Next-generation Internet Of Things Research Articles

Next-Generation Internet of Things in Fintech Ecosystem

The purpose of the present study is to comprehensively evaluate the present status of the Internet of Things (IoT) in fintech ecology and ahead. The study finds that the concept of communication between the devices and financial technology is not new yet only a considerable number of studies are done on IoT in fintech. The study highlights that both the IoT and fintech show an upward trend in the marketspace. Then, the study identifies that the increase in the demand for blockchain, Internet, mobile network, cloud storage, and IoT devices among the organization are the key drivers of IoT in fintech. The study also shows that the issues related to sustainable energy, digital payments, and security are the current area of applications and challenges for IoT in Fintech. Thereafter, the study concludes that the growth of neurotech will influence the next-generation IoT in fintech as the catalytic agent. Neurotech-enabled IoT (NIoT) in Fintech will enhance the man, machine, and memory (3Ms) relationship for business process innovation.

Hybrid NN-based green cognitive radio sensor networks for next-generation IoT

In any cognitive radio sensor networks (CRSNs), the secondary users (SUs) and primary users (PUs) share the opportunity to use the authorized frequency band. Here, the SU nodes can only transmit in the temporarily idle spectrum when it is not in use by any PU nodes. The proper estimation and detection of primary nodes are important for the energy-efficient spectrum access. The work basically presents a state of the art in implementing Bayesian-based convolutional neural network (CNN) for addressing the issue of energy constraints in next-generation Internet of things (Nx-IoT) using CRSN. Initially, we use blind source separation to extract the energy features and cyclic spectrum features of the signal and carry on the asymptotic autocorrelation calculation to the extracted signal. Finally, we construct the corresponding training set for CNN training and establish a suitable spectrum sensing model for Nx-IoT. Theoretical analysis and simulation results validate a suitable B-CNN spectrum sensing model along with energy-efficient cooperative communication between the SU nodes.

An approach using Trust Management with Next-Generation IoT Networks for Healthcare, Agriculture and Sustainable Development Goals

The Internet of Things (IoT) is one of the most promising scientific fields of the last decade. The number of devices developed that support Internet things makes the application of internet things more interesting. The field of use includes the insurance sector, intelligent monitoring Environment, Smart City, sector-health care, Intelligent City, and also many other applications enabled for smart devices.The vision of the introduction of the internet, forming a network called IoT, where various information technology devices are connected, security and trust are most important for the user, because this network presumably used a large amount of data.However, trust is a significant obstacle that can hinder the growth of the IoT and even slow down the significant compression of many applications.This paper presents an approach using trust management (TM) with next-generation IoT networks for healthcare, agriculture, and sustainable development goals. Implementation of IoT conveys various problems of security and confidentiality. The use of IoT to ensure confidentiality, integrity, authentication, authorization, trust, secrecy, and management challenges are addressed. The paper uses decentralized blockchain technology to solve security and privacy issues in applications with the support of the IoT.Ethereum virtual machine is used to implement theBlockchain distributed networkand healthcare insurance claims are taken as anexample to test the proposed solution. Also, it is provided that the lessons and opinions discussed are primarily about the purpose of the trust, which can be reproduced in the future on the internet.The results show that distributed blockchain technology provides trust management and can improve the existing security and privacy of the IoT in the health and agriculture sectors, which is helpful for next-generation IoT networks.

