Internet Of Things Challenges Research Articles

A portfolio selection of internet of things (IoTs) applications for the sustainable urban transportation: A novel hybrid multi criteria decision making approach

The nature of technological innovations is unpredictable, therefore decision making on disruptive technologies such as the Internet of Things (IoT) in many public sector organizations is undisciplined. Due to resource limitations and tremendous payoffs, there is a need to have a decision-making framework to manage technological innovations as a portfolio of investments that balances risk and reward simultaneously. This study provides a new portfolio matrix for decision making to identify IoT applications in the urban transportation sector for future investment based on two dimensions of impact on sustainable development (SD) and feasibility for implementing IoT using a novel hybrid multi criteria decision making approach. For this purpose, seventeen IoT applications in urban transportation were identified using a systematic literature review. The improved Fuzzy Cognitive Map (FCM) - Best Worth Method (BWM) method was used to determine the relationship between SD criteria and IoT challenges, and to calculate their importance. Then, the Additive Ratio Assessment (ARAS) method was used to prioritize the IoT applications based on SD criteria and IoT challenges. Finally, the adapted impact-feasibility matrix was used to simultaneously evaluate both criteria for selecting the IoT applications portfolio. Results of this paper show that bike sharing and car sharing have the first priority for investment in the case of a developing country.

Navigating Routing Challenges in Internet of Things (IoT): A Focus on Routing Protocols and Wireless Sensor Networks

This paper states the routing issues in the Internet of Things (IoT). The main point of focus is especially on routing protocols in IoT and wireless sensor networks (WSN). “Everything which is connected to the internet is alive”, is going to be the new rule for the future. The future is the Internet of Things (IoT), the world is changing every day with new technologies and inventions. The Internet of Things (IoT) has known as a future scenario of technology in human life. IoT expands the concept of the Internet from a network of rather homogenous devices such as computers to a network of heterogeneous devices such as home appliances and the conventional network is mostly replaced with IoT. Devices are the main users of the IoT framework an important part is IEEE 802.15.4, a standard that operates at the PHY layer and MAC layer of IoT devices. The IoT Ecosystem includes a large number of integrated heterogeneous low-power, low-cost devices, and secure connections are needed between them [15]. They communicate with each other to gather, share and forward the information in a multi-hop manner, this requires a complex routing between them. WSN, new technology in the IoT system comprised of a large collection of sensors, a mesh network can be used to individually gather data and send data through a router to the internet in an IoT system [6]. an enormous number of sensors are used and its growth is also fast, they are collecting inexperienced data and transforming it into meaningful and useful information. considering the full usability of these devices and a huge number of them, produce routing challenges [16]. Routing in IoT is the most critical part because we are working with devices that have restricted device resources. the optimum goal is to have enhanced routing protocols and efficient performance with such limited resource devices [15, 18].

Engineering the advances of the artificial neural networks (ANNs) for the security requirements of Internet of Things: a systematic review

Internet of Things (IoT) driven systems have been sharply growing in the recent times but this evolution is hampered by cybersecurity threats like spoofing, denial of service (DoS), distributed denial of service (DDoS) attacks, intrusions, malwares, authentication problems or other fatal attacks. The impacts of these security threats can be diminished by providing protection towards the different IoT security features. Different technological solutions have been presented to cope with the vulnerabilities and providing overall security towards IoT systems operating in numerous environments. In order to attain the full-pledged security of any IoT-driven system the significant contribution presented by artificial neural networks (ANNs) is worthy to be highlighted. Therefore, a systematic approach is presented to unfold the efforts and approaches of ANNs towards the security challenges of IoT. This systematic literature review (SLR) is composed of three (3) research questions (RQs) such that in RQ1, the major focus is to identify security requirements or criteria that defines a full-pledge IoT system. This question also focusses on pinpointing the different types of ANNs approaches that are contributing towards IoT security. In RQ2, we highlighted and discussed the contributions of ANNs approaches for individual security requirement/feature in comprehensive and detailed fashion. In this question, we also determined the various models, frameworks, techniques and algorithms suggested by ANNs for the security advancements of IoT. In RQ3, different security mechanisms presented by ANNs especially towards intrusion detection system (IDS) in IoT along with their performances are comparatively discussed. In this research, 143 research papers have been used for analysis which are providing security solutions towards IoT security issues. A comprehensive and in-depth analysis of selected studies have been made to understand the current research gaps and future research works in this domain.

