A testbed for CCAM services supported by edge computing, and use case of computation offloading

Cloud workload evolution has progressed because end-users need real-time applications such as autonomous systems, industrial IoT, and innovative healthcare. Traditional cloud computing systems cause substantial latency because they process information centrally while sending and receiving data. Artificial intelligence and Edge computing unite to provide an effective solution through network edge-based computations distribution, enabling fast real-time data processing. This research analyzes important strategies used in edge computing and artificial intelligence technology to minimize delays in cloud computing operations. The paper introduces basic principles of edge computing with AI capabilities for cloud workload management. We examine three main challenges: network bottleneck, processing capacity limitations, and security threat considerations. The proposed solution incorporates edge AI accelerators with hardware implementation and lightweight AI models, federated learning and reinforcement learning as software approaches, and 5G technology and edge caching as network optimization methods. The document demonstrates real-world applications based on case studies and experimental outcomes from autonomous vehicles, the healthcare sector, and industrial IoT implementations. The paper conducts a systematic evaluation that compares edge-based AI systems against traditional cloud computing based on their latency, power efficiency, and flexibility performance. It examines upcoming technologies that involve AI-driven self-adjusting edge networks and combinations of cloud and edge AI platforms. The discussion grows in technical depth through the addition of graphs, flowcharts, pseudocode, and system diagrams. Our research confirms that AI integration with edge computing lowers end-to-end latency while improving real-time decisions and maximizing cloud-based resource performance. The study establishes a framework for developing next-generation cloud architectural infrastructure that uses low-latency artificial intelligence.

With the rapid development of domestic power grid technology, it is difficult for traditional power grids to solve the low-voltage distribution end multi-source data comprehensive processing efficiency and poor cross-data fusion effect. At present, with the vigorous development of edge computing, cloud containers and 5G technologies, new methods are provided for the processing and integration of multi-source data in low-voltage distribution networks. This paper analyzes edge computing and cloud container technologies to design a reasonable framework for low-voltage power distribution data processing to help the construction of smart grids.

Investigation on big data evaluation and visualization of internet of things based on edge computing

Edge computing technology is an important computer operating system in China. It plays a key role in multi-system fusion and intelligent manufacturing, and can play a key role in training and testing of deep neural networks. The purpose of this paper is to study the application of edge computing technology in the collaborative optimization of intelligent transportation systems based on information and physical fusion. This article sets up monitoring points at different traffic intersections, and applies long-term and short-term memory networks to collect data at each traffic intersection. The DBN-SVR method model was used to detect the traffic flow of some intersections, and the edge computer technology was used to process the information signals generated by the intersections. The other portions of the intersections used traditional monitoring systems. By comparing the work efficiency and utility under the two methods, fitting data is performed, and mathematical statistics and mathematical analysis methods are used to verify the fitted data. The experimental data show that the edge computing technology can help the processing of traffic conditions in the intelligent transportation system integrated with information and physics, which has greatly improved the overall work efficiency of each system. Experimental data shows that intelligent transportation systems that integrate edge computing technology with information physics have improved transportation efficiency by about 20% and urban security by about 35%, which has a great effect on building smart cities and safe cities.

Dew Computing (DC) is a comparatively modern field with a wide range of applications. By examining how technological advances such as fog, edge and Dew computing, and distributed intelligence force us to reconsider traditional Cloud Computing (CC) to serve the Internet of Things. A new dew estimation theory is presented in this article. The revised definition is as follows: DC is a software and hardware cloud-based company. On-premises servers provide autonomy and collaborate with cloud networks. Dew Calculation aims to enhance the capabilities of on-premises and cloud-based applications. These categories can result in the development of new applications. In the world, there has been rapid growth in Information and Communication Technology (ICT), starting with Grid Computing (GC), CC, Fog Computing (FC), and the latest Edge Computing (EC) technology. DC technologies, infrastructure, and applications are described. We'll go through the newest developments in fog networking, QoE, cloud at the edge, platforms, security, and privacy. The dew-cloud architecture is an option concerning the current client-server architecture, where two servers are located at opposite ends. In the absence of an Internet connection, a dew server helps users browse and track their details. Data are primarily stored as a local copy on the dew server that starts the Internet and is synchronized with the cloud master copy. The local dew pages, a local online version of the current website, can be browsed, read, written, or added to the users. Mapping between different Local Dew sites has been made possible using the dew domain name scheme and dew domain redirection.

