LoRa networks have become a key enabling technology for the Industrial Internet of Things (IIoT) due to the low cost, wide coverage, and easy deployment. However, the lifetime of Industrial LoRa Networks (ILNs) leaves much to be desired due to the star topology used, i.e., the energy consumption of nodes at network edges is much higher than those near the gateway. Existing works deploy additional gateways to improve network lifetime, however, this is often limited by hardware cost and geographical constraints. In this article, we propose to trade the long communication range for better network performance by introducing intranetwork relays into ILNs. The fine-grained global views and strict time synchronization in ILNs allow us to establish multihop relays within a network, thus supporting more energy-efficient parameters at edge nodes for enhancing network lifetime. We investigate the unique challenges of LoRa relays in ILNs, and then propose HybLoRa, a novel hybrid framework which adaptively assign relays and configure nodes based on a gain-driven minimum spanning tree (MST). We conduct real-world experiments and large-scale simulations. The results show that compared with single-hop EXPLoRa, HybLoRa can reduce total network energy consumption by 36.1% and improve network lifetime by 67.2% .

The integration of satellite communications with mobile edge computing (MEC) into space-air-ground integrated networks, known as satellite edge computing (SEC), has become a crucial research field for future communication systems to provide extensive global coverage services. This paper investigates the joint dependent task offloading and resource allocation problem for remote Internet-of-Things (IoT) applications within the SEC architecture. The proposed system leverages unmanned aerial vehicles (UAVs) as mobile access points and edge servers and utilizes low- earth orbit (LEO) satellites and ground stations as cloud computing resources. Multiple applications with dependent tasks from IoT devices (IoTDs) are modeled as directed acyclic graphs (DAGs). To address the challenges of reducing the system cost in UAV-assisted SEC, we first propose a one-to-many matching algorithm to associate IoTDs with UAVs. Then, a multi-application task sequence algorithm is devoted to merging the multiple DAGs and sorting the task order. Finally, a graph-aware asynchronous multi-agent reinforcement learning approach empowers the agents to autonomously discover optimal offloading and resource allocation strategies. Extensive simulations based on real-world datasets demonstrate the effectiveness of the proposed approach in minimizing the system costs while meeting application latency requirements, outperforming other benchmark algorithms.

The deployment of multimodal large language models (MLLMs) in edge-cloud networks faces critical challenges, including computational resource heterogeneity, memory bottlenecks, and bandwidth constraints. To address these issues, we propose EdgeSD, a novel framework that accelerates MLLM inference by integrating speculative decoding (SD) with edge-cloud collaboration. First, EdgeSD decouples the vision encoding and decoding processes of the draft MLLM across heterogeneous edge servers (ESs). This disaggregation architecture overcomes single-node memory constraints, enabling optimized resource utilization and high-resolution input processing. Second, to resolve the communication bottleneck and computational burden inherent in this distributed architecture, EdgeSD integrates a bandwidth-aware dynamic image token merging (ITM) method. Unlike general pruning techniques, this EdgeSD-specific ITM method focuses on minimizing inter-ES transmission latency for vision-decoding disaggregation while maintaining draft quality. Third, to optimize SD efficiency on consumer-grade ESs, EdgeSD employs an adaptive and scalable token tree structure solved using a parallel delta-stepping algorithm. This structure maximizes the number of accepted tokens under strict edge latency constraints. Extensive experiments on six multimodal datasets and five benchmarks with various MLLM pairs demonstrate that EdgeSD achieves substantial acceleration and throughput gains in edge-cloud collaboration scenarios using a lightweight draft MLLM, achieving 3.04-5.12x speedup compared to baseline methods.

The 2-stage eigenvalue decomposition (EVD) method outperforms conventional 1-stage method on GPUs and heterogeneous architectures, especially when eigenvectors are not required. However, its performance advantage diminishes when performing back transformation to obtain eigenvectors. To address this, we propose two key solutions: 1) replacing BLAS3 operations with BLAS2 operations during the bulge-chasing back transformation for better performance, and 2) reordering the back transformation workflow from a backward pattern to a new parallelism-driven pattern to hide divide-and-conquer latency, at the cost of one additional GEMM computation. Experimentally, the proposed back transformation algorithm demonstrates significant performance improvements, outperforming the SOTA implementation in MAGMA by an average factor of 3.58x. For complete FP64 precision symmetric EVD with eigenvectors, the proposed algorithm, incorporating both solutions, surpasses the SOTA implementations in MAGMA and cuSOLVER by average factors of 2.62x and 2.21x, respectively.

