Next Generation Of Internet Research Articles

Dual Mutual Robust Graph Convolutional Network for Weakly Supervised Node Classification in Social Networks of Internet of People

Social networks are a crucial component of the Internet of People (IoP) which is the frontier of the next generation of Internet of Things (IoT). Predicting a large number of unknown node labels with few known labels is one of the challenging problems in social networks analysis. Fortunately, Graph Convolution Network (GCN) and subsequent variants have achieved impressive performance on Semi-Supervised Node Classification (SSNC). However, existing works only focus on the case of clean labels and rarely study the problem of SSNC under Noisy Labels (SSNCNL), which is a more challenging and practical problem in the field of weakly supervised learning. To address this challenge, in this paper, we propose a novel Dual Mutual Robust Graph Convolutional Network named DMRGCN, which is inspired by deep mutual learning and robust learning in the domain of image recognition. Specifically, we first employ two GCNs with different learning abilities to construct network architecture. Then we define a new loss function which is a weighted sum of the supervised loss, mutual loss, and robust loss. Finally, we train the network in the paradigm of pseudo-siamese network. Experimental results on three social network benchmark datasets with different levels of noise on labels demonstrate that DMRGCN outperforms the vanilla GCN and several variants in terms of classification accuracy. In particular, under the two conditions of clean labels and noisy labels, the accuracy of node classification obtained by our proposed DMRGCN can be 3.05% and 6.44% higher than that of the vanilla GCN, respectively.

FedFog - A federated learning based resource management framework in fog computing for zero touch networks

Fog computing offers an optimal answer to the expansion challenge of today’s networks. It boasts scaling and reduced latency. Since the concept is still nascent, many research questions remain unanswered. One of these is the challenge of Resource Management. There is a pressing need for a reliable and scalable architecture that meets the Resource Management challenge without compromising the Quality of Service. Among the proposed solutions, Artificial Intelligence based path selection techniques and automated link detection methods can provide lasting and reliable answer. An optimal approach for introducing intelligence in the networks is the infusion of Machine learning methods. Such futuristic, intelligent networks form the backbone of the next generation of Internet. These self-learning and self-healing networks are termed as the Zero-Touch networks. This paper proposes FedFog, a Federated Learning based optimal, automated Resource Management framework in Fog Computing for Zero-touch Networks. The paper describes a series of experiments focusing on Quality of Service parameters such as Network latency, Resources processed, Energy consumption and Network usage. The simulation results from these experiments depict superiority of the proposed architecture over traditional, existing architecture.

Future Internet Architectures on an Emerging Scale—A Systematic Review

Future Internet is a general term that is used to refer to the study of new Internet architectures that emphasize the advancements that are paving the way for the next generation of internet. Today’s internet has become more complicated and arduous to manage due to its increased traffic. This traffic is a result of the transfer of 247 billion emails, the management of more than a billion websites and 735 active top-level domains, the viewing of at least one billion YouTube videos per day (which is the source of main traffic), and the uploading of more than 2.5 billion photos to Facebook every year. The internet was never anticipated to provide quality of service (QoS) support, but one can have a best effort service that provides support for video streams and downloaded media applications. Therefore, the future architecture of the internet becomes crucial. Furthermore, the internet as a service has witnessed many evolving conflicts among its stakeholders, leading to extensive research. This article presents a systematic review of the internet’s evolution and discusses the ongoing research efforts towards new internet architectures, as well as the challenges that are faced in increasing the network’s performance and quality. Moreover, as part of these anticipated future developments, this article draws attention to the Metaverse, which combines the emerging areas of augmented reality, virtual reality, mixed reality, and extended reality, and is considered to be the next frontier for the future internet. This article examines the key role of the blockchain in organizing and advancing the applications and services within the Metaverse. It also discusses the potential benefits and challenges of future internet research. Finally, the article outlines certain directions for future internet research, particularly in the context of utilizing blockchains in the Metaverse.

Interpretable intrusion detection for next generation of Internet of Things

This paper presents a new framework for intrusion detection in the next-generation Internet of Things. MinMax normalization strategy is used to collect and preprocess data. The Marine Predator algorithm is then used to select relevant features to be used in the learning process. The selected features are then trained with an advanced and state-of-the-art recurrent neural network that includes an attention mechanism. Finally, Shapely values are calculated to determine how much each feature contributes to the final output. The dataset NSL-KDD was used for intensive simulations. The results show the advantages of the proposed system as well as its superiority over state-of-the-art methods. In fact, the proposed solution achieved a rate of more than 94% for both true negative and true position, while the rates of the existing solutions are below 90% for the challenging NSL-KDD datasets.

