International Journal of Network Management最新文献

Ubiquitous edge computing facilitates efficient cloud services near mobile devices, enabling mobile edge computing (MEC) to offer services more efficiently by presenting storage and processing capability within the proximity of mobile devices and in general IoT domains. However, compared with conventional mobile cloud computing, ubiquitous MEC introduces numerous complex challenges due to the heterogeneous smart devices, network infrastructures, and limited transmission bandwidth. Processing and managing such massive volumes of data generated from these devices is complex and challenging in edge infrastructures. On the other side, time-critical applications have stringent requirements such as ultra-low-latency, energy cost, mobility, resource, and security issues that cannot be neglected. For example, smart healthcare or industrial networks generate emergency information very frequently (i.e., often in terms of milliseconds), which needs to be processed near the sensing devices with minimal processing delay. In this context, future generation IoT requires robust and intelligent network management approaches that can handle the system complexity (e.g., scalability and orchestration) with little or no little human intervention and offer a better service to end-users. More precisely, AI/ML approaches designed explicitly for networks under high traffic volume of data help overcome several management challenges, such as (i) improving performance by balancing load and traffic, (ii) distributing the bandwidth spectrum based on demand, and (iii) traffic predictions. Moreover, this need also opens several new research directions such as new MEC architecture, service provisioning technique, security mechanisms, advanced 5G or beyond communication technology, ambient intelligence, and AI/ML-based solutions.

This issue collect surveys and contribution articles on emerging trends and technologies in ubiquitous MEC for future generation IoT networks and their managements. The papers related to machine learning, deep learning, optimization, blockchain, 5G, or beyond solutions, especially for domain-specific IoT network management, which use MEC environments, are collected after evaluating the review process. Each paper submitted to this special issue was reviewed by three to seven experts during the assessment process. At the end we consider one survey paper and four research contributions.

The first paper Ravi et al. proposed a survey on “Stochastic modeling and performance analysis in balancing load and traffic for vehicular ad hoc networks.” This survey presents recently published stochastic modeling-based algorithms for VANETs. This article briefly covers various queueing models for the reader's convenience. This paper discusses a variety of VANET issues such as mobility, routing, data dissemination, cooperative communication, congestion control, and traffic load balancing issues addressed by stochastic modeling techniques. The authors

The cloud environment is inherently dynamic as users are added immensely in a short duration. It is indeed difficult to manage such user profiles and associated data. Meanwhile, the cloud data expand at a twofold-to-threefold rate on average, making storage space management and data integrity maintenance a mandatory task but also risky. The main approaches for addressing these data maintenance challenges in a cloud context are deduplication and data protection. In order to manage storage space, finding and removing identical copies of the same data from the cloud are possible, resulting in a reduction in the amount of storage space needed. Furthermore, duplicate copies are considerably reduced in cloud storage owing to data deduplication. Here, a decentralized ledger public blockchain network is introduced to protect the Integrity of data stored in cloud storage. This research proposes data deduplication using speedy content-defined Chunking (SpeedyCDC) algorithm in the public blockchain. Many people and businesses outsource sensitive data to remote cloud servers because it considerably eliminates the hassle of managing software and infrastructure. However, the ownership and control rights of users data are nonetheless divided because it is outsourced to cloud storage providers (CSPs) and kept on a distant cloud. As a result, users have a great deal of difficulty in verifying the Integrity of sensitive data. Analysis using datasets from Geospatial Information Systems (GIS) revealed that the throughput increased by 5%–6% over that of the fastCDC technique, which offered Integrity since a blockchain network secured it.

The technological integration of the Internet of Things (IoT)-Cloud paradigm has enabled intelligent linkages of things, data, processes, and people for efficient decision making without human intervention. However, it poses various challenges for IoT networks that cannot handle large amounts of operation technology (OT) data due to physical storage shortages, excessive latency, higher transfer costs, a lack of context awareness, impractical resiliency, and so on. As a result, the fog network emerged as a new computing model for providing computing capacity closer to IoT edge devices. The IoT-Fog-Cloud network, on the other hand, is more vulnerable to multiple security flaws, such as missing key management problems, inappropriate access control, inadequate software update mechanism, insecure configuration files and default passwords, missing communication security, and secure key exchange algorithms over unsecured channels. Therefore, these networks cannot make good security decisions, which are significantly easier to hack than to defend the fog-enabled IoT environment. This paper proposes the cooperative flow for securing edge devices in fog-enabled IoT networks using a permissioned blockchain system (pBCS). The proposed fog-enabled IoT network provides efficient security solutions for key management issues, communication security, and secure key exchange mechanism using a blockchain system. To secure the fog-based IoT network, we proposed a mechanism for identification and authentication among fog, gateway, and edge nodes that should register with the blockchain network. The fog nodes maintain the blockchain system and hold a shared smart contract for validating edge devices. The participating fog nodes serve as validators and maintain a distributed ledger/blockchain to authenticate and validate the request of the edge nodes. The network services can only be accessed by nodes that have been authenticated against the blockchain system. We implemented the proposed pBCS network using the private Ethereum 2.0 that enables secure device-to-device communication and demonstrated performance metrics such as throughput, transaction delay, block creation response time, communication, and computation overhead using state-of-the-art techniques. Finally, we conducted a security analysis of the communication network to protect the IoT edge devices from unauthorized malicious nodes without data loss.

