Journal articles: 'Intelligent Edge Networks'

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Li, Qian, Heng Liu, and Xiaoming Zhao. "IoT Networks-Aided Perception Vocal Music Singing Learning System and Piano Teaching with Edge Computing." Mobile Information Systems 2023 (April 28, 2023): 1–9. http://dx.doi.org/10.1155/2023/2074890.

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The research on Internet of Things (IoT) network and edge computing has been a research hotspot in both industry and academia in recent years, especially for the ambient intelligence and massive communication. As a typical form of IoT network and edge computing, the intelligent perception vocal music singing learning system has attracted the attention of researchers in education and academia. Piano teaching is an important course for music majors in higher education. Strengthening piano teaching can cultivate outstanding piano talents for the country and promote the development of music art. This paper applies IoT perception technology to piano teaching, constructs an intelligent piano teaching system, and uses edge computing algorithms to accurately deploy sensors into the system by exploiting the ambient intelligence and massive communication. The system includes data acquisition, data perception, data monitoring, and other modules, making piano teaching more humanized and intelligent. Experiments show that the research in this paper provides important guidance for the application of IoT networks and edge computing, especially for the ambient intelligence and massive communication.

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Musa, Salahadin Seid, Marco Zennaro, Mulugeta Libsie, and Ermanno Pietrosemoli. "Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions." Future Internet 14, no. 7 (June 25, 2022): 192. http://dx.doi.org/10.3390/fi14070192.

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Recently the Internet of Vehicles (IoV) has become a promising research area in the field of the Internet of Things (IoT), which enables vehicles to communicate and exchange real-time information with each other, as well as with infrastructure, people, and other sensors and actuators through various communication interfaces. The realization of IoV networks faces various communication and networking challenges to meet stringent requirements of low latency, dynamic topology, high data-rate connectivity, resource allocation, multiple access, and QoS. Advances in information-centric networks (ICN), edge computing (EC), and artificial intelligence (AI) will transform and help to realize the Intelligent Internet of Vehicles (IIoV). Information-centric networks have emerged as a paradigm promising to cope with the limitations of the current host-based network architecture (TCP/IP-based networks) by providing mobility support, efficient content distribution, scalability and security based on content names, regardless of their location. Edge computing (EC), on the other hand, is a key paradigm to provide computation, storage and other cloud services in close proximity to where they are requested, thus enabling the support of real-time services. It is promising for computation-intensive applications, such as autonomous and cooperative driving, and to alleviate storage burdens (by caching). AI has recently emerged as a powerful tool to break through obstacles in various research areas including that of intelligent transport systems (ITS). ITS are smart enough to make decisions based on the status of a great variety of inputs. The convergence of ICN and EC with AI empowerment will bring new opportunities while also raising not-yet-explored obstacles to realize Intelligent IoV. In this paper, we discuss the applicability of AI techniques in solving challenging vehicular problems and enhancing the learning capacity of edge devices and ICN networks. A comprehensive review is provided of utilizing intelligence in EC and ICN to address current challenges in their application to IIoV. In particular, we focus on intelligent edge computing and networking, offloading, intelligent mobility-aware caching and forwarding and overall network performance. Furthermore, we discuss potential solutions to the presented issues. Finally, we highlight potential research directions which may illuminate efforts to develop new intelligent IoV applications.

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Zhang, Jiaxin, Xing Zhang, Peng Wang, Liangjingrong Liu, and Yuanjun Wang. "Double-edge intelligent integrated satellite terrestrial networks." China Communications 17, no. 9 (September 2020): 128–46. http://dx.doi.org/10.23919/jcc.2020.09.011.

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Zeydan, Engin, Josep Mangues-Bafalluy, and Yekta Turk. "Intelligent Service Orchestration in Edge Cloud Networks." IEEE Network 35, no. 6 (November 2021): 126–32. http://dx.doi.org/10.1109/mnet.101.2100214.

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Pencheva, Evelina, Ivaylo Atanasov, and Ventsislav Trifonov. "Towards Intelligent, Programmable, and Open Railway Networks." Applied Sciences 12, no. 8 (April 17, 2022): 4062. http://dx.doi.org/10.3390/app12084062.

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The virtualization and automation of network functions will be key features of future high-speed railway networks, which have to provide dependable, safe, and secure services. The virtualization of railway network functions will enable functions such as train control, train integrity protection, shunting control, and trackside monitoring and maintenance to be virtualized and to be run on general-purpose hardware. Network function virtualization combined with edge computing can deliver dynamic, low-latency, and reliable services. The automation of railway operations can be achieved by embedding intelligence into the network to optimize the railway operation performance and to enhance the passenger experience. This paper presents an innovative railway network architecture that features distributed intelligence, function cloudification and virtualization, openness, and programmability. The focus is on time-tolerant and time-sensitive intelligent services designed to follow the principles of service-oriented architecture. The interaction between identified logical identities is illustrated by use cases. The paper provides some details of the design of the interface between distributed intelligent services and presents the results of an emulation of the interface performance.

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Alam, Tanweer, Baha Rababah, Arshad Ali, and Shamimul Qamar. "Distributed Intelligence at the Edge on IoT Networks." Annals of Emerging Technologies in Computing 4, no. 5 (December 20, 2020): 1–18. http://dx.doi.org/10.33166/aetic.2020.05.001.

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The Internet of Things (IoT) has revolutionized innovation to collect and store the information received from physical objects or sensors. The smart devices are linked to a repository that stores intelligent information executed by sensors on IoT-based smart objects. Now, the IoT is shifted from knowledge-based technologies to operational-based technologies. The IoT integrates sensors, smart devices, and a smart grid of implementations to deliver smart strategies. Nowadays, the IoT has been pondered to be an essential technology. The transmission of information to or from the cloud has recently been found to cause many network problems to include latency, power usage, security, privacy, etc. The distributed intelligence enables IoT to help the correct communication available at the correct time and correct place. Distributed Intelligence could strengthen the IoT in a variety of ways, including evaluating the integration of different big data or enhancing efficiency and distribution in huge IoT operations. While evaluating distributed intelligence in the IoT paradigm, the implementation of distributed intelligence services should take into consideration the transmission delay and bandwidth requirements of the network. In this article, the distributed intelligence at the Edge on IoT Networks, applications, opportunities, challenges and future scopes have been presented.

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Tassiulas, Leandros. "Enabling Intelligent Services at the Network Edge." ACM SIGMETRICS Performance Evaluation Review 49, no. 1 (June 22, 2022): 69–70. http://dx.doi.org/10.1145/3543516.3453912.