AuSDiDe: Towards a New Authentication System for Distributed and Decentralized Structure based on Shamir’s Secret Sharing

Nowadays, connected devices are growing exponen-tially; their produced data traffic has increased unprecedent-edly. Information systems security and cybersecurity are critical because data typically contain sensitive personal information, requiring high data protection. An authentication system manages and controls access to this data allowing the system to ensure the legitimacy of the access request. Most of the current identification and authentication systems are based on a centralized architec-ture. However, some concepts as Cloud computing and Blockchain use respectively distributed and decentralized architectures. Users without a central server will own platforms and applications of the next generation of Internet and Web3. This paper proposes AuSDiDe, a new authentication system for the distributed and decentralized structure. This solution aims to divide and share keys toward different and distributed nodes. The main objective of AuSDiDe is to securely store and manage passwords, private keys, and authentication based on the Shamir secret sharing algo-rithm. This new proposal significantly reinforces data protection in information security.

Machine Learning Based Intelligent Intrusion Classification System for IoT Gateway Communication

Internet of Things(IoT) is a next generation of Internet in that every object in the universe connect, communicate with sensor devices through Internet. In that inter-connected communication devices as well as sensor devices share the data through IoT gateway for a relevant application like whether forecasting, healthcare, smart city, disaster management are providing without human interaction. IoT enhances comfortable for human being even security is one of the challenging tasks. Intrusion detection system (IDS) will protect IoT devices from intruders. Now a day i.e in this era, as per user requirement and day-to-day increasing new innovative technologies as IoT, cloud computing, big data analytics, AIapplications implementation a network traffic will be generating a heavy data. To manage these data intrusion detection system is essential technique to detect, collect analyze the data is transmission through IoT gateway network. It is essential to improve the accuracy as well speed of intrusion detection system model by applying machine learning approach to detect IoT systems and gateway network to protect from cyber-attacks. In this paper providing a detailed study of Intrusion detection system (IDS) classification system for IoT gateway communication to protect IoT gateway by machine learning algorithms ina intelligent fashion.

Construction of Lightweight Authenticated Joint Arithmetic Computation for 5G IoT Networks

Abstract The next generation of Internet of Things (IoT) networks and mobile communications (5G IoT networks) has the particularity of being heterogeneous, therefore, it has very strong ability to compute, store, etc. Group-oriented applications demonstrate its potential ability in 5G IoT networks. One of the main challenges for secure group-oriented applications (SGA) in 5G IoT networks is how to secure communication and computation among these heterogeneous devices. Conventional protocols are not suitable for SGA in 5G IoT networks since multiparty joint computation in this environment requires lightweight communication and computation overhead. Furthermore, the primary task of SGA is to securely transmit various types of jointly computing data. Hence, membership authentication and secure multiparty joint arithmetic computation become two fundamental security services in SGA for 5G IoT networks. The membership authentication allows communication entities to authenticate their communication partners and the multiparty joint computations allow a secret output to be shared among all communication entities. The multiparty joint computation result can be used to protect exchange information in the communication or be used as a result that all users jointly compute by using their secret inputs. A novel construction of computation/communications-efficient membership authenticated joint arithmetic computation is proposed in this paper for 5G IoT networks, which not only integrates the function of membership authentication and joint arithmetic computation but also realizes both computation and communication efficiency on each group member side. Our protocol is secure against inside attackers and outside attackers, and also meets all the described security goals. Meanwhile, in this construction the privacy of tokens can be well protected so tokens can be reused multiple times. This proposal is noninteractive and can be easily extended to joint arithmetic computation with any number of inputs. Hence, our design has more attraction for lightweight membership authenticated joint arithmetic computation in 5G IoT networks.

Cyberspace: A Digital Ecosystem

Cyberspace is a new frontier, not just for hackers, but for engineers. It is a digital ecosystem, the next generation of Internet and network applications, promising a whole new world of distributed and open systems that can interact, self-organize, evolve, and adapt. These ecosystems transcend traditional collaborative environments, such as client-server, peer-to-peer, or hybrid models (e.g., web services), to become a self-organized, evolving, interactive environment. Understanding cyberspace as a system is critical if we are to properly design systems to exist within it. Considering it to be a digital ecosystem, where systems can adapt and evolve, will enable systems engineering to become more effective in the future of networks and the Internet. While most systems engineers have only anecdotal experience with large segments of this ecosystem, in today’s world all of them must come to understand it. Engineering any system, or portion of a system, begins with an understanding of the system. This paper presents two interrelated yet distinct foundational models of the ecosystem of cyberspace: a Systemigram to narrate the cyclical nature of cyber warfare, and a modified predator–prey model, as a mathematical model. Systems engineers can utilize these models to design digital “species” that function and adapt within this ecosystem.

