Edge Computing Research Articles

Green Edge: Harnessing situational awareness for sustainable systems

Green computing and Building Management Systems (BMS)are closely related through their shared goal of improving energy efficiency and reducing environmental impact. Field controllers that connect with the sensors and actuators at the field level act as an interface between field devices and enterprise applications. Data transfer, protocol conversion, data processing, computing, and control actuators based on predefined logic are the functions of the field controllers. Fast response time is one of the crucial parameters of these field controllers hence reducing the load on these field controllers by enabling Edge computing functionality is very much required. Maharudrayya Nandimath and others [16] in their “Edge Computing in IBMS (Integrated Building Management Systems). (2024 International Conference on Computer Communication and Informatics (iccci. in)) the paper suggested many of the measures to reduce the load on these field controllers. Building Management Systems are essential for optimizing the operation of modern buildings, ensuring comfort, safety, and energy efficiency. By integrating advanced technologies such as Situation Awareness (SA) and Artificial Intelligence (AI), BMS can be significantly enhanced to provide real-time monitoring, predictive maintenance, and intelligent decision-making. This paper explores the potential of combining SA and AI with BMS, leveraging insights from recent research on industrial processes, IoT cloud-edge continuum, and smart cities. The proposed system architecture includes feedback loops and contextual awareness, aiming to improve building operations and reduce energy consumption, thereby supporting global sustainability goals.

Advantages of Big Data Analysis and AI Technology in Data Collecting And Processing of Preventive Maintenance

In recent years, the emergence of Cyber-Physical systems (CPS), big data, cloud computing, and industrial wireless networks have promoted the development of Industry 4.0. Intelligent manufacturing systems are the leaders of this change. In smart manufacturing, the emphasis will be placed on using technologies such as big data analytics, cloud computing, edge computing, and artificial intelligence (AI). The advantages of new technologies are significantly reflected in the comparison of preventive maintenance and traditional maintenance based on intelligent technology: preventive maintenance significantly reduces the cost of prevention and, at the same time, dramatically improves the accuracy and timeliness of maintenance, bringing inestimable value to intelligent manufacturing. This paper will analyze new technologies’ innovative advantages and technical prospects through big data analysis and specific application scenarios of AI technology in preventive maintenance. Firstly, in data acquisition, this paper examines the architecture of preventive maintenance systems based on big data. Then, it analyzes the composition of the data pipeline: in the realization of data collection and processing in the field of cloud computing and edge computing, and the aspect of data processing, it discusses the participation of AI technology in data understanding and processing and the unprecedented changes it brings.

The C-V2V-assisted early small cell data offloading (E-SCDO) method using the multi-access edge computing (MEC) mechanism

The C-V2V-assisted early small cell data offloading (E-SCDO) method using the multi-access edge computing (MEC) mechanism

Cost-Effective Power Management for Smart Homes: Innovative Scheduling Techniques and Integrating Battery Optimization in 6G Networks

This paper presents an Optimal Power Management System (OPMS) for smart homes in 6G environments, which are designed to enhance the sustainability of Green Internet of Everything (GIoT) applications. The system employs a brute-force search using an exact solution to identify the optimal decision for adapting power consumption to renewable power availability. Key techniques, including priority-based allocation, time-shifting, quality degradation, battery utilization and service rejection, will be adopted. Given the NP-hard nature of this problem, the brute-force approach is feasible for smaller scenarios but sets the stage for future heuristic methods in large-scale applications like smart cities. The OPMS, deployed on Multi-Access Edge Computing (MEC) nodes, integrates a novel demand response (DR) strategy to manage real-time power use effectively. Synthetic data tests achieved a 100% acceptance rate with zero reliance on non-renewable power, while real-world tests reduced non-renewable power consumption by over 90%, demonstrating the system’s flexibility. These results provide a foundation for further AI-based heuristics optimization techniques to improve scalability and power efficiency in broader smart city deployments.

