Edge Computing Applications Research Articles

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.

Comparison of Reinforcement Learning Algorithms for Edge Computing Applications Deployed by Serverless Technologies

Edge computing is one of the technological areas currently considered among the most promising for the implementation of many types of applications. In particular, IoT-type applications can benefit from reduced latency and better data protection. However, the price typically to be paid in order to benefit from the offered opportunities includes the need to use a reduced amount of resources compared to the traditional cloud environment. Indeed, it may happen that only one computing node can be used. In these situations, it is essential to introduce computing and memory resource management techniques that allow resources to be optimized while still guaranteeing acceptable performance, in terms of latency and probability of rejection. For this reason, the use of serverless technologies, managed by reinforcement learning algorithms, is an active area of research. In this paper, we explore and compare the performance of some machine learning algorithms for managing horizontal function autoscaling in a serverless edge computing system. In particular, we make use of open serverless technologies, deployed in a Kubernetes cluster, to experimentally fine-tune the performance of the algorithms. The results obtained allow both the understanding of some basic mechanisms typical of edge computing systems and related technologies that determine system performance and the guiding of configuration choices for systems in operation.

Edge computing and AI-driven intelligent traffic monitoring and optimization

Intelligent transportation system is a comprehensive system engineering, involving real-time data processing, security and privacy protection and other challenges. This paper discusses the key role of edge computing in intelligent transportation, especially its combination with SLAM technology. Edge computing enables faster data processing and response times by placing computing and data storage resources near the data source and end users, improving the efficiency and reliability of intelligent transportation systems. At the same time, edge computing can also enhance information security and privacy protection, and bring more possibilities for the future development of intelligent transportation systems. In the future, with the progress and development of technology, we can look forward to the deeper application of edge computing and SLAM technology in the field of intelligent transportation, bringing more innovation and development to smart city transportation.

Retraction: Application of edge computing and GIS in ecological water requirement prediction and optimal allocation of water resources in irrigation area.

Retraction: Application of edge computing and GIS in ecological water requirement prediction and optimal allocation of water resources in irrigation area.

Empowering the Edge: Research advances from doors 2024

The 4th Edge Computing Workshop (doors 2024) held in Zhytomyr, Ukraine, aimed to bring together researchers to share advances and applications in edge computing, a critical technology for enabling the Internet of Things and analytics close to data sources. This report summarises the event, highlighting the peer-reviewed papers covering topics from edge hardware to AI and security. The nine accepted papers, selected from 19 submissions, propose innovative approaches to advance the field. Common themes include leveraging edge computing for sensing applications and addressing resource utilisation and management challenges. The workshop stimulated valuable discussion and collaboration around these important research directions.

Dynamic Resource Allocation for AI/ML Applications in Edge Computing: Framework Architecture and Optimization Methods

This scholarly paper introduces an extensive architectural framework and optimization strategies designed specifically for dynamic resource allocation in edge computing environments, with a focus on AI/ML applications. The rise of edge computing presents a viable solution for managing the computational complexities of AI/ML tasks by utilizing resources in proximity to data sources. Nevertheless, effective resource allocation encounters significant hurdles due to the diverse and ever-changing nature of edge environments. In addressing these challenges, the paper introduces an innovative framework that integrates dynamic resource allocation methodologies with the unique requirements of AI/ML applications. This framework encompasses a range of optimization techniques customized to efficiently distribute resources, taking into account factors such as workload attributes, resource availability, and latency limitations. Through extensive simulations and evaluations, the study showcases the effectiveness of the proposed approach in enhancing resource utilization, reducing latency, and bolstering overall performance for AI/ML workloads within edge computing scenarios.

