, prior coflow-based solutions have significant limitations. They use a one-shot performance metric-CCT (coflow completion time), which cannot continuously reflect the staleness of the output results for a streaming application.To this end, we propose a new performance metric-coflow age (CA), for coflows generated by distributed streaming applications. The CA tracks the longest time-since-last-service among all flows in a coflow. In such a context, we consider a data center network with multiple coflows that continuously transmit packets between their source-destination pairs and address the problem of minimizing the average long-term CA while simultaneously satisfying the throughput constraints from the coflows. To solve this problem efficiently, we design a randomized algorithm and a drift-plus-age algorithm, and show that they can make the average long-term CA to achieve nearly two times and arbitrarily close to the optimal value, respectively. Through extensive simulations, we further demonstrate that both of the proposed algorithms can significantly reduce the CA of coflows, without violating the throughput requirement of any coflow, when compared to the state-of-the-art solution.

This paper discusses the data transmission strategy based on underwater multimodal communication for marine applications in underwater wireless sensor networks (UWSNs). Underwater data required by various applications have different values of information (VoIs) depending on the type and timeliness of events. These data should be transmitted in different time latency according to their VoIs to accommodate the both application requirements and network performance. Our objective is to design a multi-level transmission strategy by using underwater multimodal communication system so that multiple paths with different transmission latency and energy consumption are provided for underwater data in UWSNs. For this purpose, we first define a minimum cost flow (MCF) model for the design of transmission strategy that considers transmission latency, energy efficiency, and transfer load. Then a distributed multilevel transmission strategy EMTS is proposed based on time backoff method for large-scale UWSNs. Finally, we compared the transmission latency, energy efficiency and network lifetime obtained by our EMTS strategy to those of the optimum solution of the MCF model, and a transmission algorithm based on greedy strategy. Although the latency of EMTS is slightly higher than that of other algorithms, our average network lifetime can reach 88.7% of that of the optimum solution of the MCF model.

Unknown RFID tags appear when tagged items are not scanned before being moved into a warehouse, which can even cause serious security issues. This paper studies the practically important problem of unknown tag detection. Existing solutions either require low-cost tags to perform complex operations or beget a long detection time. To this end, we propose the Collision-Seeking Detection (CSD) protocol, in which the server finds out a collision-seed to make massive known tags hash-collide in the last $N$ slots of a time frame with size $f$ . Thus, all the leading ${f-N}$ pre-empty slots become useful for detection of unknown tags. A challenging issue is that, computation cost for finding the collision-seed is very huge. Hence, we propose a supplementary protocol called Balanced Group Partition (BGP), which divides tag population into $n$ small groups. The group number $n$ is able to trade off between communication cost and computation cost. We also give theoretical analysis to investigate the parameters to ensure the required detection accuracy. The major advantages of our CSD+BGP are two-fold: (i) it only requires tags to perform lightweight operations, which are widely used in classical framed slotted Aloha algorithms. Thus, it is more suitable for low-cost tags; (ii) it is more time-efficient to detect the unknown tags. Simulation results reveal that CSD+BGP can ensure the required detection accuracy, meanwhile achieving $1.7\times $ speedup in the single-reader scenarios and $3.9\times $ speedup in the multi-reader scenarios than the state-of-the-art detection protocol.

Recent trends show that cloud computing is growing to span more and more globally distributed datacenters. For geo-distributed datacenters, there is an increasingly need for scheduling algorithms to place tasks across datacenters, by jointly considering WAN traffic and computation. This scheduling must deal with situations such as wide-area distributed data, data sharing, WAN bandwidth costs and datacenter capacity limits, while also minimizing makespan. However, this scheduling problem is NP-hard. We propose a new resource allocation algorithm called HPS+, an extension to Hypergraph Partition-based Scheduling. HPS+ models the combined task-data dependencies and data-datacenter dependencies as an augmented hypergraph, and adopts an improved hypergraph partition technique to minimize WAN traffic. It further uses a coordination mechanism to allocate network resources closely following the guidelines of task requirements, for minimizing the makespan. Evaluation across the real China-Astronomy-Cloud model and Google datacenter model show that HPS+ saves the amount of data transfers by upto 53 percent and reduces the makespan by 39 percent compared to existing algorithms.

For the development of the Underwater Internet of Things, reliable transmission of underwater wireless sensor networks to monitor the marine environment is important. However, for ocean monitoring, the reliability of data transmission is difficult to guarantee because of node mobility. In addition, energy consumption must be reduced during data transmission because node energy is limited. To entirely address these problems, this paper proposes a self-organising routing algorithm based on a joint clustering and routing strategy for ocean monitoring (JCR-OM) to increase reliable data transmission in underwater wireless sensor networks. Firstly, the reliable communication distance of the node is calculated in a multilayer current model by using a force analysis of the anchor node. Then, in cluster head selection, the reliable transmission distance and a backoff strategy are introduced to improve the impact of node mobility on data transmission. In intercluster routing selection, a greedy strategy is used to construct a routing strategy with minimum communication cost. The simulation results verify that JCR-OM can improve data transmission and prolong network lifetime.

The increasing scale of software defined networks (SDN) raises the requirement of distributed control plane, for providing scalable, reliable and high performance network management capabilities. In particular, the flat design of distributed control plane enables the management of networks with multiple independent domains that are incapable of deploying a root controller. However, it is very difficult to avoid policy conflicts between multiple controllers in flat plane due to the lack of arbitration. In this paper, we address the problem of traffic engineering in a flat control plane, and design a distributed traffic engineering algorithm, called DisTE, which can provide max-min fair bandwidth allocation for flows and maximize the resource utilization, using a fully distributed arbitration mechanism. DisTE also preserves the local topology of each domain using the topology aggregation method, and supports consistency by multiple rounds of synchronizations. We examine four strategies for determining the synchronization timings, and find that linearly decreasing interval method provides a better trade-off between network utilization and time costs. Experiments on a 717-switches 5-domain network topology demonstrate that DisTE could drive the link utilization ratio to more than 93%, and reduce up to 95% convergence time at cost of 3% relative error on fairness, compared to the centralized approach.

