Aiming at the real-time computing problems in large-scale internet of things devices (IoTDs) scenarios, a framework for terahertz (THz) -based mobile edge computing (MEC) network with multi-unmanned aerial vehicles (UAV) collaboration is proposed. In this framework, a utility model based on latency and connection scheduling is first presented. Its significance lies in enabling high-priority tasks to obtain more computing resources, thereby reducing computing latency. Then, we formulate an optimization problem that jointly optimizes connection scheduling, computing resource allocation, and UAV flight trajectories under the objective of maximizing the average utility of IoTDs. To solve this Mixed Integer Nonlinear Programming Problem (MINLP), we use Deep Reinforcement Learning (DRL) based on learning rate decay and Prioritized Experience Replay (PER) to optimize the UAVs trajectories, and design a low-time complexity heuristic algorithm to solve the connection scheduling and resolve the computing resource allocation by an iterative algorithm. Subsequently, to evaluate the performance of our proposed algorithm, we compare it with Soft Actor-Critic (SAC), Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), and Particle Swarm Optimization (PSO). Simulation results show that our proposed algorithm significantly improves the average utility of IoTDs and reduces the latency of high-priority tasks. Besides, our proposed algorithm has better convergence than the above algorithms.

Sketch is widely used in many traffic estimation tasks due to its good balance among accuracy, speed, and memory usage. In scenarios with priority flows, priority-aware sketch, as an emerging method, provides differentiated detection accuracy for flows of different priorities, optimizing resource allocation and improving the detection accuracy of high-priority flows. However, existing priority-aware sketches methods struggle to effectively handle the dynamic changes in flow priority distribution in realworld detection environments, leading to wasted or insufficient storage space. To address this issue, this paper proposes a new priority-aware sketch with Dynamic Memory Allocation called DMA-Sketch. It dynamically adjusts the detection framework based on flow priority distribution information and adaptively allocates appropriate memory space to each storage region. The experimental results show that DMA-Sketch improves the overall priority accuracy, high-priority accuracy and throughput by up to 1.33×, 16.39× and 1.88×, respectively, under the scenarios with changing flow priority distribution over the state-of-the-art schemes.

With the rapid development of generative models, the visual quality of generated images has become almost indistinguishable from real images, which poses a huge challenge to content authenticity verification. A key limitation of existing detectors is their reliance on model-specific cues, resulting in poor generalization to unseen models. Based on the observation of local differences in the generated images, we found that the generated images lack device-specific sensor noise and unnatural pixel intensity variations caused by the oversimplified generation process. These discrepancies provide important forensic cues for distinguishing between real and generated images. We propose the Feature Aggregation for Localized Context and Noise Network (FALCON-Net), which leverages these discrepancies to enhance detection capabilities. FALCON-Net integrates two complementary modules to enhance detection capabilities: the Intrinsic Noise Pattern Isolation (INP) module isolates device-specific noise patterns by analyzing high-frequency features in the frequency domain, while the Local Variation Pattern (LVP) module models the complex relationships between local pixels to capture directional intensity variations and reveal unnatural regularities in generated images. By combining these sensor-level and local structural cues, FALCON-Net identifies fundamental generative inconsistencies, ensuring robustness to post-processing and strong generalization to unseen models. Extensive experimental results show that FALCON-Net achieves the state-of-the-art performance in detecting generated images and shows good generalization ability to unseen generative models. The code is available at https://github.com/humiaomiaohaha/FALCON-Net.

Remote direct memory access (RDMA) is widely deployed in production data centers to enable low-latency transmission. The current multipath RDMA transmission protocols effectively improve link utilization by allocating traffic to equal-cost parallel paths. To address packet reordering, they struggle to control the level of out-of-order packets by using bitmaps. However, under asymmetric path status and highly dynamic traffic scenarios, a large number of out-of-order packets easily cause bitmap overflow and frequent unnecessary retransmission, resulting in goodput far below throughput. Motivated by this, we present MPTR, an efficient multipath transport with robust reordering for RDMA networks. At its core, MPTR continuously monitors the multipath congestion status at the receiver and distributes the traffic in a congestion-aware manner to proactively reduce the degree of out-of-order and avoid triggering retransmission due to bitmap cache overflow. The NS-3 simulation results show that MPTR effectively reduces unnecessary retransmission and improves goodput under realistic workloads by up to 34%, 49%, and 51% compared to multi-path remote direct memory access (MP-RDMA), ConWeave, and data center quantized congestion notification (DCQCN), respectively.

The rapid advancement of Industry 5.0 has accelerated the adoption of the Industrial Internet of Things (IIoT). However, challenges such as data privacy breaches, malicious attacks, and the absence of trustworthy mechanisms continue to hinder its secure and efficient operation. To overcome these issues, this paper proposes an enhanced blockchain-based data storage framework and systematically improves the Delegated Proof of Stake (DPoS) consensus mechanism. A four-party evolutionary game model is developed, involving agent nodes, voting nodes, malicious nodes, and supervisory nodes, to comprehensively analyze the dynamic effects of key factors—including bribery intensity, malicious costs, supervision, and reputation mechanisms—on system stability. Furthermore, novel incentive and punishment strategies are introduced to foster node collaboration and suppress malicious behaviors. The simulation results show that the improved DPoS mechanism achieves significant enhancements across multiple performance dimensions. Under high-load conditions, the system increases transaction throughput by approximately 5%, reduces consensus latency, and maintains stable operation even as the network scale expands. In adversarial scenarios, the double-spending attack success rate decreases to about 2.6%, indicating strengthened security resilience. In addition, the convergence of strategy evolution is notably accelerated, enabling the system to reach cooperative and stable states more efficiently. These results demonstrate that the proposed mechanism effectively improves the efficiency, security, and dynamic stability of IIoT data storage systems, providing strong support for reliable operation in complex industrial environments.

To meet the massive compute and high-speed communication demands of Large Language Model (LLM) training, modern datacenters typically adopt multipath topologies such as Fat-Tree and Clos to host parallel jobs across hundreds to thousands of GPUs. However, LLM training exhibits periodic, high-bandwidth communication patterns. Existing load-balancing schemes become misaligned under dynamic congestion and anomalous surges: they struggle to promptly mitigate iteration-peak congestion and lack effective isolation of anomalous traffic. To address this, we propose Security-Aware Load Balancing (SALB) for LLM training. SALB leverages a Deep Reinforcement Learning (DRL) controller with queue and delay signals for packet-level multipath load balancing and employs path binding to confine suspicious flows. By integrating data security into load balancing, SALB simultaneously achieves high throughput and robust traffic isolation. NS-3 simulation results show that, compared with CONGA, Hermes, and ConWeave, SALB reduces the 99th-percentile flow completion time (FCT) of short flows by an average of 65% and increases the throughput of long flows by an average of 54%. It further outperforms the baselines in aggregate throughput, path utilization, and packet loss rate, thereby significantly enhancing system stability, robustness, and data security.

Remote Direct Memory Access (RDMA) over Converged Ethernet (RoCE) cooperating with Priority Flow Control (PFC) has been widely deployed in production datacenters to enable low latency, lossless transmission. At the same time, modern datacenters typically offer parallel transmission paths between any pair of end-hosts, underscoring the importance of load balancing. However, the well-studied load balancing mechanisms designed for lossy datacenter networks (DCNs) are ill-suited for such lossless environments. Through extensive experiments, we are among the first to comprehensively inspect the interactions between PFC and load balancing, and uncover that existing fine-grained rerouting schemes can be counterproductive to spread the congested flows among more paths, further aggravating PFC’s head-of-line (HoL) blocking. Motivated by this, we present FLB, a Fine-grained Load Balancing scheme for lossless DCNs. At its core, FLB employs threshold-free rerouting to effectively balance traffic load and improve link utilization during normal conditions and leverages timely congested flow isolation to eliminate HoL blocking on non-congested flows when congestion occurs. To handle complex multi-bottleneck scenarios, we further introduce FLB*, which incorporates an enhanced congestion-point-aware isolation mechanism using Congestion Point Identifiers (CPI) to eliminate HoL blocking among different congested flows.We have fully implemented a FLB prototype, and our evaluation results show that FLB reduces PFC PAUSE rate by up to 96% and avoids HoL blocking, translating to up to 45% improvement in goodput over CONGA+DCQCN and 40%, 36%, 29% and 18% reduction in average flow completion time (FCT) over LetFlow+Swift, MP-RDMA, Proteus+DCQCN and LetFlow+PCN, respectively.