RETRACTED ARTICLE: Energy efficient IRS assisted 6G network for Industry 5.0

The real world applications are more prone to difficulties of challenges due to fast growth of technologies and inclusion of artificial intelligence (AI) based logical solutions. The massive internet-of-things (IoT) devices are involved in number of Industry 5.0 applications like smart healthcare, smart manufacturing, smart agriculture, smart transportation. Advanced wireless techniques, customization of services and different technologies are experiencing a major transformation. The desire to increase the communication reliability without adding energy overhead is the major challenge for massive IoT enabled networks. To cope up with the above challenges, Industry 5.0 requirements needs to be monitored at the remote level which again adds on the communication challenge. Use of relays in 6G based wireless networks is denied due to high requirement of energy. Therefore in this paper, Intelligent reflecting surfaces (IRSs) assisted energy constrained 6G wireless networks are studied. To provide seamless connection between the communicating mobile nodes, IRS with an array of reflecting elements are configured in the system set up. A use-case scenario of IRS enabled network in Internet-of-Underwater things (IoUT) for smart ocean transportation is also provided. The IRS assisted wireless network is evaluated for target rates achieved. A power consumption model of the IRS supported system is also proposed to optimise the energy efficiency of the system. Further, the paper evaluates the impact of number of reflecting elements N on the IRS and the phase resolution b of each element on the system performance. The energy efficiency improves by 20% for IRS with N=100\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=100$$\\end{document} with b=2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$b=2$$\\end{document} over IRS with b=1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$b=1$$\\end{document}.

Multi-criteria Decision Support System for Prioritizing Challenges of Internet of Things (IoT)

Abstract: The adoption and utilization of Internet of Things (IoT) technologies such as IoT Platforms, 5G, connected sensors, WAN, etc mostly come with numerous, seemingly complex and expensive challenges for institutions and organisations, particularly in developing countries as they work to fill their fair share of the global IoT market. Multi-criteria decision support systems (MCDSS) can be indispensable tools for evaluating and prioritizing these complex competing difficulties by multistakeholder decision makers. While eliminating conventional decision problems such as, human biases, time consumption, cost of decision, etc, MCDSS make it possible for companies to concentrate on solving challenges which their limited resources can cater for. This study presents an approach for design and implementation of a prototype MCDSS for prioritizing challenges that institutions and organizations face while adopting and utilizing IoT technologies using Fuzzy Analytic Hierarchy Process (FAHP) methodology. ASP.Net model View controller (MVC) framework and C Sharp programming Language was used for the GUI and the Logic development. The system's default IoT challenges and dataset were adapted from the works of A.K. Mohammadzadeh (Baseline dataset). The system usability test result obtained shows that the system is friendly and usable. The output weights and rating of the IoT technology adoption challenges/sub-challenges exhibit over 80% similarity when compared to the Baseline dataset. Further studies are recommended to make the system a