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The proliferation of novel mobile applications and the associated AI services necessitates a fresh view on the architecture, algorithms and services at the network edge in order to meet stringent performance requirements. Some recent work addressing these challenges is presented. In order to meet the requirement for low-latency, the execution of computing tasks moves form the cloud to the network edge, closer to the end-users. The joint optimization of service placement and request routing in dense mobile edge computing networks is considered. Multidimensional constraints are introduced to capture the storage requirements of the vast amounts of data needed. An algorithm that achieves close-to-optimal performance using a randomized rounding technique is presented. Recent advances in network virtualization and programmability enable realization of services as chains, where flows can be steered through a pre-defined sequence of functions deployed at different network locations. The optimal deployment of such service chains where storage is a stringent constraint in addition to computation and bandwidth is considered and an approximation algorithm with provable performance guarantees is proposed and evaluated. Finally the problem of traffic flow classification as it arises in firewalls and intrusion detection applications is presented. An approach for realizing such functions based on a novel two-stage deep learning method for attack detection is presented. Leveraging the high level of data plane programmability in modern network hardware, the realization of these mechanisms at the network edge is demonstrated.

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Guo, Hongzhi, Jiajia Liu, Ju Ren, and Yanning Zhang. "Intelligent Task Offloading in Vehicular Edge Computing Networks." IEEE Wireless Communications 27, no. 4 (August 2020): 126–32. http://dx.doi.org/10.1109/mwc.001.1900489.

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Bourechak, Amira, Ouarda Zedadra, Mohamed Nadjib Kouahla, Antonio Guerrieri, Hamid Seridi, and Giancarlo Fortino. "At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives." Sensors 23, no. 3 (February 2, 2023): 1639. http://dx.doi.org/10.3390/s23031639.

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Given its advantages in low latency, fast response, context-aware services, mobility, and privacy preservation, edge computing has emerged as the key support for intelligent applications and 5G/6G Internet of things (IoT) networks. This technology extends the cloud by providing intermediate services at the edge of the network and improving the quality of service for latency-sensitive applications. Many AI-based solutions with machine learning, deep learning, and swarm intelligence have exhibited the high potential to perform intelligent cognitive sensing, intelligent network management, big data analytics, and security enhancement for edge-based smart applications. Despite its many benefits, there are still concerns about the required capabilities of intelligent edge computing to deal with the computational complexity of machine learning techniques for big IoT data analytics. Resource constraints of edge computing, distributed computing, efficient orchestration, and synchronization of resources are all factors that require attention for quality of service improvement and cost-effective development of edge-based smart applications. In this context, this paper aims to explore the confluence of AI and edge in many application domains in order to leverage the potential of the existing research around these factors and identify new perspectives. The confluence of edge computing and AI improves the quality of user experience in emergency situations, such as in the Internet of vehicles, where critical inaccuracies or delays can lead to damage and accidents. These are the same factors that most studies have used to evaluate the success of an edge-based application. In this review, we first provide an in-depth analysis of the state of the art of AI in edge-based applications with a focus on eight application areas: smart agriculture, smart environment, smart grid, smart healthcare, smart industry, smart education, smart transportation, and security and privacy. Then, we present a qualitative comparison that emphasizes the main objective of the confluence, the roles and the use of artificial intelligence at the network edge, and the key enabling technologies for edge analytics. Then, open challenges, future research directions, and perspectives are identified and discussed. Finally, some conclusions are drawn.

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Yang, Yang, Rui Lyu, Zhipeng Gao, Lanlan Rui, and Yu Yan. "Semisupervised Graph Neural Networks for Traffic Classification in Edge Networks." Discrete Dynamics in Nature and Society 2023 (July 3, 2023): 1–13. http://dx.doi.org/10.1155/2023/2879563.

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Edge networking brings computation and data storage as close to the point of request as possible. Various intelligent devices are connected to the edge nodes where traffic packets flow. Traffic classification tasks are thought to be a keystone for network management; researchers can analyze packets captured to understand the traffic as it hits their network. However, the existing traffic classification framework needs to conduct a unified analysis, which leads to the huge bandwidth resources required in the process of transferring all captured packet files to train a global classifier. In this paper, a semisupervised graph neural network traffic classifier is proposed for cloud-edge architecture so that cloud servers and edge nodes could cooperate to perform the traffic classification tasks in order to deliver low latency and save bandwidth on the edge nodes. To preserve the structural information and interrelationships conveyed in packets within a session, we transform traffic sessions into graphs. We segment the frequently combined consecutive packets into granules, which are later transformed into the nodes in graphs. Edges could extract the adjacency of the granules in the sessions; the edge node side then selects the highly representative samples and sends them to the cloud server; the server side uses graph neural networks to perform semisupervised classification tasks on the selected training set. Our method has been trained and tested on several datasets, such as the VPN-nonVPN dataset, and the experimental results show good performance on accuracy, recall, and F-score.

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Jin, Wenquan, Rongxu Xu, Sunhwan Lim, Dong-Hwan Park, Chanwon Park, and Dohyeun Kim. "Dynamic Inference Approach Based on Rules Engine in Intelligent Edge Computing for Building Environment Control." Sensors 21, no. 2 (January 18, 2021): 630. http://dx.doi.org/10.3390/s21020630.

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Computation offloading enables intensive computational tasks in edge computing to be separated into multiple computing resources of the server to overcome hardware limitations. Deep learning derives the inference approach based on the learning approach with a volume of data using a sufficient computing resource. However, deploying the domain-specific inference approaches to edge computing provides intelligent services close to the edge of the networks. In this paper, we propose intelligent edge computing by providing a dynamic inference approach for building environment control. The dynamic inference approach is provided based on the rules engine that is deployed on the edge gateway to select an inference function by the triggered rule. The edge gateway is deployed in the entry of a network edge and provides comprehensive functions, including device management, device proxy, client service, intelligent service and rules engine. The functions are provided by microservices provider modules that enable flexibility, extensibility and light weight for offloading domain-specific solutions to the edge gateway. Additionally, the intelligent services can be updated through offloading the microservices provider module with the inference models. Then, using the rules engine, the edge gateway operates an intelligent scenario based on the deployed rule profile by requesting the inference model of the intelligent service provider. The inference models are derived by training the building user data with the deep learning model using the edge server, which provides a high-performance computing resource. The intelligent service provider includes inference models and provides intelligent functions in the edge gateway using a constrained hardware resource based on microservices. Moreover, for bridging the Internet of Things (IoT) device network to the Internet, the gateway provides device management and proxy to enable device access to web clients.

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Han, Yibo, Weiwei Zhang, and Zheng Zhang. "Security Analysis of Intelligent System Based on Edge Computing." Security and Communication Networks 2021 (August 16, 2021): 1–10. http://dx.doi.org/10.1155/2021/1224333.