To reduce the traffic volume and accelerate communication in distributed training (DT) jobs, recent works introduce In-Network Aggregation (INA) to move the gradient summation into network programmable switches. However, switch memory is a scarce resource, unable to support massive DT jobs in data centers, and existing INA solutions have not utilized switch memory to the best extent. We propose DSA, an Efficient Data-Plane switch memory Scheduler for in-network Aggregation. DSA introduces preemption to the switch memory management for INA jobs. Furthermore, under packet preemption scenarios, DSA optimizes the selective retransmission mechanism to reduce redundant retransimtting packets to alleviate congestion. In the data plane, DSA allows gradient tensors with high priority to preempt the switch aggregators (basic computation unit in INA) from tensors with low priority, which avoids an aggregator wasting time in idle. In the control plane, DSA devises a priority policy which assigns high priority to gradient tensors that benefit overall job efficiency more, e.g., communication-intensive jobs. We implement the prototype of DSA. The experimental results show that DSA can improve the average job completion time (JCT) by up to 1.35x compared with baseline solutions.

ByteScheduler partitions and rearranges tensor transmissions to improve the communication efficiency of distributed Deep Neural Network (DNN) training. The configuration of hyper-parameters (i.e., the partition size and the credit size) is critical to the effectiveness of partitioning and rearrangement. Currently ByteScheduler adopts Bayesian Optimization (BO) to find the optimal configuration for the hyper-parameters beforehand. In practice, however, various runtime factors (such as worker node status and network conditions) change over time, making the statically-determined one-shot configuration result suboptimal for real-world DNN training. To address this problem, in this paper we present a realtime configuration method (called AutoByte) that automatically and timely searches the optimal hyper-parameters as the training systems dynamically change. AutoByte extends the ByteScheduler framework with a meta network, which takes the systems’ runtime statistics as its input, dynamically adjusts the triggering threshold based on system environment characteristics, and outputs predictions for speedups under specific configurations. Evaluation results on various DNN models show that AutoByte can dynamically tune the hyper-parameters with low resource usage, and deliver up to 33.2% higher performance than the best static configuration method on the ByteScheduler framework.

Distributed AI training generates unique low-entropy flow patterns with predictable, singular and repetitive flows that differ fundamentally from traditional network flow with heavy-tailed distributions. While sketch-based methods are widely used for network measurement, existing approaches fail to exploit these distinctive characteristics, resulting in poor measurement accuracy. To address this issue, this paper proposes FP-Sketch, a high-accuracy sketch for measuring low-entropy flows with Flow Prediction. FP-Sketch utilizes a staging queue to predict and classify flows of different sizes, thereby leveraging the singular, repetitive, and predictable nature of low-entropy AI flows. Combined with hierarchical storage, our method achieves superior measurement precision for distributed AI workloads. We establish rigorous error bounds for FP-Sketch through theoretical analysis. The experimental results show that FP-Sketch reduces flow estimation error by 38.6% and improves insertion throughput by 49.4% compared to the state-of-the-art alternatives.

Sketch is widely used in many traffic estimation tasks due to its good balance among accuracy, speed, and memory usage. In scenarios with priority flows, priority-aware sketch, as an emerging method, provides differentiated detection accuracy for flows of different priorities, optimizing resource allocation and improving the detection accuracy of high-priority flows. However, existing priority-aware sketches methods struggle to effectively handle the dynamic changes in flow priority skew in realworld detection environments, leading to wasted or insufficient storage space. To address this issue, this paper proposes a new priority-aware sketch with Dynamic Memory Allocation called DMA-Sketch. It dynamically adjusts the detection framework based on flow priority skew information and adaptively allocates appropriate memory space to each storage region. The experimental results show that DMA-Sketch improves the overall priority accuracy, high-priority accuracy and throughput by up to$1.33 \times, 16.39 \times$and$1.88 \times$, respectively, under the scenarios with changing flow priority skew over the state-of-theart schemes.

With the swift advancement of the Internet of Things (IoT) and artificial intelligence (AI), various technologies have been integrated into wearable medical health devices, improving users’ awareness of their physical states and enabling the analysis of a greater amount of human data. However, these sensitive pieces of information are prone to tampering or theft during storage and transmission, posing security risks. In this article, we propose a multiattribute sketch secure data sharing scheme for IoT wearable medical devices based on blockchain (MASS). We introduce a multiattribute sketch storage method that stores the encrypted hash of health data transmitted by medical wearable devices on the blockchain. This work also designs a ciphertext-policy attribute-based encryption (CP-ABE) access control mechanism that effectively addresses the secure sharing of data from wearable medical devices among healthcare professionals. Experimental findings indicate that with the rise in the number of medical health data documents, the costs associated with index generation and search time decrease by 55.3% and 10.83%, respectively. Additionally, as the frequency of data access increases, there is a 13.5% reduction in encryption time, and the implementation of multiattribute sketches results in a 24.8% and 11.3% reduction in index generation and search times, respectively.

Unmanned Aerial Vehicles (UAVs) play a significant role in wireless communication because of their high maneuverability and the advantage of forming Line-of-Sight (LoS) links with ground users. In this research, we investigate the trajectory design and resource scheduling problem of UAV relay communication optimization scenarios, considering heterogeneous tasks and QoS (Quality of Service) awareness. First, we model the optimization scenario and transform the problem-solving into a Markov Decision Process (MDP). Next, we propose R-DQN, a robust DQN (Deep Q-Network) algorithm tailored for heterogeneous tasks and QoS-aware UAV relay communication optimization scenarios. R-DQN introduces corresponding mechanisms in the training process, network structure, and sampling method to improve the effective exploration capability of DQN, making it more robust and suitable for the dynamic constrained optimization scenario tackled. Simulations and experimental results show that the proposed R-DQN algorithm has better convergence and global optimization abilities than other algorithms, such as Dueling DQN, Noisy DQN, and DDQN.

The explosive growth of Internet of Things (IoT) data demands secure and reliable storage, where traditional centralized solutions often fall short. Blockchain offers decentralization and tamper-resistance, making it a promising foundation for IoT. However, IoT blockchain systems based on Delegated Proof-of-Stake (DPoS) face challenges such as weak node incentives, unfair reward distribution, and low consensus efficiency. This paper proposes a fairness-aware incentive mechanism that accounts for both node capability and effort under information asymmetry. By incorporating fairness preferences into the contract design, the mechanism improves participation and motivates sustained effort. Theoretical analysis and simulation results show that our approach enhances throughput by about 15%, while achieving revenue fairness, incentive compatibility, and stronger consensus performance. The mechanism’s adaptability makes it suitable for diverse IoT application scenarios.

In Unmanned Aerial Vehicle Ad Hoc Networks (UANETs), rapid movement of nodes leads to frequent changes in network topology, increasing the risk of packet loss and affecting data transmission. Furthermore, current research on drone clustering lacks security considerations, which reduces the reliability of data transmission. Due to this, improving the stability and reliability of network data transmission in dynamically changing UANETs remains a challenge. In this work, we propose the reputation mechanism DSCR (Dynamically Secure Cluster Routing) Scheme for UANETs, a design for cluster head election in UANETs using the evaluated value and reputation value of drones and forms clusters through this reputation mechanism to avoid malicious nodes from interfering with the clusters to improve the security of UANETs. We apply a residual link survival time-based intra-and inter-cluster algorithm based on residual link survival time for data forwarding of nodes, optimizing the dynamic routing strategy and reducing the packet loss rate of nodes. In addition, reinforcement learning is used in UANETs to achieve cluster decisions based on the current network state and cluster dynamics, effectively improving the stability of clusters. Experiments were conducted on the proposed method to verify its efficiency and stability. Compared to the ICRA, RICR, and DCA algorithms, the stability of the cluster is improved by 11.92%, 28.12%, and 75.2%, respectively, and the packet loss rate reduced by 41.48%, 44.17%, and 55.74%, demonstrating that DSCR is a compelling dynamic routing solution applicable to UANETs.