IoT-based generalized multi-granulation sequential three-way decisions

In the context of the Internet of Things (IoT), decision models and GRC (Governance, Risk, and Compliance) can be used for data processing and decision-making processes. The IoT involves a large number of sensors and devices, some of which enable “device-free” functionality, meaning that specific devices or sensors are not required to obtain the necessary information. This technology utilizes the signals present in the environment to determine the location of individuals without them carrying any specific devices. By incorporating “device-free” sensor data into decision models, we can process IoT data more efficiently and achieve accurate location positioning. The prevailing hierarchical sequential three-way decision models (STDM) follow a common pattern of constructing a hierarchical tree of conditional attributes to acquire knowledge. However, these models fail to meet the diverse needs of users when it comes to rule mining complex hierarchical data with multiple levels and views in real-world applications. Additionally, the pursuit of simplified rules remains a significant topic of interest. To address these challenges, we propose a novel approach that integrates multi-granularity, device-free localization, and STDM. By leveraging IoT and device-free localization, we collect data from various devices and sensors to accurately determine the positions of individuals. Using this data, we construct a generalized hierarchical decision table by creating a concept hierarchical tree with decision attributes. This framework offers a comprehensive solution for overcoming the challenges of device-free localization and IoT. Subsequently, we establish a granular structure by partitioning the complete set of attributes. In this paper, our proposed approach improves the efficiency and accuracy of data processing in machine learning models and enables users to better process and understand complex datasets. Finally, the performance is experimentally validated for the models in this paper. Such models greatly enrich the theoretical framework of three-way decision making. Overall, the abstract provides a concise and succinct overview, outlining the motivation behind the research and presenting the contributions made.

LIGHTWEIGHT CRYPTOGRAPHY METHOD IN THE INTERNET OF THINGS USING ELLIPTIC CURVE AND CROW SEARCH ALGORITHM

The Internet of Things (IoT) as an important technology consists of a heterogeneous and huge number of devices that generates an enormous amount of data in various applications. However, generating and transmitting huge amount of data in the IoT makes it crucial to implement a secure and safe data transmission scheme. Cryptography methods can secure the confidentiality, data integrity, access control, and authentication. Due to constrained resources in IoT devices, providing classical cryptography schemes isn’t efficient for IoT applications, so a lightweight cryptography scheme is one of the most important solutions to overcome security challenges in IoT. In this paper, a new security scheme called ECCCSASHA256 based on Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm (SHA-256) using Crow Search Algorithm (CSA) has been proposed for secure data transmission in IoT devices. The ECCCSASHA256 model uses the CSA for generating a private key to encode the elliptic curve. Furthermore, the proposed scheme uses SHA-256 model for hashing the incoming encoded data using ECC. The simulation results indicate that the average throughput of the proposed model was about 8.22% and 8.97% higher in encryption and 8.72% and 9.81% higher in decryption compared to 3DES&ECC&SHA-256 and RC4&ECC&SHA-256, respectively.

Current Trends and Challenges of IOT Next 5 Years

bstract: The world is moving forward at smart , and the credit goes to ever growing technology. One such concept is IOT (Internet of things) with which automation is no longer a virtual reality. IOT connects various physical objects through the internet and enables them to share information with their community network to automate processes for humans and makes their lives easier and confortable . The paper presents the current trends and future challenges of IoT next 5 years , such as the technical, business and legal challenges.

A systematic survey on content caching in ICN and ICN-IoT: Challenges, approaches and strategies

Context:Data traffic increased in recent years due to the expansion of IoT (Internet of Things) applications, and most IoT applications follow a content-oriented paradigm. The host-to-host nature of the existing internet architecture makes it inefficient for handling content distribution and information sharing. ICN (Information Centric Network) is a content-centric network that offers guaranteed solutions for data transfer between consumers and producers. ICN caching enables the caching technique in available resources, which can enhance the overall network performance by enabling users to access data within the network. Objective:This study aims to review the literature on caching approaches and strategies in ICN-IoT to identify areas that need more attention in current and future research directions. Method:A systematic mapping study was conducted to identify the relevant literature, and 88 publications that are categorized as caching approaches and caching strategies were chosen as the most relevant studies. Result:Based on caching approaches and caching strategies, the articles are grouped. Edge caching (50%), in-network caching (25%), and hybrid caching (25%) represent the majority of the articles on caching approaches. Popularity-based caching (26%), cooperative caching (17%), collaborative caching (17%), machine learning-based caching (19%), and probabilistic caching (7%) represent the majority of the articles in caching strategies. This study further presents the simulators, performance metrics, parameters and performance evaluation of the existing literature. Conclusion:The results confirm that ICN is a futuristic internet architecture that assigns a content name to each content in the network and enables in-network caching; it is a superior technique for resolving IoT challenges and increases the overall network efficiency.