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At present, artificial intelligence technology is widely used in society, and various intelligent systems emerge as the times require. Due to the uniqueness of biometrics, most intelligent systems use biometric-based recognition technology, among which face recognition is the most widely used. To improve the security of intelligent system, this paper proposes a face authentication system based on edge computing and innovatively extracts the features of face image by convolution neural network, verifies the face by cosine similarity, and introduces a user privacy protection scheme based on secure nearest neighbor algorithm and secret sharing homomorphism technology. The results show that when the threshold is 0.51, the correct rate of face verification reaches 92.46%, which is far higher than the recognition strength of human eyes. In face recognition time consumption and recognition accuracy, the encryption scheme is basically consistent with the recognition time consumption in plaintext state. It can be seen that the security of the intelligent system with this scheme can be significantly improved. This research provides a certain reference value for the research on the ways to improve the security of intelligent system.

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Liu, Jun, Yuwei Zhang, Jing Wang, Tao Cui, Lin Zhang, Chao Li, Kai Chen, et al. "Intelligent Bi-directional Relaying Communication for Edge Intelligence based Industrial IoT Networks." EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 9, no. 32 (August 10, 2022): e4. http://dx.doi.org/10.4108/eetinis.v9i32.1909.

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Within this specific record, our group study the two-way interact body (TWRN) that has a number of amplify-and-forward (AF) relays. In that, the best one is actually really got to help the info communication among sources. A interact option is actually really according to the obsolete channel problem information (CSI) in addition to our group analyze its own very personal effect on the system effectiveness in the Rayleigh fading atmospheres. Especially, we extremely preliminary acquire a restricted decreased connected for the outage opportunity and afterward current an asymptotic assessment for greater signal-to-noise ratio (SNR). Our group extra acquire a restricted decreased connected along with an asymptotic result on the authorize error cost (SER). Originating got via these results, our group easily quickly obtain that body system range order remain at unity offered that the CSI is actually really obsolete. Relative results reveal the rigidness on the effectiveness bounds along with the effects of obsolete interact option on the body system effectiveness. Simulation outcomes are likewise offered to corroborate the scholastic evaluation.

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Lalapura, Varsha S., J. Amudha, and Hariramn Selvamuruga Satheesh. "Recurrent Neural Networks for Edge Intelligence." ACM Computing Surveys 54, no. 4 (May 2021): 1–38. http://dx.doi.org/10.1145/3448974.

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Recurrent Neural Networks are ubiquitous and pervasive in many artificial intelligence applications such as speech recognition, predictive healthcare, creative art, and so on. Although they provide accurate superior solutions, they pose a massive challenge “training havoc.” Current expansion of IoT demands intelligent models to be deployed at the edge. This is precisely to handle increasing model sizes and complex network architectures. Design efforts to meet these for greater performance have had inverse effects on portability on edge devices with real-time constraints of memory, latency, and energy. This article provides a detailed insight into various compression techniques widely disseminated in the deep learning regime. They have become key in mapping powerful RNNs onto resource-constrained devices. While compression of RNNs is the main focus of the survey, it also highlights challenges encountered while training. The training procedure directly influences model performance and compression alongside. Recent advancements to overcome the training challenges with their strengths and drawbacks are discussed. In short, the survey covers the three-step process, namely, architecture selection, efficient training process, and suitable compression technique applicable to a resource-constrained environment. It is thus one of the comprehensive survey guides a developer can adapt for a time-series problem context and an RNN solution for the edge.

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Rahmadika, Sandi, Muhammad Firdaus, Seolah Jang, and Kyung-Hyune Rhee. "Blockchain-Enabled 5G Edge Networks and Beyond: An Intelligent Cross-Silo Federated Learning Approach." Security and Communication Networks 2021 (March 27, 2021): 1–14. http://dx.doi.org/10.1155/2021/5550153.

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Edge networks (ENs) in 5G have the capability to protect traffic between edge entry points (edge-to-edge), enabling the design of various flexible and customizable applications. The advantage of edge networks is their pioneering integration of other prominent technologies such as blockchain and federated learning (FL) to produce better services on wireless networks. In this paper, we propose an intelligent system integrating blockchain technologies, 5G ENs, and FL to create an efficient and secure framework for transactions. FL enables user equipment (UE) to train the artificial intelligence model without exposing the UE’s valuable data to the public, or to the model providers. Furthermore, the blockchain is an immutable data approach that can be leveraged for FL across 5G ENs and beyond. The recorded transactions cannot be altered maliciously, and they remain unchanged by design. We further propose a dynamic authentication protocol for UE to interact with a diverse base station. We apply blockchain as a reward mechanism in FL to enable computational offloading in wireless networks. Additionally, we implement and investigate blockchain technology for FL in 5G UE.

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Alrowaili, Dalal, Zohaib Zahid, Muhammad Ahsan, Sohail Zafar, and Imran Siddique. "Edge Metric Dimension of Some Classes of Toeplitz Networks." Journal of Mathematics 2021 (December 17, 2021): 1–11. http://dx.doi.org/10.1155/2021/3402275.

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Toeplitz networks are used as interconnection networks due to their smaller diameter, symmetry, simpler routing, high connectivity, and reliability. The edge metric dimension of a network is recently introduced, and its applications can be seen in several areas including robot navigation, intelligent systems, network designing, and image processing. For a vertex s and an edge g = s 1 s 2 of a connected graph G , the minimum number from distances of s with s 1 and s 2 is called the distance between s and g . If for every two distinct edges s 1 , s 2 ∈ E G , there always exists w 1 ɛ W E ⊆ V G , such that d s 1 , w 1 ≠ d s 2 , w 1 ; then, W E is named as an edge metric generator. The minimum number of vertices in W E is known as the edge metric dimension of G . In this study, we consider four families of Toeplitz networks T n 1,2 , T n 1,3 , T n 1,4 , and T n 1,2,3 and studied their edge metric dimension. We prove that for all n ≥ 4 , e dim T n 1,2 = 4 , for n ≥ 5 , e dim T n 1,3 = 3 , and for n ≥ 6 , e dim T n 1,4 = 3 . We further prove that for all n ≥ 5 , e dim T n 1,2,3 ≤ 6 , and hence, it is bounded.

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Wang, Pin, Zhijian Gao, Yimin Li, Lingyu Zeng, and Hongmei Zhong. "Design and Implementation of a Radioactive Source Intelligent Search Robot Based on Artificial Intelligence Edge Computing." Wireless Communications and Mobile Computing 2022 (May 17, 2022): 1–12. http://dx.doi.org/10.1155/2022/3940348.