With the rapid development of the Internet of Things (IoT) and blockchain technology, e-voting has been widely used in all aspects of people’s lives. However, there is a common problem in the vast majority of e-voting solutions: the inability to complete vote counting without a trusted third-party organization, which may lead to security risks. When designing an e-voting system, ensuring the trustworthiness of the voting results as well as protecting the privacy of the voters are always the most important issues. To address this challenge, we propose improved secure and efficient (ISE)-Voting, an ISE e-voting scheme for blockchain-assisted IoT devices. Our proposed ISE-Voting achieves voter privacy anonymity, distributed vote counting, and public verifiability of counting results in e-voting systems by using secret-sharing and identity-based ring signatures in the blockchain system. In addition, we introduce a cloud service provider (CSP), which is used to share the computational pressure of the system and assist ISE-Voting to complete the final counting. According to the experimental analysis and results, our scheme is not only able to meet the basic security goals of satisfying correctness, anonymity, unforgeability and verifiability, and provide 128-bit identity security for the voters in the post-quantum environment. Moreover, it can complete the distributed counting of voters’ ballots within an effective time, which provides a feasible solution for future e-voting systems.

Modern industrial datacenter networks employ multirooted tree topologies to accommodate a diverse range of cloud applications, which generate heterogeneous traffic with low-latency short flows and high-throughput long flows. Recently, the proposed learning-based load balancing mechanisms are resilient to dynamic network, but they are agnostic to heterogeneous traffic, resulting in large tail delay. In this article, we propose a new deep reinforcement learning (DRL) based load balancing called DRLB, which uses DRL with the distributed distributional deterministic policy gradients algorithm to make (re)routing for long flows, and adopts the weighted cost multipathing mechanism for short flows. Furthermore, this article introduces a traffic feature-based dynamic training cycle mechanism to adaptively adjust the training cycles. The experimental results show DRLB reduces the flow completion time of short flows by up to 58% and improves the throughput of long flows by 38% compared to the state-of-the-art load balancing mechanisms.

To meet the stringent requirements of industrial applications, modern Ethernet datacenter networks widely deployed with remote direct memory access (RDMA) technology and priority-based flow control (PFC) scheme aim at providing low latency and high throughput transmission performance. However, the existing end-to-end congestion control cannot handle the transient congestion timely due to the round-trip-time (RTT) level control loop, inevitably resulting in PFC triggering. In this article, we propose a Sub-RTT congestion control mechanism called SRCC to alleviate bursty congestion timely. Specifically, SRCC identifies the congested flows accurately, notifies congestion directly from the hotspot to the corresponding source at the sub-RTT control loop and adjusts the sending rate to avoid PFC's head-of-line blocking. Compared to the state-of-the-art end-to-end transmission protocols, the evaluation results show that SRCC effectively reduces the average flow completion time (FCT) by up to 61%, 52%, 40%, and 24% over datacenter quantized congestion notification (DCQCN), Swift, high precision congestion control (HPCC), and photonic congestion notification (PCN), respectively.

Modern industrial datacenter networks carry latency-sensitive and throughput-oriented applications with diverse requirements. Recent load balancing mechanisms effectively reduce latency and improve throughput for heterogeneous traffic. However, deadline-sensitive flows still often miss deadlines due to being blocked. In this article, we introduce an adaptive routing system (ARS) to avoid missing deadlines. Specifically, an ARS computes a heuristic function using three influence factors, derives the probability of choosing the next node, and finds optimal (re)routing path. The experimental results show that an ARS enhances the throughput for long flows and decreases the average flow completion time and the deadline miss rate by 24% and 55%, respectively, compared to state-of-the-art load balancing schemes.

Mobile Edge Computing (MEC) effectively alleviates the pressure on limited in-vehicle computing resources and energy supply caused by computation-intensive vehicular applications. However, the uneven spatial distribution of users leads to load imbalance among adjacent MEC servers, significantly increase the latency and energy consumption costs for vehicles. Therefore, achieving optimal configuration of available computing resources in MEC servers to accomplish the goal of low-latency and low-energy task offloading has become a critical issue to address. To tackle this problem, this study proposes a Multi-RSU Load Balancing (MRLB) strategy based on multi-hop network technology. This strategy dynamically allocates computing tasks to neighboring RSU server clusters with available computing resources through task segmentation and computation offloading mechanisms. Meanwhile, adaptive resource allocation strategies are implemented based on task quantity and task scale characteristics. Specifically, this study designs a multi-RSU collaborative offloading algorithm based on Deep Deterministic Policy Gradient (DDPG) to solve the optimal offloading decision. Additionally, by integrating the Lagrange multiplier method and Sequential Quadratic Programming (SQP) algorithm, the joint optimization of imbalanced task segmentation decisions and optimal CPU frequency allocation decisions for RSU servers is achieved. Experimental results demonstrate that the proposed method can achieve efficient multi-RSU resource allocation and ensure coordinated optimization of both system latency and energy consumption costs across diverse device conditions and varying network scenarios, particularly in load-imbalanced situations.

Multi-camera setups find widespread use across various applications, such as autonomous driving, as they greatly expand sensing capabilities. Despite the fast development of Neural radiance field (NeRF) techniques and their wide applications in both indoor and outdoor scenes, applying NeRF to multi-camera systems remains very challenging. This is primarily due to the inherent under-calibration issues in multi-camera setup, including inconsistent imaging effects stemming from separately calibrated image signal processing units in diverse cameras, and system errors arising from mechanical vibrations during driving that affect relative camera poses. In this paper, we present UC-NeRF, a novel method tailored for novel view synthesis in under-calibrated multi-view camera systems. Firstly, we propose a layer-based color correction to rectify the color inconsistency in different image regions. Second, we propose virtual warping to generate more viewpoint-diverse but color-consistent virtual views for color correction and 3D recovery. Finally, a spatiotemporally constrained pose refinement is designed for more robust and accurate pose calibration in multi-camera systems. Our method not only achieves state-of-the-art performance of novel view synthesis in multi-camera setups, but also effectively facilitates depth estimation in large-scale outdoor scenes with the synthesized novel views.

As training Deep Neural Network (DNN) is time-consuming, people resort to parallelization across multiple accelerators. A plethora of solutions adopt data/model parallelization, but they suffer from frequent weight synchronization overhead or resource under-utilization. Recent work introduces pipeline parallelism to improve the utilization of accelerators, however, most existing pipeline parallelism approaches take a one-shot configuration, while ignoring the fluctuation of available resources, e.g., bandwidth and GPUs. Moreover, the heuristic work partition methods oversimplify the computation and communication process, leading to sub-optimal results. To address this challenge, we present AutoPipe, a self-adaptive pipeline parallelism optimization solution. At its core, AutoPipe introduces a reinforcement learning (RL) based work partitioning model, which takes into account both exact communication procedure and dynamic state switching. To mitigate the stalls on state switching, AutoPipe adopts layer-by-layer computation under switching. We have implemented an AutoPipe prototype and evaluated it via testbed experiments. Our results show that the AutoPipe-enhanced PipeDream can find better work partitioning and benefit from dynamic configuration, outperforming the vanilla solutions by up to 89% for exclusive tasks and 143% in dynamic workloads. Furthermore, we show that AutoPipe can also work well with other pipeline parallelism schemes and achieve considerable performance gains.

