Edge Computing Research Articles

A method for simultaneously implementing trajectory planning and DAG task scheduling in multi-UAV assisted edge computing

UAV-assisted edge computing(UEC) as a new framework is able to provide computing services to remote areas. However, facing computationally intensive tasks with huge computation time forces them to hover near the user’s devices(UDs) for long periods of time. To better utilize the available arithmetic resources and reduce the computation time of UAVs, it is imperative to introduce directed acyclic graph (DAG) task scheduling into the UEC framework. Therefore, this article proposes a DAG-type task-driven trajectory planning (DAG-TDTP) model, which can plan UAV routes while scheduling DAG subtasks between UAVs that offload from UDs. To implement the DAG-TDTP model, we propose a distance-based heterogeneous earliest-finish-time (D-HEFT) algorithm and a time segmentation method based on the cooperative task offloading matrix. To stimulate the potential of the DAG-TDTP model in reducing energy consumption, we propose a genetic algorithm based on temporary key nodes (TKNGA) for the proposed model. Through simulation analysis, we verify the superiority of the proposed model in reducing UAV system energy consumption and the superiority of TKNGA compared to other algorithms.

Multi-UAV Assisted Air–Ground Collaborative MEC System: DRL-Based Joint Task Offloading and Resource Allocation and 3D UAV Trajectory Optimization

In disaster-stricken areas that were severely damaged by earthquakes, typhoons, floods, mudslides, and the like, employing unmanned aerial vehicles (UAVs) as airborne base stations for mobile edge computing (MEC) constitutes an effective solution. Concerning this, we investigate a 3D air–ground collaborative MEC scenario facilitated by multi-UAV for multiple ground devices (GDs). Specifically, we first design a 3D multi-UAV-assisted air–ground cooperative MEC system, and construct system communication, computation, and UAV flight energy consumption models. Subsequently, a cooperative resource optimization (CRO) problem is proposed by jointly optimizing task offloading, UAV flight trajectories, and edge computing resource allocation to minimize the total energy consumption of the system. Further, the CRO problem is decoupled into two sub-problems. Among them, the MATD3 deep reinforcement learning algorithm is utilized to jointly optimize the offloading decisions of GDs and the flight trajectories of UAVs; subsequently, the optimal resource allocation scheme at the edge is demonstrated through the derivation of KKT conditions. Finally, the simulation results show that the algorithm has good convergence compared with other algorithms and can effectively reduce the system energy consumption.

Application of edge computing and IoT technology in supply chain finance

This study proposes an IoT data management framework based on blockchain and edge computing and conducts detailed simulation experiment design and performance evaluation for the field of supply chain finance. Previous methods often struggled with high latency, limited scalability, and inadequate risk management, and to address these issues, the experimental platform includes traditional centralized systems, distributed systems, blockchain-based systems, and edge computing + blockchain systems. By monitoring indicators such as data processing delay, data transmission delay, data retrieval time, system throughput, and data integrity of each platform, and introducing evaluation formulas for credit risk, operational risk, and market risk, the performance of different systems in processing supply chain financial data is comprehensively analyzed. The experimental results show that the edge computing + blockchain system performs well in data processing efficiency, security, real-time, and system throughput, especially under high load conditions. Our market risk value is reduced to 0.015. This framework improves the efficiency and security of data transmission and effectively reduces credit risk and operational risk, providing an efficient and reliable solution for data management in supply chain finance.

Analysis and Justification of Data Structure Selection for Traffic Flow Management Tasks

Abstract. Problem. Cities around the world are increasingly facing challenges related to congestion, delays and the environmental impact of those problems. The paper examines the critical need for innovative solutions to improve the efficiency of traffic flow management, offering a comprehensive study of the role of data arrays in solving the problem of urban mobility. The main goal is to explore and find out how the use of data sets can improve traffic management, contributing to the development of new traffic management systems and informed decision-making. By summarizing information from various sources, the article aims to provide a holistic understanding of the theoretical foundations, practical applications and new trends in the use of datasets for traffic optimization. Drawing on a range of sources, ranging from dynamic algorithms to real-world case studies in Los Angeles, the paper examines the theoretical foundations, applications of dynamic arrays, and the development of traffic data management. The results demonstrate the transformative effect of dynamic arrays on traffic flow management. Real-world implementations demonstrate tangible benefits, including improved signal synchronization, increased security, and data-driven decision-making. New trends such as 5G, edge computing and advanced sensor networks are considered to be the main elements shaping the future of traffic data management. The practical value of the work lies in its potential to inform urban planners and technology developers about the transformative possibilities of data sets. By understanding practical applications, challenges and emerging trends, stakeholders can make informed decisions to improve urban mobility, reduce congestion and shape the future of intelligent traffic solutions. The work provides a structured study of data sets in the management of traffic flows, offering ideas that contribute to the possible integration of intelligent solutions for traffic and a more comfortable urban life.