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Artificial intelligence is a very broad science, which consists of different fields, such as machine learning, and computer vision. In recent years, the world nuclear industry has developed vigorously. At the same time, incidents of loss of radioactive sources also occur from time to time. At present, most of the search for radioactive sources adopt manual search, which is inefficient, and the searchers are vulnerable to radiation damage. Sending a robot to the search an area where there may be an uncontrolled radioactive source is different. Not only does it improve efficiency, it also protects people from radiation. Therefore, it is of great practical significance to design a radioactive source search robot. This paper mainly introduces the design and implementation of a radioactive source intelligent search robot based on artificial intelligence edge computing, aiming to provide some ideas and directions for the research of radioactive source intelligent search robot. In this paper, a research method for the design and implementation of a radioactive source intelligent search robot based on artificial intelligence edge computing is proposed, including intelligent edge computing and gamma-ray imaging algorithms, which are used to carry out related experiments on the design and implementation of radioactive sources, an intelligent search robot based on edge computing. The experimental results of this paper show that the average resolution of the radioactive source search robot is 90.55%, and the resolution results are more prominent.

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Wu, J. Y., R. Xin, J. B. Zhao, T. Zheng, D. Jiang, and P. F. Zhang. "Study on Delay Optimization of Fog Computing Edge Nodes Based on the CPSO-LB Algorithm." Wireless Communications and Mobile Computing 2020 (December 1, 2020): 1–12. http://dx.doi.org/10.1155/2020/8811175.

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With the development of modern science and technology as well as the steady advancement of urbanization, intelligent networks have emerged and are replacing traditional networks with the identity of next-generation networks. And information security is one of the most important research directions in the intelligent network construction. In order to resist the threat of privacy leakage during the data transmission of intelligent terminal, an original four-layer fog computing system which is suitable for intelligent network data collection, transmission, and processing structure is established in the paper. With the help of the Paillier algorithm for encryption and fine-grained aggregation, the fine-grained aggregated data as coefficients are embed in the cloud node, and Horner’s rule is conformed for unary polynomials, which further aggregates to reduce the amount of transmitted data, so that communication overhead is reduced as well. Meanwhile, the resolvability of Horner’s rules allows EPSI to finally obtain the subregional information plain text, and it is summed up to obtain cloud-level information data. Therefore, the comparative analysis of simulation experiments with other algorithms proves that the rational optimization of the research content in this paper plays a higher security role.

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Chen, Liming, Xiaoyun Kuang, Fusheng Zhu, and Junjuan Xia. "Intelligent Mobile Edge Computing Networks for Internet of Things." IEEE Access 9 (2021): 95665–74. http://dx.doi.org/10.1109/access.2021.3093886.

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Deng, Dan, and Junjuan Xia. "Cache-Enabled Cooperative Edge Networks for Intelligent Connected Vehicles." IEEE Access 7 (2019): 166939–49. http://dx.doi.org/10.1109/access.2019.2952172.

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Koufos, Konstantinos, Karim EI Haloui, Mehrdad Dianati, Matthew Higgins, Jaafar Elmirghani, Muhammad Ali Imran, and Rahim Tafazolli. "Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps." Journal of Sensor and Actuator Networks 10, no. 4 (October 12, 2021): 60. http://dx.doi.org/10.3390/jsan10040060.

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The increasing complexity of communication systems, following the advent of heterogeneous technologies, services and use cases with diverse technical requirements, provide a strong case for the use of artificial intelligence (AI) and data-driven machine learning (ML) techniques in studying, designing and operating emerging communication networks. At the same time, the access and ability to process large volumes of network data can unleash the full potential of a network orchestrated by AI/ML to optimise the usage of available resources while keeping both CapEx and OpEx low. Driven by these new opportunities, the ongoing standardisation activities indicate strong interest to reap the benefits of incorporating AI and ML techniques in communication networks. For instance, 3GPP has introduced the network data analytics function (NWDAF) at the 5G core network for the control and management of network slices, and for providing predictive analytics, or statistics, about past events to other network functions, leveraging AI/ML and big data analytics. Likewise, at the radio access network (RAN), the O-RAN Alliance has already defined an architecture to infuse intelligence into the RAN, where closed-loop control models are classified based on their operational timescale, i.e., real-time, near real-time, and non-real-time RAN intelligent control (RIC). Different from the existing related surveys, in this review article, we group the major research studies in the design of model-aided ML-based transceivers following the breakdown suggested by the O-RAN Alliance. At the core and the edge networks, we review the ongoing standardisation activities in intelligent networking and the existing works cognisant of the architecture recommended by 3GPP and ETSI. We also review the existing trends in ML algorithms running on low-power micro-controller units, known as TinyML. We conclude with a summary of recent and currently funded projects on intelligent communications and networking. This review reveals that the telecommunication industry and standardisation bodies have been mostly focused on non-real-time RIC, data analytics at the core and the edge, AI-based network slicing, and vendor inter-operability issues, whereas most recent academic research has focused on real-time RIC. In addition, intelligent radio resource management and aspects of intelligent control of the propagation channel using reflecting intelligent surfaces have captured the attention of ongoing research projects.

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Gallyamov, D., V. Kisel, R. Kirichek, A. Borodin, and A. Koucheryavy. "NETWORK LATENCY COMPENSATION APPROACHES FOR APPLICATIONS IN 2030 COMMUNICATIONS NETWORKS." Telecom IT 7, no. 2 (December 2019): 1–11. http://dx.doi.org/10.31854/2307-1303-2019-7-2-1-11.

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Research subject. Communication network architecture 2030, delay compensation mechanisms. Objective. Consider the architecture of communication networks with intelligent edge computing, propose mechanisms for compensating network latency for tactile Internet applications based on predictive analytics. Core results. Scientific publications and specifications from open sources were analyzed, on tactile Internet topics, delay compensation mechanisms, intelligent boundary computing. A model of 2030 network architecture and a method of minimizing delay based on machine learning are proposed. Main conclusions. Modernization of the network architecture and software package will significantly increase the efficiency of communication networks, implement new models and methods of data processing, and will also have a significant impact on the digital economy.

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Moreno-Vozmediano, Rafael, Rubén S. Montero, Eduardo Huedo, and Ignacio M. Llorente. "Intelligent Resource Orchestration for 5G Edge Infrastructures." Future Internet 16, no. 3 (March 19, 2024): 103. http://dx.doi.org/10.3390/fi16030103.

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The adoption of edge infrastructure in 5G environments stands out as a transformative technology aimed at meeting the increasing demands of latency-sensitive and data-intensive applications. This research paper presents a comprehensive study on the intelligent orchestration of 5G edge computing infrastructures. The proposed Smart 5G Edge-Cloud Management Architecture, built upon an OpenNebula foundation, incorporates a ONEedge5G experimental component, which offers intelligent workload forecasting and infrastructure orchestration and automation capabilities, for optimal allocation of virtual resources across diverse edge locations. The research evaluated different forecasting models, based both on traditional statistical techniques and machine learning techniques, comparing their accuracy in CPU usage prediction for a dataset of virtual machines (VMs). Additionally, an integer linear programming formulation was proposed to solve the optimization problem of mapping VMs to physical servers in distributed edge infrastructure. Different optimization criteria such as minimizing server usage, load balancing, and reducing latency violations were considered, along with mapping constraints. Comprehensive tests and experiments were conducted to evaluate the efficacy of the proposed architecture.