Remote Direct Memory Access (RDMA) is widely deployed in datacenter networks (DCNs) due to its ultra-low latency, high throughput, and low CPU overhead. Since RDMA is sensitive to out-of-order packets, the previous load balancing schemes designed based on TCP do not work well in RDMA networks. Recently proposed load balancing schemes focus on solving packet reordering within the network. However, the existing solutions cannot be extended in practice because the required queues far exceed common switch capabilities. In this paper, we propose a scalable and efficient load balancing called SELB to improve availability and scalability. SELB employs a clustering algorithm to categorize equal-cost paths and then reroutes traffic to the same cluster parallel paths to reduce the degree of out-of-order and improve queue utilization. The NS-3 simulation results demonstrate that SELB reduces the average flow completion time (FCT) and the 99th percentile FCT by up to 33% and 21%, respectively, compared to the state-of-the-art load balancing schemes.

In modern datacenter networks (DCNs), the booming online data-intensive applications generate mix-flows with or without deadlines. Balancing these heterogenous flows among parallel equal-cost paths to meet the tight deadlines is crucial. However, due to the unaware of deadlines, the existing load balancing mechanisms cannot choose suitable (re)routing path for mix-flows to meet their respective stringent requirements. In this paper, we propose a deadline-aware rerouting scheme called DAR, which applies different routing strategies for mix-flows. Specifically, DAR first perceives the deadline flows and then categorizes them based on the urgency of the deadline, and employs different (re)routing strategies to ensure that flows with more urgent deadlines are completed earlier. The NS-3 simulation results show that DAR effectively balances mix-flows. For example, compared to the state-of-the-art load balancing schemes, DAR reduces the deadline miss rate and the average flow completion time (AFCT) by up to 38% and 35.5%, respectively.

Increasingly large-scale models and rich data sets make communication overhead a key bottleneck for distributed Deep Neural Network (DNN) training, constantly attracting the attention of academia and industry. Despite continuous efforts, prior solutions such as pipelining computation/communication and in-network gradient compression/scheduling do not focus on how to accelerate DNN training through load balancing in datacenter networks (DCNs). However, the existing load balancing mechanisms are unaware of tensor integrity and priority for gradient parameter synchronization during the DNN training iterations, resulting in severe tensor tail latency and slow model convergence speed. In this paper, we present a Tensor-aware Load Balancing (TaLB) scheme to accelerate DNN training. Specifically, TaLB identifies the different priority tensors and makes (re)routing decisions based on the tensor-level granularity to cut the high-priority tensors tail delay. The testbed implementation and large-scale NS-3 simulation results show that TaLB effectively accelerates DNN training speed. For example, TaLB significantly reduces the average flow completion time (FCT) by up to 55%, and accelerates the model training speed up to 2.37× on VGG19, ResNet50 and AlexNet models.

As a new type of digital living space that blends virtual and reality, Metaverse combines many emerging technologies. It provides an immersive experience based on VR technology and stores and protects users’ digital content and digital assets through blockchain technology. However, different virtual environments are often highly heterogeneous in terms of underlying architecture and software implementation technology, which leads to many challenges in scalability and interoperability for blockchains serving the Metaverse. Cross-chain technology is an essential technology to realize the scalability and interoperability of blockchain. However, the current cross-chain technologies all have their own merits and demerits, and there is no cross-chain solution that can be fully applied to any scenario. To this end, in the blockchain-based Metaverse, this article proposes a cross-chain transaction scheme based on improved hash timelock, HCNCT. By combining the notary mechanism, this scheme uses a group of notaries to supervise and participate in cross-chain transactions, effectively solving the problem that malicious users create a large number of time-out transactions to block the transaction channel, which exists in the traditional hash timelock method. Besides, this article uses the verifiable secret sharing method in the notary group, which can effectively prevent the centralization problem of the notary mechanism. Moreover, this article discusses the process of key processing, cross-chain transaction and transaction verification of the scheme, and designs the user credibility evaluation mechanism, which can effectively reduce the occurrence of malicious default of users. Compared with existing solutions, our solution has the advantage of effectively addressing time-out transaction attacks and centralization issues while guaranteeing security. The experiments also verify the effectiveness of the proposed scheme.

Many existing load balancing mechanisms work effectively in lossy datacenter networks (DCNs), but they suffer from serious packet reordering in lossless Ethernet DCNs deployed with the hop-by-hop Priority-based Flow Control (PFC). The key reason is that the prior solutions are not able to perceive PFC triggering correctly and in a timely manner when making load balancing decisions. Once the forwarding path pauses transmission due to PFC triggering, the packets allocated on it are blocked, inevitably leading to out-of-order packets and retransmission. In this paper, we present an Reordering-robust Load Balancing (RLB) scheme with PFC prediction in lossless DCNs. At its heart, RLB leverages the derivative of ingress queue length to predict PFC triggering and proactively notifies the upstream switches to choose an appropriate rerouting path or perform packet recirculation to avoid reordering. Furthermore, under switch failure scenarios, RLB adjusts the recirculation threshold adaptively to mitigate the risk of packets over-recirculation. We have implemented RLB in the hardware programmable switch. As a building block for existing load balancing mechanisms, we have integrated RLB into Presto, LetFlow, Hermes and DRILL. The evaluation results show that the RLB-enhanced solutions deliver significant performance by avoiding packet reordering. For example, it reduces the$99^{th}$percentile flow completion time (FCT) by up to 72%, 67%, 58% and 54% over DRILL, Presto, LetFlow and Hermes, respectively.

Many existing load balancing mechanisms work effectively in lossy datacenter networks (DCNs), but they suffer from serious packet reordering in lossless Ethernet DCNs deployed with the hop-by-hop Priority-based Flow Control (PFC). The key reason is that the prior solutions are not able to correctly and timely perceive PFC triggering when making load balancing decisions. Once the forwarding path pauses transmission due to PFC triggering, the packets allocated on it are blocked, inevitably leading to out-of-order packets and retransmission. In this paper, we present a Reordering-robust Load Balancing (RLB) scheme with PFC prediction in lossless DCNs. At its heart, RLB leverages the derivative of ingress queue length to predict PFC triggering and proactively notifies the upstream switches to choose an appropriate rerouting path or perform packet recirculation to avoid reordering. As a building block for existing load balancing mechanisms, we have integrated RLB into Presto, LetFlow, Hermes and DRILL. The test results show that the RLB-enhanced solutions deliver significant performance by avoiding packet reordering. For example, it reduces the 99th percentile flow completion time (FCT) by up to 58%, 67%, 72% and 54% over Presto, LetFlow, Hermes and DRILL, respectively.

With the development of the Internet of Things (IoT) , a large-scale, heterogeneous, and dynamic distributed network has been formed among IoT devices. There is an extreme need to establish a trust mechanism between devices, and blockchain can provide a zero-trust security framework for IoT. However, the efficiency of the blockchain is far from meeting the application requirements of the IoT, which has become the biggest resistance to the application of the blockchain in the IoT. Therefore, this paper combines sharding to build an effective Blockchain-based IoT data storage scheme (S-BDS) . Sharding can solve the problem of blockchain capacity and scalability. While the blockchain provides data immutability and traceability for the IoT, it also brings huge demands for data credibility verification. The communication delay in the IoT system seriously affects the security of the system, while the Merkle proof of traditional blockchain occupies a lot of communication resources. This paper constructs Insertable Vector Commitment (IVC) in the bilinear group and replaces the Merkle tree with IVC to store IoT data in the blockchain. The construct has small-sized proof. It also has the ability to record the number of updates, which can prevent replay-attacks. Experiments show that each block processes 1,000 transactions, the proof size of a single data piece is 30% of the original scheme, and proofs from different shards can be aggregated. IVC can effectively reduce communication congestion and improve the stability and security of the IoT system.

Retraction: Multiscale fast correlation filtering tracking algorithm based on a feature fusion model Yuantao Chen, Jin Wang, Songjie Liu, Xi Chen, Jie Xiong, Jingbo Xie, Kai Yang, 2021, 33 (15), (https://doi.org/10.1002/cpe.5533) The above article, published online on 23 October 2019 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the authors, the journal Editors, David W. Walker, Jinjun Chen, Nitin Auluck and Martin Berzins, and John Wiley and Sons Ltd. The retraction has been agreed due to scientific errors arising from the incorrect use of materials and data, which have led to conclusions that are unreliable.