Generative AI-Enabled Energy-Efficient Mobile Augmented Reality in Multi-Access Edge Computing

This paper proposes a novel offloading and super-resolution (SR) control scheme for energy-efficient mobile augmented reality (MAR) in multi-access edge computing (MEC) using SR as a promising generative artificial intelligence (GAI) technology. Specifically, SR can enhance low-resolution images into high-resolution versions using GAI technologies. This capability is particularly advantageous in MAR by lowering the bitrate required for network transmission. However, this SR process requires considerable computational resources and can introduce latency, potentially overloading the MEC server if there are numerous offload requests for MAR services. In this context, we conduct an empirical study to verify that the computational latency of SR increases with the upscaling level. Therefore, we demonstrate a trade-off between computational latency and improved service satisfaction when upscaling images for object detection, as it enhances the detection accuracy. From this perspective, determining whether to apply SR for MAR, while jointly controlling offloading decisions, is challenging. Consequently, to design energy-efficient MAR, we rigorously formulate analytical models for the energy consumption of a MAR device, the overall latency and the MAR satisfaction of service quality from the enforcement of the service accuracy, taking into account the SR process at the MEC server. Finally, we develop a theoretical framework that optimizes the computation offloading and SR control problem for MAR clients by jointly optimizing the offloading and SR decisions, considering their trade-off in MAR with MEC. Finally, the performance evaluation indicates that our proposed framework effectively supports MAR services by efficiently managing offloading and SR decisions, balancing trade-offs between energy consumption, latency, and service satisfaction compared to benchmarks.

Optimizing Energy and Delay in Task Offloading for Connected Vehicles with Proximal Policy Optimization

Electric-powered intelligent connected vehicles are becoming the pivotal point of the automotive industry. As vehicles integrate more applications, the computation tasks they generate increase substantially. Cloud servers cannot handle these tasks promptly, and current electric vehicles (EVs) have limited energy and computing resources. Multi-Access edge computing (MEC) performs various activities in proximity to the vehicles, resulting in decreased latency and the preservation of EV battery power. However, MEC servers have finite processing resources and may be unable to satisfy the required latency restrictions. We propose a task offloading scheme to optimize the allocation of computational resources from roadside servers across several EVs. We develop a mathematical model to optimize both computation latency and EV energy, represented as a Markov decision process (MDP). To address this, we employ the deep reinforcement learning-proximal policy optimization (DRL-PPO) algorithm. The implementation of our mathematical model, which is based on an MDP, together with the use of the DRL-PPO algorithm, showcases notable decreases in both energy consumption and latency when compared to alternative benchmark deep reinforcement learning (DRL) approaches.

Real-time optical-wireless cooperative switching on wireless quality degradation for end-to-end video delay guarantee

This paper proposes an optical-wireless cooperative control method to guarantee end-to-end delays in the case of radio quality degradation in wireless networks by switching optical paths. The method leverages the control information from mobile systems to dynamically switch the optical path between the distributed unit (DU) and the far aggregated central unit (CU) to another path forward to the nearest virtualized central unit (vCU) at the edge sight, on the basis of the radio quality. This enables demanding applications that require a combination of high bandwidth and low latency, such as remote operation using drones. Preliminary simulations were conducted to investigate the effect of quality degradation in the wireless domain and the potential effectiveness of the proposed delay compensation method. In the experiments conducted to verify the feasibility of the proposed method, the results demonstrated that the optical path switching was successfully performed without packet loss and within 4 ms from the transmission of the control frame. The proposed method was also able to correctly switch back when the processing rate of mobile edge computing (MEC) was exceeded after the radio quality recovered. This study demonstrates the effectiveness of further coordination between wired and wireless control for a wider range of real-time applications using video.