In cloud computing, profitability among others, is the driving force that encourages full utilization of computing resources. Hence, scenarios where one or more users are co-located on the same CPU but on different virtual machines are normal. Even though this setting brings profitability, unfortunately it also brings security and privacy issues. Software-based sand-boxing techniques are not perfect, as a result, users through the shared memory of an application, can leak secret information. To tackle this problem, some work has been published proving how it is possible to detect cache side-channel attacks using HPCs (Hardware Performance Counters). However, this particular method of detection may only be effective for cache side-channel attacks that cause a lot of cache activity, especially in the form of cache misses, among others. Using HPCs, we observe that the Firefox Web Browser (a benign application) causes more page-faults and cache misses among others, in comparison with the FLUSH+FLUSH, FLUSH+RELOAD and PRIME+PROBE in our attack. In this paper, we focus on using the FLUSH+FLUSH method of cache side-channel attacks, to distinguish between input against the Links text-based browser in order to gain private information about last visited websites. Because the FLUSH+FLUSH attack method causes little or no cache misses, as a result, the attack is more stealthy and in some cases more accurate compared to the state of the art FLUSH+RELOAD attack method. We make the assumption that both the victim and the adversary are co-located on the same CPU but on different virtual machines and different cores, and that they have the same application installed, then through the shared memory of the application, the attack is made possible. Our experiment results indicate that the shared memory of computer applications may be vulnerable to cache side-channel attacks and that more work needs to be done to protect users in the cloud computing system in this aspect.

Underwater wireless sensor networks (UWSNs) have become crucial for many different applications, such as marine pastures, which needs stratified aquaculture according to the habitat and range of activities of marine organisms. This type of application poses a significant challenge to network topology because strengthening the monitoring of the living conditions and collecting underwater data on organisms in different regions is necessary. In this paper, a new concept of "subregional monitoring" is proposed, and subregions with frequent activities of marine organisms are taken as the key monitoring areas, i.e., the areas of interest (AOIs). Then, SFRG, an energy-balanced and robust topology based on scale-free network and rigid graph theory, is proposed to strengthen the monitoring of the AOIs. The topology of a rigid graph is constructed in AOI, and the entire network is scale-free network with power-law degree distribution, when the rigid graph is regarded as a "node". Furthermore, a transmission algorithm based on the SFRG is suggested to prolong the network lifetime. The algorithm guarantees that 90% of the received packets are from the AOIs. The simulation results demonstrate that the SFRG effectively prolongs the network lifetime and improves the network robustness, which provides a strong support in strengthening the monitoring of AOIs and balancing the energy consumption.

In Industrial Internet of Things (IIoT), sensor nodes are vulnerable to withstand node failures due to energy exhaustion or external attacks, which leads to the low connectivity of networks. In this situation, how to improve network reliability has became a crucial problem. Adding a small amount of shortcuts to build a small world model in IIoT not only can reduce the delay, but also increases the reliability of networks. In this paper, we propose a Topology Optimization Scheme with Global Small World (TOSG) based on ant colony for IIoT. First, according to the number of appearing on the all shortest paths obtained by the ant colony optimization algorithm, we give the definition of importance for each node. The node with the highest importance in the communication range of a node is defined as an important node. We can find the important nodes in the network topology so that some shortcuts can be created between them to build a global small world model. The experiment results show that the TOSG model has a smaller average shortest path length and higher reliability compared with Greedy Model with Small World properties and Directed Angulation toward the Sink Node Model.

This paper studies the important sensory data collection problem in the sensor-augmented RFID systems, which is to quickly and accurately collect sensory data from a predefined set of target tags with the coexistence of unexpected tags. The existing RFID data collection schemes suffer from either low time-efficiency due to tag-collisions or serious data corruption issue due to interference of unexpected tags. To overcome these limitations, we propose the hierarchical-hashing data collection (HDC) protocol, which can not only significantly improve the utilization of RFID wireless communication channel by establishing bijective mapping between k target tags and the first k slots in time frame, but also effectively filter out the serious interference of unexpected tags. Although HDC has attractive advantages, the theoretical analysis reveals that the computation cost involved in it is as huge as O(k2k), where k is normally large in practice. By making some modifications to the basic HDC protocol, we propose the multi-framed hierarchical-hashing data collection (MHDC) protocol to effectively reduce the involved computation complexity. Unlike HDC that only issues a single time frame, MHDC uses multiple time frames to collaboratively collect sensory data from the k target tags. It can be understood as that a big computation task is disintegrated into multiple small pieces and then shared by multiple time frames. As a result, the computation cost involved in MHDC is reduced to O(k2n), where n ≪ k is the expected number of target tags that each time frame handles. Theoretical analysis is given to jointly consider the communication cost and computation cost thereby maximizing the overall time-efficiency of MHDC. Extensive simulation results reveal that the proposed MHDC protocol can correctly collect all sensory data and is always about more than 2× faster than the state-of-the-art RFID sensory data collection protocols.

In recent years, many fields have begun to use Internet of Things(IoT) to monitor the environment, especially in mountain terrain. Node failures bring a significant challenge in mountain terrain monitoring. Thus, how to improve the robustness of networks withstand node failures becomes a critical issue. To address this shortcoming, this article proposes a strategy to improve the robustness of IoT topology based on Genetic Algorithm (GA). First, Gauss Integration is used to build a 3D terrain to simulate the mountain terrain. Then, an initial scale-free topology according to the characteristics of IoT in 3D terrain is built. Furthermore, a novel crossover operator and a novel mutation operator are proposed to optimize the robustness of IoT topology in 3D terrain. Our proposed model keeps the initial degree of each node unchanged such that the edges overhead will not increase. The extensive experiment results show that our algorithm can significantly improve the robustness of topology in 3D terrain. Especially, the robustness of topology still keeps a high level in the case of partial node failures.

Summary The segmentation of a shape into a series of meaningful parts is a fundamental problem in shape analysis and part‐based object representation. However, it is difficult to make the result of shape segmentation accord with the expectations of humans performing the same task. There is still a need for an effective way to segment the shape although a variety of methods have been proposed. In this paper, we present a novel shape decomposition algorithm, which is implemented in a coarse‐to‐fine manner, taking into account the critical points on the silhouette. First, a part‐cut hypotheses candidate set is generated and classified into 2 categories. Then, the hypotheses with adjacent endpoints are determined first, and later, the other kinds of hypotheses are finely determined by our presented measures such as chord arc ratio and inner angle. We note that the proposed coarse‐to‐fine decomposition conforms to the mechanism of human vision. The extensive experimental results on a large set of shapes show that our algorithm can generate shape decomposition results that better accord with human intuition compared to competing algorithms.