Due to high capacity and fast transmission speed, 5G plays a key role in modern electronic infrastructure. Meanwhile, sparse tensor factorization (STF) is a useful tool for dimension reduction to analyze high-order, high-dimension, and sparse tensor (HOHDST) data, which is transmitted on 5G Internet-of-things (IoT). Hence, HOHDST data relies on STF to obtain complete data and discover rules for real time and accurate analysis. From another view of computation and data security, the current STF solution seeks to improve the computational efficiency but neglects privacy security of the IoT data, e.g., data analysis for network traffic monitor system. To overcome these problems, this article proposes a multiple-strategies differential privacy framework on STF (MDPSTF) for HOHDST network traffic data analysis.MDPSTFcomprises three differential privacy (DP) mechanisms, i.e.,$\varepsilon -$DP, concentrated DP, and local DP. Furthermore, the theoretical proof of privacy bound is presented. Hence,MDPSTFcan provide general data protection for HOHDST network traffic data with high-security promise. We conduct experiments on two real network traffic datasets ($Abilene$and$G\grave{E}ANT$). The experimental results show thatMDPSTFhas high universality on the various degrees of privacy protection demands and high recovery accuracy for the HOHDST network traffic data.

Taxi demand prediction plays a significant role in assisting the pre-allocation of taxi resources to avoid mismatches between demand and service, particularly in the era of the sharing economy and autonomous driving. However, most studies have only tried to figure out the complex spatial-temporal pattern of taxi demand from historical taxi demand series, neglecting the intrinsic influences of regional functions, and failing to effectively capture the dynamic long-term periodicity. In this paper, we make two important observations: (1) taxi demand pattern varies significantly between different functional regions; and (2) taxi demand follows a dynamic daily and weekly pattern. To address these two issues, we adopt Points of Interest (POIs) to identify regional functions, and propose a novel BERT-based Deep Spatial-Temporal Network (BDSTN) to model the complex spatial-temporal relations from heterogeneous local and global features. In BDSTN, a Spatiotemporal Pattern Matching module is introduced to capture the complex spatiotemporal pattern of taxi demand while considering its dynamic temporal periodicity, and a Functional Similarity Embedding module is adopted to learn the functional similarity among all regions via POIs. To the best of our knowledge, this is the first work to use BERT-based architecture to learn taxi demand patterns, and is also the first to take functional similarity represented by POIs into consideration. Our experimental results on real-world traffic datasets in New York City demonstrate that the effectiveness of the proposed method outperforms the state-of-the-art methods, and that the efficiency of our proposed model is higher than other deep learning methods.

Obtaining timely and accurate traffic information is one of the most important problems in intelligent transportation system, which will make vehicles run smoothly, avoid road congestion, save road running time and reduce vehicle energy consumption. In the current Internet of Vehicles system, the traffic management center can learn from the feedback information of all vehicles to improve the ability of decision-making and traffic command. However, the existing feedback mechanism does not respond to the spatial-temporal characteristics of data in time, due to the lack of communication capability of the current equipment. So, it cannot meet the requirements of ultra-low delay, high reliability and high security in the Internet of Vehicles. To solve this problem, this paper proposes a blockchain-based proxy vote and revocation scheme for decision feedback in Internet of Vehicles, which allows the intelligent system to ignore the unevenness and heterogeneity in the 6G technology. In addition, blockchain technology notarizes the vote data of vehicles and outsources microservices. Secondly, we use the attributes of decision-related nodes instead of their identities to enable anonymous vote. Smart contracts can automatically expand the scalability of outsourced microservices. Finally, the security proof of the proposed scheme ensures the security and consistency of outsourced microservices. The simulation results also show that our scheme greatly improves the efficiency of voting feedback.

Security and forensics represent two key components for network management, especially to guarantee the trusted operation of massive access networks such as the Internet of Things (IoT). As a core technology to provide low latency and high communication for IoT, Mobile Edge Computing (MEC) pulls computing resources from remote cloud centers to devices. The process of MEC service involves three types of entities: devices, data generated by devices and digital evidence generated after the data interaction. These entities are fully distributed and difficult to protect through traditional, highly centralized security and authentication mechanisms. As a decentralized shared ledger and database, the emerging blockchain is considered to provide cooperative trust and collaborative action among multiple subjects while ensuring the integrity and confidentiality of data. Because of its anonymity, non-tampering and traceability, the blockchain arouses research on the combination of blockchain and edge computing for device security, data security and forensics in IoT. This survey analyzes the application of blockchain in MEC-IoT systems and mainly focuses on approaches and technologies to manage the security and forensics issues for IoT. Finally, we present open issues and prospects for future work and research directions.

Cryptography chips are often used in some applications, such as smart grids and Internet of Things (IoT) to ensure their security. Cryptographic chips must be strictly tested to guarantee the correctness of the encryption and decryption. Scan-based design-for-testability (DFT) provides high test quality. However, it can also be misused to steal the cipher key of cryptographic chips by hackers. In this article, we present a new scan design methodology that can resist scan-based side-channel attacks by the dynamical obfuscation of scan input data and scan output data. The scan test is managed by a test password, which consists of load password and scan password. When the chip enters into the test mode, it is required to apply the test password via some external input ports. Once the correct load password is delivered, the scan password can be loaded into a special shift register. If the scan password is also correct, the chip testing can proceed normally. In case the load password or the scan password is wrong, the data in scan chains cannot be propagated correctly. Specifically, some elusory bits are sneaked into scan chains dynamically. The advantage of the proposed method is that it has no negative impact on design performance and test flow when powerfully protecting cryptographic chips. The area penalty is also acceptably low compared with other schemes.

Internet of Things (IoT) realizes the interconnection of heterogeneous devices by the technology of wireless and mobile communication. The data of target regions are collected by widely distributed sensing devices and transmitted to the processing center for aggregation and analysis as the basis of IoT. The quality of IoT services usually depends on the accuracy and integrity of data. However, due to the adverse environment or device defects, the collected data will be anomalous. Therefore, the effective method of anomaly detection is the crucial issue for guaranteeing service quality. Deep learning is one of the most concerned technology in recent years which realizes automatic feature extraction from raw data. In this article, the integrated model of the convolutional neural network (CNN) and recurrent autoencoder is proposed for anomaly detection. Simple combination of CNN and autoencoder cannot improve classification performance, especially, for time series. Therefore, we utilize the two-stage sliding window in data preprocessing to learn better representations. Based on the characteristics of the Yahoo Webscope S5 dataset, raw time series with anomalous points are extended to fixed-length sequences with normal or anomaly label via the first-stage sliding window. Then, each sequence is transformed into continuous time-dependent subsequences by another smaller sliding window. The preprocessing of the two-stage sliding window can be considered as low-level temporal feature extraction, and we empirically prove that the preprocessing of the two-stage sliding window will be useful for high-level feature extraction in the integrated model. After data preprocessing, spatial and temporal features are extracted in CNN and recurrent autoencoder for the classification in fully connected networks. Empiric results show that the proposed model has better performances on multiple classification metrics and achieves preferable effect on anomaly detection.

Summary In this paper, a multi‐carrier cognitive decision engine based on a binary particle swarm optimization with a non‐linear decreasing inertia‐weight (NDI‐BPSO) is presented. Our main goal is to solve the optimization problem of transmitter parameters in different wireless communication modes for cognitive radio systems (CRSs), especially for the transmitter in communication systems based on the environment sensing. In the new algorithm, the multi‐carrier cognitive decision engine based on an NDI‐BPSO algorithm can mitigate the local extreme points effectively and reduce the oscillation phenomenon in the process of optimization. We apply the NDI‐BPSO to the cognitive orthogonal frequency division multiplexing (OFDM) system to determine the best parameters to obtain good performances in different communication modes. The simulation results show that the proposed multi‐objective cognitive decision engine, which has a high fitness value and strong robustness for different communication modes, is better than the existing engines. The novel NDI‐BPSO algorithm achieves the objective of parameter optimization effectively.