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Dong, Chao, Yun Shen, Yuben Qu, Kun Wang, Jianchao Zheng, Qihui Wu, and Fan Wu. "UAVs as an Intelligent Service: Boosting Edge Intelligence for Air-Ground Integrated Networks." IEEE Network 35, no. 4 (July 2021): 167–75. http://dx.doi.org/10.1109/mnet.011.2000651.

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Tuli, Shreshth. "SplitPlace." ACM SIGMETRICS Performance Evaluation Review 49, no. 2 (January 17, 2022): 63–65. http://dx.doi.org/10.1145/3512798.3512821.

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In recent years, deep learning models have become ubiquitous in industry and academia alike. Modern deep neural networks can solve one of the most complex problems today, but coming with the price of massive compute and storage requirements. This makes deploying such massive neural networks challenging in the mobile edge computing paradigm, where edge nodes are resource-constrained, hence limiting the input analysis power of such frameworks. Semantic and layer-wise splitting of neural networks for distributed processing show some hope in this direction. However, there are no intelligent algorithms that place such modular splits to edge nodes for optimal performance. This work proposes a novel placement policy, SplitPlace, for the placement of such neural network split fragments on mobile edge hosts for efficient and scalable computing.

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Tang, Yajuan, Shiwei Lai, Yanyi Rao, Wen Zhou, Fusheng Zhu, Liming Chen, Dan Deng, et al. "Intelligent Distributed Data Storage for Wireless Communications in B5G Networks." ICST Transactions on Mobile Communications and Applications 7, no. 2 (August 25, 2022): e2. http://dx.doi.org/10.4108/eetmca.v7i2.2415.

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With the deployment and commercialization of the fifth-generation (5G) mobile communication network, the access nodes and data volume of wireless network show a massive and blowout growth trend. Taking beyond 5G (B5G) edge intelligent network as the research object, based on the deep integration of storage / computing and communication, this paper focuses on the theory and key technology of system intelligent transmission, so as to effectively support the related applications of B5G edge intelligent network in the future. This paper analyzes the research status of data storage, studies the real field distributed storage computing system, and designs the corresponding flashback shift code and error correction scheme with low storage space overhead.

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Liu, Xun. "Blockchain-Enabled Collaborative Edge Computing for Intelligent Education Systems Using Social IoT." International Journal of Distributed Systems and Technologies 13, no. 7 (July 12, 2022): 1–19. http://dx.doi.org/10.4018/ijdst.307958.

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As new technologies such as the internet of things, big data analysis, artificial intelligence, and cloud computing are widely used, intelligent learning platforms and web-based educational platforms are gaining popularity. Social internet of things (SIoT) uses mobile edge computing and interpersonal interactions among SIoT users to take advantage of the benefits that collaborative edge computing (CEC) offers, even while posing new challenges. The communication efficiency and the security of intelligent education systems must be considerably developed to ensure real-time services. Therefore, this work deliberates an advanced structural framework for a blockchain-enabled 6G communication network (BC-6GCN) for the intelligent education system. Schools must analyze massive data volumes to provide intelligent education services, leaving the data open to manipulation by malicious hackers. The challenges discussed can lead to the potential advancement of protected, reliable, and smart SIoT frameworks.

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Wei, Junyong, Jiarong Han, and Suzhi Cao. "Satellite IoT Edge Intelligent Computing: A Research on Architecture." Electronics 8, no. 11 (October 31, 2019): 1247. http://dx.doi.org/10.3390/electronics8111247.

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As the number of satellites continues to increase, satellites become an important part of the IoT and 5G/6G communications. How to deal with the data of the satellite Internet of Things is a problem worth considering and paying attention to. Due to the current on-board processing capability and the limitation of the inter-satellite communication rate, the data acquisition from the satellite has a higher delay and the data utilization rate is lower. In order to use the data generated by the satellite IoT more effectively, we propose a satellite IoT edge intelligent computing architecture. In the article, we analyze the current methods of satellite data processing, combined with the development trend of future satellites, and use the characteristics of edge computing and machine learning to describe the satellite IoT edge intelligent computing architecture. Finally, we verify that the architecture can speed up the processing of satellite data. By demonstrating the performance of different neural network models in the satellite edge intelligent computing architecture, we can find that the lightweight of neural networks can promote the development of satellite IoT edge intelligent computing architecture.

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An, Xin, Baigen Cai, and Linguo Chai. "Research on Over-the-Horizon Perception Distance Division of Optical Fiber Communication Based on Intelligent Roadways." Sensors 24, no. 1 (January 3, 2024): 276. http://dx.doi.org/10.3390/s24010276.

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With the construction and application of more and more intelligent networking demonstration projects, a large number of advanced roadside digital infrastructures are deployed on both sides of the intelligent road. These devices sense the road situation in real time through algorithms and transmit it to edge computing units and cloud control platforms through high-speed optical fiber transmission networks. This article proposes a cloud edge terminal architecture system based on cloud edge cooperation, as well as a data exchange protocol for cloud control basic platforms. The over-the-horizon scene division and optical fiber network communication model are verified by deploying intelligent roadside devices on the intelligent highway. At the same time, this article uses the optical fiber network communication algorithm and ModelScope large model to model inference on real-time video data. The actual data results show that the StreamYOLO (Stream You Only Look Once) model can use the Streaming Perception method to detect and continuously track target vehicles in real-time videos. Finally, the method proposed in this article was experimentally validated in an actual smart highway digital infrastructure construction project. The experimental results demonstrate the high application value and promotion prospects of the fiber optic network in the division of over the horizon perception distance in intelligent roadways construction.

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Jebari, Hakim, Meriem Hayani Mechkouri, Siham Rekiek, and Kamal Reklaoui. "Poultry-Edge-AI-IoT System for Real-Time Monitoring and Predicting by Using Artificial Intelligence." International Journal of Interactive Mobile Technologies (iJIM) 17, no. 12 (June 20, 2023): 149–70. http://dx.doi.org/10.3991/ijim.v17i12.38095.