The deployment of multimodal large language models (MLLMs) on resource-constrained mobile devices poses significant challenges due to their high computational demands. This paper introduces a novel two-timescale optimization framework for efficient MLLM inference in Edge-Cloud networks, addressing the problem of multi-timescale resource management by jointly optimizing slow-timescale MLLMs deployment decisions and fast-timescale batch scheduling, GPU resource allocation, and bandwidth allocation under dynamic network conditions and spatiotemporal request heterogeneity. Our key innovation is a hierarchical twin delayed deep deterministic policy gradient (HALTD3) algorithm that integrates attention mechanisms and long short-term memory networks to optimize slow-timescale MLLMs deployment and fast-timescale resource allocation, minimizing weighted system costs including deployment cost, end-to-end latency, and energy consumption, while meeting stringent quality-of-service requirements. Extensive experiments demonstrate that the HALTD3 algorithm substantially outperforms baseline methods in reducing system costs across diverse MLLM workloads and dynamic network scenarios, validating its effectiveness for practical edge-cloud collaborative inference.

Workflow scheduling plays a critical role in optimizing completion time and throughput in distributed cloud environments, leveraging the parallelism of heterogeneous computing resources. However, existing workflow scheduling algorithms often fall short due to heuristic limitations and the challenges in adaptability within heterogeneous settings, leading to suboptimal scheduling solutions. In this paper, we present a novel deep reinforcement learning (DRL) framework tailored for continuous workflow scheduling in heterogeneous environments. First, we propose an intelligent scheduler that updates the policy network through interactions with a multi-tenant environment, triggered by scheduling events. Next, the framework incorporates a Graph Attention Network (GAT) and a self-attention MultiLayer Perceptron (MLP) to preprocess the workflow topology and embed dynamic features of ready tasks and available processors into the state input at each scheduling step. Additionally, a k-dimensional tree-based k-nearest neighbors (kNN) algorithm is employed to map the output action vector to a pair of executed ready task and processor, facilitating the transition from continuous to discrete action spaces and addressing challenges associated with dynamic action spaces. Experimental results demonstrate that our method converges effectively in continuous workflow scheduling scenarios and significantly outperforms the best-known methods in terms of average makespan and load balancing efficiency.

The recent breakthrough in Wireless Power Transfer (WPT) provides a promising way to prolong network lifetime by employing a charging vehicle to replenish energy. Data transmissions from nodes typically happen in response to physical sensory events, leading to time-varying energy consumption. To improve charging efficiency, the existing schemes collect energy information by employing a data-gathering vehicle or data collection protocol. However, in duty cycle networks, these schemes either incur extra vehicles or high data collection delay. To solve this problem, we propose an mobile adaptive charging scheme with rapid data sharing (rShare), which establishes multi-layer collection trees and collects overall energy data to the vehicle. A spatial predicted active sending (SPAS) algorithm is proposed for distant nodes to actively estimate the future position and transmit their data to cover potential positions of the charging vehicle, which significantly reduces data collection delay. We also propose an estimated time of arrival (ETA)-aware scheme based on the TSP Nearest Neighbor algorithm that updates the charging path based on the collected data. Extensive simulation results demonstrate that our scheme outperforms the state-of-the-arts in terms of dead node avoidance with less communication overhead.

Federated learning (FL) has shown its great potential for achieving distributed intelligence in privacy-sensitive IoT. However, popular FL approaches, such as FedAvg and its variants share model parameters among clients during the training process and thus cause significant communication overhead in IoT. Moreover, nonindependent and identically distributed (non-IID) data across learning devices severely affect the convergence and speed of FL. To address these challenges, we propose a communication-efficient FL framework based on Two-step Knowledge Distillation, Fed2KD, which boosts the classification accuracy through privacy-preserving data generation while improving communication efficiency through a new knowledge distillation scheme empowered by an attention mechanism and metric learning. The generalization ability of Fed2KD is analyzed from the view of domain adaption. Extensive simulation experiments are conducted on Fashion-MNIST, CIFAR-10, and ImageNet data sets with various non-IID data distributions. The performance results show that Fed2KD can reduce the communication overhead and improve classification accuracy compared to FedAvg and its latest variants.