For massive access to the Internet of Things, edge computing servers are installed on cellular ground base stations (GBSs) with fixed geographical locations, which easily suffer from traffic overload of the end user (EU) with high density and mobility. To provide reliable and flexible offloading service, unmanned aerial vehicles (UAV) are explored to assist edge computing, which relieves the computation offloading pressure of both EUs and GBS. However, most existing UAV researches focus on trajectory design to reduce offloading delay, which ignoring the variability of user distribution and the energy limitation of UAV. This article proposes a novel UAV-assisted edge computing framework, named as HOTSPOT, which locates the UAV in 3-D space according to the time-varying hot spot of user distribution and provides the corresponding edge computing offloading assistance. By formulating the UAV positioning problem into a maximum clique problem, a light-weighted deterministic algorithm is proposed based on stochastic gradient descent to search the optimal location of UAV. With the elaborate UAV position, HOTSPOT further gives an opportunistic offloading balanced scheme to reach low latency. Simulation results show that when the GBS load is 75%, HOTSPOT reduces the average offloading delay by 33%. When the GBS load reaches 90%, the average delay reduction is up to 80%.

Mobile-edge computing (MEC) is expected to provide reliable and low-latency computation offloading for massive Internet of Things (IoT) with the next generation networks, such as the sixth-generation (6G) network. However, the successful implementation of 6G depends on network densification, which brings new offloading challenges for edge computing, one of which is how to make offloading decisions facing densified servers considering both channel interference and queuing, which is an NP-hard problem. This article proposes a distributed-two-stage offloading (DTSO) strategy to give tradeoff solutions. In the first stage, by introducing the queuing theory and considering channel interference, a combinatorial optimization problem is formulated to calculate the offloading probability of each station. In the second stage, the original problem is converted to a nonlinear optimization problem, which is solved by a designed sequential quadratic programming (SQP) algorithm. To make an adjustable tradeoff between the latency and energy requirement among heterogeneous applications, an elasticity parameter is specially designed in DTSO. Simulation results show that compared to the latest works, DTSO can effectively reduce latency and energy consumption and achieve a balance between them based on application preferences.

As an important application of the Internet of Things (IoT) devices, sentiment analysis has been paid more attention with the rapid development of artificial intelligence. As a widely used method in artificial intelligence applications, traditional deep learning methods need massive data for training. However, due to the limitations of hardware, IoT devices have deficiencies in processing big data. In the case of insufficient sample size, how to carry out a machine learning method for IoT devices has become a common concern of the industry. In order to perform sentiment analysis on text with few data samples from the IoT devices, we propose FSLM, which is an intelligent few-shot learning model based on Siamese networks. The FSLM model consists of two self-attention models with the same parameters, which are divided into two parts. First, for two input texts, a self-attention model is used to extract sentiment features, and then the Mahalanobis distance is adopted to measure the similarity between two feature vectors to determine whether they belong to the same category. The FSLM is tested on the Amazon Review Sentiment Classification (ARSC) data set. The extensive experimental results on this data set demonstrate that the FSLM model has better accuracy and robustness for text sentiment analysis than other main existing models with a small number of samples.

Intelligent anomaly detection for key performance indicators (KPIs) is important for keeping services reliable in industrial-based cyber-physical systems (CPS). However, it is common in practice for various KPI sampling strategies to be utilized. We experimentally verify that anomaly detection is highly sensitive to irregular sampling, and accordingly go on to investigate low-cost anomaly detection for large-scale irregular KPIs. Irregular KPIs can be classified into four types: equal interval and unequal quantity (EIUQ) KPIs, unequal interval (UI) KPIs, unequal interval with equal duration (UIED) KPIs, and segmented irregular KPIs. In this article, we propose an anomaly detection framework based on these irregular types. Moreover, to handle the various lengths and phase shifts among EIUQ KPIs, we propose a normalized version of unequal cross-correlation, which slides the KPIs to enable finding the most similar position. To avoid high computational costs, we analyze the low-rank feature of KPIs data and propose a matrix factorization-based alignment algorithm for UIED KPIs; this algorithm treats UIED KPIs as an incomplete matrix and recovers the KPIs to align them before performing anomaly detection. Extensive simulations using three public datasets and two real-world datasets demonstrate that our algorithm can achieve a larger F1-score than Minkowski distance and less time than dynamic time warping distance.

With the explosive growth in the number of IoT devices, ensuring the integrity of the massive data generated by these devices has become an important issue. Due to the limitation of hardware, most past data integrity verification schemes randomly select partial data blocks and then perform integrity validation on those blocks instead of examining the entire dataset. This will result in that unsampled data blocks cannot be detected even if they are tampered with. To solve this problem, we propose a new and effective integrity auditing mechanism of sensor data based on a bilinear map accumulator. Using the proposed approach will examine all the data blocks in the dataset, not just some of the data blocks, thus, eliminating the possibility of any cloud manipulation. Compared with other schemes, our proposed solution has been proved to be highly secure for all necessary security requirements, including tag forgery, data deletion, replacement, replay, and data leakage attacks. The solution reduces the computational and storage costs of cloud storage providers and verifiers, and also supports dynamic operations for data owners to insert, delete, and update data by using a tag index table (TIT). Compared with existing schemes based on RSA accumulator, our scheme has the advantages of fast verification and witness generation and no need to map data blocks to prime numbers. The new solution supports all the characteristics of a data integrity verification scheme.

Once defined, the access control policies and regulations would never be changed in a running and state transition process. However, it will give attackers the possibility of discovering vulnerabilities in the system, and the control systems lack the ability of dynamic perception of security state and risk, causing the systems to be exposed to risks. In this article, a dynamic Bell-LaPadula (BLP) model is proposed. The conditional random field (CRF) is introduced into the BLP model to optimize the rules. First, the model formalizes the security attributes, states of system, transition rules, and constraint models on the basis of the state transition of CRFs. After the historical system access logs are processed as the original dataset, a feature selection method is proposed to extract the requests and current states as feature vectors. Second, this article presents a rules training algorithm based on L-BFGS to implement the study and training of datasets, and then marks the logs in the test set through Viterbi algorithm automatically. On the base of these, a rule generation algorithm is proposed to dynamically adjust the access control rules based on the current security status and events of the system. Third, the security of CRFs-BLP is proved by theoretical analysis. Finally, the validity and accuracy of the model are verified by estimating the value of the precision, recall, and F1-score. As the system threats are shown to be decreased obviously from these experiments, this dynamic model can decrease the vulnerabilities and risk effectively.

Mobile edge computing (MEC) has been proposed in recent years to process resource-intensive and delay-sensitive applications at the edge of mobile networks, which can break the hardware limitations and resource constraints at user equipment (UE). In order to fully use the MEC server resource, how to maximize the number of offloaded tasks is meaningful especially for crowded place or disaster area. In this paper, an optimal partial offloading scheme POSMU (Partial Offloading Strategy Maximizing the User task number) is proposed to obtain the optimal offloading ratio, local computing frequency, transmission power and MEC server computing frequency for each UE. The problem is formulated as a mixed integer nonlinear programming problem (MINLP), which is NP-hard and challenging to solve. As such, we convert the problem into multiple nonlinear programming problems (NLPs) and propose an efficient algorithm to solve them by applying the block coordinate descent (BCD) as well as convex optimization techniques. Besides, we can seamlessly apply POSMU to UAV (Unmanned Aerial Vehicle) enabled MEC system by analyzing the 3D communication model. The optimality of POSMU is illustrated in numerical results, and POSMU can approximately maximize the number of offloaded tasks compared to other schemes.