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Poultry farms have played a significant role throughout human history, in feeding the growing population. A good environment is a perfect condition for the growth of poultry, preventing disease, and effective production. The temperature higher and the humidity favor the growth of bacteria and hence the production of ammonia (NH3) by the decomposition of organic matter. Ammonia (NH3), carbon monoxide (CO), and carbon dioxide (CO2), Methane (CH4), hydrogen sulfide (H2S) are poisonous gases that can cause poultry diseases and mortality. The combination of Artificial Intelligence (AI), Internet of Things (IoT), and Edge Computing offer efficient and intelligent stand-alone systems of monitoring in real-time, predicting, and advanced automation. The paper aims to monitor in real-time and predict poultry barns' environmental conditions using an artificial intelligence algorithm. An intelligent system called Poultry-Edge-AI-IoT has been developed to gather, hash, store, pretreat, filter, knowledge extract, and transmit information from a heterogeneous wireless sensor network. The Poultry-Edge-AI-IoT system is based on IoT, AI, and Edge Computing for the detection of potential stress, the harmful gas concentration, and the prediction of poultry barns' environmental conditions. The system is modular and upgradeable.

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Tang, Xiangdong, Fei Chen, and Yunlong He. "Intelligent Video Streaming at Network Edge: An Attention-Based Multiagent Reinforcement Learning Solution." Future Internet 15, no. 7 (July 3, 2023): 234. http://dx.doi.org/10.3390/fi15070234.

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Video viewing is currently the primary form of entertainment for modern people due to the rapid development of mobile devices and 5G networks. The combination of pervasive edge devices and adaptive bitrate streaming technologies can lessen the effects of network changes, boosting user quality of experience (QoE). Even while edge servers can offer near-end services to local users, it is challenging to accommodate a high number of mobile users in a dynamic environment due to their restricted capacity to maximize user long-term QoE. We are motivated to integrate user allocation and bitrate adaptation into one optimization objective and propose a multiagent reinforcement learning method combined with an attention mechanism to solve the problem of multiedge servers cooperatively serving users. Through comparative experiments, we demonstrate the superiority of our proposed solution in various network configurations. To tackle the edge user allocation problem, we proposed a method called attention-based multiagent reinforcement learning (AMARL), which optimized the problem in two directions, i.e., maximizing the QoE of users and minimizing the number of leased edge servers. The performance of AMARL is proved by experiments.

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R, Anuvidhya. "RANFO: An Intelligent Anomaly Detection in IoT Edge Devices." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 2900–2907. http://dx.doi.org/10.22214/ijraset.2021.35622.

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As devices, applications, and communication networks become more connected and integrated, computer attacks on the Internet of Things (IoT) become more sophisticated. When attacks on IoT networks cause long-term outages, it affects the availability of critical end-user programmers, increases the number of data breaches and fraud, raises prices, and reduces revenue. In this paper we present the RANFO (IDS), prepared to protect inherently linked Iot systems. The proposed entry-level system can successfully enter real-world entrance, according to our experimental results. We'll illustrate how RANFO can identify a variety of harmful assaults, including DOS, R2L, Probe, and U2L.

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Lai, Shiwei, Rui Zhao, Shunpu Tang, Junjuan Xia, Fasheng Zhou, and Liseng Fan. "Intelligent secure mobile edge computing for beyond 5G wireless networks." Physical Communication 45 (April 2021): 101283. http://dx.doi.org/10.1016/j.phycom.2021.101283.

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Hazra, Abhishek, Mainak Adhikari, Sudarshan Nandy, Khushbu Doulani, and Varun G. Menon. "Federated-Learning-Aided Next-Generation Edge Networks for Intelligent Services." IEEE Network 36, no. 3 (May 2022): 56–64. http://dx.doi.org/10.1109/mnet.007.2100549.

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Math, Sa, Prohim Tam, and Seokhoon Kim. "Intelligent Real-Time IoT Traffic Steering in 5G Edge Networks." Computers, Materials & Continua 67, no. 3 (2021): 3433–50. http://dx.doi.org/10.32604/cmc.2021.015490.

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Guo, Yinghao, Zichao Zhao, Rui Zhao, Shiwei Lai, Zou Dan, Junjuan Xia, and Liseng Fan. "Intelligent Offloading Strategy Design for Relaying Mobile Edge Computing Networks." IEEE Access 8 (2020): 35127–35. http://dx.doi.org/10.1109/access.2020.2972106.

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Karimi, Elham, Yuanzhu Chen, and Behzad Akbari. "Intelligent and Decentralized Resource Allocation in Vehicular Edge Computing Networks." IEEE Internet of Things Magazine 6, no. 4 (December 2023): 112–17. http://dx.doi.org/10.1109/iotm.001.2200268.

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Wang, Xiaonan, Yang Guo, and Yuan Gao. "Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network." Information 15, no. 1 (January 11, 2024): 38. http://dx.doi.org/10.3390/info15010038.

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Non-terrestrial network (NTN) is a trending topic in the field of communication, as it shows promise for scenarios in which terrestrial infrastructure is unavailable. Unmanned autonomous intelligent systems (UAISs), as a physical form of artificial intelligence (AI), have gained significant attention from academia and industry. These systems have various applications in autonomous driving, logistics, area surveillance, and medical services. With the rapid evolution of information and communication technology (ICT), 5G and beyond-5G communication have enabled numerous intelligent applications through the comprehensive utilization of advanced NTN communication technology and artificial intelligence. To meet the demands of complex tasks in remote or communication-challenged areas, there is an urgent need for reliable, ultra-low latency communication networks to enable unmanned autonomous intelligent systems for applications such as localization, navigation, perception, decision-making, and motion planning. However, in remote areas, reliable communication coverage is not available, which poses a significant challenge for intelligent systems applications. The rapid development of non-terrestrial networks (NTNs) communication has shed new light on intelligent applications that require ubiquitous network connections in space, air, ground, and sea. However, challenges arise when using NTN technology in unmanned autonomous intelligent systems. Our research examines the advancements and obstacles in academic research and industry applications of NTN technology concerning UAIS, which is supported by unmanned aerial vehicles (UAV) and other low-altitude platforms. Nevertheless, edge computing and cloud computing are crucial for unmanned autonomous intelligent systems, which also necessitate distributed computation architectures for computationally intensive tasks and massive data offloading. This paper presents a comprehensive analysis of the opportunities and challenges of unmanned autonomous intelligent systems in UAV NTN, along with NTN-based unmanned autonomous intelligent systems and their applications. A field trial case study is presented to demonstrate the application of NTN in UAIS.

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Islam, Shafkat, Shahriar Badsha, Shamik Sengupta, Hung La, Ibrahim Khalil, and Mohammed Atiquzzaman. "Blockchain-Enabled Intelligent Vehicular Edge Computing." IEEE Network 35, no. 3 (May 2021): 125–31. http://dx.doi.org/10.1109/mnet.011.2000554.

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Alladi, Tejasvi, Ayush Agrawal, Bhavya Gera, Vinay Chamola, and F. Richard Yu. "Ambient Intelligence for Securing Intelligent Vehicular Networks: Edge-Enabled Intrusion and Anomaly Detection Strategies." IEEE Internet of Things Magazine 6, no. 1 (March 2023): 128–32. http://dx.doi.org/10.1109/iotm.001.2200197.