Organizing shapes by convex parts is a fundamental procedure for many shape-related applications. However, convexity is sensitive to noise and shape variations. Recent publications in the field concentrated on decomposing shapes into near-convex parts. Although a variety of methods have been presented, there is still a need for a robust and versatile method, especially when a shape possesses long curved branches such as a lizard with a long curved tail. It is difficult to capture the tail as a whole part because its concavity is too high based on classic measures. To address this issue, we propose a `Visibility Range', novel shape signature in this paper. Visibility range reaches low values for points in concave regions and high values in convex regions. Moreover, a novel concavity measure based on visibility range is presented. Compared to previous measures, the novel measure describes long curved branches better. With these, a simple but effective shape decomposition algorithm is designed. The decomposition is formulated as a problem of detecting points with extreme visibility range in a visibility matrix. Extensive experiments have been done on shapes with various kinds of near-convex parts, demonstrating that the proposed method is more robust and effective than the state-of-art methods based on other concave-convex features.

As a novel architecture, software-defined networking (SDN) is viewed as the key technology of future networking. The core idea of SDN is to decouple the control plane and the data plane, enabling centralized, flexible, and programmable network control. Although local area networks like data center networks have benefited from SDN, it is still a problem to deploy SDN in wide area networks (WANs) or large-scale networks. Existing works show that multiple controllers are required in WANs with each covering one small SDN domain. However, the problems of SDN domain partition and controller placement should be further addressed. Therefore, we propose the spectral clustering based partition and placement algorithms, by which we can partition a large network into several small SDN domains efficiently and effectively. In our algorithms, the matrix perturbation theory and eigengap are used to discover the stability of SDN domains and decide the optimal number of SDN domains automatically. To evaluate our algorithms, we develop a new experimental framework with the Internet2 topology and other available WAN topologies. The results show the effectiveness of our algorithm for the SDN domain partition and controller placement problems.

Internet attacks, such as distributed denial-of-service attacks and worm attacks, are increasing in severity and frequency. Identifying and mitigating realtime attacks are an important and challenging task for network administrators. An infected host can make a large number of connections to distinct destinations during a short time. Such a host is called a superpoint. Detecting superpoints can be utilized for traffic engineering and anomaly detection. This paper proposes a novel data streaming method for detecting superpoints and proves guarantees on its accuracy with low memory requirements. The superior performance of this method comes from a new data structure, called vector bloom filter (VBF), which is a variant of standard BF. The VBF consists of six hash functions, four of which take some consecutive bits from the input string as the corresponding value, respectively. The information of superpoints is obtained by using the overlapping of hash bit strings of the VBF. Theoretical analysis and experimental results show that the proposed method can detect superpoints precisely and efficiently through comparison with other existing approaches.

Subspace skyline, as an important variant of skyline, has been widely applied for multiple-criteria decisions, business planning. With the development of mobile internet, subspace skyline query in mobile distributed environments has recently attracted considerable attention. However, efficiently obtaining the meaningful subset of skyline points in any subspace remains a challenging task in the current mobile internet. For more and more mobile applications, subspace skyline query on mobile units is usually limited by big data and wireless bandwidth. To address this issue, in this paper, we propose a system model that can support subspace skyline query in mobile distributed environment. An efficient algorithm for processing the Subspace Skyline Query using MapReduce (SSQ) is also presented which can obtain the meaningful subset of points from the full set of skyline points in any subspace. The SSQ algorithm divides a subspace skyline query into two processing phases: the preprocess phase and the query phase. The preprocess phase includes the pruning process and constructing index process which is designed to reduce network delay and response time . Additionally, the query phase provides two filtering methods, SQM-filtering and ε-filtering, to filter the skyline points according to user preference and reduce network cost. Extensive experiments on real and synthetic data are conducted and the experimental results indicate that our algorithm is much efficient, meanwhile, the pruning strategy can further improve the efficiency of the algorithm.

Summary Reverse nearest neighbor (RNN) queries are the complimentary problem and particular interest in the past few years, such as location‐based services, profile‐based marketing, resource allocation, and traffic monitoring system. The one major drawback for the existing RNN is that it has inherent sequential nature and uses in‐memory algorithm, which limits its applicability to large‐scale spatial data queries. This paper proposes scalable algorithms for RNN queries in a distributed environment. Firstly, we investigate the Basic‐scalable reverse nearest neighbor (SRNN) initialization query method based on the inverted grid index. Secondly, two optimization methods Lazy‐SRNN and Eager‐SRNN are proposed to effectively process scalable multi‐dimensional RNN queries. Among them, Lazy‐SRNN prunes the search space when all RNN objects are discovered in one pass; Eager‐SRNN attempts to prune spatial objects incrementally as soon as they are visited. In addition, the SRNN algorithm is proved to be the first attempt for the exact scalable RNN algorithms in a distributed environment on multi‐dimensional data sets. We show in an extensive experimental evaluation on real‐world and synthetic data the scalability and the performance of our novel approach. Copyright © 2015 John Wiley & Sons, Ltd.

Summary Cloud computing came into being and is currently an essential infrastructure of many commerce facilities. To achieve the promising potentials of cloud computing, effective and efficient scheduling algorithms are fundamentally important. However, conventional scheduling methodology encounters a number of challenges. During the tasks scheduling in cloud systems, how to make full use of resources and how to effectively select resources are also important factors. At the same time, communication delay also plays an important role in cloud scheduling, which not only leads to waiting between tasks but also results in much idle interval time between processing units. In this paper, a fuzzy clustering method is used to effectively preprocess the cloud resources. Combining the list scheduling with the task duplication scheduling scheme, a new directed acyclic graph based scheduling algorithm called earliest finish time duplication algorithm for heterogeneous cloud systems is presented. Earliest finish time duplication attempts to insert suitable immediate parent nodes of the current selected node in order to reduce its waiting time on the processor. The case study and experimental results illustrate that the algorithm proposed in this paper is better than the popular heterogeneous earliest finish time algorithms. Copyright © 2014 John Wiley & Sons, Ltd.

Radio Frequency Identification (RFID) technology has been widely used in inventory management in many scenarios, e.g., warehouses, retail stores, hospitals, etc. This paper investigates a challenging problem of complete identification of missing tags in large-scale RFID systems. Although this problem has attracted extensive attention from academy and industry, the existing work can hardly satisfy the stringent real-time requirements. In this paper, a Slot Filter-based Missing Tag Identification (SFMTI) protocol is proposed to reconcile some expected collision slots into singleton slots and filter out the expected empty slots as well as the unreconcilable collision slots, thereby achieving the improved time-efficiency. The theoretical analysis is conducted to minimize the execution time of the proposed SFMTI. We then propose a cost-effective method to extend SFMTI to the multi-reader scenarios. The extensive simulation experiments and performance results demonstrate that the proposed SFMTI protocol outperforms the most promising Iterative ID-free Protocol (IIP) by reducing nearly 45% of the required execution time, and is just within a factor of 1.18 from the lower bound of the minimum execution time.