E-CARGO-based dynamic weight offload strategy with resource contention mitigation for edge networks

With the widespread use of Mobile Edge Computing (MEC) in smart manufacturing systems in Industrial Internet of Things (IIoT) and 5G networks, determining how to efficiently offload computing tasks has become a hot research area. The Role-Based Collaboration (RBC) Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model is introduced to comprehensively manage MEC servers and user computation tasks in edge network environments, thereby improving the effectiveness and performance of task offloading in smart manufacturing systems. To begin with, latency and energy consumption are important indicators for evaluating the offloading effect. A pre-allocation algorithm based on user latency tolerance is proposed to dynamically adjust the latency-energy consumption weighting factor to optimize system resource allocation for real-time adjustment of offloading decisions. Second, the Group Role Assignment of Agent Role Conflicts (GRACAR) model based on E-CARGO is extended, along with a dynamic weighting of the GRACAR (GRACAR-DW) model and formal modeling. By introducing resource contention constraints, the resource contention caused by excessive task data offloading to the same MEC server is proactively mitigated. Finally, a Gurobi solution based on Mixed-Integer Linear Programming (MILP) is developed to help validate and synthesize the proposed model. Simulation results show that the strategy considerably enhances the MEC system's overall performance in terms of latency and energy consumption while also providing new ideas and technological support for offloading decisions in edge networks.

Towards Mine 4.0: A Proposed Multi-Layered Architecture for Real-Time Surveillance and Anomaly Detection in an Open-Pit Phosphate Mine

Given the global importance and limited availability of phosphate, optimizing the use of this critical resource and minimizing its wastage are of paramount importance. In this context, this paper proposes an innovative architecture for the implementation of an intelligent video surveillance system specifically designed for open-pit phosphate mines. The proposed architecture is designed to meet the overall functional requirements of a surveillance system in the challenging environment of open-pit mining, while aligning with the guidelines of the Mine 4.0 revolution. It incorporates advanced technologies that address the critical challenges of latency, data security, and transparency commonly encountered in traditional monitoring systems. By adopting a multi-layered approach that leverages edge, fog, and cloud computing, coupled with blockchain technology and expert collaboration, our architecture offers a comprehensive framework for efficient data processing at every stage, from initial data acquisition to real-time anomaly detection and decision-making.

Detection of aberration in human behavior using shallow neural network over smartphone inertial sensors data

AbstractThe integration of different Mobile Edge Computing (MEC) applications has significantly enhanced the realm of security and surveillance, with Human Activity Recognition (HAR) standing out as a crucial application. The diverse sensors found in smartphones have made it convenient for monitoring applications to gather and analyze data, rendering them valuable for HAR purposes. Moreover, MEC can be employed to automate surveillance, allowing intelligent monitoring of restricted areas to identify and respond to unwanted or suspicious activities. This research develops a system using motion sensors in smartphones to identify unusual human activities. People's smartphones were employed to monitor both suspicious and regular activities. Information was collected for various actions categorized as either suspicious or regular. When a person performs a certain action, the smartphone records a series of sensory data, analyse important patterns from the basic data, and then determines what the person is doing by combining information from different sensors. To prepare the data, information from different sensors was aligned to a shared timeline. In this study, we used a sliding window approach on synchronized data to feed sequences into LSTM and CNN models. These models, which include initial layers of LSTM and CNN, automatically find important patterns in the order of human activities. We combined SVM with the features extracted by the shallow Neural Network to make a mixed model that predicts suspicious activities. Lastly, we compared LSTM, CNN, and our new shallow mixed neural network using a new real‐time dataset. The mixed model of CNN and SVM achieved an accuracy of 94.43%. Additionally, the sliding window method's effectiveness was confirmed with a 4.28% improvement in accuracy.

Improved ECC cryptosystem-based lightweight authentication for SDN-based edge computing in IoT-enabled healthcare systems

The Internet of Things (IoT) consists of smart devices with limited resources that can identify and analyze data. In IoT-enabled healthcare systems, the security of IoT devices and the data they contain is complex. These devices in the healthcare industry, edge computing can provide low-latency information services at a reasonable cost. This work proposes a security infrastructure for Software Defined Network (SDN)-based edge computing in IoT-enabled healthcare systems consisting of three steps: Lightweight authentication, collaborative edge computing and job migration. The lightweight authentication step involves both Improved Lightweight Key Management (ILKM) and Improved Elliptic Curve Cryptography (IECC) schemes to ensure authentication among the devices and edge servers. Moreover, the patient’s data in IoT devices are scheduled to the appropriate edge server by examining the load balancing in the collaborative edge computing phase. This is done optimally using the adopted hybrid optimization model, Osprey Assisted Coati Optimization Algorithm (OACOA). Further, job migration takes place, in which the data is allocated to the edge server by comparing the capacity of edge servers and the data gets migrated to other servers by considering migration cost when the capacity of the edge server is overloaded. Finally, the efficiency of the suggested OACOA scheme is evaluated over traditional models with regard to several metrics. When considering the edge-server 30, the OACOA scheme achieves a makespan of 385, while conventional methods acquired fewer makespan ratings. Also, the OACOA approach obtained the highest security ratings (0.7143) on edge-server 20 when compared to existing schemes.