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Shyh-Wei Chen, Shyh-Wei Chen, Chun-Ju Tsai Shyh-Wei Chen, Chia-Hui Liu Chun-Ju Tsai, William Cheng-Chung Chu Chia-Hui Liu, and Ching-Tsorng Tsai William Cheng-Chung Chu. "Development of an Intelligent Defect Detection System for Gummy Candy under Edge Computing." 網際網路技術學刊 23, no. 5 (September 2022): 981–88. http://dx.doi.org/10.53106/160792642022092305006.

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<p>Gummy candies are one of the products of the food industry. It has invested more resources in all aspects of the food production chain to improve production processes. The defective candies cause the unevenness of the product that will cause the appearance, taste and flavor poor. That will lead to economic losses for the company. Most traditional candy companies set up product inspection personnel to eliminate defective product. In this paper, an intelligent defect detection system for gummy candy industry under edge computing environment is proposed. It can replace manual visual inspection, even shorten the processing time to reduce production costs, thereby improving product quality, the efficiency of the production line, and the number of inspections. The system includes: (1) The intelligent defect detection system by deep learning algorithms. (2) The edge computing architecture with AIoT. The proposed system adopted the YOLO deep learning algorithm. The results show that the Precision is 93%, Recall is 87% and the F1 Score is 90. It has certain empirical reference significance for the intelligent defect detection system of candies products. By adopting deep learning algorithm in the detection system, it can reduce the inspection man-power needs and long-term data collection.</p> <p> </p>

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Artem, Volkov, Kovalenko Vadim, Ibrahim A. Elgendy, Ammar Muthanna, and Andrey Koucheryavy. "DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework." Future Internet 14, no. 1 (December 27, 2021): 13. http://dx.doi.org/10.3390/fi14010013.

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Nowadays, 5G networks are emerged and designed to integrate all the achievements of mobile and fixed communication networks, in which it can provide ultra-high data speeds and enable a broad range of new services with new cloud computing structures such as fog and edge. In spite of this, the complex nature of the system, especially with the varying network conditions, variety of possible mechanisms, hardware, and protocols, makes communication between these technologies challenging. To this end, in this paper, we proposed a new distributed and fog (DD-fog) framework for software development, in which fog and mobile edge computing (MEC) technologies and microservices approach are jointly considered. More specifically, based on the computational and network capabilities, this framework provides a microservices migration between fog structures and elements, in which user query statistics in each of the fog structures are considered. In addition, a new modern solution was proposed for IoT-based application development and deployment, which provides new time constraint services like a tactile internet, autonomous vehicles, etc. Moreover, to maintain quality service delivery services, two different algorithms have been developed to pick load points in the search mechanism for congestion of users and find the fog migration node. Finally, simulation results proved that the proposed framework could reduce the execution time of the microservice function by up to 70% by deploying the rational allocation of resources reasonably.

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Math, Sa, Prohim Tam, and Seokhoon Kim. "Intelligent Media Forensics and Traffic Handling Scheme in 5G Edge Networks." Security and Communication Networks 2021 (April 21, 2021): 1–11. http://dx.doi.org/10.1155/2021/5589352.

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The 5th generation (5G) communications evolved with heterogeneous user terminals and applications. A convergence of Mobile Edge Computing (MEC) and Software-Defined Networks (SDN) delivers gigantic challenges and opportunities for enhancing computing resources and user Quality of Service (QoS) in fronthaul and backhaul networks. Due to the precipitous expansion of user media in the 5G epoch, efficient media forensics methods are mandatory for specifying and offering effective safety handling based on individual application requirements. According to the exponential increment of Heterogeneous Internet of Things (HetIoT) devices, gigantic traffic will generate through bottleneck 5G fronthaul gateways. 5G fronthaul network environments consist of inadequate resources to surmount the enormous user traffic and communications, QoS will be reduced when the existence of traffic congestion occurs. To confront the aforementioned issues, this paper proposed intelligent media forensics and traffic handling scheme for controlling the Uplink (UL) transmission according to the Downlink (DL) statuses. Support Vector Machine (SVM) algorithm was applied to conduct the media forensics and MEC server integrated into fronthaul gateways, in which gateways resources are divided into UL and DL. Caching technology will be a part of 5G environments, and DL will be utilized for traffic caching. So, it is compulsory to adjust the communication traffic according to UL/DL resource utilization and control the forwarding traffic which relies on resource availability. The experiment was conducted by using computer software, and the proposed scheme illustrated a noteworthy outperformance over the conventional method in terms of diverse significant QoS factors including reliability, latency, and communication throughput.

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Huo, Yiming, Xingqin Lin, Boya Di, Hongliang Zhang, Francisco Javier Lorca Hernando, Ahmet Serdar Tan, Shahid Mumtaz, Özlem Tuğfe Demir, and Kun Chen-Hu. "Technology Trends for Massive MIMO towards 6G." Sensors 23, no. 13 (June 30, 2023): 6062. http://dx.doi.org/10.3390/s23136062.

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At the dawn of the next-generation wireless systems and networks, massive multiple-input multiple-output (MIMO) in combination with leading-edge technologies, methodologies, and architectures are poised to be a cornerstone technology. Capitalizing on its successful integration and scalability within 5G and beyond, massive MIMO has proven its merits and adaptability. Notably, a series of evolutionary advancements and revolutionary trends have begun to materialize in recent years, envisioned to redefine the landscape of future 6G wireless systems and networks. In particular, the capabilities and performance of future massive MIMO systems will be amplified through the incorporation of cutting-edge technologies, structures, and strategies. These include intelligent omni-surfaces (IOSs)/intelligent reflecting surfaces (IRSs), artificial intelligence (AI), Terahertz (THz) communications, and cell-free architectures. In addition, an array of diverse applications built on the foundation of massive MIMO will continue to proliferate and thrive. These encompass wireless localization and sensing, vehicular communications, non-terrestrial communications, remote sensing, and inter-planetary communications, among others.

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Liu, Xiao, Jiong Jin, and Fang Dong. "Edge-Computing-Based Intelligent IoT: Architectures, Algorithms and Applications." Sensors 22, no. 12 (June 13, 2022): 4464. http://dx.doi.org/10.3390/s22124464.

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With the rapid growth of the Internet of Things (IoT), 5G networks and beyond, the computing paradigm for intelligent IoT systems is shifting from conventional centralized-cloud computing to distributed edge computing [...]

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Thipparthi Upendra Chari and B Srinivasa S P Kumar. "RBP: a website fingerprinting obfuscation method against intelligent fingerprintingattacks." international journal of engineering technology and management sciences 6, no. 6 (November 28, 2022): 398–403. http://dx.doi.org/10.46647/ijetms.2022.v06i06.071.