Unknown RFID tags appear when the unread tagged objects are moved in or tagged objects are misplaced. This paper studies the practically important problem of unknown tag detection while taking both time-efficiency and energy-efficiency of battery-powered active tags into consideration. We first propose a Sampling Bloom Filter which generalizes the standard Bloom Filter. Using the new filtering technique, we propose the Sampling Bloom Filter-based Unknown tag Detection Protocol (SBF-UDP), whose detection accuracy is tunable by the end users. We present the theoretical analysis to minimize the time and energy costs. SBF-UDP can be tuned to either the time-saving mode or the energy-saving mode, according to the specific requirements. Extensive simulations are conducted to evaluate the performance of the proposed protocol. The experimental results show that SBF-UDP considerably outperforms the previous related protocols in terms of both time-efficiency and energy-efficiency. For example, when 3 or more unknown tags appear in the RFID system with 30000 known tags, the proposed SBF-UDP is able to successfully report the existence of unknown tags with a confidence more than 99%. While our protocol runs 9 times faster than the fastest existing scheme and reducing the energy consumption by more than 80%.

Skyline queries are useful in decision making applications. Skyline queries in highly mobile distributed environments have attracted many attentions recently due to the development of mobile internet device. The properties of distributed computing make skyline queries more complicated especially in any subspace. Conventional skyline algorithms do not support subspace skyline queries in distributed environment. In this paper, we focus on how to perform distributed skyline queries in any subspace according to user preference. So we propose a system model in a mobile and distributed environment. An efficient parallel algorithm for processing the Subspace Skyline Query (SSQ) using MapReduce is applied to the system model. This algorithm can report skyline points in any subspace. Meanwhile, a pruning strategy is also proposed in order to reduce the network communication and minimize the response time. We conduct experiments on real and synthetic data. Experimental results indicate that our SSQ algorithm is much more efficient. Furthermore, the pruning strategy can further improve the performance of the algorithm.

Collaborative Filtering (CF) algorithm is the common solution to Recommender System (RS). With the development of network and storage technology, the amount of users and items in RS system is exclusively growing. How to increase the scalability and recommendation accuracy of CF are the main concerns in the related research. In this paper, an efficient implementation for user-based CF algorithm on MapReduce is presented. We exploit Bag of Word (BoW) method and design a hierarchical inverted index to further increase the scalability of our method. Meanwhile, a soft-assignment mechanism for the hierarchical inverted index is proposed to make up the recommendation accuracy decrease caused by the index. The Mapreduce implementations of our methods are detailed discussed and analyzed on both simulated data and real data, demonstrating that our implementation has the ability to scale to huge numbers of users and items, eanwhile ensures recommendation accuracy.

How to reduce the transmission latency is a main concern in the context of Mobile Visual Search (MVS). Transmitting extremely compacted visual descriptor in a progressive manner is the start-of-art solution. In this paper, we present a novel MVS system following the client-server architecture. To reduce the transmission latency, the inquiry image is represented by a set of hash bits, which are then progressively transmitted. In the server side, all images are indexed by their hash bits, similar as the classic Bag-of-Word (BoW) model. Owe to the merit of the proposed system, the IDF weight of the hash bits are encoded into a sparse vector which retained in the mobile client, and provides a transmission order of the inquiry hash bits. The hash bit with lower IDF weight will be more discriminative, which should have higher priority during the transmission. As far as we know, this work is the first one attempting to transmit the hash bits in a proper progressive manner in MVS. Extensive experiments have been done on the public Stanford MVS database, demonstrating that the proposed progressive transmission strategy achieves higher recognition rate compared to other strategies, when delivering the same amount of data.

As a kind of interesting mobile application, Mobile Visual Search (MVS) has attracted extensive research efforts from both academy and industry. Most of the MVS systems adopt the client-server framework, in which transmission latency caused by the limited bandwidth in wireless network is a big problem. To address this problem, the state-of-the-art work focuses on designing low bit-rate descriptors for MVS. However, few work focuses on reducing the number of descriptors. To further reduce the latency, we propose a novel framework of MVS based on the weighted matching of dominant descriptor. Firstly, we present an affinity propagation based algorithm for dominant descriptor selection. Secondly, we propose a weighted feature matching method to consider the differences of dominant descriptors in feature matching. By the proposed framework, we not only reduce the network latency in MVS, but also avoid transmitting useless descriptors to improve the retrieval accuracy of MVS. The experimental results on Stanford MVS data set show that when using CHoG descriptors, the proposed framework outperforms the existing framework by reducing more than 40% of the amount of data transmission and increasing 5% of the average retrieval accuracy.

Owing to its superior properties, such as fast identification and relatively long interrogating range over barcode systems, Radio Frequency Identification (RFID) technology has promising application prospects in inventory management. This paper studies the problem of complete identification of missing RFID tag, which is important in practice. Time efficiency is the key performance metric of missing tag identification. However, the existing protocols are ineffective in terms of execution time and can hardly satisfy the requirements of realtime applications. In this paper, a Multi-hashing based Missing Tag Identification (MMTI) protocol is proposed, which achieves better time efficiency by improving the utilization of the time frame used for identification. Specifically, the reader recursively sends bitmaps that reflect the current slot occupation state to guide the slot selection of the next hashing process, thereby changing more empty or collision slots to the expected singleton slots. We investigate the optimal parameter settings to maximize the performance of the MMTI protocol. Furthermore, we discuss the case of channel error and propose the countermeasures to make the MMTI workable in the scenarios with imperfect communication channels. Extensive simulation experiments are conducted to evaluate the performance of MMTI, and the results demonstrate that this new protocol significantly outperforms other related protocols reported in the current literature.

Due to the bandwidth limitation in wireless networks, transmission overhead is a big problem in Mobile Visual Search (MVS). Existing work proposes transmitting the compressed local feature descriptors instead of the query image to reduce the transmission overhead. Although many kinds of compressed descriptors are proposed, designing a suitable lossless compressed descriptor has proven elusive. In this paper, we propose a novel framework for MVS with low transmission overhead rather than focusing on compressed descriptors. The key point of the proposed framework is to migrate the vector quantization in the bag of visual words model from the server to the client. In this framework, no matter what descriptors are used, the client only transmits the ID numbers of the visual words to the server, thereby reaching the minimal possible transmission overhead. To achieve this goal, we present vocabulary decomposition by which we can decompose the large vocabulary into several small ones satisfying storage constraints on mobile devices. In this paper, we first formulate vocabulary decomposition as an optimization problem. We then present Joint Product Quantization (JPQ) and Joint Optimized Product Quantization (JOPQ) to address the proposed optimization problem. Finally , we conduct a large number of simulation experiments and real experiments. The experimental results show that the proposed framework outperforms the existing framework by reducing more than 95% of the transmission overhead.