Edge Computing vs. Cloud Computing: A Comparative Analysis for Real-Time AI Applications

AbstractReal-time AI has been evidenced to be supported by two models, namely edge computing and cloud computing. This research seeks to compare the two methods: Latency, security features, and data processing capabilities will be among the most critical aspects of comparison. Real-time AI applications are envisioned to be robust through 2020, most of which will be used in applications such as autonomous automobile systems and IoT devices that demand low latency for data processing. As data processing is outsourced in clouds, it is elastic and centralized, but this deal faces latency problems when the distance between the source of data and the processing center is large. On the other hand, edge computing refers to conducting computation closer to the data source, which could reduce latency and enhance everyday real-time performance.This research assesses the above models based on the literature review, technical papers, and case studies from industries that depend more on real-time AI. According to the research, edge computing is typically more effective for latency-sensitive workloads, while cloud computing outperforms throughput-intensive applications. Security concerns, however, present themselves as having dual effects; while advancing the privacy of handling data through edge computing, it elevates new risks. As such, this study concludes that no clear winner depends on the necessary application, therefore recommending a symmetric mode for the requirements, where both latency time and computational needs are required. Further studies should be conducted to establish the coordinated implementation approach of these models.

A Narrative Review of Identity, Data and Location Privacy Techniques in Edge Computing and Mobile Crowdsourcing

As digital technology advances, the proliferation of connected devices poses significant challenges and opportunities in mobile crowdsourcing and edge computing. This narrative review focuses on the need for privacy protection in these fields, emphasizing the increasing importance of data security in a data-driven world. Through an analysis of contemporary academic literature, this review provides an understanding of the current trends and privacy concerns in mobile crowdsourcing and edge computing. We present insights and highlight advancements in privacy-preserving techniques, addressing identity, data, and location privacy. This review also discusses the potential directions that can be useful resources for researchers, industry professionals, and policymakers.

Enhancing System Efficiency through AI, Edge Computing, and Resource Optimization in Modern Infrastructure

This paper explores innovative strategies for enhancing system efficiency in modern infrastructure by integrating artificial intelligence (AI), edge computing, and resource optimization techniques. As the complexity of infrastructure systems increases, traditional methods often fall short in addressing the evolving demands of operational efficiency and reliability. By leveraging AI algorithms for predictive analytics and resource allocation, and utilizing edge computing for real-time data processing, organizations can significantly improve performance and responsiveness. The study examines case studies that highlight successful implementations of these technologies across various sectors, including infrastructure monitoring, and grid maintenance. Insights from this research provide a framework for practitioners to adopt these advanced methodologies, ultimately leading to more resilient and efficient infrastructure systems.

Cloud Computing in Insurance: Emerging Trends and Transformative Technologies

This article examines the transformative impact of emerging cloud computing trends on the insurance industry. As insurers increasingly adopt cloud-based solutions, several key technologies are poised to revolutionize operations, customer experiences, and business models. The integration of artificial intelligence and machine learning is enhancing underwriting accuracy, claims processing efficiency, and fraud detection capabilities. Edge computing is enabling real-time data analysis and decision-making, while serverless architectures are optimizing resource allocation and reducing costs. Blockchain technology is improving data security, transparency, and trust in transactions. Additionally, hybrid and multi-cloud strategies are providing insurers with greater flexibility and regulatory compliance. This comprehensive analysis explores these trends, their interconnections, and their potential to drive innovation in insurance. The article concludes by discussing the implications of these advancements for insurers, policyholders, and the broader financial services ecosystem, offering insights into future research directions and industry adaptations.

Mastering Microservices Architecture and Cloud Computing : In-Depth Strategies for Domain-Specific Optimization

This comprehensive article explores the intricate landscape of microservices architecture and cloud computing, specifically focusing on domain-specific optimization strategies. It delves into the foundational principles of microservices, examining their synergistic relationship with cloud technologies and their transformative impact on modern software development. The article addresses critical aspects such as service-to-service communication, API management, and data security in distributed environments. It also investigates the role of cloud-native services, including serverless computing and container orchestration, in enhancing microservices performance. Analyzing real-world case studies and emerging trends, the article highlights how organizations can leverage these technologies to drive innovation and maintain a competitive edge. Key challenges in implementing and managing microservices architectures and strategies for overcoming them are discussed. The research emphasizes the importance of tailoring microservices to specific business needs and provides insights into performance tuning for high-traffic systems. This article offers a forward-looking perspective on the evolution of microservices and cloud computing by exploring future trends and considerations, including the potential impact of emerging technologies like edge computing and artificial intelligence. It serves as a valuable resource for IT professionals, architects, and decision-makers seeking to master these technologies and implement domain-specific optimizations effectively in their organizations.