Resource Allocation Based on Task Priority and Resource Consumption in Edge Computing

The computational power of Internet of Things (IoT) devices is usually low, which makes it necessary to process data and extract relevant information on devices with higher processing capacity. Edge Computing emerged as a complementary solution to cloud computing, providing devices at the network edge with computational resources to handle the data processing and analysis that constrained IoT devices eventually cannot perform. This solution allows data processing closer to the IoT devices, reducing latency for IoT applications. However, the resource constraints of edge nodes, which have lower computational power than the cloud nodes, make resource allocation and processing massive requests challenging. This study proposes an edge resource allocation mechanism based on task priority and machine learning. The proposed approach efficiently allocates resources for IoT requests based on their task priorities while monitoring the resource consumption of edge nodes. This study evaluates the performance of different classification algorithms by using well-known metrics for classifying models. The most efficient classifier achieved an accuracy of 92% and a precision of 90%. The results indicate good performance when using this classifier in the evaluated approach. The proposed mechanism demonstrated that resource management can be done more efficiently with significantly lower resource utilization when compared to an allocation method based only on distance. The study tested different scenarios regarding the number of requests, edge nodes, and a proposed failure mechanism to schedule failed node tasks to functional nodes. This failure control mechanism is a significant contribution of the proposal. Therefore, the proposed method in this study can become a valuable tool for efficient resource management with reduced computational cost and efficient resource allocation.

A Consortium Blockchain-Based Edge Task Offloading Method for Connected Autonomous Vehicles

In recent years, the proliferation of Connected Autonomous Vehicles(CAV) has revolutionizing the transportation industry. However, these vehicles often face limitations in terms of local computing resources, leading to the need for offloading interactive-intensive application tasks to servers for processing. Traditional paradigm has its limitations in meeting the demands of massive task processing. The combination of Web3.0 and edge computing offers users high-reliable, low-latency, and highly flexible services. Nevertheless, the new paradigm also presents its own challenges such as ensuring privacy data protection, and reducing the time and energy costs associated with task offloading. To tackle these challenges, a edge task offloading framework based on consortium blockchain for CAVs has been developed. Within this framework, a consortium blockchain-based interaction-intensive task offloading method, called CBIToMe, has been designed. CBIToMe specifically addresses the multi-stage nature of interactive-intensive CAV tasks and aims to minimize task completion time and offloading costs, particularly when the waiting time for interaction is uncertain. Additionally, CBIToMe effectively utilizes consortium blockchain technology to safeguard the CAV privacy data. Results from experiments conducted in various scenarios demonstrate that CBIToMe outperforms three representative methods, showcasing its superior performance.

Minimizing Service Latency Through Image-Based Microservice Caching and Randomized Request Routing in Mobile Edge Computing

Minimizing Service Latency Through Image-Based Microservice Caching and Randomized Request Routing in Mobile Edge Computing

Self-Adaptive Evolutionary Multitasking Algorithm for Mobile Edge Computing in Internet of Things

Self-Adaptive Evolutionary Multitasking Algorithm for Mobile Edge Computing in Internet of Things

Enhancing security offloading performance in NOMA heterogeneous MEC networks using access point selection and meta-heuristic algorithm

The research delves into the intricate domain of security offloading within the context of non-orthogonal multiple access (NOMA) heterogeneous mobile edge computing (het-MEC) networks operating over Rayleigh fading channels. The investigation centers on a system model comprising a single antenna-equipped edge user, denoted as U, which strategically offloads computational tasks to two distinct heterogeneous wireless access points (APs): the far AP (AP1) and the near one (AP2), employing NOMA techniques. Notably, the research accounts for a passive eavesdropper (E) intending to intercept the U−AP2 transmission. A four-phase protocol is proposed to ensure the security offloading process, namely SAPS, which leverages wireless access point selection (APS) and physical layer security (PLS) techniques. The focus extends to derive a closed-form expression for a novel critical system performance metric: the secrecy successful computation probability (SSCP). Furthermore, an algorithm based on Ant Colony Optimization (ACO) within the continuous domain is introduced, which aims to enhance the SSCP by intelligently determining system parameters. The impact of critical factors such as transmit power, power allocation coefficient, bandwidth, CPU frequency, and task division ratio under the SAPS scheme is explored and compared to the conventional approach using pure NOMA. Remarkably, the algorithm in the proposed scheme demonstrates up to a 3% performance improvement. The validity and accuracy of the study findings are verified through Monte-Carlo simulations. The work contributes significantly to advancing secure offloading strategies in NOMA-based MEC networks, offering valuable insights for practical deployment and optimization.