Design and Evaluation of a New Machine Learning Framework for IoT and Embedded Devices

Low-cost, high-performance embedded devices are proliferating and a plethora of new platforms are available on the market. Some of them either have embedded GPUs or the possibility to be connected to external Machine Learning (ML) algorithm hardware accelerators. These enhanced hardware features enable new applications in which AI-powered smart objects can effectively and pervasively run in real-time distributed ML algorithms, shifting part of the raw data analysis and processing from cloud or edge to the device itself. In such context, Artificial Intelligence (AI) can be considered as the backbone of the next generation of Internet of the Things (IoT) devices, which will no longer merely be data collectors and forwarders, but really “smart” devices with built-in data wrangling and data analysis features that leverage lightweight machine learning algorithms to make autonomous decisions on the field. This work thoroughly reviews and analyses the most popular ML algorithms, with particular emphasis on those that are more suitable to run on resource-constrained embedded devices. In addition, several machine learning algorithms have been built on top of a custom multi-dimensional array library. The designed framework has been evaluated and its performance stressed on Raspberry Pi III- and IV-embedded computers.

A First Outlook of Sputtered FeWO4 Thin Films for Micro-Supercapacitor Electrodes

The next generation of Internet of Things devices requires micro-supercapacitors operating at high voltage which is difficult to achieve using symmetrical design. Thus, their fabrication in an asymmetric configuration is mandatory. While MnO2 is well-established as positive electrode, the scarcity of existing efficient materials able to be used at the negative side drives the research towards new promising materials. Since few years, a new class of oxide materials, named multicationic oxides, were demonstrated to be attractive solutions as bulk electrodes for electrochemical capacitor. Among them, the wolframite-type FeWO4 oxide was proposed as an interesting negative electrode material for asymmetric FeWO4/MnO2 electrochemical capacitors. The present paper reports for the first time on the successful thin film synthesis of such iron-tungstate oxide films by reactive DC magnetron sputtering, a deposition method widely used in the semiconductor industry to manufacture micro-devices. The pseudocapacitive behaviour documented at the bulk scale is preserved at the thin film level as well, and opens-up the possibility to use FeWO4 in the next generations of micro-supercapacitors.

Smart Power Unit—mW-to-nW Power Management and Control for Self-Sustainable IoT Devices

Self-sustainability and intelligence will be critical features of the next generation of Internet of Things devices. Power management circuits need to be energy-efficient in multiple power modes, from nW sleep to mW active, and to support energy harvesting (EH) from multiple environmental sources. This article presents an adaptive and firmware-configurable power supply unit that enables intelligent devices to operate in the sub μW range while achieving self-sustainability through EH. The microcontroller-based design allows efficient conversion from batteries, dc, ac environmental sources, and nA quiescent currents. Source and load power points are decoupled with multiple dc–dc converters aiming to supply loads with adaptive voltage scaling and achieving high reliability. Experimental results show the flexibility and efficiency of this approach: the proposed power supply unit achieves a quiescent current of 54 nA and a maximum peak load current of 300 mA, delivered with an end-to-end power efficiency above 85%.

Dynamic Hierarchical Caching Resource Allocation for 5G-ICN Slice

Network slicing and Multiple-Access Edge Computing (MEC) are key technologies in fifth-generation (5G) networks. The flexible programmability of network slicing and the decentralization of MEC facilitate the deployment of Information-Centric Networking (ICN). The caching feature of ICN can provide users with low-latency data services. Although many existing works have addressed the cache deployment problem or the cache optimization problem, most of them do not consider the issue of caching resource allocation in the dynamic and hierarchical environment. Dynamic deployment of cache nodes can improve the operator’s revenue as much as possible while accurately allocating the caching resources can reduce the user-requested latency. Therefore, in this study, a problem of the operator’s expected revenue maximization is presented in an environment combining dynamic deployment of the MECs and the caching-enabled node ICN-Gateway (ICN-GW). To solve this problem, we propose an optimal stopping theory (OST)-based dynamic hierarchical caching resources allocation (ODH-CRA) algorithm. The algorithm consists of three parts. Firstly, an Integer Linear Programming (ILP) solution is proposed to determine the optimal deployment of the MECs. This method determines the optimal location and number of the MECs by considering deployment costs and service requirement costs synthetically. Secondly, a redeployment technique based on the OST is proposed to determine the best redeployment time of the MECs according to the values of latency violations and the service latency requirements. Finally, an improved elite genetic algorithm (IEGA) is proposed to find the optimal solution of the hierarchical caching resource allocation. This method searches the optimal scheme by maximizing the operator’s revenue joint caching costs and energy consumption. Ultimately, we perform a series of simulation experiments to compare the proposed method’s performance to dynamic and hierarchical methods. Our solution can effectively reduce the latency for users’ requesting, improve the revenue of ICN Communication Service Provider (ICSP), and provide an effective caching resource allocation scheme for the next generation of Internet of Things (IoT) networks.