Owing to its attractive features such as fast identification and relatively long interrogating range over the classical barcode systems, radio-frequency identification (RFID) technology possesses a promising prospect in many practical applications such as inventory control and supply chain management. However, unknown tags appear in RFID systems when the tagged objects are misplaced or unregistered tagged objects are moved in, which often causes huge economic losses. This paper addresses an important and challenging problem of unknown tag identification in large-scale RFID systems. The existing protocols leverage the Aloha-like schemes to distinguish the unknown tags from known tags at the slot level, which are of low time-efficiency, and thus can hardly satisfy the delay-sensitive applications. To fill in this gap, two filtering-based protocols (at the bit level) are proposed in this paper to address the problem of unknown tag identification efficiently. Theoretical analysis of the protocol parameters is performed to minimize the execution time of the proposed protocols. Extensive simulation experiments are conducted to evaluate the performance of the protocols. The results demonstrate that the proposed protocols significantly outperform the currently most promising protocols.

k-means is undoubtedly one of the most popular clustering algorithms owing to its simplicity and efficiency. However, this algorithm is highly sensitive to the chosen initial centers and thus a proper initialization is crucial for obtaining an ideal solution. To address this problem, k-means++ is proposed to sequentially choose the centers so as to achieve a solution that is provably close to the optimal one. However, due to its weak scalability, k-means++ becomes inefficient as the size of data increases. To improve its scalability and efficiency, this paper presents Map Reduce k-means++ method which can drastically reduce the number of Map Reduce jobs by using only one MapReduce job to obtain k centers. The k-means++ initialization algorithm is executed in the Mapper phase and the weighted k-means++ initialization algorithm is run in the Reducer phase. As this new Map Reduce k-means++ method replaces the iterations among multiple machines with a single machine, it can reduce the communication and I/O costs significantly. We also prove that the proposed Map Reduce k-means++ method obtains O(α2)approximation to the optimal solution of k-means. To reduce the expensive distance computation of the proposed method, we further propose a pruning strategy that can greatly avoid a large number of redundant distance computations. Extensive experiments on real and synthetic data are conducted and the performance results indicate that the proposed Map Reduce k-means++ method is much more efficient and can achieve a good approximation.

A shape signature is defined as any 1-D function on a shape, which is a compact and concise representation for some essence of the shape. Although a variety of shape signatures are proposed and utilized in shape retrieval and recognition tasks, the existing signatures cannot yet provide entirely satisfactory solutions to describe the shape variations well, especially when significant noise or articulation occurs. Motivated by the fact that electrical charge distributions are almost the same for similar shapes but not vice versa when shapes reach their electrical equilibrium condition, we propose a novel shape signature based on the electrical charge distribution on the shape (ECDS). Compared to other shape descriptors, ECDS is more intuitively and robust, which is computed in a global manner. Furthermore, as well as being invariant to translation, scale and rotation, ECDS is articulation insensitive and therefore exhibits better performance by the introduction of generalized coulomb potentials. This allows it to better match shapes whose parts can move independently, such as scissors. Finally, numerous experiments have done on public databases, demonstrating that ECDS has the above properties and compares well with other shape descriptors in many kinds of shape retrieval and recognition tasks.

Radio Frequency Identification (RFID) technology has been widely applied in many scenarios such as inventory control, supply chain management due to its superior properties including fast identification and relatively long interrogating range over barcode systems. It is critical to efficiently identify the unknown tags because these tags can appear when new tagged objects are moved in or wrongly placed. The state-of-the-art Basic Unknown tag Identification Protocol-with Collision-Fresh slot paring (BUIP-CF) protocol can first deactivate all the known tags and then collect all the unknown tags. However, BUIP-CF protocol investigates an ALOHA-like technique and causes too many tag responses, which results in low efficiency. This paper proposes a Fast Unknown tag Identification (FUI) protocol which investigates an indicator vector to label the unknown tags with a given accuracy and removes the time-consuming tag responses in the deactivation phase. FUI also adopts the classical Enhanced Dynamic Framed Slotted ALOHA (EDFSA) protocol to collect the labeled unknown tags. We then investigate the optimal parameter settings to maximize the performance of the proposed FUI protocol. Extensive simulation experiments are conducted to evaluate the performance of the proposed FUI protocol and the experimental results show that it considerably outperforms the state-of-the-art protocol.

Radio Frequency Identification (RFID) technology is widely used in the the retail, warehouse and supply chain management. However, unknown RFID tags appear when the unregistered tagged objects are moved in or tagged objects are misplaced, which leads to huge economic losses (e.g., misplaced chilled food in a warehouse may quickly decay). This paper studies the practically important problem of unknown tag detection. To the best of our knowledge, this is the first piece of work taking both time-efficiency and energy-efficiency into consideration, where the energy-efficiency is very important when the battery-powered active tags are used. This paper proposes two efficient protocols to address the problem of unknown tag detection. Specifically, the Basic Unknown Tag Detection (B-UTD) protocol leverages a cost-effective filter vector to detect the unknown tags, based on which we then propose a Sampling based Unknown Tag Detection (SUTD) protocol by adopting the well-known sampling idea. We present theoretical analysis to optimize the performance of the proposed protocols. Extensive simulations are conducted to evaluate the performance of the proposed protocols. And the experimental results show that the proposed S-UTD protocol considerably outperforms the most related protocol by reducing more than 90% of the required execution time and energy consumption.

SUMMARY The anonymity and dynamic character of a Peer‐to‐Peer (P2P) network makes it an ideal medium for selfish and vicious action. In order to solve this problem, P2P reputation systems are proposed to evaluate the trustworthiness of peers and to prevent the selfish, dishonest, and malicious peers' behaviors, which collects local reputation scores and aggregates them into the global reputation. In this paper we propose a DHT (Distributed Hash Table) trust overlay network (DHTON) to model the network structure and the storage of reputation information. We also design a robust and distributing reputation system, DHTrust, which takes full advantage of the DHT to distribute local reputation to trade‐off the damage of fake reputation information by genuine reputation information. By using the trust evaluation towards two reputation scores, we can also distinguish and evaluate the fundamental behaviors of peers in the P2P network, i.e. providing service and issuing reputation scores. To adapt to the dynamic P2P networks, we take dynamic node mechanism into account. Our scheme can assure convergence effectiveness and robustness, when nodes enter or leave the system. We conduct extensive simulations to evaluate the performance of DHTrust. The results show that our system makes significant improvement in convergence speed and aggregation accuracy. Moreover, it is robust to malicious peers. Copyright © 2011 John Wiley & Sons, Ltd.