Transformers are transforming the landscape of computer vision, especially for image-level recognition and instance-level detection tasks. Human-object interaction detection transformer (HOI-TR) is the first transformer-based end-to-end learning system for human-object interaction (HOI) detection; vision transformers build a simple multi-stage structure for multi-scale representation with single-scale patch and are the first patch-based transformer architecture for image-level recognition and instance-level detection. In this paper, we build a transformer-based multi-scale human-object interaction detector (MHOI), a novel method to integrate Vision and HOI detection Transformer, instead of directly incorporating two types of transformers, since the vision transformer lacks hierarchical architecture to handle the large variations in the scale of visual entities due to the single-scale patch partitioning. Specifically, MHOI embeds features of the same size (i.e., sequence length) with patches of variable scales simultaneously by utilizing overlapping convolutional patch embedding, then introduces an efficient transformer decoder that designs the query based on anchor points and essential auxiliary techniques to boost the HOI detection performance. Numerically, extensive experiments on several benchmarks demonstrate that our proposed framework outperforms prior existing methods coherently and achieves the impressive performance of 29.67 mAP on HICO-DET and 58.7 mAP on V-COCO, respectively.

In the modern era of big data, large-scale graph computing has become challenging because of the dramatic rise in graph data size. Graph edge partitioning (GEP) is a crucial preprocessing step to distributed graph platforms, yet it is challenging to partition the large-scale graphs. GEP has shown better partition quality than the graph vertex partitioning for the graph's skewed degree distribution. Existing GEP approaches are classified into two as stream and offline. The former category assigns edges to the partitions based on the previously received edge information. It has less partitioning quality and is affected by stream order compared to the latter while supporting big graph partitioning. The latter uses complete knowledge of a graph during partitioning and hence has a better partitioning quality than the former; however, it does not support large-scale graphs. In this study, we propose a novel OffStream partitioning approach (OSPA) and hybrid graph edge partitioner OffStreamNH. OSPA leverages both the offline and stream graph partitioning approaches through stateful partitioning by introducing a state layer. This stateful partition state is recorded while offline is partitioning its input graph. It contains partial knowledge of previously partitioned data and is used by the stream partitioner. The OffStreamNH uses Neighborhood Expansion (NE) and Higher Degree Replicated First (HDRF) algorithms for the offline and online; respectively, with minor modifications of both algorithms. Experimental results show that OffStreamNH outperforms the state of the art stream partitioners in terms of replication factor, load balance and tolerates the effect of stream orders.

Driven by the tremendous application demands, the Internet of Things (IoT) systems are expected to fulfill computation-intensive and latency-sensitive sensing and computational tasks, which pose a significant challenge for the IoT devices with limited computational ability and battery capacity. To address this problem, edge computing is a promising architecture where the IoT devices can offload their tasks to the edge servers. Current works on task offloading often overlook the unique task topologies and schedules from the IoT devices, leading to degraded performance and underutilization of the edge resources. In this article, we investigate the problem of fine-grained task offloading in edge computing for low-power IoT systems. By explicitly considering: 1) the topology/schedules of the IoT tasks; 2) the heterogeneous resources on edge servers; and 3) the wireless interference in the multiaccess edge networks, we propose a lightweight yet efficient offloading scheme for multiuser edge systems, which offloads the most appropriate IoT tasks/subtasks to edge servers such that the expected execution time is minimized. To support the multiuser offloading, we also propose a distributed consensus algorithm for low-power IoT devices. We conduct extensive simulation experiments and the results show that the proposed offloading algorithms can effectively reduce the end-to-end task execution time and improve the resource utilization of the edge servers.

Vehicular edge computing (VEC) is a new computing paradigm that has great potential to enhance the capability of vehicle terminals (VTs) to support resource-hungry in-vehicle applications with low latency and high energy efficiency. In this article, we investigate an important computation offloading scheduling problem in a typical VEC scenario, where a VT traveling along an expressway intends to schedule its tasks waiting in the queue to minimize the long-term cost in terms of a tradeoff between task latency and energy consumption. Due to diverse task characteristics, dynamic wireless environment, and frequent handover events caused by vehicle movements, an optimal solution should take into account both where to schedule (i.e., local computation or offloading) and when to schedule (i.e., the order and time for execution) each task. To solve such a complicated stochastic optimization problem, we model it by a carefully designed Markov decision process (MDP) and resort to deep reinforcement learning (DRL) to deal with the enormous state space. Our DRL implementation is designed based on the state-of-the-art proximal policy optimization (PPO) algorithm. A parameter-shared network architecture combined with a convolutional neural network (CNN) is utilized to approximate both policy and value function, which can effectively extract representative features. A series of adjustments to the state and reward representations are taken to further improve the training efficiency. Extensive simulation experiments and comprehensive comparisons with six known baseline algorithms and their heuristic combinations clearly demonstrate the advantages of the proposed DRL-based offloading scheduling method.