The conventional cloud-centric Internet of Things (IoT) application fails to meet the latency requirement of time-critical applications. The idea of edge and fog computing arrived to distribute workloads across the fog devices located in the local area. However, achieving seamless interoperability, platform independence, and automatic deployment of services becomes the major challenge over heterogeneous fog devices. This paper proposes an integrated and standards-based fog computing federation framework, FogDEFT, that adapts OASIS–Topology and Orchestration Specification for Cloud Applications (TOSCA) for service deployment in fog. The framework standardizes the distributed application design with TOSCA Service Template to deploy Docker Containers in Swarm mode and manages interoperability over heterogeneous fog devices. The framework uses a lightweight TOSCA compliant orchestrator to dynamically deploy various fog applications (user-developed services) on the fly.

Internet of things (IoT) devices are equipped with a number of interconnected sensor nodes that relies on ubiquitous connectivity between sensor devices to optimize information automation processes. Because of the extensive deployments in adverse areas and unsupervised nature of wireless sensor networks (WSNs), energy efficiency is a significant aim in these networks. Network survival time can be extended by optimizing its energy consumption. It has been a complex struggle for researchers to develop energy-efficient routing protocols in the field of WSNs. Energy consumption, path reliability and Quality of Service (QoS) in WSNs became important factors to be focused on enforcing an efficient routing strategy. A hybrid optimization technique presented in this paper is a combination of fuzzy c-means and Grey Wolf optimization (GWO) techniques for clustering. The proposed scheme was evaluated on different parameters such as total energy consumed, packet delivery ratio, packet drop rate, throughput, delay, remaining energy and total network lifetime. According to the results of the simulation, the proposed scheme improves energy efficiency and throughput by about 30% and packet delivery ratio and latency by about 10%, compared with existing protocols such as Chemical Reaction Approach based Cluster Formation (CHRA), Hybrid Optimal Based Cluster Formation (HOBCF), GWO-based clustering (GWO-C) and Cat Swarm Optimization based Energy-Efficient Reliable sectoring Scheme with prediction algorithms (P_CSO_EERSS). The study concludes that the protocol suitable for creating IoT monitoring system network lifetime is an important criteria.

This paper presents a survey of non-fungible tokens (NFTs), including its history, technologies, standards, and challenges in their development. An NFT is a unique digital entity that is created and maintained using blockchain technology. Each NFT is identified using a unique smart contract and a token ID, so the whole history of the NFT can be globally identified by its address and token ID. The blockchain information indelibly identifies the current owner of any asset, previous owners, and original creator. NFTs are used to manage ownership of digital and physical assets and cryptocurrencies. The prices of popular NFTs have become very high, and the market for them has overheated in recent years. NFT technology and its ecosystem have evolved since Quantum, the first NFT, was stored in the Namecoin blockchain. Ethereum has become the main platform for NFT projects because it provides support for smart contracts. Currently, almost all NFT projects are launched on the Ethereum blockchain. NFT has two major standards called ERC-721 and ERC-1155, which have had important functions in the development of NFT. Starting with these two standards, other standards for NFT continue to emerge; they expand the functionality of NFT such as by adding utility. However, NFT is a very early technology, and it has not been long after the NFT concept was created and used. So there are several challenges for further improving NFT technology, in terms of usability, interoperability, and evolution. This paper presents a survey of NFT, including its history, technologies, standards, and challenges of NFT.

Mobile Internet services are developing rapidly for several applications based on computational ability such as augmented/virtual reality, vehicular networks, etc. The mobile terminals are enabled using mobile edge computing (MEC) for offloading the task at the edge of the cellular networks, but offloading is still a challenging issue due to the dynamism, and uncertainty of upcoming IoT requests and wireless channel state. Moreover, securing the offloading data enhanced the challenges of computational complexities and required a secure and efficient offloading technique. To tackle the mentioned issues, a reinforcement learning-based Markov decision process offloading model is proposed that optimized energy efficiency, and mobile users' time by considering the constrained computation of IoT devices, moreover guarantees efficient resource sharing among multiple users. An advanced encryption standard is employed in this work to fulfil the requirements of data security. The simulation outputs reveal that the proposed approach surpasses the existing baseline models for offloading overhead and service cost QoS parameters ensuring secure data offloading.