In object-based image retrieval, there are two important issues: an effective image representation method for representing image content and an effective image classification method for processing user feedback to find more images containing the user-desired object categories. In the image representation method, the local-based representation is the best selection for object-based image retrieval. As a kernel-based classification method, Support Vector Machine (SVM) has shown impressive performance on image classification. But SVM cannot work on the local-based representation unless there is an appropriate kernel. To address this problem, some representative kernels are proposed in literatures. However, these kernels cannot work effectively in object-based image retrieval due to ignoring the spatial context and the combination of local features. In this article, we present Adjacent Matrix (AM) and the Local Combined Features (LCF) to incorporate the spatial context and the combination of local features into the kernel. We propose the AM-LCF feature vector to represent image content and the AM-LCF kernel to measure the similarities between AM-LCF feature vectors. According to the detailed analysis, we show that the proposed kernel can overcome the deficiencies of existing kernels. Moreover, we evaluate the proposed kernel through experiments of object-based image retrieval on two public image sets. The experimental results show that the performance of object-based image retrieval can be improved by the proposed kernel.

Due to the use of acoustic channels with limited available bandwidth, Underwater Sensor Networks (USNs) often suffer from significant performance restrictions such as low reliability, low energy-efficiency, and high end-to-end packet delay. The provisioning of reliable, energy-efficient, and low-delay communication in USNs has become a challenging research issue. In this paper, we take noise attenuation in deep water areas into account and propose a novel layered multipath power control (LMPC) scheme in order to reduce the energy consumption as well as enhance reliable and robust communication in USNs. To this end, we first formalize an optimization problem to manage transmission power and control data rate across the whole network. The objective is to minimize energy consumption and simultaneously guarantee the other performance metrics. After proving that this optimization problem is NP-complete, we solve the key problems of LMPC including establishment of the energy-efficient tree and management of energy distribution and further develop a heuristic algorithm to achieve the feasible solution of the optimization problem. Finally, the extensive simulation experiments are conducted to evaluate the network performance under different working conditions. The results reveal that the proposed LMPC scheme outperforms the existing mechanism significantly.

The open and anonymous nature of peer-to-peer (P2P) networks makes it an ideal medium for attackers to spread malicious contents, which in turn leads to lower quality of network services due to lack of effective trust management mechanism. To improve the quality of services (or transactions), this paper proposes a novel trust and reputation model, named as GroupTrust, based on peer group and evaluation similarity degree in P2P networks. In the proposed model, trust relationships between peers are divided into three categories: trust relationship within a peer group, trust relationship between different groups, and trust relationship between a peer in a peer group with another peer out of this peer group. The model presents the evaluation similarity degree under different context of services and gives local and global reputation computation. Experimental results demonstrate that this model can get more real trust value and deal with the malicious attacks efficiently by comparison with existing models. © 2010 Wiley Periodicals, Inc.

The anonymity and dynamic characters of Peer-to-Peer (P2P) system makes it an ideal medium for selfish and vicious action. In order to solve this problem, P2P reputation systems are proposed to evaluate the trustworthiness of peers and to prevent the selfish, dishonest, and malicious peers' behaviors, which collects local reputation scores and aggregates them into the global reputation. In this paper we propose a DHT trust overlay network (DHTON) to model the network structure and the storage of reputation information. We also design a robust and distributing reputation system, DHTrust, which takes full advantage of the Distributed Hash Table (DHT) to distribute local reputation to trade off the damage of fake reputation information by genuine reputation information. By using the trust evaluation towards two reputation scores, we can also distinguish and evaluate the fundamental behaviors of peers in P2P network, i.e., providing service and issuing reputation scores. Through the simulation experiments, we find our system makes significant performance gains in convergence speed and aggregation accuracy, and the most important, is robust to malicious peers.

Since the generation of detailed traffic statistics does not scale well with link speed, increasingly passive traffic measurement employs sampling at the packet or flow level. Sampling has become an attractive and scalable means to measure flow data on high-speed links. However, knowing the length distributions of traffic flows passing through a network link is useful for some applications such as inferring traffic demands, characterizing source traffic, and detecting traffic anomalies. Passive traffic measurement increasingly makes inferences from sampled network traffic. However, previous work has shown the inaccuracy of estimating flow length distributions from sampled traffic when the sampling is performed at the packet level. In this paper, we propose a novel method that uses flow statistics formed from double-sampled packet stream to infer the absolute frequencies of lengths of flows in the unsampled stream. We achieve this through statistical inference and by exploiting heavy-tailed feather. The method allow us to recover the complete flow length distribution.

With the increasing number of technology areas using Virtual Machine (VM) platforms, challenges exist in Virtual Machine migrating from one physical host to another. However, the complexity of these virtualized environments presents additional management challenges. Unfortunately, many traditional approaches may be either not effective well for reducing downtime or migration time, or not suitable well for Xen VMs platforms. This paper presents the design and implementation of a novel Slowdown Scheduling Algorithm (SSA) for Xen live VM migration. In our SSA methodology, the CPU resources which have been assigned to migration domain are decrease properly. That is, the dirtying page rate is reduced according to the decrease of CPU activity. Experimental results illustrate that our SSA approach can shorten both the total migration time and downtime obviously under high dirty page rate environment.