Future Smart City with Less Traffic Congestion Model Enabled by (IoT)

The advancement in wireless telecommunication network has increase the accessibility of more users to wireless connectivity. With the advent of the fifth-generation (5G) wireless network, a seamless connectivity is available for internet users globally. A future smart city is a metropolis that utilizes information and communication technologies (ICT) to grow its functionality effectively to disseminate information among the public and to develop the quality of government facilities and the welfare of the citizen. The Internet of Things (IoT) refer to the interconnection of several systems, devices or physical objects/things which are driven by sensors, software, and other equipment in order to interconnect and interchange data with other devices and systems through the internet. The Internet of things (IoT), is a revolutionary method that allows a diverse number of applications to be interconnected in order to create a single communication architecture. Future smart city has resulted in the increase in population, hence there is need to develop a smart traffic light system to help in managing the problem of future smart city; traffic congestion. The Internet of Things (IoT) a key features necessary for employing a large-scale in IoTS are low-cost sensors, high-speed and error-tolerant data communications, smart computations, and numerous applications which helps in solving these challenges associated with traffic congestion. It enables a smart environment, smart energy, and smart transportation system. In this paper, we shall discuss IoT technology, review some literatures on application area of Internet of Things (IoT), and challenges of IoT. And also discuss the applications of IoT, in smart city development, and traffic congestion management in smart city design, and how it proffers solution to future smart city problem.

Machine Learning-Driven Ubiquitous Mobile Edge Computing as a Solution to Network Challenges in Next-Generation IoT

Ubiquitous mobile edge computing (MEC) using the internet of things (IoT) is a promising technology for providing low-latency and high-throughput services to end-users. Resource allocation and quality of service (QoS) optimization are critical challenges in MEC systems due to the large number of devices and applications involved. This results in poor latency with minimum throughput and energy consumption as well as a high delay rate. Therefore, this paper proposes a novel approach for resource allocation and QoS optimization in MEC using IoT by combining the hybrid kernel random Forest (HKRF) and ensemble support vector machine (ESVM) algorithms with crossover-based hunter–prey optimization (CHPO). The HKRF algorithm uses decision trees and kernel functions to capture the complex relationships between input features and output labels. The ESVM algorithm combines multiple SVM classifiers to improve the classification accuracy and robustness. The CHPO algorithm is a metaheuristic optimization algorithm that mimics the hunting behavior of predators and prey in nature. The proposed approach aims to optimize the parameters of the HKRF and ESVM algorithms and allocate resources to different applications running on the MEC network to improve the QoS metrics such as latency, throughput, and energy efficiency. The experimental results show that the proposed approach outperforms other algorithms in terms of QoS metrics and resource allocation efficiency. The throughput and the energy consumption attained by our proposed approach are 595 mbit/s and 9.4 mJ, respectively.