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A significant danger to website privacy and web security is posed by website fingerprinting (WF), a passive traffic analysis attack. It gathers the network packets created when a user accesses a website and then employs a number of approaches to identify patterns in the network packets that can be used to determine the kind of website the user is accessing. Numerous anonymous networks, like Tor, can satisfy the need to conceal identify from users while participating in network activities, but they are also vulnerable to WF assaults. In this research, we present Random Bidirectional Padding, a website fingerprinting obfuscation technique against sophisticated fingerprinting techniques (RBP). It is a cutting-edge website fingerprinting defence technology built on time sampling and random bidirectional packet padding that can change real packet distribution to destroy Inter-Arrival Time (IAT) features in the traffic sequence and increase the difference between datasets with random bidirectional virtual packet padding. We test the efficiency of thedefence against cutting-edge website fingerprinting attacks in actual circumstances.

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Alsamhi, Saeed Hamood, Alexey V. Shvetsov, Santosh Kumar, Jahan Hassan, Mohammed A. Alhartomi, Svetlana V. Shvetsova, Radhya Sahal, and Ammar Hawbani. "Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0." Drones 6, no. 7 (July 18, 2022): 177. http://dx.doi.org/10.3390/drones6070177.

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Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless communication networks. These networks have an avenue for generating a considerable amount of heterogeneous data by the expanding number of Internet of Things (IoT) devices in smart environments. However, storing and processing massive data with limited computational capability and energy availability at local nodes in the IoT network has been a significant difficulty, mainly when deploying Artificial Intelligence (AI) techniques to extract discriminatory information from the massive amount of data for different tasks.Therefore, Mobile Edge Computing (MEC) has evolved as a promising computing paradigm leveraged with efficient technology to improve the quality of services of edge devices and network performance better than cloud computing networks, addressing challenging problems of latency and computation-intensive offloading in a UAV-assisted framework. This paper provides a comprehensive review of intelligent UAV computing technology to enable 6G networks over smart environments. We highlight the utility of UAV computing and the critical role of Federated Learning (FL) in meeting the challenges related to energy, security, task offloading, and latency of IoT data in smart environments. We present the reader with an insight into UAV computing, advantages, applications, and challenges that can provide helpful guidance for future research.

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Sun, Zhengjie, Hui Yang, Chao Li, Qiuyan Yao, Ao Yu, Jie Zhang, Yang Zhao, Sheng Liu, and Yunbo Li. "Task Offloading Scheme for Survivability Guarantee Based on Traffic Prediction in 6G Edge Networks." Electronics 12, no. 21 (November 1, 2023): 4497. http://dx.doi.org/10.3390/electronics12214497.

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With the development of sixth-generation (6G) mobile networks, the rise of emerging intelligent services has led to a huge increase in traffic. As an important technology to support the development of 6G, mobile edge computing (MEC) effectively meets the ultra-low latency requirements of most emerging services. However, due to the limited processing capacity of edge nodes, overload on any edge node will cause service degradation, interruption, and even node failure, weakening the advantages of MEC and reducing the survivability of the whole network. In this paper, we propose a task offloading scheme based on traffic prediction for node-overload protection to ensure the survivability of the 6G edge networks. We transformed the network survivability guarantee problem into a task offloading problem under the constraint of future available resources based on traffic prediction and developed a particle swarm optimization algorithm based on policy gradient (PSO-PG) to jointly optimize offloading decisions, routing, and computing resource allocation. Simulations verify the effectiveness of our proposed scheme and guarantee the survivability of 6G edge networks. Meanwhile, evaluation in multiple scenarios with different node scales has verified the wide applicability of our work.

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Duan, Qiang. "Intelligent and Autonomous Management in Cloud-Native Future Networks—A Survey on Related Standards from an Architectural Perspective." Future Internet 13, no. 2 (February 5, 2021): 42. http://dx.doi.org/10.3390/fi13020042.

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Cloud-native network design, which leverages network virtualization and softwarization together with the service-oriented architectural principle, is transforming communication networks to a versatile platform for converged network-cloud/edge service provisioning. Intelligent and autonomous management is one of the most challenging issues in cloud-native future networks, and a wide range of machine learning (ML)-based technologies have been proposed for addressing different aspects of the management challenge. It becomes critical that the various management technologies are applied on the foundation of a consistent architectural framework with a holistic vision. This calls for standardization of new management architecture that supports seamless the integration of diverse ML-based technologies in cloud-native future networks. The goal of this paper is to provide a big picture of the recent developments of architectural frameworks for intelligent and autonomous management for future networks. The paper surveys the latest progress in the standardization of network management architectures including works by 3GPP, ETSI, and ITU-Tand analyzes how cloud-native network design may facilitate the architecture development for addressing management challenges. Open issues related to intelligent and autonomous management in cloud-native future networks are also discussed in this paper to identify some possible directions for future research and development.

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K, Priyadarsini, Sri Lakshmi Chandana, Severo Simón Calderón Samaniego, Dr Megha Gupta Chaudhary, Dr Vipul Vekariya, and Mr Abhay Chaturvedi. "Intelligent Mobile Edge Computing Integrated with Blockchain Security Analysis for Millimetre-Wave Communication." International Journal of Communication Networks and Information Security (IJCNIS) 14, no. 3 (December 23, 2022): 100–122. http://dx.doi.org/10.17762/ijcnis.v14i3.5577.

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With the increase in number of devices enabled the Internet of Things (IoT) communication with the centralized cloud computing model. With the implementation of the cloud computing model leads to increased Quality of Service (QoS). The cloud computing model provides the edge computing technologies for the real-time application to achieve reliability and security. Edge computing is considered the extension of the cloud computing technology involved in transfer of the sensitive information in the cloud edge to increase the network security. The real-time data transmission realizes the interaction with the high frequency to derive improved network security. However, with edge computing server security is considered as sensitive privacy information maintenance. The information generated from the IoT devices are separated based on stored edge servers based on the service location. Edge computing data is separated based in edge servers for the guaranteed data integrity for the data loss and storage. Blockchain technologies are subjected to different security problem for the data integrity through integrated blockchain technologies. This paper developed a Voted Blockchain Elliptical Curve Cryptography (VBECC) model for the millimetre wave application. The examination of the blockchain model is evaluated based on the edge computing architecture. The VBECC model develop an architectural model based Blockchain technology with the voting scheme for the millimetre application. The estimated voting scheme computes the edge computing technologies for the estimation of features through ECC model. The VBECC model computes the security model for the data transmission in the edge computing-based millimetre application. The experimental analysis stated that VBECC model uses the data security model ~8% increased performance than the conventional technique.

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Journal articles on the topic 'Intelligent Edge Networks'

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