Dynamic Resource Allocation for AI/ML Applications in Edge Computing: Framework Architecture and Optimization Methods

This scholarly paper introduces an extensive architectural framework and optimization strategies designed specifically for dynamic resource allocation in edge computing environments, with a focus on AI/ML applications. The rise of edge computing presents a viable solution for managing the computational complexities of AI/ML tasks by utilizing resources in proximity to data sources. Nevertheless, effective resource allocation encounters significant hurdles due to the diverse and ever-changing nature of edge environments. In addressing these challenges, the paper introduces an innovative framework that integrates dynamic resource allocation methodologies with the unique requirements of AI/ML applications. This framework encompasses a range of optimization techniques customized to efficiently distribute resources, taking into account factors such as workload attributes, resource availability, and latency limitations. Through extensive simulations and evaluations, the study showcases the effectiveness of the proposed approach in enhancing resource utilization, reducing latency, and bolstering overall performance for AI/ML workloads within edge computing scenarios.

Enabling The E-NOSE: Evaluating Network Technologies for Optimized Odor Data Transmission in IoT and Edge Computing Applications

Electronic Noses (e-noses) are advanced sensors that are being increasingly used in the Internet of Things (IoT) field. They have various applications in environmental monitoring and medical diagnostics. Optimal transmission of odor-related data is essential for fully harnessing the capabilities of these electronic noses in various network environments. This research paper examines the issue of transmitting odor data in Internet of Things (IoT) applications by assessing recent progress in communication and network technologies. A thorough analysis of various network alternatives, such as Ethernet, cellular (4G/5G), Wi-Fi, Bluetooth, Zigbee, and LoRa, is provided, accompanied by an elaborate table displaying precise technical specifications. The analysis of LoRa, a Low-Power Wide-Area Network (LPWAN) technology, encompasses an investigation into its architecture, modulation method, device classifications, and protocols such as LoRaWAN. The analysis of Edge Computing and cloud-based approaches for managing odor data in IoT culminates in a tabular presentation that succinctly outlines their advantages and disadvantages. This study provides valuable information to enhance the efficiency of transmitting odor data and effectively utilize electronic nose technology in different emerging applications in the Internet of Things (IoT) field.

Enabling Efficient On-Edge Spiking Neural Network Acceleration with Highly Flexible FPGA Architectures

Spiking neural networks (SNNs) promise to perform tasks currently performed by classical artificial neural networks (ANNs) faster, in smaller footprints, and using less energy. Neuromorphic processors are set out to revolutionize computing at a large scale, but the move to edge-computing applications calls for finely-tuned custom implementations to keep pushing towards more efficient systems. To that end, we examined the architectural design space for executing spiking neuron models on FPGA platforms, focusing on achieving ultra-low area and power consumption. This work presents an efficient clock-driven spiking neuron architecture used for the implementation of both fully-connected cores and 2D convolutional cores, which rely on deep pipelines for synaptic processing and distributed memory for weight and neuron states. With them, we developed an accelerator for an SNN version of the LeNet-5 network trained on the MNIST dataset. At around 5.5 slices/neuron and only 348 mW, it is able to use 33% less area and four times less power per neuron as current state-of-the-art implementations while keeping low simulation step times.

A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems

This paper proposes a novel event-driven architecture for enhancing edge-based vehicular systems within smart transportation. Leveraging the inherent real-time, scalable, and fault-tolerant nature of the Elixir language, we present an innovative architecture tailored for edge computing. This architecture employs MQTT for efficient event transport and utilizes Elixir’s lightweight concurrency model for distributed processing. Robustness and scalability are further ensured through the EMQX broker. We demonstrate the effectiveness of our approach through two smart transportation case studies: a traffic light system for dynamically adjusting signal timing, and a cab dispatch prototype designed for high concurrency and real-time data processing. Evaluations on an Apple M1 chip reveal consistently low latency responses below 5 ms and efficient multicore utilization under load. These findings showcase the system’s robust throughput and multicore programming capabilities, confirming its suitability for real-time, distributed edge computing applications in smart transportation. Therefore, our work suggests that integrating Elixir with an event-driven model represents a promising approach for developing scalable, responsive applications in edge computing. This opens avenues for further exploration and adoption of Elixir in addressing the evolving demands of edge-based smart transportation systems.

DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor

With the remarkable progress that technology has made, the need for processing data near the sensors at the edge has increased dramatically. The electronic systems used in these applications must process data continuously, in real-time, and extract relevant information using the smallest possible energy budgets. A promising approach for implementing always-on processing of sensory signals that supports on-demand, sparse, and edge-computing is to take inspiration from biological nervous system. Following this approach, we present a brain-inspired platform for prototyping real-time event-based spiking neural networks. The system proposed supports the direct emulation of dynamic and realistic neural processing phenomena such as short-term plasticity, NMDA gating, AMPA diffusion, homeostasis, spike frequency adaptation, conductance-based dendritic compartments and spike transmission delays. The analog circuits that implement such primitives are paired with a low latency asynchronous digital circuits for routing and mapping events. This asynchronous infrastructure enables the definition of different network architectures, and provides direct event-based interfaces to convert and encode data from event-based and continuous-signal sensors. Here we describe the overall system architecture, we characterize the mixed signal analog-digital circuits that emulate neural dynamics, demonstrate their features with experimental measurements, and present a low- and high-level software ecosystem that can be used for configuring the system. The flexibility to emulate different biologically plausible neural networks, and the chip’s ability to monitor both population and single neuron signals in real-time, allow to develop and validate complex models of neural processing for both basic research and edge-computing applications.

Research and Application of Edge Computing and Deep Learning in a Recommender System

Recommendation systems play a pivotal role in improving product competitiveness. Traditional recommendation models predominantly use centralized feature processing to operate, leading to issues such as excessive resource consumption and low real-time recommendation concurrency. This paper introduces a recommendation model founded on deep learning, incorporating edge computing and knowledge distillation to address these challenges. Recognizing the intricate relationship between the accuracy of deep learning algorithms and their complexity, our model employs knowledge distillation to compress deep learning. Teacher–student models were initially chosen and constructed in the cloud, focusing on developing structurally complex teacher models that incorporate passenger and production characteristics. The knowledge acquired from these models was then transferred to a student model, characterized by weaker learning capabilities and a simpler structure, facilitating the compression and acceleration of an intelligent ranking model. Following this, the student model underwent segmentation, and certain computational tasks were shifted to end devices, aligning with edge computing principles. This collaborative approach between the cloud and end devices enabled the realization of an intelligent ranking for product listings. Finally, a random selection of the passengers’ travel records from the last five years was taken to test the accuracy and performance of the proposed model, as well as to validate the intelligent ranking of the remaining tickets. The results indicate that, on the one hand, an intelligent recommendation system based on knowledge distillation and edge computing successfully achieved the concurrency and timeliness of the existing remaining ticket queries. Simultaneously, it guaranteed a certain level of accuracy, and reduced computing resource and traffic load on the cloud, showcasing its potential applicability in highly concurrent recommendation service scenarios.

DESIGN REAL-TIME DATA PROCESSING SYSTEMS FOR AI APPLICATIONS.

Online analytics systems are vital for ensuring the high efficiency of AI in response to real-time situations requiring agile decision-making. The present paper explores real-time data processing and topology, featuring the application of edge computing and cloud-based services and systems. Through simulation reports, the study shows how these systems handle significant data traffic and minimal delays in healthcare monitoring, automated transport systems, and smart homes. Possible data consistency, system growth, and redundancy issues are recognized, and recommendations are made to improve navigation system dependability and effectiveness. It is possible to improve AI in various industries with the support of progressive apt processing solutions.

Application of edge computing and artificial intelligence in English MOOC assisted teaching instruction

Application of edge computing and artificial intelligence in English MOOC assisted teaching instruction

Retracted: Application of Edge Computing in Physical Education Teaching Management

Retracted: Application of Edge Computing in Physical Education Teaching Management

A 47 TOPS/W 10T SRAM-Based Multi-Bit Signed CIM With Self-Adaptive Bias Voltage Generator for Edge Computing Applications