A Resource-Efficient Hybrid Proxy Mobile IPv6 Extension for Next-Generation IoT Networks

The future communication technologies like 6G are capable to provide higher mobility and better quality-of-service requirements to Internet of Things (IoT). To ensure mobility, the 6G technologies need more reliable and scalable solutions, which are capable to integrate large-scale heterogeneous IoT networks. In a heterogeneous environment, seamless mobility along with the demands of IP addresses requires a proxy mobile IPv6 (PMIPv6) protocol that provides cost-effective solutions in next-generation IoT networks. The PMIPv6 has been exploited for resource efficiency in IoT-enabled next-generation networks. In this article, we have proposed a demand-based resource-efficient location-aware PMIPv6 extension for seamless mobility in the next-generation IoT networks. The proposed approach efficiently utilizes the network resources using location information and received signal strength (RSS). This solution enhances the performance of the PMIPv6 protocol in terms of signaling cost, and load on network entities. Furthermore, mathematical models are derived in terms of signaling cost and load distribution. The proposed solution is compared with the existing RSS-based PMIPv6 extension protocols. The results show that the proposed scheme enhances the performance and is a resource-friendly for the next-generation large-scale IoT networks.

Harvesting Devices’ Heterogeneous Energy Profiles and QoS Requirements in IoT: WPT-NOMA vs BAC-NOMA

The next generation Internet of Things (IoT) exhibits a unique feature that IoT devices have different energy profiles and quality of service (QoS) requirements. In this paper, two energy and spectrally efficient transmission strategies, namely wireless power transfer assisted non-orthogonal multiple access (WPT-NOMA) and backscatter communication (Back-Com) assisted NOMA (BAC-NOMA), are proposed by utilizing this feature of IoT and employing spectrum and energy cooperation among the devices. In particular, the use of NOMA ensures that the devices with different QoS requirements can share the same spectrum, and WPT and Back-Com are employed to utilize the cooperation among the devices with different energy profiles, which avoids the use of a dedicated power beacon. Furthermore, for the proposed WPT-NOMA scheme, the application of hybrid successive interference cancelation (SIC) decoding order is also considered, and analytical results are developed to demonstrate that WPT-NOMA can avoid outage probability error floors and realize the full diversity gain. Unlike WPT-NOMA, BAC-NOMA suffers from an outage probability error floor, and the asymptotic behavior of this error floor is analyzed in the paper by applying the extreme value theory. In addition, the effect of a unique feature of BAC-NOMA, i.e., employing one device's signal as the carrier signal for another device, is studied, and its impact on the diversity gain is revealed. Simulation results are also provided to compare the performance of the proposed strategies and verify the developed analytical results.

Artificial Intelligence-Based Sensors for Next Generation IoT Applications: A Review

Sensors play a vital role in our daily lives and are an essential component for Internet of Things (IoT) based systems as they enable the IoT to collect data to take smart and intelligent decisions. Recent advances in IoT systems, applications, and technologies, including industrial Cyber-Physical Systems (CPSs), are being supported by a wide range of different types of sensors based on artificial intelligence (AI). These smart AI-based sensors are typically characterized by onboard intelligence and have the ability to communicate collaboratively or through the Internet. To achieve the high level of automation required in today’s smart IoT applications, sensors incorporated into nodes must be efficient, intelligent, context-aware, reliable, accurate, and connected. Such sensors must also be robust, safety- and privacy-aware for users interacting with them. Sensors leveraging advanced AI technologies, new capabilities have recently emerged which have the potential to detect, identify, and avoid performance degradation and discover new patterns. Along with knowledge from complex sensor datasets, they can promote product innovation, improve operation level, and open up novel business models. We review sensors, smart data processing, communication protocol, and artificial intelligence which will enable the deployment of AI-based sensors for next-generation IoT applications.

Ensemble Deep Learning Assisted VNF Deployment Strategy for Next-Generation IoT Services