Empirical accounting is a relatively new course. It was first produced in the United States in the 1960s. After years of research and development, a relatively complete theoretical system has been formed. Empirical accounting teaching is a new content of accounting higher education in our country, which plays an important role in popularizing and improving the empirical study of accounting in our country. After investigating and analysing the empirical accounting teaching in most colleges in my country, this paper summarizes and analyzes the current status of empirical accounting teaching and discusses the optimization measures of its empirical accounting teaching mode based on the Internet of Things and deep learning. The experimental results show that this method can effectively improve students' learning efficiency and teachers' teaching satisfaction.

Mobile edge computing (MEC) integrates mobile and edge computing technologies to provide efficient computing services with low latency. It includes several Internet of Things (IoT) and edge devices that process the user data at the network's edge. The architectural characteristic of MEC supports many internet-based services, which attract more number of users, including attackers. The safety and privacy of the MEC environment, especially user information is a significant concern. A lightweight accessing and sharing protocol is required because edge devices are resource constraints. This paper addresses this issue by proposing a blockchain-enabled security management framework for MEC environments. This approach provides another level of security and includes blockchain security features like temper resistance, immutable, transparent, traceable, and distributed ledger in the MEC environment. The framework guarantees secure data storage in the MEC environment. The contributions of this paper are twofold: (1) We propose a blockchain-enabled security management framework for MEC environments that address the security and privacy concerns, and (2) we demonstrate through simulations that the framework has high performance and is suitable for resource-constrained MEC devices. In addition, a smart contract-based access and sharing mechanism is proposed. Our research uses a combination of theoretical analysis and simulation experiments to demonstrate that the proposed framework offers high security, low latency, legitimate access, high throughput, and low operations cost.

This special issue contains extended versions of three best papers from the IEEE International Conference on Network Softwarization (NetSoft 2021, https://netsoft2021.ieee-netsoft.org/). The theme of NetSoft 2021 was “Accelerating Network Softwarization in the Cognitive Age.” The General Co-Chairs were Kohei Shiomoto, Tokyo City University, Japan and Young-Tak Kim, Yeungnam University, Korea. The TPC-Co-Chairs were Christian Esteve Rothenberg, University of Campinas, Brazil; Barbara Martini, CNIT, Italy; and Eiji Oki, Kyoto University, Japan.

The first paper, titled “RNN-EdgeQL: An Auto-Scaling and Placement Approach for SFC,” proposes an innovative prediction-based scaling and placement algorithm for service function chains (SFCs) that improves service level agreements (SLAs) and reduces operational costs. The authors use a variant of recurrent neural networks (RNNs) called gated recurrent unit (GRU) for resource demand prediction and an algorithm that applies Q-Learning on Edge computing environment (EdgeQL) to place scaled-out virtual network functions (VNFs) in appropriate locations. The proposed algorithm is tested on realistic temporal dynamic load models and achieves the lowest overall latency, lowest SLA violations, and lowest VNFs requirement compared with existing algorithms.

The second paper, titled “SRv6-based Time-Sensitive Networks (TSN) with Low Overhead Rerouting,” proposes a software-defined network (SDN)-based approach for low-overhead TSN network updates using segment routing over IPv6 (SRv6) for path control. The authors introduce the concept of TSN subgraphs to quickly reschedule flows traversing problematic areas and propose a TSN-aware routing heuristic to minimize convergence time. The proposed approach yields faster recovery and significantly reduces the number of required reconfigurations upon failures, at the expense of a small SRv6 encoding/decoding overhead.

The third paper, titled “Analysis of Network Function Sharing in Content Delivery Network-as-a-Service Slicing Scenarios,” investigates the potential efficiencies that can be achieved when sharing a virtual cache function among Internet service providers (ISPs) that are sharing a common physical infrastructure. The authors simulate the sharing of virtualized cache functions and analyze the implications of limiting the storage capacity of the caches at the edge. The paper provides insights into the potential cost savings that can be achieved by sharing network infrastructures and virtualized cache functions among ISPs.

We believe that these three papers make significant contributions and offer valuable insights into the challenges and opportunities of managing softwarized networks. We hope that this special issue will inspire further research in this area and lead to the development of more efficient and effective network management solutions.

We would like to thank the authors for their excellent contributions and the reviewers for