This paper presents a 10T static random-access memory (SRAM) -computing in memory (CIM) structure of 32kb macro unit for convolutional neural networks (CNNs). The proposed CIM handles ± computation with signed input signals in a single bitcell and obtains 3 times BL range for a larger sensing margin. Although the area overhead of the SRAM bitcell is 27 larger due to the positive branch PMOS, the array size is reduced to half for the same computation. A serial multi-bit weight technique is used to extend the number of weight bits with improved linearity and no significant time penalty. A bias voltage generator is implemented to regulate the discharge current under different PT variations. Both weight and input can be adjusted to 1, 2, and 4-b. For 4-b weight and 4-b input, the proposed structure achieves 85.7% accuracy with the CIFAR-10 dataset and 47 TOPS/W energy consumption in CMOS 28nm technology.

Integration of Blockchain and Edge Computing in Healthcare: Accountability and Collaboration

A decentralized, safe, and effective ecosystem is created in the healthcare industry through the integration of blockchain and edge computing. Secure data interchange, real-time analytics, enhanced privacy, and patient-centered treatment are all made possible. Realizing the full potential of integrating blockchain and edge computing for health care will need accountability and collaboration. It will make it possible to create reliable, secure, and cooperative healthcare organizations that will increase patient care, protect the confidentiality of information, and support cutting-edge applications for healthcare. Our Solution is to share data safely and cooperatively, improve patient confidentiality, and support healthcare data's ethical and accountable use. In this paper, we propose that combining blockchain technology with edge computing in healthcare is intended to improve accountability and teamwork. The methodologies used in integrating deep learning deploy various models on edge devices such as Q-Learning and Deep Q-Networks (DQN), SVM, etc. In conclusion, the application of edge computing and blockchain in the healthcare sector offers fascinating possibilities for cooperation and accountability. Healthcare systems may improve data security, privacy, interoperability, and real-time analytics by combining the advantages of the two technologies. The delivery of healthcare might change as a result of this integration, which could also foster cooperative research and eventually enhance patient outcomes.

Application of edge computing and APP software services in graphic product art design

Application of edge computing and APP software services in graphic product art design

Efficient Container Assignment and Layer Sequencing in Edge Computing

Containers are becoming a popular way of running applications in edge computing. Before running the application, the edge node must download the application’s container image consisting of multiple layers. However, given the limited bandwidth in edge computing, the container startup latency due to long image download time seriously affects the real-time performance. In this article, we jointly determine the container assignment and the layer download sequence to reduce the total startup latency. We formulate the Container Assignment and Layer Sequencing (CALS) problem and prove its NP-hardness. A Layer-Aware Scheduling Algorithm (LASA) is proposed, fully considering layer sharing among images. First, layers shared by the same set of images are grouped to reduce CALS’s problem scale without affecting the optimal result. Second, considering both layer sharing and existing layer size on edge nodes, a layer-aware algorithm is designed to assign containers to appropriate edge nodes. Finally, to determine the layer download sequence on each edge node, an approximation algorithm is proposed. We further analyze the approximation ratio of LASA in the case of identical edge nodes with sufficient capacity. Extensive experiments based on real-world data show the effectiveness of LASA, which reduces the total startup latency by 40% to 60%.

GASE: A Lightweight Group Authentication Scheme With Key Agreement for Edge Computing Applications

Motivated by the fact that mass authentication is one of the desirable security features in the edge computing paradigm, we propose a lightweight group authentication protocol with a session key-agreement. Most of the previously proposed group authentication schemes (GASs) are heavyweight and do not support multiple authentications or key-agreement. On the other hand, our protocol, which is based on secret sharing scheme and aggregated message authentication code, is lightweight and provides multiple asynchronous authentications. Furthermore, we implement a simple key refreshing mechanism in which, in each session, a new session-key between an Internet of Things node and the authenticating server is established without the need for redistributing new shares. Our security analysis includes proving that our protocol provides group authentication, message forward secrecy, and prevents several attacks. Additionally, we present a formal automated verification using Verifpal tool. Furthermore, we show that our scheme has better performance than other relative schemes in terms of communication complexity, secret-share redistribution, and session key derivations.