As a successful application of edge computing in the industrial production environment, prolonging the smart devices' (SDs') battery lifetime has become an important issue. In some special practical applications, the uploaded data from SDs to vehicle base stations (VBSs) or servers can be merged between SDs with a fixed size, which is called superposed data. In this article, we consider the superposed data uploading problem in a decentralized device-to-device communication system. The task of the problem is to minimize the total energy consumption of uploading data. We reduce it into a combinatorial optimization problem from the graph theory perspective. For VBSs or servers with infinite capacities, we propose an optimal algorithm with polynomial running time. When VBSs or servers have limited capacities, the problem is NP-hard even in very special cases. For this NP-hard problem, we give two heuristic algorithms and the corresponding numerical simulation results.

The Internet of Things (IoT) plays a vital role in the real world by providing autonomous support for communications and operations, thus enabling and promoting novel services that are commonly used in day-to-day life. It is important to do research on security frameworks for next-generation IoT and develop state-of-the-art confidentiality protection schemes to deal with various attacks on IoT networks. In order to offer prominent features like continuous confidentiality, authentication, and robustness, the blockchain technology comes out as a sustainable solution. A blockchain-enabled distributed security framework using edge cloud and software-defined networking (SDN) is presented in this article. The security attack detection is achieved at the cloud layer, and security attacks are consequently reduced at the edge layer of the IoT network. The SDN-enabled gateway offers dynamic network traffic flow management, which contributes to the security attack recognition through determining doubtful network traffic flows and diminishes security attacks through hindering doubtful flows. The results obtained show that the proposed security framework can efficiently and effectively meet the data confidentiality challenges introduced by the integration of blockchain, edge cloud, and SDN paradigm.

In the era of data deluge, data analysis has become a key task for many industrial applications, e.g., master data management, and data integration. In particular, similarity join is an important primitive operator to support data analysis, which is to find similar pairs based on similarity functions and thresholds. In this article, we first propose a new similarity join operation called the dynamic skyline join without having to specify any similarity function or similarity threshold, which measures the similarity through multicriteria optimization. The dynamic skyline join operator makes the similarity join more flexible to support different criteria in multidimensional space. However, it is nontrivial to achieve dynamic skyline joins as both join operations and dynamic skyline queries are computationally complex in the increasing volume of real-world data. Therefore, we further propose Grid-SkyJoin, a framework to enable efficient parallel dynamic skyline joins on a shared-nothing cluster. Specifically, we use a grid partitioning to facilitate the data filtering and grouping strategies to provide load balancing and reduce the number of replicas. We also propose a multilevel filtering scheme to prune away a large fraction of unpromising points that do not fit into join results without actual join operations. Extensive experiments using benchmark datasets demonstrate that our filtering scheme can greatly reduce the number of data points to be joined, and our approach is about two times faster compared with the straightforward method in average.

The electrocardiogram (ECG) signal can easily be affected by various types of noises while being recorded, which decreases the accuracy of subsequent diagnosis. Therefore, the efficient denoising of ECG signals has become an important research topic. In the paper, we proposed an efficient ECG denoising approach based on empirical mode decomposition (EMD), sample entropy, and improved threshold function. This method can better remove the noise of ECG signals and provide better diagnosis service for the computer-based automatic medical system. The proposed work includes three stages of analysis: (1) EMD is used to decompose the signal into finite intrinsic mode functions (IMFs), and according to the sample entropy of each order of IMF following EMD, the order of IMFs denoised is determined; (2) the new threshold function is adopted to denoise these IMFs after the order of IMFs denoised is determined; and (3) the signal is reconstructed and smoothed. The proposed method solves the shortcoming of discarding the first-order IMF directly in traditional EMD denoising and proposes a new threshold denoising function to improve the traditional soft and hard threshold functions. We further conduct simulation experiments of ECG signals from the MIT-BIH database, in which three types of noise are simulated: white Gaussian noise, electromyogram (EMG), and power line interference. The experimental results show that the proposed method is robust to a variety of noise types. Moreover, we analyze the effectiveness of the proposed method under different input SNR with reference to improving SNR ( SNR imp ) and mean square error ( MSE ), then compare the denoising algorithm proposed in this paper with previous ECG signal denoising techniques. The results demonstrate that the proposed method has a higher SNR imp and a lower MSE . Qualitative and quantitative studies demonstrate that the proposed algorithm is a good ECG signal denoising method.

Existing image annotation methods that employ convolutional features of deep learning methods from the Internet of Things (IoT) have a number of limitations, including complex training and high space/time expenses associated with the image annotation procedure. Accordingly, this paper proposes an innovative method in which the visual features of the image are presented by the intermediate layer features of deep learning, while semantic concepts are represented by mean vectors of positive samples. Firstly, the convolutional result is directly output in the form of low-level visual features through the mid-level of the pre-trained deep learning model, with the image being represented by sparse coding in the IoT. Secondly, the positive mean vector method is used to construct visual feature vectors for each text vocabulary item, so that a visual feature vector database is created. Finally, the visual feature vector similarity between the testing image and all text vocabulary is calculated, and the vocabulary with the largest similarity is taken from the IoT as the words used for annotation. Experiments on multiple datasets demonstrate the effectiveness of the proposed method; in terms of F1 score, the proposed method's performance on the Corel5k and IAPR TC-12 datasets is superior to that of MBRM, JEC-AF, JEC-DF, and 2PKNN with end-to-end deep features.

Location estimation technology under the wireless environment has become a vital technology in the field of mobile edge computing. Especially, under the mobile edge of entire networks environment, indoor location estimation is gradually getting the interest research and application topic, due to technical constraints of global positioning system technology for indoor environment and the popularity of the mobile edge computing servers. In this paper, the widely used single-model framework for indoor localization is presented as an introduction, which consists of three stages: 1) sample data collection; 2) model building; and 3) localization estimation. And then, through analyzing of the actual scene of indoor localization, a new framework for indoor localization under mobile edge computing environment, named Multimodel, is proposed from the theoretical perspective. It is mainly based on the observation that the environment of the sample data collection and that of localization data collection may change seriously. In order to make up for the shortcomings of this framework, two combinatorial optimization problems are proposed. Later, we discuss the NP-hardness of them in several different cases. In addition, two heuristic algorithms are given, and the performance of which are illustrated by the corresponding experimental results.

The smart grid solves the growing load demand of electrical customers through two-way real-time communication of electricity supply and demand sides and home energy management system (HEMS). However, these technical features also bring network security risks to the real-time price signal of the smart grid. The real-time price attack (RTPA) can maliciously raise the real-time price in smart meter, resulting in an increase in electrical customers load demand, causing the extensive damage to the power transmission lines due to overload. In this paper, we based on the behavioral relationship between load demand of electrical customers and real-time price of electricity suppliers (ES), defined the game relationship between RTPA, ES, and electrical customers, established a price elasticity of electricity demand (PEED) model, and proposed a defensive strategy of real-time price attack based on multiperson zero-determinant strategy (MPZDS). The experimental results show that the combination of MPZDS to some extent cut the expected load demand of electrical customers and protect the safety of power transmission lines.

In the last decade, wireless multimedia device is widely used in many fields, which leads to efficiency improvement, reliability, security, and economic benefits in our daily life. However, with the rapid development of new technologies, the wireless multimedia data transmission security is confronted with a series of new threats and challenges. In physical layer, Radio Frequency Fingerprinting (RFF) is a unique characteristic of IoT devices themselves, which can difficultly be tampered. The wireless multimedia device identification via Radio Frequency Fingerprinting (RFF) extracted from radio signals is a physical-layer method for data transmission security. Just as people’s unique fingerprinting, different Internet of Things (IoT) devices exhibit different RFF which can be used for identification and authentication. In this paper, a wireless multimedia device identification system based on Ensemble Learning is proposed. The key technologies such as signal detection, RFF extraction, and classification model are discussed. According to the theoretical modeling and experiment validation, the reliability and the differentiability of the RFFs are evaluated and the classification results are shown under the real wireless multimedia device environments.