Swarm Intelligence-Based Task Scheduling for Enhancing Security for IoT Devices

Due to the great advancement in computation, communication and control technologies, the Internet of Things (IoT) can provide ubiquitous connectivity for anyone and anything at any time and any place, leading to a revolution in information society. Protecting devices against various security threats is one of the most important challenges in IoT since IoT applications are generally security-critical systems while IoT devices are often poorly secured. For IoT devices, employing security services provided by smart gateways or edge/cloud servers to defend against various threats is an effective way to enhance their security. However, the finite battery energy of devices and the limited fund of device users hinder the wide application of security services in IoT. This necessitates the demand for designing new methodologies to tackle the trade-off among security, energy and fund of IoT devices. Therefore, this paper attempts to optimize system security of IoT devices under energy and fund constraints. Specifically, to formulate the energy and fund constrained security optimization problem, we firstly propose a pricing model for the security services provided by the smart gateway. We then formulate the problem as a mixed-integer linear programming (MILP) problem. Since using a solver to address the MILP problem may be time-consuming, we leverage the swarm intelligence technique to design a new task scheduling scheme that can efficiently solve the optimization problem. Extensive experiments are conducted to validate our proposed MILP and swarm intelligence based task scheduling algorithms. Simulation results show that our scheme outperforms two state-of-the-art methods in improving system quality of security and guaranteeing schedule feasibility.

Analysis on issues and challenges of IoT and the solution based on blockchain technology

With the development of science and technology, the Internet of Things (IoT) has become an area of great influence and potential. However, the deficient data security and privacy problems of the current IoT systems seriously limit its application. This is due to the fact that the information exchange and data authentication in IoT must be done through the central server. In order to solve the fraud possibility of device, false authentication, unreliable data sharing, eliminating the concept of a central server, the block chain (BC) technology was adopted as part of the Internet of things used to improve the above problems. This paper elaborates the component interaction in the Internet of things which bring security and privacy problems, and introduces the existing block chain technology, the application of Internet of things. Various challenges of IoT and IoT with BC as well as future research directions are summarized.

DNS for IoT: A Survey.

The Internet of Things (IoT) is paving the way to becoming necessary in numerous aspects of people's lives. IoT is becoming integrated in many domains, such as medical, industrial, and personal. Recent years have witnessed the creation of many IoT technologies that differ not only in their applications and use cases but also in standards. The absence of universally accepted standards and the variety of technologies are only some challenges the IoT market faces. Other challenges include the constrained nature of most IoT devices, the diverse identification schemes, the inadequate security mechanisms, and the lack of interoperability between different technologies. The Domain Name System (DNS) persisted throughout the years as the Internet's naming service and accumulated more trust from users with the introduction of its security extensions. DNS could be utilized to address some of the challenges the IoT market faces. However, using DNS for IoT applications might jeopardize DNS infrastructure. In this survey, we study the coexistence of DNS and IoT. We define IoT, present its architecture and discuss its main challenges. We then introduce DNS and its function; we discuss its security and privacy drawbacks and the extensions standardized to address them. We further discuss the uses of DNS in IoT environments to address some of IoT's challenges and the impact these uses might have on DNS.

Generic Board for Industrial Appliances using IoT and GUI

The paper focuses on the integration of the Internet of Things (IoT) with Graphical User Interface (GUI) in the design and development of industrial appliances. The integration of these two technologies enhances the usability, flexibility, and efficiency of industrial appliances. The paper presents a detailed discussion of the benefits and challenges of IoT and GUI integration in industrial appliances. It also explores various applications of IoT and GUI in industrial appliances such as smart factories, logistics management, and supply chain management. The paper concludes by highlighting the potential of IoT and GUI integration in revolutionizing the industrial sector by improving productivity and reducing costs

A comprehensive study on cybersecurity challenges and opportunities in the IoT world

AbstractIt has become possible to link anything and everything to the Internet in recent decades due to the expanding Internet of Things (IoT). As a result, our usage of technology has changed a lot, causing digital disruption in the real world. IoT allows drones, sensors, digital set‐top boxes, surveillance cameras, wearable technology, and medical equipment to be connected to the internet. Healthcare, manufacturing, utilities, transportation, and housing are among the various sectors that has become intelligent. Recently, we have seen a surge in cybersecurity challenges and opportunities for the improvement of various IoT applications. Although cybersecurity and the IoT are extensively researched, there is a dearth of studies that exclusively focus on the evolution of cybersecurity challenges in the area of AI and machine learning, blockchain and zero trust, lightweight security, integration of IoT with 5G networks, and many more in the IoT world. The availability of environment‐capturing sensors and internet‐connected tracking devices allows for private life surveillance and cloud data transmission. Therefore, a significant problem for researchers and developers is to ensure the CIA (Confidentiality, Integrity, and Availability) security triangle for people. This paper presents a comprehensive study of cybersecurity applications, challenges, and opportunities in the IoT world. The IoT architectural layer, attacks against the IoT layer, and related issues are highlighted. Furthermore, cybersecurity issues and challenges in IoT along with the strength and weaknesses of existing techniques are discussed in detail. Our study will provide insight into various current cybersecurity research trends in the IoT world.