Due to the massive Internet of Things (IoT) connectivity and substantial growth of communication traffic, Virtual Network Function (VNF) orchestration scheme is anticipated to function promptly, dynamically, and intelligently for next-generation networks. Hence, we urge the necessity to move beyond the traditional paradigm and employ VNFs on the network edge located cloudlet. Overall, multi-access edge computing can intensify the performance of delay-sensitive IoT applications compared to the core cloud based VNF deployments. In this paper, we intend to investigate how to simultaneously leverage the ensembling of multiple deep learning models for proper calibration to provide real-time VNF placement solutions. We also address the challenges associated with state-of-the-art approaches to deal with dynamic network traffic and topology patterns. Our envisioned methods, based on Convolutional Neural Networks and Artificial Neural Networks named as E-ConvNets and E-ANN respectively, suggest two proactive VNF deployment strategies. These ensembled VNF deployment strategies demonstrate encouraging performance (optimality gap nearly 7%) in terms of minimizing relocation and communication costs, and high scalability intelligence factor (around 0.93) through simulation results compared to standalone deep learning models. Furthermore, the presented results indicate the potentialities of applying deep learning-based strategies into similar research enigmas for future telecommunication network researches.

Triboelectric Nanogenerators and Hybridized Systems for Enabling Next-Generation IoT Applications.

In the past few years, triboelectric nanogenerator-based (TENG-based) hybrid generators and systems have experienced a widespread and flourishing development, ranging among almost every aspect of our lives, e.g., from industry to consumer, outdoor to indoor, and wearable to implantable applications. Although TENG technology has been extensively investigated for mechanical energy harvesting, most developed TENGs still have limitations of small output current, unstable power generation, and low energy utilization rate of multisource energies. To harvest the ubiquitous/coexisted energy forms including mechanical, thermal, and solar energy simultaneously, a promising direction is to integrate TENG with other transducing mechanisms, e.g., electromagnetic generator, piezoelectric nanogenerator, pyroelectric nanogenerator, thermoelectric generator, and solar cell, forming the hybrid generator for synergetic single-source and multisource energy harvesting. The resultant TENG-based hybrid generators utilizing integrated transducing mechanisms are able to compensate for the shortcomings of each mechanism and overcome the above limitations, toward achieving a maximum, reliable, and stable output generation. Hence, in this review, we systematically introduce the key technologies of the TENG-based hybrid generators and hybridized systems, in the aspects of operation principles, structure designs, optimization strategies, power management, and system integration. The recent progress of TENG-based hybrid generators and hybridized systems for the outdoor, indoor, wearable, and implantable applications is also provided. Lastly, we discuss our perspectives on the future development trend of hybrid generators and hybridized systems in environmental monitoring, human activity sensation, human-machine interaction, smart home, healthcare, wearables, implants, robotics, Internet of things (IoT), and many other fields.

Systematic Approach for State-of-the-Art Architectures and System-on-Chip Selection for Heterogeneous IoT Applications

The Internet of Things (IoT) refers to a network of physical devices, which collects data and processes into a system without human intervention. In the commercialized market, IoT architectures are upgrading day by day to reduce data transmission costs, latency, and bandwidth usage for various application requirements. The extensively available IoT architectures and their specification resist the researchers to select a system-on-chip (SoC) for heterogeneous IoT applications. This paper seeks to comprehend the various IoT device specifications and their characteristics to support multiple applications. Moreover, microprocessor architectures and their components are detailed to facilitate developer knowledge in advanced methodology and technology. The various instructions set architectures (ISA) are implemented in a Zynq-7000 (xc7Zz20clg484-1) FPGA device to examine the feasibility of design space requirements for real-time hardware execution. To select specific system-on-chip (SoC) architecture for heterogeneous IoT applications, a genetic algorithm (GA) based optimization method is implemented in MATLAB. The proposed algorithm identifies the optimized SoC architecture concerning device parameters such as a clock, cache, RAM space, external storage, network support, etc. Further, the confusion matrix method evaluates the proposed algorithm's accuracy, which yields 84.62% accuracy. The outcome of SoCs attained through the GA are tested by analyzing their execution time and performance using various evaluation benchmarks. This article helps the researchers and field engineers to comprehend the microarchitecture device configurations and to identify the superior SoC for next-generation IoT practices.

Optimal Placement of Social Digital Twins in Edge IoT Networks.