This study focuses on safeguarding the privacy of farmers’ personal information data during the enhancement of employment resources and services. By leveraging the integrated features of blockchain, including decentralization, immutability, and distributed storage, along with the efficiency of edge computing technology in processing distributed storage data, the research presents solutions for the collection, sharing, and identity authentication of farmers’ personal information within the strategic framework of targeted poverty alleviation. It can effectively protect against various security threats, thus ensuring the security and privacy of farmers’ personal information data during the aggregation and uploading process. Meanwhile, it combines the Paillier homomorphic encryption algorithm, batch aggregation signature algorithm, and anonymous authentication mechanism to propose a secure and anonymous data aggregation mechanism. It can use Horner’s rule to parse regional data, and then realize the aggregation and decryption of fine-grained privacy data, which provides strong support for optimizing rural employment resources and services. Finally, through the comparison experiment, it is found that when ACA is 200 and 700, the communication overhead of this scheme is 0.47 bits and 1.81 bits, both of which have lower communication overhead than the previous scheme.

Background: The pervasive adoption of edge computing is reshaping real-time big data analysis, smart city management, intelligent transportation, and various other domains. Its appeal lies in its distributed nature, decentralization, low latency, mobile support, and spatial awareness. However, this ubiquity exposes data to security threats, jeopardizing privacy and integrity. Consequently, access control assumes paramount importance in securing data sharing within edge computing and blockchain technologies. Methods: This research addresses this critical issue by conducting a comprehensive study on access control techniques within the context of edge computing and blockchain for secure data sharing. Our methodology commences with an exhaustive review of relevant articles, aiming to identify and expound upon gaps in existing research. Subsequently, we perform a meticulous analysis of access control methods, mechanisms, and performance metrics, seeking to establish a holistic understanding of the landscape. Results: The culmination of this research effort is a multifaceted contribution. We distill insights from a diverse range of access control schemes, shedding light on their nuances and effectiveness. Our analysis extends to evaluating the performance metrics vital for ensuring robust access control. Through this research, we also pinpoint critical research gaps within traditional access control methods, creating a foundation for innovative approaches to address the evolving challenges within edge computing and blockchain environments. Conclusion: In conclusion, this research venture paves the way for secure data sharing in edge computing and blockchain by offering a thorough examination of access control. The findings from this study are anticipated to guide future developments in access control techniques and facilitate the evolution of secure, privacy-conscious, and efficient data sharing practices in the dynamic landscape of digital technology.

The health monitoring component is the essential block, a pillar of several e-health systems. Plenty of health tracking applications and specific technologies such as smart devices, wearables, and data management systems are available. To be effective, promptly reacting to issues, a health monitoring service must ensure short delays in data sensing, collection, and processing activities. This is an open problem that distributed computing paradigms, such as Internet of Things (IoT), Cloud, and Edge computing, could address. The solution proposed in this paper is based on Stack4Things, an IoT-Cloud framework to manage edge nodes such as mobiles, smart objects, network devices, workstations, as a whole, a computing infrastructure allowing to provide resources on-demand, as services, to end users. Through Stack4Things facilities, the health tracking system can locate the closer computing resource to offload processing and thus reducing latency per the Edge computing paradigm.

Mobile applications are advancing toward higher network and compute requirements, which are similar to the requirements of server applications. Users prefer to perform their tasks on the go from mobile devices instead of stationary desktop and server systems. Modern mobile applications limit mobile battery times as charging is lost quickly due to high processing and data demands. Resources other than energy are also limited in mobile devices (memory, CPU millions of instructions per second, etc.).Mobile edge computing (MEC) technologies facilitate resource-scarcemobile devices to enhance their capabilities and execute data/computeintensive applications while collaborating with resource-rich network servers to enable ubiquitous computing. On the other hand, mobile cloud computing provides higher resources for applications that have low response requirements (noninteractive applications). Bothmobile edge computing andmobile cloud computing enable technologies for Internet of Things (IoT), smart grids, and e-health applications.