Design and analysis of routing algorithm with energy efficiency based on clustering and backwards in IoT system

One of the biggest challenges for Internet of Things (IoT) systems is traffic congestion in large networks. For this reason, the bandwidth should be increased in such systems. In addition, the issue of routing is raised in sending packets from the origin to the destination. Therefore, if there are many IoT devices in the network, it will increase the traffic, which makes faultless routing important in these networks. In this paper, a novel routing method based on Routing Protocol for Low-Power (RPL) is presented to minimize the energy consumption of the Internet of Things. Using the backward method based on the A* method to reduce energy consumption in a large graph, promising nodes are selected. A coordinate node is used to manage packets and transfer them. The selection of the coordinator node helps to receive packets with less energy and less delay from its neighbors, and the head node selects the best coordinator node with the shortest distance and the highest residual energy. The proposed method improves the energy consumption criteria, the delay between nodes, and the network overhead criterion by considering the estimated energy to the destination with the A* method.

Small But Mighty: The Power of Lightweight Cryptography in IoT

The Internet of Things (IoT) has brought about significant changes to various domains such as healthcare, transportation, and manufacturing. However, security remains a critical challenge in IoT due to the large number of connected devices with varying processing capabilities and memory constraints. Traditional cryptographic algorithms are not well-suited for IoT devices due to their high computational and memory requirements. Lightweight cryptography algorithms have emerged as a promising solution for securing IoT devices with limited resources. In this abstract, we provide an overview of lightweight cryptography algorithms for IoT, including their design principles, security properties, and performance evaluation.

Machine learning‐based clustering protocols for Internet of Things networks: An overview

SummaryThe Internet of Things (IoT) continues to expand the current Internet, opening the door to a wide range of novel applications. The increasing volume of the IoT requires effective strategies to overcome its challenges. Machine Learning (ML) has led to a growing technology that enables computers to solve problems without the need for knowledge of their intricate details. Over the past years, various ML techniques have been used to efficiently manage IoT networks. Clustering is a technique that has proven its performance in the networking domain. Many works in the literature have studied ML‐based clustering methods for IoT networks, including their main properties, characteristics, underlying technologies, and open issues. In this paper, we focus on topology‐centered ML‐based clustering protocols for IoT networks. Specifically, we investigate the potential benefits of adopting the clustering approach to address several IoT challenges. Moreover, we provide a comprehensive taxonomy of ML‐based clustering algorithms for IoT networks. Finally, we statistically analyze the incorporation of ML techniques for clustering in various IoT systems and highlight the related open issues.

Cloud and internet-of-things secure integration along with security concerns

<span lang="EN-US">Cloud computing is a new technology which refers to an infrastructure where both software and hardware application are operate for the network with the help of internet. Cloud computing provide these services with the help of rule know as you pay as you go on. Internet of things (IoT) is a new technology which is growing rapidly in the field of telecommunications. The aim of IoT devices is to connect all things around us to the Internet and thus provide us with smarter cities, intelligent homes and generally more comfortable lives. The combation of cloud computing and IoT devices make rapid development of both technologies. In this paper, we present information about IoT and cloud computing with a focus on the security issues of both technologies. Concluding we present the contribution of cloud computing to the IoT technology. Thus, it shows how the cloud computing technology improves the function of the IoT. Finally present the security challenges of both technologies IoT and cloud computing.</span>