Inspired by mobile edge computing (MEC), vehicular edge computing (VEC) enables vehicle terminals to support resource-hungry on-vehicle applications with significantly lower latency and less energy consumption. In this paper, we investigate the computation offloading problem in a typical VEC scenario, where a vehicle offloads its computation tasks to the VEC servers deployed in the road side unit (RSU) to minimize its long-term user cost. The mobility of the vehicle coupled with the high dynamics of the environment makes the problem particularly difficult. To tackle this challenge, a deep reinforcement learning (DRL) based offloading method is proposed, which approximates the offloading policy (OP) by a deep neural network (DNN) and trains the DNN with the proximal policy optimization (PPO) algorithm without a priori knowledge of the environment dynamics. Extensive simulation experiments and comprehensive comparison with six baseline algorithms demonstrate that it can achieve the lowest user cost in most cases.

Suspension-based locks are widely used in realtime systems to coordinate simultaneous accesses to exclusive shared resources. Although suspension-based locks have been well studied for sequential real-time tasks, little work has been done on this topic for parallel real-time tasks. This paper for the first time studies the problem of how to extend existing sequential-task locking protocols and their analysis techniques to the parallel task model. More specifically, we extend two locking protocols OMLP and OMIP, which were designed for clustered scheduling of sequential real-time tasks, to federated scheduling of parallel real-time tasks, and develop path-oriented techniques to analyze and count blocking time. Experiments are conducted to evaluate the performance of our proposed approaches and compare them against the state-of-the-art.

The recent advances in microelectronics and communications have led to the development of large-scale Internet of Things (IoT) networks, where tremendous sensory data is generated and needs to be processed. To support realtime processing for large-scale IoT, deploying edge servers with storage and computational capability is a promising approach. In this paper, we carefully analyze the impacting factors and key challenges for edge node (EN) deployment. We then propose a novel three-phase deployment approach which considers both traffic diversity and the wireless diversity of IoT. The proposed work aims at providing real-time processing service for the IoT network and reducing the required number of ENs. We conducted extensive simulation experiments, the results show that compared to the existing works that overlooked the two kinds of diversities, the proposed work greatly reduces the number of ENs and improves the throughput between IoT and ENs.

Summary Metadata operations have become dominant file operations in the storage systems. In the scenarios of read‐more and write‐less of massive small files, the current distributed file systems suffer from the unsatisfying performance and scalability of metadata service because of random disk I/O during metadata operations. In this paper, a highly efficient metadata management architecture for cloud storage systems is proposed. The cluster design significantly improves the scalability of the system. A concept of the disk I/O channel is introduced, which is an independent data storage pipe by binding an independent CPU‐core to each physical disk. In addition, a multi‐channel fast key‐value storage engine is proposed to provide the extremely efficient performance for the underlying storage service, which takes full advantages of multi‐core processors and parallel disks I/O. Besides, a new dynamic load‐balancing strategy is proposed to reduce load thrashing and improve the precision of rebalancing among the clusters. Performance measurements under a variety of benchmarks show that the metadata management is capable of handling the massive small files storage and the performance is improved significantly compared to the existing solutions. Copyright © 2016 John Wiley & Sons, Ltd.

Summary With the rapid development of intelligent transportation technology, high‐definition data storage, and processing of massive amounts of video surveillance have become key issues. When thousands of high‐bit‐rate video streaming concurrently writes, disk I/O throughput becomes a bottleneck. In addition, this leads to serious energy consumption and disk abrasion. To solve these problems, a new distributed file system for high concurrent and high‐bit‐rate writing is designed. It combines an optimized data storage model, efficient metadata management, and exquisite disk schedule mechanism. The optimized data storage model uses a file pre‐allocation strategy and multiple‐stream input modulating technology to convert the randomly concurrent writes on the disk into sequential writes; the metadata management provides an efficient means of retrieving the specified data; and the dual‐partition schedule mechanism can ensure the disk stability with less abrasion. Through this distributed file system, the disk I/O throughput can be saturated in a high concurrent writing environment. The performance evaluation results demonstrate that the I/O throughput of a normal 7200RPM SATA III disk in our scheme can be stabilized at 150MB/s, easily to support 300 concurrent high‐definition video streams (4Mbit/s each). The distributed file system with eight commodity servers can afford the ability of supporting 8000 high‐definition video streams concurrently writing, which is far greater than the existing video surveillance storage solutions. Copyright © 2015 John Wiley & Sons, Ltd.