In next-generation Internet of Things (IoT) deployments, every object such as a wearable device, a smartphone, a vehicle, and even a sensor or an actuator will be provided with a digital counterpart (twin) with the aim of augmenting the physical object’s capabilities and acting on its behalf when interacting with third parties. Moreover, such objects can be able to interact and autonomously establish social relationships according to the Social Internet of Things (SIoT) paradigm. In such a context, the goal of this work is to provide an optimal solution for the social-aware placement of IoT digital twins (DTs) at the network edge, with the twofold aim of reducing the latency (i) between physical devices and corresponding DTs for efficient data exchange, and (ii) among DTs of friend devices to speed-up the service discovery and chaining procedures across the SIoT network. To this aim, we formulate the problem as a mixed-integer linear programming model taking into account limited computing resources in the edge cloud and social relationships among IoT devices.

A genetic algorithm based energy efficient group paging approach for IoT over 5G

Cellular networks are evolving to the era of 5th Generation (5G), where 5G new radio (NR) and Long-Term Evolution: Advanced (LTE-A) Pro technologies are being envisioned for enabling smart and innovative services of the Internet-of-Things (IoT). However, existing LTE-A Pro protocols such as the group paging approach is still heavily inclined towards human-to-human communications owing to the heterogeneous characteristics of a wide variety of IoT devices. Since most IoT devices are battery operated and their power consumption rate decides the battery lifetime, hence energy-efficient data transfer protocols are of paramount importance for next-generation IoT networks. Group paging is one such mechanism that has been widely accepted to improve energy efficiency of IoT networks. However, grouping approach for IoT devices is still not a much addressed topic, though a few novel group paging approaches have been studied that focus on varied IoT characteristics and mobility; though such approaches are not computationally efficient particularly for massive IoT deployments. Therefore, this paper proposes a novel multi-parameter evolutionary optimization, namely, genetic algorithm (GA) based grouping approach that also considers IoT features such as traffic patterns, delay requirements, and mobility patterns. Results obtained from simulations validate that our proposed method can significantly improve IoT devices’ energy efficiency over random grouping schemes and other approaches.

Low-cost Security for Next-generation IoT Networks

In recent years, the ubiquitous nature of Internet-of-Things (IoT) applications as well as the pervasive character of next-generation communication protocols, such as the 5G technology, have become widely evident. In this work, we identify the need for low-cost security in current and next-generation IoT networks and address this demand through the implementation, testing, and validation of an intrinsic low-cost and low-overhead hardware-based security primitive within an inherent network component. In particular, an intrinsic Physical Unclonable Function (PUF) is implemented in the peripheral network module of a tri-band commercial off-the-shelf router. Subsequently, we demonstrate the robustness of this PUF to ambient temperature variations and to limited natural aging, and examine in detail its potential for securing the next generation of IoT networks and other applications. Finally, the security of the proposed PUF-based schemes is briefly assessed and discussed.

Harvesting Devices' Heterogeneous Energy Profiles and QoS Requirements in IoT: WPT-NOMA vs BAC-NOMA

The next generation Internet of Things (IoT) exhibits a unique feature that IoT devices have different energy profiles and quality of service (QoS) requirements. In this paper, two energy and spectrally efficient transmission strategies, namely wireless power transfer assisted non-orthogonal multiple access (WPT-NOMA) and backscatter communication assisted NOMA (BAC-NOMA), are proposed by utilizing this feature of IoT and employing spectrum and energy cooperation among the devices. Furthermore, for the proposed WPT-NOMA scheme, the application of hybrid successive interference cancelation (SIC) is also considered, and analytical results are developed to demonstrate that WPT-NOMA can avoid outage probability error floors and realize the full diversity gain. Unlike WPT-NOMA, BAC-NOMA suffers from an outage probability error floor, and the asymptotic behaviour of this error floor is analyzed in the paper by applying the extreme value theory. In addition, the effect of a unique feature of BAC-NOMA, i.e., employing one device's signal as the carrier signal for another device, is studied, and its impact on the diversity gain is revealed. Simulation results are also provided to compare the performance of the proposed strategies and verify the developed analytical results.