In the research of location privacy protection, the existing methods are mostly based on the traditional anonymization, fuzzy and cryptography technology, and little success in the big data environment, for example, the sensor networks contain sensitive information, which is compulsory to be appropriately protected. Current trends, such as “Industrie 4.0” and Internet of Things (IoT), generate, process, and exchange vast amounts of security-critical and privacy-sensitive data, which makes them attractive targets of attacks. However, previous methods overlooked the privacy protection issue, leading to privacy violation. In this paper, we propose a location privacy protection method that satisfies differential privacy constraint to protect location data privacy and maximizes the utility of data and algorithm in Industrial IoT. In view of the high value and low density of location data, we combine the utility with the privacy and build a multilevel location information tree model. Furthermore, the index mechanism of differential privacy is used to select data according to the tree node accessing frequency. Finally, the Laplace scheme is used to add noises to accessing frequency of the selecting data. As is shown in the theoretical analysis and the experimental results, the proposed strategy can achieve significant improvements in terms of security, privacy, and applicability.

Energy efficiency has been a hot research topic for many years and many routing algorithms have been proposed to improve energy efficiency and to prolong lifetime for wireless sensor networks (WSNs). Since nodes close to the sink usually need to consume more energy to forward data of its neighbours to sink, they will exhaust energy more quickly. These nodes are called hot spot nodes and we call this phenomenon hot spot problem. In this paper, an Enhanced Power Efficient Gathering in Sensor Information Systems (EPEGASIS) algorithm is proposed to alleviate the hot spots problem from four aspects. Firstly, optimal communication distance is determined to reduce the energy consumption during transmission. Then threshold value is set to protect the dying nodes and mobile sink technology is used to balance the energy consumption among nodes. Next, the node can adjust its communication range according to its distance to the sink node. Finally, extensive experiments have been performed to show that our proposed EPEGASIS performs better in terms of lifetime, energy consumption, and network latency.

Summary The accumulation of massive data generates the new concept of big data. The relationships hidden in big data can bring great benefits, which have attracted public attentions. Meanwhile, the challenges of big data security are also more serious than ever. Privacy disclosure is one of the most concerned problems, and the privacy protection of big data is more difficult than traditional information protection. The technology of data publishing anonymous protection can provide privacy protection with the respect of data releasing. K‐anonymity and L‐diversity are two kinds of anonymity model. Their main idea is to generalize the value of quasi‐identifier and make the data accord with the model. In this paper, we propose the improved model which integrate K‐anonymity with L‐diversity and can solve the problem of imbalanced sensitive attribute distribution. K‐member clustering algorithm can translate the problem of anonymity into the problem of clustering and find a set of equivalence classes in which the records will be generalized to the same value. We utilize K‐member clustering algorithm to realize the improved anonymity model which can reduce the algorithm execution time and information loss. The integration of anonymity model and clustering algorithm makes the generalization process more efficient, which is particularly important for big data. Copyright © 2016 John Wiley & Sons, Ltd.

A trust-aware recommender system (TARS) is widely used in social media to find useful information. S_Searching is one of the most effective recommender searching mechanisms of TARS. It is based on the scale-freeness of the trust network: a skeleton, which consists of hub nodes of the trust network, is involved in trust propagations. Trusts are first propagated from active users to the skeleton, and then recommenders are searched via the skeleton. One fundamental research issue in S_Searching is to search the skeleton for active users efficiently. Existing methods fully search the trust network to find hub nodes in the skeleton for active users. It has high computational cost. In this paper, we propose a novel iterative deepening-based skeleton searching strategy for S_Searching, in which a depth-limited search is run repeatedly. The depth limit is increased with each iteration until it reaches the maximum allowable trust propagation distance. Simulation results show that the computational complexity of our proposed strategy is much less expensive than that of existing methods.

Summary In recent years, many energy‐efficient algorithms and data dissemination protocols have been proposed for wireless sensor networks (WSNs). Because sensor nodes close to sink node have more traffic loads, they will quickly deplete their limited energy in practical implementation, and it will finally lead to energy hole and network partition problem. Adding sink mobility into sensor networks can bring in new opportunities to improve energy efficiency for WSNs. In this paper, we proposed our multiple mobile sink‐based routing algorithm for data dissemination to improve WSNs performance. Multiple mobile sinks will be utilized to collect the interested raw data. They will move back and forth along predetermined paths; one of which is the diameter of the circle, and the other two are fixed on arc lines. Mobile sinks will sojourn at some fixed points to collect raw data from relevant areas. Extensive simulation results show that our proposed algorithm can efficiently mitigate the hot spots problem and prolong the network lifetime of WSNs. Copyright © 2014 John Wiley & Sons, Ltd.

Summary We study the cloud resource auction problem where users can bid for resource bundle containing heterogeneous types of virtual machines, and providers allocate virtual machines to their users through group price model. Compared with fixed price model, which is not always the best approach for trading resources as its economically inefficient and inflexible nature, the group price model possess the better flexibility and monetary benefits for auction participants (e.g., cloud providers and users). The proposed auction mechanism strategy‐proof group buying‐based auction mechanism, which formulates the problem of virtual machine allocation in clouds as a combinatorial auction problem, and holds some important property such as individual rationality, ex‐post budget balance, and truthfulness, meanwhile guaranteeing efficiency in both the provider's revenue and system efficiency. Extensive simulation results show that the proposed mechanism yields the allocation efficiency and computational tractability compared with the mechanism with fixed price model. Copyright © 2015 John Wiley & Sons, Ltd.

With the aim of prolonging network lifetime, many energy efficient algorithms and data dissemination protocols have been proposed for wireless sensor networks (WSNs) recently. Since sensor nodes close to sink have to bear more traffic burden to forward data, they will quickly deplete their energy which leads to energy hole and network partition problem. Adding mobility into sensor networks brings in new opportunities to improve network performance in terms of energy consumption, network lifetime, latency etc. In this paper, our Mobility based Data Collection Algorithm (MDCA) for Wireless Sensor Networks is proposed. Multiple mobile sinks are utilized, instead of one static sink, to move back and forth along predetermined paths to collect data. One of the predetermined paths is the diameter of the circle area and the other two are fixed on arc lines. The mobile sinks only sojourn at some fixed points to collect data from relevant areas. The effectiveness of our proposed algorithm is demonstrated through extensive simulation results.

This paper presents an approach for scheduling U-Life care applications in the cloud computing environment based on virtual resources to support real-time services and to improve user Quality of Service (QoS) requirements. We design and develop an architecture called ULC3 (Ubiquitous Life Care Cloud Computing) that uses virtual resources provided by cloud computing to schedule U-Life care applications. The ULC3 is based on the concepts of cloud computing and wireless sensor networks. The architecture is very important and necessary to support create virtual clusters dynamically, deploys the required number of virtual machines (VMs) in potential computing resources to meet the application requirements, and to configure with the required software execution environment. Thus, the system can improve computation time, guarantee the QoS, and support real-time services. Finally, the results from the execute applications are provided to the end-users as a service.

Abstract Cognitive radio (CR) has been considered a key technology to enhance overall spectrum utilization by opportunistic transmissions in CR transmitter–receiver link(s). However, CRs must form a cognitive radio network (CRN) so that the messages can be forwarded from source to destination, on top of a number of opportunistic links from co‐existing multi‐radio systems. Unfortunately, appropriate routing in CRN of coexisting multi‐radio systems remains an open problem. We explore the fundamental behaviors of CR links to conclude three major challenges, and thus decompose general CRN into cognitive radio relay network (CRRN), CR uplink relay network, CR downlink relay network, and tunneling (or core) network. Due to extremely dynamic nature of CR links, traditional routing to maintain end‐to‐end routing table for ad hoc networks is not feasible. We locally build up one‐step forward table at each CR to proceed based on spectrum sensing to determine trend of paths from source to destination, while primary systems (PSs) follow original ways to forward packets like tunneling. From simulations over ad hoc with infrastructure network topology and random network topology, we demonstrate such simple routing concept known as CRN local on‐demand (CLOD) routing to be realistic at reasonable routing delay to route packets through. Copyright © 2009 John Wiley & Sons, Ltd.