A novel energy-efficient and cost-effective task offloading approach for UAV-enabled MEC with LEO enhancement in Internet of Remote Things networks

The Internet of Remote Things (IoRT) involves networks of devices deployed in extensive and often remote areas, collecting data for transmission and processing. In such networks, Unmanned Aerial Vehicles (UAVs) gather data, which is then sent to Low Earth Orbit (LEO) satellites for processing. These systems often face significant challenges, particularly in task offloading. Conventional methods typically rely on static routing and scheduling algorithms that do not adapt to changing conditions and usually overlook the complexity of dynamic decision-making in harsh or isolated environments, thus failing to address the critical challenges of energy efficiency and latency. In this paper, we introduce a method comprised of a three-layer architecture. The first layer, the IoRT computing layer, uses Deep Q-Network (DQN) to optimize local decisions based on device constraints and task urgency. The second layer features UAVs serve as Mobile Edge Computing (MEC), which not only processes data but also decides whether to process tasks locally or offload them to LEO satellites, utilizing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for this decision-making process. The third LEO Satellite Layer has a high computational capacity to handle offloaded tasks. Simulation results demonstrate notable improvements: compared to another method, the proposed model shows a 14.73 % reduction in energy consumption and a 23.13 % decrease in latency while reducing execution costs by an average of 28.7 %.

Multimedia Cognitive Wireless Sensor Network Cluster Routing based on Intelligent Robot Edge Computing and Collection

Multimedia Cognitive Wireless Sensor Network Cluster Routing based on Intelligent Robot Edge Computing and Collection

UAV-IRS-assisted energy harvesting for edge computing based on deep reinforcement learning

In the internet of everything (IoE) era, the proliferation of internet of things (IoT) devices is accelerating rapidly. Particularly, smaller devices are increasingly constrained by hardware limitations that impact their computational capacity, communication bandwidth, and battery longevity. Our research explores a multi-device, multi-access edge computing (MEC) environment within small cells to address the challenges posed by the hardware limitations of IoT devices in this environment. We employ wireless power transfer (WPT) to ensure these IoT devices have sufficient energy for task processing. We propose a system architecture in which an intelligent reflective surface (IRS) is carried by an unmanned aerial vehicle (UAV) to enhance communication conditions. For sustainable energy harvesting (EH), we integrate a normal distribution into the objective function. We utilize a softmax deep double deterministic policy gradients (SD3) algorithm, based on deep reinforcement learning (DRL), to optimize the computational and communication capabilities of IoT devices. Simulation experiments demonstrate that our SD3-based EH edge computing (EHEC-SD3) algorithm surpasses existing DRL algorithms in our explored environments, achieving more than 90% in overall optimization and EH performance.

Transport properties of interface-type analog memristors

Interface-type, analog memristors have quite a reputation for real-time applications in edge sensorics, edge computing, and neuromorphic computing. The -type conducting BiFeO3 (BFO) is such an interface-type, analog memristor, which is also nonlinear and can therefore not only store, but also process data in the same memristor cell without data transfer between the data-storage unit and the data-processing unit. Here we present a physical memristor model, which describes the hysteretic current-voltage curves of the BFO memristor in the small and large current-voltage range. Extracted internal state variables are reconfigured by the ion drift in the two write branches and are determining the electron transport in the two read branches. Simulation of electronic circuits with the BFO interface-type, analog memristors was not possible so far because previous physical memristor models have not captured the full range of internal state variables. We show quantitative agreement between modeled and experimental current-voltage curves exemplarily of three different BFO memristors in the small and large current-voltage ranges. Extracted dynamic and static internal state variables in the two full write branches and in the two full read branches, respectively, can be used for simulating electronic circuits with BFO memristors, e.g., in edge sensorics, edge computing, and neuromorphic computing. Published by the American Physical Society 2024