Identifying and mitigating security risks for secure and robust NGI networks

Smart city development is important to achieve sustainable cities and societies which help enhance urban services, reduce resource consumption and decrease overall cost. The incorporation of smart cities with the Internet has given us the Next Generation of Internet (NGI) where every smart device exploits the interconnected services and infrastructure of the Internet. The underlying structure of NGI is composed of large scale heterogeneous multilevel systems-of-systems (SoSs) where each system represents a sensor, mobile phone, computer or smart device.Security and privacy is a fundamental requirement of NGI which is heavily dependent on the composition of services and connectivity of the underlying systems. Meaning any unsecure system can affect the security of the entire networked infrastructure/SoSs. Therefore, it is important to analyse and understand the composition of different systems at different levels in NGI in order to identify and mitigate vulnerabilities. This paper proposes a solution to identify and mitigate vulnerabilities within multilevel SoSs, to enhance security without deploying additional security at endpoints, and quantify security levels of individual systems and the entire composed system. The solution was tested and evaluated using simulation and a network testbed. Results show that NGI security can be enhanced with better composition of systems.

Interaction of Edge-Cloud Computing Based on SDN and NFV for Next Generation IoT

In order to improve the stability of mobile network system for application of the next generation of Internet of Things (IoT), balance the network load and guarantee the quality of user service experience, this article first introduces the computing migration framework for the network of the next generation, and summarizes the concept and content of mobile edge computing (MEC) using software-defined network (SDN) and network function virtualization (NFV). And then, this article proceeds to introduce the MEC strategy based on SDN and NFV technology as well as multiattribute decision making, computing migration, multiattribute decision, the MEC decision model based on SDN and NFV technology and the solving process of the MEC decision model based on SDN and NFV. Finally, the three sets of simulation experiments based on MATLAB are designed to validate the multiattribute decision of MEC migration strategy based on SDN and NFV. The results show that the multiattribute decision making based on SDN and NFV can select the appropriate MEC center, further reduce the server response time and improve the quality of user service experience. This article is of great significance to the application of IoT terminal in the next generation of network environment.

Assessing risks and threats with layered approach to Internet of Things security

Internet of Things is the next-generation Internet network created by intelligent objects with software and sensors, employed in a wide range of fields such as automotive, construction, health, textile, education and transportation. With the advent of Industry 4.0, Internet of Things has been started to be used and it has led to the emergence of innovative business models. The processing and production capabilities of Internet of Things objects in hidden and critical data provide great advantages for the next generation of Internet. However, the integrated features of Internet of Things objects cause vulnerabilities in terms of security, making them the target of cyber threats. In this study, a security model which offers an integrated risk-based Internet of Things security approach for the Internet of Things vulnerabilities while providing detailed information about Internet of Things and the types of attacks targeting Internet of Things is proposed. In addition, in this study, the vulnerabilities of Internet of Things were explained by classifying attack types threatening the physical layer, network layer, data processing layer and application layer. Moreover, the risk-based security model has been proposed by examining the vulnerabilities and threats of smart objects that generate the Internet of Things. The proposed Internet of Things model is a holistic security model that separately evaluates the Internet of Things layers against vulnerabilities and threats based on the risk-level approach.