Guest Editorial Special Issue on Edge-Cloud Interplay Based on SDN and NFV for Next-Generation IoT Applications

With significant and continuing advances in information and communication technologies, the Internet of Things (IoT) will play an increasingly important role in domains, such as healthcare, transportation, finance, and energy. In an IoT system, billions of devices (e.g., sensors, wearables, and smart appliances) are connected to the global network infrastructure, and one associated phenomenon is the generation of a large volume of data. Apart from data volume, the velocity, variety, and veracity of these data will pose a significant burden on conventional networking infrastructures. However, as sensor and fifth-generation (5G) cellular technologies advance, so will the pervasiveness of IoT deployment. Parallel to this trend, cloud computing has been integrated with IoT in order to address limitations in existing IoT networks (e.g., storage and computing resources), and examples include Google cloud dataflow and Amazon IoT. However, cloud-centric IoT solutions may not be suited for delay-sensitive and computationally intensive applications, for example, due to resource availability, end-to-end latency, bandwidth, etc. Increasingly, large-scale IoT deployments demand high connectivity, interoperability, and orchestration which are necessary for minimizing latency and maximizing throughput. This highlights the importance of a distributed computing platform that can support the interactions between IoT and cloud computing systems.

Blockchain-Enabled Distributed Security Framework for Next-Generation IoT: An Edge Cloud and Software-Defined Network-Integrated Approach

The Internet of Things (IoT) plays a vital role in the real world by providing autonomous support for communications and operations, thus enabling and promoting novel services that are commonly used in day-to-day life. It is important to do research on security frameworks for next-generation IoT and develop state-of-the-art confidentiality protection schemes to deal with various attacks on IoT networks. In order to offer prominent features like continuous confidentiality, authentication, and robustness, the blockchain technology comes out as a sustainable solution. A blockchain-enabled distributed security framework using edge cloud and software-defined networking (SDN) is presented in this article. The security attack detection is achieved at the cloud layer, and security attacks are consequently reduced at the edge layer of the IoT network. The SDN-enabled gateway offers dynamic network traffic flow management, which contributes to the security attack recognition through determining doubtful network traffic flows and diminishes security attacks through hindering doubtful flows. The results obtained show that the proposed security framework can efficiently and effectively meet the data confidentiality challenges introduced by the integration of blockchain, edge cloud, and SDN paradigm.

Quality-enabled decentralized IoT architecture with efficient resources utilization

The Internet of Things (IoT) is a paradigm aimed at connecting everyday objects to the internet. IoT applications include smart cities, healthcare, agriculture, as well as the industry and manufacturing. The ability to monitor and control the physical world using information technology creates many opportunities. However, it also comes with some costs. The exponential growth of connected devices, the heterogeneity of IoT use cases, and the diversity of the network technologies yield a concern regarding IoT sustainability. With this work, we aim to contribute to this concern. In doing so, we introduce a novel representation model that is destined for (i) monitoring the IoT environment at runtime, (ii) expressing the overall quality of the system, and (iii) helping to utilize the available resources efficiently. We also define a feature set that describes the best the expectations of decentralized IoT platforms. Furthermore, we describe a quality-enabled decentralized IoT architecture too that incorporates the specified feature set as well as our representation model. Such solutions are necessary to improve and maintain IoT of the future and all its application domains, including the Industrial Internet of Things (IIoT). With the presented research, we aim to encourage the efficient utilization of resources and simplify the production of next-generation IoT solutions.

