Internet Of Things Components Research Articles

Multi-Class Imbalanced Data Handling with Concept Drift in Fog Computing: A Taxonomy, Review, and Future Directions

A network of actual physical objects or “IoT components” linked to the internet and equipped with sensors, electronics, software, and network connectivity is known as the Internet of Things (IoT). This ability of the IoT components to gather and share data is made possible by this network connectivity. Many IoT devices are currently operating, which generate a lot of data. When these IoT devices started collecting data, the cloud was the only place to analyze, filter, pre-process, and aggregate it. However, when it comes to IoT, the cloud has restrictions regarding latency and a more centralized method of distributing programs. A new form of computing called Fog computing has been proposed to address the shortcomings of current cloud computing. In an IoT context, sensors regularly communicate signal information, and edge devices process the data obtained from these sensors using Fog computing. The sensors’ internal or external problems, security breaches, or the integration of heterogeneous equipment contribute to the imbalanced data, i.e., comparatively speaking, one class has more instances than the other. As a result of this data, the pattern extraction is imbalanced . Recent attempts have concentrated heavily on binary-class imbalanced concerns with exactly two classes. However, the classification of multi-class imbalanced data is an issue that needs to be fixed in Fog computing, even if it is widespread in other fields, including text categorization, human activity detection, and medical diagnosis. The study intends to deal with this problem. It presents a systematic, thorough, and in-depth comparative analysis of several binary-class and multi-class imbalanced data handling strategies for batch and streaming data in IoT networks and Fog computing. There are five major objectives in this study. First, reviewing the Fog computing concept. Second, outlining the optimization metric used in Fog computing. Third, focusing on binary and multi-class batch data handling for IoT networks and Fog computing. Fourth, reviewing and comparing the current imbalanced data handling methodologies for multi-class data streams. Fifth, explaining how to cope with the concept drift, including novel and recurring classes, targeted optimization measures, and evaluation tools. Finally, the best performance metrics and tools for concept drift, binary-class (batch and stream) data, and multi-class (batch and stream) data are highlighted.

IoT enabled carbon cloth-based 3D printed hydrogen fuel cell integrated with supercapacitor for low-power microelectronic devices

A Hydrogen fuel cell (HFC) broad range associated with Internet of Things (IoT) technologies that require slightly less and constant electricity made possible by remote climate monitoring connections. Novelty demonstrates a miniature HFC based on carbon cloth electrodes and sealing elements manufactured via 3D printing. Cobalt (II) Oxide (Co3O4)—reduced Graphene Oxide (rGO) and Platinum (Pt) based nanoparticles are coated over carbon cloth to increase the catalytic activity at the anode and cathode. Hydrogen is produced by using an aluminium foil (Al) that is stored in between the filter paper and through capillary action the sodium hydroxide pellets (NaOH) are applied and reacted with Al foil to produce hydrogen. The single HFC device working surface area of 1 × 1 cm2 effectively generates an open circuit voltage (OCV) of 1.3 V, a current density of 1.602 mA/cm2, and a peak power density of 761 mW/cm2. The fuel cell stability performance is monitored for up to 10 h. The power obtained from the HFC is stored in a supercapacitor and used to supply energy to the IoT component. The module includes a built-in sensor that monitors the temperature, pressure, and humidity. The measured data is then transmitted to a smartphone via Bluetooth.

A New Approach to FRAND Royalty Determination in IoT Value Chains: Insights from Connected Cars

The complexities surrounding connected cars, especially the integration of Telematics Control Units (TCUs), intensify uncertainties within their value chains. Disputes arise regarding rightful licensees and applicable rates. Standard Essential Patent (SEP) holders push for royalties from end-product manufacturers, pegged to final product prices. Conversely, manufacturers and suppliers argue for licensing directed at suppliers, restricting royalties to the TCU’s price. This discord unfolded in the legal tussle involving Nokia (SEP holder), Daimler (car manufacturer), and its supplier, litigated in German courts. This context underscores the significance behind the EU Commission’s proposal on SEP licensing issued in April 2023. Addressing escalating disputes and uncertainties surrounding Fair, Reasonable, and Non-Discriminatory (FRAND) royalty rates, the proposal aims to establish transparency and predictability in SEP licensing. In our study, we have devised a proposal aimed at reshaping this landscape. We challenge the conventional linkage of SEP royalties solely to end-product prices within intricate value chains. Instead, we advocate tying SEP royalties to the economic value attributed to the SEP, specifically focusing on connectivity within Internet of Things (IoT) applications. Our proposal suggests granting licences to the party responsible for designing the IoT component, irrespective of its role in the value chain. This functionalistic approach also proposes establishing the royalty base through technical, measurable factors like data consumption. This model offers objectivity in determining FRAND rates, potentially fostering constructive negotiations and offering environmental benefits.

Unveiling the core of IoT: comprehensive review on data security challenges and mitigation strategies

The Internet of Things (IoT) is a collection of devices such as sensors for collecting data, actuators that perform mechanical actions on the sensor's collected data, and gateways used as an interface for effective communication with the external world. The IoT has been successfully applied to various fields, from small households to large industries. The IoT environment consists of heterogeneous networks and billions of devices increasing daily, making the system more complex and this need for privacy and security of IoT devices become a major concern. The critical components of IoT are device identification, a large number of sensors, hardware operating systems, and IoT semantics and services. The layers of a core IoT application are presented in this paper with the protocols used in each layer. The security challenges at various IoT layers are unveiled in this review paper along with the existing mitigation strategies such as machine learning, deep learning, lightweight encryption techniques, and Intrusion Detection Systems (IDS) to overcome these security challenges and future scope. It has been concluded after doing an intensive review that Spoofing and Distributed Denial of Service (DDoS) attacks are two of the most common attacks in IoT applications. While spoofing tricks systems by impersonating devices, DDoS attacks flood IoT systems with traffic. IoT security is also compromised by other attacks, such as botnet attacks, man-in-middle attacks etc. which call for strong defenses including IDS framework, deep neural networks, and multifactor authentication system.