The Role of Automation in Enhancing Cybersecurity: a Technical Analysis

Intelligent automation, a convergence of artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA), is reshaping modern business operations across various sectors. This article examines the transformative impact of intelligent automation on organizational efficiency, cost structures, and innovation capabilities. Recent industry surveys indicate a significant increase in adoption rates, with 67% of business leaders implementing intelligent automation at scale in 2023, up from 48% in 2020. The integration of AI and ML with RPA has enabled businesses to automate complex, cognitive tasks, leading to an average 35% reduction in operational costs and a 50% improvement in process cycle times. While challenges such as data privacy, ethical considerations, and workforce reskilling persist, the potential benefits of intelligent automation are substantial. This article explores quantifiable impacts across industries, examines real-world case studies, and anticipates future trends including hyperautomation, autonomous decision-making, and integration with emerging technologies like blockchain and edge computing. The article concludes that strategic implementation of intelligent automation solutions will be crucial for organizations to maintain competitiveness and drive innovation in the increasingly digital business landscape.

Enhancing 5G Vehicular Edge Computing Efficiency with the Hungarian Algorithm for Optimal Task Offloading

The rapid advancements in vehicular technologies have enabled modern autonomous vehicles (AVs) to perform complex tasks, such as augmented reality, real-time video surveillance, and automated parking. However, these applications require significant computational resources, which AVs often lack. To address this limitation, Vehicular Edge Computing (VEC) has emerged as a promising solution, allowing AVs to offload computational tasks to nearby vehicles and edge servers. This offloading process, however, is complicated by factors such as high vehicle mobility and intermittent connectivity. In this paper, we propose the Hungarian Algorithm for Task Offloading (HATO), a novel approach designed to optimize the distribution of computational tasks in 5G-enabled VEC systems. HATO leverages 5G’s low-latency, high-bandwidth communication to efficiently allocate tasks across edge servers and nearby vehicles, utilizing the Hungarian algorithm for optimal task assignment. By designating an edge server to gather contextual information from surrounding nodes and compute the best offloading scheme, HATO reduces computational burdens on AVs and minimizes task failures. Through extensive simulations in both urban and highway scenarios, HATO achieved a significant performance improvement, reducing execution time by up to 75.4% compared to existing methods under full 5G coverage in high-density environments. Additionally, HATO demonstrated zero energy constraint violations and achieved the highest task processing reliability, with an offloading success rate of 87.75% in high-density urban areas. These results highlight the potential of HATO to enhance the efficiency and scalability of VEC systems for autonomous vehicles.

The New Method for Ground Insulation Monitoring of the Secondary Circuit in Substations

When two-or-more-point grounding faults occur in the secondary circuit of a substation, they will lead to large data measurement errors in the protection, measurement, and control devices, which can easily cause protection malfunction. At present, there is a lack of an online panoramic monitoring system for the secondary circuits of substations, and it is difficult to accurately identify the faults in these secondary circuits. In order to solve this problem, this paper proposes a cloud–edge collaboration framework for the power Internet of Things (IoT), which can accurately identify faults by monitoring the secondary circuit of power grid substations with panoramic online status, reducing maintenance costs and improving overall operational efficiency. Firstly, the collection requirements of current transformer (CT) and potential transformer (PT) fault characteristics in the secondary circuit of the substation are analyzed. The platform architecture of the secondary loop panoramic monitoring system of the cloud-side cooperative substation is proposed, along with the algorithm and data wireless acquisition scheme of the edge computing, cloud computing, and cloud-side system. Furthermore, the edge computing platform and the current and voltage wireless sensors are developed. The maximum error in the accuracy test of the voltage sensor is 1.9180%, while the maximum error in the accuracy test of the current sensor is 1.3406%.