Scalability is a desirable quality for contemporary and future computing and communication systems and becomes one of the most important considerations during the design and deployment of these systems. With rapid increases in the information volume and system complexity, new architecture and techniques are required to support scalable computing and communications. Scalable computing Network technologies and applications Parallel and distributed systems This special issue assembles state-of-the-art researches for scalable computing and communications. In ‘An Optimal Multimedia Object Allocation Solution in Multi-powermode Storage Systems’, the authors consider the allocation problem of multimedia objects in multi-powermode storage systems. They design an underlying infrastructure of a storage system and propose a dynamic multimedia object allocation policy to minimize the energy consumption with consideration of the system performance. The authors not only devise a multimedia object placement algorithm for a wide-area storage system by inserting the function of transcoding into storage servers, but also, in light of the concerns on energy consumption from industrial parks, optimize the overall effect of both access latency and energy consumption. The paper shows that the proposed mechanism will lead to an optimal solution of the energy consumption problem to the system 1. The paper titled ‘Dynamic Scratch-pad Memory Management with Data Pipelining for Embedded Systems’ proposes an effective data pipelining technique, Scratch-Pad Data Pipelining (SPDP), for dynamic scratch-pad memory (SPM) management with Direct Memory Access (DMA). The basic idea is to overlap the execution of CPU instructions and DMA operations. When the CPU executes instructions and accesses data from one portion of the SPM, DMA operations can be performed to transfer data between the off-chip memory and another portion of SPM simultaneously. The authors have implemented the SPDP technique with the IMPACT compiler, and conduct experiments using a set of loop kernels from DSPstone, Mibench and Mediabench on the cycle-accurate VLIW simulator of Trimaran. The experimental results show that the technique achieves performance improvement compared with the previous work 2. The paper titled ‘Building Dynamic and Transparent Integrity Measurement and Protection for Virtualized Platform in Cloud Computing’ deals with the problem of runtime system integrity measurement and protection in the Cloud Computing environment. The authors present an integrity measurement and protection architecture for software stacks running on a guest operating system (OS) of a virtualized platform in cloud platform. The solution does not change the guest OS and thus is transparent to the OS authority. Furthermore, the architecture ensures that sensitive information of users is protected once the integrity of software stacks is broken during runtime. The evaluation results show that the solution is effective for integrity protection with acceptable performance overhead 3. The paper titled ‘Performance Modeling and Analysis of Deficit Round Robin (DRR) Scheduling Scheme with Self-Similar Traffic’ investigates the queuing performance of DRR and develops a new analytical model for deriving the upper and lower bounds of the queue length distributions of individual traffic flows in DRR scheduling systems subject to self-similar traffic. Extensive comparison between simulation and analytical results validates the accuracy of the developed model. To demonstrate its applications, the analytical model is used to investigate the effects of packet size on the performance of the queuing system. The developed model is further applied to study the configuration of weights of individual traffic flows4. The paper titled ‘An Automatic Application Signature Construction System for Unknown Traffic’ is about Identifying and classifying network traffic flows for a broad range of network activities. The authors propose a traffic classification system based on application signatures, with a novel approach to fully automate the process of deriving signatures from unidentified traffic. The key idea is to integrate statistics-based flow clustering with payload-based signature matching method, so as to eliminate the requirement of pre-labeled training data sets. The paper evaluates the efficiency of their approach using real-world traffic trace, and the results indicate that signature classifiers built from clustered data and pre-labeled data are able to achieve a similar high accuracy better than 99% 5. The paper titled ‘Improving Grid Performance by Dynamically Deploying Applications’ presents the idea of Hierarchical and Dynamic Deployment of Application (HDDA) in Grid to improve the system performance. With HDDA, an application can be dynamically deployed and undeployed when necessary. The Average Latency Ratio Minimum (ALR-MIN) replacement strategy is also proposed to reduce the overhead caused by HDDA. It deploys applications to nodes with minimum ALR of Node (NALR), and evicts applications with minimum increment of ALR. Results of the experiment show that HDDA can achieve 10 and 24% less Average Complete Time than the schemes of non-HDDA and Static Deployment of Application, respectively. Additionally, throughput and load balancing of HDDA are also better than the other two schemas 6. All the papers in this special issue are focused entirely on advanced topics on scalable computing and communications. We sincerely hope that you will enjoy reading these papers and find them very useful. We expect this special issue to play an important role in promoting scalable computing and communications research. The guest editors of this special issue thank all the authors and international reviewers for their excellent contributions to this special issue. We deeply thank Prof Geoffrey Fox for his efforts in making this issue possible. This work is supported by the NSFC of China under grand No. of 90818002, 90718030, 60973115 and 60973116.

Storage QoS methodology has been extensively mentioned in large-scale storage systems. Since storage resources tend to be heterogeneous and dynamic changed, efficient storage states capturing becomes essential. In this paper we propose a novel capturing and preserving mechanism, called change state capture (CSC). CSC can capture the changed state when storage provider QoS modified. The changed event can trigger the broker module to reorganize the storage resources to meet this change. The experimental results show that CSC can achieve better performance than existing periodic scanning algorithm in terms of efficiency.

It is challenging to deal with the tradeoff between energy efficiency and quality of service (QoS) for multimedia applications in wireless sensor networks (WSNs). The packet aggregation scheme is widely applied to reduce overhead for high throughput and energy saving in wireless sensor networks. However, it may cause unacceptable delay in WSNs. In order to solve the problem for voice delivering with energy efficiency and proper QoS in WSNs, in this paper, we propose an adaptive waiting packet aggregation (AWPA) algorithm. First, we formulate the problem as an optimization problem. Then we optimize the problem and implement the proposed algorithm. Furthermore, we analyze the performance of the proposed algorithm. Finally, we conduct simulations in the NS-2 platform to evaluate the performance. The results show that AWPA outperforms existing algorithms for the performance.

Selfish behaviors significantly affect the overall performance of mobile ad hoc networks (MANETs). Reputation systems have been proved to be an efficient way to block such behaviors in MANETs. Several reputation models based on subjective logic have been proposed to improve the reputation mechanism, in which an uncertainty value is introduced for reputation computation when the local information is not sufficient. However, these reputation models fail to utilize the recommended opinions effectively and reduce the uncertainty value while these opinions are combined. In this paper, we propose a novel reputation computation model based on subjective logic to overcome the above deficiencies. We consider not only the recommenders' trustworthiness but also the familiarities among the recommended nodes during reputation computations. This familiarity is defined as a certainty value which is used to weight opinions in reputation computation. In our model, the recommendations of nodes with low trustworthiness or high uncertainty on the recommended nodes have little impact on the recommended nodes' reputations so that nodes can reach opinions with lower uncertainty value through reputation computations. We conduct simulations to evaluate our model on its performance. The simulation results show that the proposed model achieves about 40% improvement in the time of discovering and isolating selfish nodes compared with a previous model based on subjective logic and selfish nodes' success rate is further reduced by up to 10%.

Abstract Multi‐core platforms are being widely used in many technology areas. These technology shifts not only provide a compute‐intensive service, but also require us to improve the I/O‐intensive service in performance. However, the existing I/O scheduling approaches lack either quantitative analysis or workload adaptability. These approaches become worse when the application scenarios have either diverse I/O tasks or heterogeneous multi‐cores. Based on the analysis of traditional software pipelining technology, we first propose a reverse interleaved pipelining scheduling strategy to decrease the total I/O execution time and balance the workloads of homogeneous processors in this paper. Theoretical analysis shows that this balancing strategy is fair. As for the heterogeneous processors' circumstance, a Self‐adaptive scheduling method is presented for selecting one of the most‐appropriate multi‐core services for the expected I/O tasks. Simulation studies show that the performance of this scheduling strategy self‐adapts to application and multi‐core diversity well. Copyright © 2009 John Wiley & Sons, Ltd.