A Service Delay Minimization Scheme for QoS-Constrained, Context-Aware Unified IoT Applications

With the unprecedented advancements in the field of Internet of Things (IoT), novel increasingly complex services are getting conceived and implemented with the day. A new trend in this landscape of next-generation IoT applications has been those orchestrated by the seamless integration of numerous vertical IoT applications, also known as, unified IoT services. However, such unified IoT applications are resource hungry (computation, storage, as well as network resources) and are usually characterized by real-time constraints. These render cloud and fog enactments insufficient. This article employs a context-aware computing approach that helps alleviating massive data transfers across fog nodes in real time. User requests are managed at fog nodes were depending upon their required contexts, availability of such contexts in the fog node and respective deadline, the requests are serviced either locally or at other fog nodes and Quality of Service (QoS) is fulfilled. To this end, a distributed service management algorithm is proposed that services the user requests at every fog node by either sharing contexts among context requests within a fog node, or by bringing in unavailable contexts from other fog nodes or by migrating service requests to remote fog nodes with available contexts in a deadline-aware fashion. Accordingly, this article proposes a novel smart multichannel queuing (SMCQ) model that schedules requests to virtual machines (VMs) at individual fog nodes by assigning them to specific priority groups (based on deadline vicinity) and thus minimizes service delay. The proposed algorithms for delay-tolerant service migration have been presented and simulation results carried out to demonstrate the efficacy of the proposed methodology.

Machine learning based solutions for security of Internet of Things (IoT): A survey

Over the last decade, IoT platforms have been developed into a global giant that grabs every aspect of our daily lives by advancing human life with its unaccountable smart services. Because of easy accessibility and fast-growing demand for smart devices and network, IoT is now facing more security challenges than ever before. There are existing security measures that can be applied to protect IoT. However, traditional techniques are not as efficient with the advancement booms as well as different attack types and their severeness. Thus, a strong-dynamically enhanced and up to date security system is required for next-generation IoT system. A huge technological advancement has been noticed in Machine Learning (ML) which has opened many possible research windows to address ongoing and future challenges in IoT. In order to detect attacks and identify abnormal behaviors of smart devices and networks, ML is being utilized as a powerful technology to fulfill this purpose. In this survey paper, the architecture of IoT is discussed, following a comprehensive literature review on ML approaches the importance of security of IoT in terms of different types of possible attacks. Moreover, ML-based potential solutions for IoT security has been presented and future challenges are discussed.

Continuous direct current by charge transportation for next-generation IoT and real-time virtual reality applications

Abstract A conversion from mechanical energy into direct current (DC) is of great interest to realize the next-generation self-sustained Internet of Things (IoT) and real-time virtual reality (VR) control. Inspired by ancient waterwheel transport water and P–N junction theory, a continuous DC nanogenerator was developed using charges unidirectional transportation and dual-intersection triboelectric nanogenerators (TENGs). By employing tribo-polarity reversal porous material as charges transportation carrier sliding among the ultra-negative and ultra-positive materials, the charges were unidirectionally transported by the discharge of repulsion via line electrodes, forming a stability DC output. Due to the charge transport and repulsive discharge, a much higher DC output voltage was easily obtained than the air breakdown mechanism. Moving forward the realistic application, the capability of the dual-intersection TENGs as a DC power source for the actuator and sensor was well demonstrated, and it further expanded all kinds of wireless networks for next-generation IoT to directly power sensor nodes. To get across the discontinuous characteristic identification from triboelectric (e.g. frequency, number of peaks, pulse absolute value) as status stimulation in software, we first time successfully realized a continuous motion control in virtual space for next-generation real-time VR application in triboelectric.

Guest Editorial: Special Issue on Large-Scale Wireless-Powered Networks With Backscatter Communications

The Proliferation of connected and embedded devices has played a critical role in the development of next generation Internet of Things (IoT), and consequently, the diffusion of low power networks. The sustainability of such networks entails energy aware devices that are capable of self-sustainable operations by harvesting and recycling energy from various sources. Accordingly, integration of wireless-powered networks (WPN) with ambient backscatter communications (AmBC), in which a backscatter transmitter conveys data to a destination by backscattering ambient signals, has recently emerged as a promising key technology for low power wireless networks. However, these technologies are not optimized and/or designed for large-scale networks and a massive number of IoT devices.