A Novel Clinching Auction Mechanism for Edge Computing Resource Allocation With Budget Limits

ABSTRACTAn auction mechanism is an effective resource allocation method that can increase the revenue of resource providers in the field of edge computing. Existing auction mechanism designs mostly aim to maximize social welfare when allocating resources, but these schemes lead to low revenue. In contrast, clinching auctions have achieved good results in spectrum allocation and advertising due to their high revenue. Therefore, a clinching auction mechanism is a promising tool for allocating edge computing resources. However, clinching auctions have the drawback that they can only allocate homogeneous finitely divisible goods, meaning that they cannot be directly applied for resource allocation in edge computing. This article presents two new auction mechanisms that improve on the clinching auction. Specifically, based on the principle of increasing global prices and local competition, two mechanisms are designed, one from the perspective of resource providers (MDCAM‐ECS) and the other from the perspective of users (MDCAM‐User), to solve the problem of edge computing resource allocation and pricing with deployment constraints and user budget constraints. The mechanisms proposed in this article have the properties of individual rationality, truthfulness, and computational efficiency. In the experiments, in terms of social welfare and revenue, our algorithms can achieve a 20% improvement over existing algorithms, such as fixed‐price, Vickery–Clarke–Groves (VCG), and monotonic critical‐price mechanisms. Additionally, in most experiments, our algorithm can ensure resource utilization greater than 80%.

Efficient Schemes for Optimizing Load Balancing and Communication Cost in Edge Computing Networks

Edge computing architectures promise increased quality of service with low communication delays by bringing cloud services closer to the end-users, at the distributed edge servers of the network edge. Hosting server capabilities at access nodes, thereby yielding edge service nodes, offers service proximity to users and provides QoS guarantees. However, the placement of edge servers should match the level of demand for computing resources and the location of user load. Thus, it is necessary to devise schemes that select the most appropriate access nodes to host computing services and associate every remaining access node with the most proper service node to ensure optimal service delivery. In this paper, we formulate this problem as an optimization problem with a bi-objective function that aims at both communication cost minimization and load balance optimization. We propose schemes that tackle this problem and compare their performance against previously proposed heuristics that have been also adapted to target both optimization goals. We study how these algorithms behave in lattice and random grid network topologies with uniform and non-uniform workloads. The results validate the efficiency of our proposed schemes in addition to the significantly lower execution times compared to the other heuristics.

Cache-assisted computation offloading for workflow applications in industrial internet of things

The Internet of Things plays an important role in the process of industrial intelligence. It facilitates the connection between devices and the Internet to enable the gathering and analysis of industrial data. However, industrial Internet of Things (IIoT) applications are highly susceptible to latency, have strict energy consumption limitations, and impose specific demands on the resources of IIoT devices. Fortunately, edge computing can migrate tasks to the edge server or cloud platform to deal with the above shortcomings. Nevertheless, tasks generated by IIoT devices in industrial production have a higher rate of repetition, and repetitive execution of the same task diminishes the operational efficiency of the edge system. In this regard, we can cache high-value tasks through task caching to reduce redundant execution. The aforementioned idea can be described as a joint optimization problem of computation offloading and task caching with resource constraints in IIoT. To address this challenge, we establish an edge-empowered service model and propose a cache-assisted computation offloading algorithm for IIoT applications. Extensive experiments demonstrate that the proposed algorithm outperforms some critical methods in terms of energy consumption and latency.

Charge-Selective 2D Heterointerface-Driven Multifunctional Floating Gate Memory for In Situ Sensing-Memory-Computing.

Flash memory, dominating data storage due to its substantial storage density and cost efficiency, faces limitations such as slow response, high operating voltages, absence of optoelectronic response, etc., hindering the development of sensing-memory-computing capability. Here, we present an ultrathin platinum disulfide (PtS2)/hexagonal boron nitride (hBN)/multilayer graphene (MLG) van der Waals heterojunction with atomically sharp interfaces, achieving selective charge tunneling behavior and demonstrating ultrafast operations, a high on/off ratio (108), extremely low operating voltage, robust endurance (105 cycles), and retention exceeding 10 years. Additionally, we achieve highly linear synaptic potentiation and depression, and observe the reversibly gate-tunable transitions between positive and negative photoconductivity. Furthermore, we employed the VGG11 neural network for in situ trained in-sensor-memory-computing to classify the CIFAR-10 data set, pushing accuracy levels comparable to pure digital systems. This work could pave the way for seamlessly integrated sensing, memory, and computing capabilities for diverse edge computing.