FRAM: Framed ALOHA for 5G Super Real-Time Multimedia Random Access with Packet Slicing

Internet of Things is gaining ground in the next generation of Internet. Random access protocols such as ALOHA and its variants play an important role for the successful implementation of 5G. The framed ALOHA is not suitable for such environment due to the relatively low throughput and no guarantee on delay. In this paper, we propose a Finite Projective Plane based random access scheme, FRAM, for multimedia super real-time traffic in the coming 5G Internet. FRAM allows for high throughput by expanding the selection base such that the collisions among users are most likely to be partial. In FRAM, a user packet is sliced into sections, i.e., packet slicing, and transmitted in the time slots of a frame corresponding to the points of a set of a Finite Projective Plane. The error bits induced in a user packet of partial collisions can be removed by forward error control coding. In the performance evaluation, we compare the throughput, pseudo throughput, and probability of blackout of FRAM with those of the framed ALOHA and SIC based CRDSA. The results show that the proposed FRAM has better pseudo throughput and much lower probability of blackout over both the framed ALOHA and CRDSA depending on threshold of partial collision.

Design and Integration of Flexible Sensor Matrix for in Situ Monitoring of Polymer Composites.

Sensory polymer composites are highly desirable for applications such as in situ and real-time production processes and structural health monitoring, and for technologies that include human-machine interfaces for the next generation of Internet of Things. However, the development of these materials is still in its infancy: these materials have been reported, but the large-scale fabrication of polymer composites with versatile and customizable sensing capabilities has yet to be demonstrated. Here, we report on a scalable fabrication strategy that enables such materials by designing and integrating PCB technology-inspired large-area flexible sensor matrices into polymer composites. The integrated sensor matrices successfully monitored in situ the production processes and structural health of an industrial polymer composite: from the application of vacuum, resin flow and polymerization, production defects, and temperature distribution. Our results demonstrate that the proposed strategy is a simple and effective solution as a distributed monitoring platform for polymer composites and shows the potential toward next generation of sensory polymer composites.

The effect of “Internet of Things” on supply chain integration and performance: An organisational capability perspective

The Internet of things (IoT) is a next generation of Internet connected embedded ICT systems in a digital environment to seamlessly integrate supply chain and logistics processes. Integrating emerging IoT into the current ICT systems can be unique because of its intelligence, autonomous and pervasive applications. However, research on the IoT adoption in supply chain domain is scarce and acceptance of the IoT into the retail services in specific has been overly rhetoric. This study is drawn upon the organisational capability theory for developing an empirical model considering the effect of IoT capabilities on multiple dimensions of supply chain process integration, and in turn improves supply chain performance as well as organisational performance. Cross-sectional survey data from 227 Australian retail firms was analysed using structural equation modelling (SEM). The results indicate that IoT capability has a positive and significant effect on internal, customer-, and supplier-related process integration that in turn positively affects supply chain performance and organisational performance. Theoretically, the study contributes to a body of knowledge that integrates information systems research into supply chain integration by establishing an empirical evidence of how IoT-enabled process integration can enhance the performance at both supply chain and organisational level. Practically, the results inform the managers of the likely investment on IoT that can lead to chain’s performance outcome.

IPv6-Based Architecture of Community Medical Internet of Things

The effective integration of Community Medical Internet of Things (CMIoT) and the core technology IPv6 about the next generation of Internet will create a new pattern for the medical field. However, as a heterogeneous network system, the interconnection of different CMIoT components is the primary problem, which needs to be solved. Traditional protocol conversion gateway in the Internet of Things only implements the packet format conversion. When the network environment changes, it is difficult to effectively implement the data path. We propose a CMIoT architecture with the assist of the communication auxiliary gateway, and design the simplified protocol message format under the premise of satisfying the functional requirements. At the same time, the CMIoT architecture model is built based on the Ptolemy II modeling environment and implemented in a community, and it is proved that the interconnection between the IPv6 network and the physical network can be realized more effectively.

Efficient Traffic Engineering Strategies for Improving the Performance of TCP Friendly Rate Control Protocol

Multimedia services will play a prominent role in the next generation of internet. With increasing real time requirements, internet technology has to provide Quality of Service (QoS) for various kinds of real time streaming services. When the bandwidth required exceeds the available network resources, network paths can get congested, which results in a delay in packet delivery and packet loss. This situation leads to the design of new strategies for congestion avoidance and control. One of the popular and appropriate congestion control mechanisms that is useful in transmitting multimedia applications in the transport layer is TCP Friendly Rate Control Protocol (TFRC). However, TFRC still suffers from packet loss and delay due to long distance heavy traffic and network fluctuations. This paper introduces a number of key concerns like enhanced Round Trip Time (RTT) and Retransmission Time Out (RTO) calculations, Enhanced Average Loss Interval (ALI) methods and improved Time to Live (TTL) features are applied to TFRC to enhance the performance of TFRC over wired networks.