Mobile edge computing (MEC) technology guarantees the privacy and security of large-scale data in the Narrowband-IoT (NB-IoT) by deploying MEC servers near base stations to provide sufficient computing, storage, and data processing capacity to meet the delay and energy consumption requirements of NB-IoT terminal equipment. For the NB-IoT MEC system, this paper proposes a resource allocation algorithm based on deep reinforcement learning to optimize the total cost of task offloading and execution. Since the formulated problem is a mixed-integer non-linear programming (MINLP), we cast our problem as a multi-agent distributed deep reinforcement learning (DRL) problem and address it using dueling Q-learning network algorithm. Simulation results show that compared with the deep Q-learning network and the all-local cost and all-offload cost algorithms, the proposed algorithm can effectively guarantee the success rates of task offloading and execution. In addition, when the execution task volume is 200KBit, the total system cost of the proposed algorithm can be reduced by at least 1.3%,and when the execution task volume is 600KBit, the total cost of system execution tasks can be reduced by 16.7% at most.

In the face of the differentiation of business requirements, the diversification of application scenarios and the challenge of isomerization of network equipment, the construction of universal Internet of things needs to introduce 5G technologies such as network slicing, edge computing, flexible return and low delay technology. Aiming at these problems, this paper studies the key technologies of 5G network with low delay and high reliability, and a mobile edge computing platform for distribution network differential protection is designed. Through the platform, the information flow does not need to return to the 5G core network, and can complete the information interaction and transmission at the edge of the network, which can meet the delay requirement of the distribution network. This scheme can replace optical fiber communication for real-time communication between distribution network differential protection devices, and provide a solution for low-delay and high-reliability application scenarios of universal Internet of things access network.Keywords5GNetwork slicingEdge calculationLow-delayService bearing

Minimizing energy consumption in UAV assisted NOMA-MEC networks

The integration of edge computing and artificial intelligence (AI) in smart prosthetics and implants represents a significant advancement in healthcare technology, enabling ultra-fast processing, real-time decision-making, and enhanced user control. This chapter explores the critical role of edge AI in addressing latency and computational constraints in prosthetic systems, focusing on hardware acceleration techniques, real-time data acquisition, and neural signal processing. By leveraging edge computing, prosthetic devices can achieve low-latency performance, providing users with seamless interaction and improved functionality. Additionally, the chapter delves into the impact of AI algorithms in optimizing control, offering insights into adaptive learning and neural interface technologies that contribute to more intuitive and responsive prosthetics. The challenges of balancing bandwidth, latency, and computational power in edge-based prosthetic systems are also examined, with a focus on strategies for minimizing delays and enhancing system efficiency. The convergence of AI, edge computing, and prosthetic technologies promises to revolutionize the field, offering users enhanced comfort, mobility, and performance.

Hyperspectral imaging (HSI) enables real-time, non-invasive tissue characterization by capturing biochemical signatures across hundreds of spectral bands simultaneously. However, the computational demands of processing vast hyperspectral datasets create significant bottlenecks for clinical deployment. This chapter examines how edge computing and hardware acceleration technologies—including Field-Programmable Gate Arrays (FPGAs), Graphics Processing Units (GPUs), and specialized neural accelerators—enable real-time HSI processing at the point of care. We analyze architectural trade-offs, implementation strategies, and clinical workflow integration approaches that reduce processing times from minutes to milliseconds. Recent advances in embedded platforms achieve throughput rates exceeding 250 MSamples/s while maintaining power consumption below 15W, making intraoperative HSI guidance practically achievable.

With the rapid development of smart grid and the rise of energy Internet, the LTE 230MHz electric wireless private network presents problems such as low network resource utilization, coupling of control and forwarding planes, lack of network openness, and inability to adapt to different service application scenarios. In order to solve these problems, this paper analyzes the evolution trend of LTE 230MHz electric wireless private network to 5G technology. The improvement of the network architecture, the introduction of 5G network slicing, edge computing and other technologies reduce end-to-end delay of electric wireless private network and improve its reliability, capacity and coverage, which will promote the construction of ubiquitous electric Internet of Things (IoT), and make the 230MHz system better adapt to the needs of future multi-scenarios and differentiated services.