Enhancing Situational Awareness with VAS-Compass Net for the Recognition of Directional Vehicle Alert Sounds.

People with hearing impairments often face increased risks related to traffic accidents due to their reduced ability to perceive surrounding sounds. Given the cost and usage limitations of traditional hearing aids and cochlear implants, this study aims to develop a sound alert assistance system (SAAS) to enhance situational awareness and improve travel safety for people with hearing impairments. We proposed the VAS-Compass Net (Vehicle Alert Sound-Compass Net), which integrates three lightweight convolutional neural networks: EfficientNet-lite0, MobileNetV3-Small, and GhostNet. Through employing a fuzzy ranking ensemble technique, our proposed model can identify different categories of vehicle alert sounds and directions of sound sources on an edge computing device. The experimental dataset consisted of images derived from the sounds of approaching police cars, ambulances, fire trucks, and car horns from various directions. The audio signals were converted into spectrogram images and Mel-frequency cepstral coefficient images, and they were fused into a complete image using image stitching techniques. We successfully deployed our proposed model on a Raspberry Pi 5 microcomputer, paired with a customized smartwatch to realize an SAAS. Our experimental results demonstrated that VAS-Compass Net achieved an accuracy of 84.38% based on server-based computing and an accuracy of 83.01% based on edge computing. Our proposed SAAS has the potential to significantly enhance the situational awareness, alertness, and safety of people with hearing impairments on the road.

A Deep Learning-Based Car Accident Detection Framework Using Edge and Cloud Computing

The evolving technological landscape has seen a pivotal shift with the advent of edge computing, transforming various sectors, particularly accident detection. Edge computing enhances road safety by enabling realtime data processing from onboard sensors, cameras, and connected devices, addressing limitations in traditional cloudbased systems. This paper introduces a deep learning-based accident detection framework within an edge-cloud setup. Utilizing a CNN model, accidents are detected at the edge node near the data source, ensuring low latency, reduced network usage, and faster execution times. The model achieves a remarkable 95.91% accuracy.

Internet of Things (IoT): Sensors Application in Dairy Cattle Farming.

The expansion of dairy cattle farms and the increase in herd size have made the control and management of animals more complex, with potentially negative effects on animal welfare, health, productive/reproductive performance and consequently farm income. Precision Livestock Farming (PLF) is based on the use of sensors to monitor individual animals in real time, enabling farmers to manage their herds more efficiently and optimise their performance. The integration of sensors and devices used in PLF with the Internet of Things (IoT) technologies (edge computing, cloud computing, and machine learning) creates a network of connected objects that improve the management of individual animals through data-driven decision-making processes. This paper illustrates the main PLF technologies used in the dairy cattle sector, highlighting how the integration of sensors and devices with IoT addresses the challenges of modern dairy cattle farming, leading to improved farm management.

Dual-timescale resource management for multi-type caching placement and multi-user computation offloading in Internet of Vehicle

In Internet of Vehicle (IoV), edge computing can effectively reduce task processing delays and meet the real-time needs of connected-vehicle applications. However, since the requirements for caching and computing resources vary across heterogeneous vehicle requests, a new challenge is posed on the resource management in the three-tier cloud–edge–end architecture, particularly when multi users offload tasks in the same time. Our work comprehensively considers various scenarios involving the deployment of multiple caching types from multi-users and the distinct time scales of offloading and updating, then builds a joint optimization caching placement, computation offloading and computational resource allocation model, aiming to minimize overall latency. Meanwhile, to better solving the model, we propose the Multi-node Collaborative Caching, Offloading, and Resource Allocation Algorithm (MCCO-RAA). MCCO-RAA utilizes dual time scales to optimize the problem: employing a Bellman optimization idea-based multi-node collaborative greedy caching placement strategy at large time scales, and a computational offloading and resource allocation strategy based on a two-tier iterative Deep Deterministic Policy Gradient (DDPG) and cooperative game at small time scales. Experimental results demonstrate that our proposed scheme achieves a 28% reduction in overall system latency compared to the baseline scheme, with smoother latency variations under different parameters.