The administration of resources and application scheduling in a large-scale distributed storage environment is a formidable task nowadays, especially in dynamic changing scenario both in storage resources and application requirements. In order to make a difference in application oriented storage service, this paper shows a new autonomic approach to match individual applications to most appropriate storage systems and storage services based on their particular characteristic matrix, such as capacity, performance, availability, dependability, recoverability, user requirements, scalability, and value to the organization etc.

A key problem in research on wireless sensor networks (WSNs) is how to improve energy efficiency and extend the lifetime of sensor nodes and therefore the WSNs. In this paper, we address this problem and present a geography-based heterogeneous hierarchy routing (GBHHR) protocol. GBHHR adopts hierarchy structure for better data congregation, which can improve the efficiency of data collection and uses heterogeneous nodes as cluster heads to solve the problem of selecting cluster head in traditional hierarchy routing algorithms. A dormancy mechanism is introduced to heterogeneous node for energy saving and a multi-jump data transmission mode is applied for remote and wide-area applications. Furthermore, a new geographical routing algorithm, named hole-forewarning routing algorithm (HFR), is proposed to solve the problem of hole during routing path. Simulation results show that GBHHR has higher energy efficiency[1]than traditional geographical routing and hierarchy routing.

Due to lack of effective trust management mechanism, there are a lot of deceptive behaviors in P2P networks, which seriously decrease the quality of network services. In order to improve the quality of services (or transactions), this paper proposes a novel trust and reputation model based on similarity evaluation in P2P environments. According to the different context of services, the model gives the similarity degree evaluation, and presents local reputation and global reputation computation. Experimental results demonstrate that this model can get more real trust value and deal with the malicious attacks.

The variable precision rough set (VPRS) model generalizes the Pawlak rough set model with variable parameters. The Bayesian rough set (BRS) model improves the VPRS model with non-parametric modification by using the prior probability as a reference. This paper presents two research results related to rough set model and rough vague sets. One is a modification to the Bayesian rough set model and the other is a modification to rough vague sets. First, the Bayesian rough set model is analyzed and discussed. Second, a modification to this model is proposed and verified. Finally, a modification to rough vague sets is presented and its related properties are discussed..

Two important factors that affect the performance of wireless sensor networks (WSNs) are data quality and network lifetime. This paper exploits the tradeoff between data quality and network lifetime to improve data collection precision while the network lifetime is adapted. The problem is to minimize the total error bound for approximate data aggregation in both single-hop and multi-hop WSNs to achieve the adaptive network lifetime. This problem is formulated as an optimization problem by combining the changing pattern of sensor readings, the residual energy of sensor nodes, and the communication cost from the sensor node to the base station. Our method is theoretically analyzed and further evaluated by conducting simulation experiments. To the best of our knowledge, this is the first study on minimizing the total error bound while achieving the adaptive network lifetime.

The main concern of the Internet user-base has shifted from what kind of information are available to how to find the desired information on the Internet thanks to the explosive growth of the WWW and the increasing amount of data available via the Internet. Since mobile agent technology is expected to be a promising technology for information retrieval, there are a number of mobile agent based-information retrieval approaches have been proposed in recent years. For a better understanding and efficiency improvement of these approaches, performance evaluation of great importance. However, most of existing evaluation results are experimental and there is a lack of theoretical performance analysis which is helpful to reveal the insight of the working mechanisms. In this paper, we further the study in W. Qu et al. (2007) in which some primary studies on the performance of several mobile agent-based information retrieval approaches are provided and provide the exact probability distributions of execution time for each approach. Our results reveal the insight of mobile agent-based information retrieval approaches and our analytical method provides a useful tool for further research to information retrieval.

Multimedia object caching, by which the same multimedia object can be adapted to diverse mobile appliances through the technique of transcoding, is an important technology for improving the scalability of Web services, especially in the environment of mobile networks. In this paper, we address the cache replacement problem for multimedia object caching by exploring the aggregate effect of caching multiple versions of the same multimedia object. First, we present an optimal solution for calculating the minimal access cost of caching multiple versions of the same multimedia object. Second, based on this solution, we propose an effective cache replacement algorithm for multimedia object caching. Finally, we evaluate the performance of the proposed solution with a set of simulation experiments for various performance metrics over a wide range of system parameters.

Abstract Advances in the Internet and the computer industry have created many new application areas for network routing such as Grid computing and also brings new challenges to traditional routing techniques. In this paper we propose a mobile agent‐based routing model in vulnerable networks for these applications. To characterize the behaviors of mobile agents and their effects on the network performance, we analyze the population distribution of mobile agents as a measurement of the computational resource consumption. Our analysis reveals theoretical insights into the statistical behaviors of mobile agents and provides useful tools for effectively managing mobile agents in large networks. Copyright © 2006 John Wiley & Sons, Ltd.

Mobile agent-based technology has attracted considerable interest in both academia and industry in recent years. Many agent-based execution models have been proposed and their effectiveness have been demonstrated in the literature. However, these models require a high overhead to achieve the reliable execution of mobile agents. In this paper, we propose a new mobile agent-based execution model, which is based on a surveillant mechanism. Extensive theoretical analysis of a stochastic nature is provided to evaluate the performance of our model, including the transaction time from node to node, the life expectancy of mobile agents, and the population distribution of mobile agents. The analytical results reveal new theoretical insights into the fault-tolerant execution of mobile agents and show that our model outperforms the existing fault-tolerant models. Our model provides an efficient way to increase overall performance and a promising method in achieving mobile agent system reliability.

This paper surveys the state of the art of agentbased fault tolerance techniques. Existing mobile agent-based fault-tolerant techniques are identified on prevent mobile agents from being blocked by a failure.

Mobile agent-based technology has attracted considerable interest in both academia and industry in recent years. Fault tolerance is of paramount importance to integrate mobile agent-based technology into today's e-society. In this paper, we propose a mobile agent-based fault-tolerant execution model and analyze the stochastic nature of mobile agents, including the transaction time from node to node, the life expectancy of mobile agents, and the population distribution of mobile agents. Our approach exploits a new way to design fault-tolerant mobile agent-driven system. Our analysis provides useful tools for effectively estimating the performance of agent-driven systems.