Аналіз технологій реконфігурації систем інтернету речей на рівні програмних модулів та завантажувачів

The subject of study and research in this article are the technology of programming and interaction of nodes, as well as individual small-sized components of embedded Internet of Things (IoT) systems. The goal is to increase the flexibility of IoT system components by simplifying the reconfiguration process of individual nodes and components. The task is: to analyze existing approaches to building configurable systems, analyze and describe existing solutions that implement joint approaches; to analyze the tools for implementing the selected approaches; to describe the method of building a living system based on the analysis approach; and to provide a technical example of the proposed programming method. According to the tasks, the following results were obtained. Possible ways of reconfiguration of IoT components are analyzed. Methods of reconfiguration at the network level and partial node reconfiguration are considered. Methods to ensure the flexibility of IoT components are analyzed and classified. A comparative analysis of the summary parameters of the loaders was carried out. The possibility of using software components as immutable blocks of code to modify the microcontroller program as a method of reconfiguration was analyzed. The idea of using variable software modules to represent the microcontroller program at a higher level of abstraction with a modified bootloader is thus proposed. Elements of a method for reprogramming variable software modules of microcontrollers as middleware are proposed. Conclusions. According to a review of the existing types of systems, they were classified according to the level of organization of their reconfigured parts. According to the obtained preliminary classifications, some architectural solutions were considered from the point of view of improving operational characteristics. The main advantages for each type of organization of the reconfigurable parts of the systems are highlighted. An analysis of practical solutions regarding the modification of the built-in components of microcontrollers for managing and updating their firmware was conducted. The practical significance of this study is the proposal of a method of programming with variable software modules for microcontrollers and the obtained results of a competitive analysis of methods of modifying components and loader characteristics.

IOT Based Food Minitoring System

Abstract: The Internet of Things (IoT) has emerged as a transformative technology with wide-ranging applications, and one of its promising domains is in the field of food monitoring systems. This abstract introduces an innovative IoT-based Food Monitoring System designed to enhance food quality assurance and reduce waste in the supply chain. The proposed system integrates various IoT components, including sensors, actuators, and communication devices, to create a comprehensive solution for monitoring the entire lifecycle of food products. Key objectives of the system include real-time tracking of food conditions, ensuring compliance with quality standards, and minimizing the environmental impact associated with food waste. The implementation of this IoT-based Food Monitoring System offers several benefits, including improved food quality, reduced waste, increased operational efficiency, and enhanced consumer satisfaction. By leveraging the capabilities of IoT, this system addresses the challenges associated with maintaining food quality and safety while contributing to the overall sustainability of the food industry

Design, Fabrication and Monitoring of Automated Polyhouse

The agriculture sector faces the challenge of feeding a growing population while dealing with the effects of climate change. With the limited expansion of cropland, agriculture automation has become necessary. Internet of Things (IoT) and Artificial Intelligence (AI) have made significant strides in various industries, including agriculture. These digital technologies offer smart systems that can monitor, control, and visualize farm operations in realtime, approaching human expertise. The potential applications of IoT and AI in agriculture include smart machinery, irrigation systems, pest control, crop monitoring, and more. Automated polyhouses equipped with IoT and AI components can optimize crop conditions, enhancing yield and quality while minimizing climate impacts. Over the past decade, research in this field has matured, transitioning from conceptual stages to implementation. The integration of AI and IoT into agriculture practices poses technical challenges, but understanding these technologies will facilitate their adoption and the development of IoT and AI-based solutions in farming.

Design and Evaluation of a Low-Power Wide-Area Network (LPWAN)-Based Emergency Response System for Individuals with Special Needs in Smart Buildings.

The Internet of Things (IoT) is a growing network of interconnected devices used in transportation, finance, public services, healthcare, smart cities, surveillance, and agriculture. IoT devices are increasingly integrated into mobile assets like trains, cars, and airplanes. Among the IoT components, wearable sensors are expected to reach three billion by 2050, becoming more common in smart environments like buildings, campuses, and healthcare facilities. A notable IoT application is the smart campus for educational purposes. Timely notifications are essential in critical scenarios. IoT devices gather and relay important information in real time to individuals with special needs via mobile applications and connected devices, aiding health-monitoring and decision-making. Ensuring IoT connectivity with end users requires long-range communication, low power consumption, and cost-effectiveness. The LPWAN is a promising technology for meeting these needs, offering a low cost, long range, and minimal power use. Despite their potential, mobile IoT and LPWANs in healthcare, especially for emergency response systems, have not received adequate research attention. Our study evaluated an LPWAN-based emergency response system for visually impaired individuals on the Hazara University campus in Mansehra, Pakistan. Experiments showed that the LPWAN technology is reliable, with 98% reliability, and suitable for implementing emergency response systems in smart campus environments.

A Robust End-to-End IoT System for Supporting Workers in Mining Industries.

The adoption of the Internet of Things (IoT) in the mining industry can dramatically enhance the safety of workers while simultaneously decreasing monitoring costs. By implementing an IoT solution consisting of a number of interconnected smart devices and sensors, mining industries can improve response times during emergencies and also reduce the number of accidents, resulting in an overall improvement of the social image of mines. Thus, in this paper, a robust end-to-end IoT system for supporting workers in harsh environments such as in mining industries is presented. The full IoT solution includes both edge devices worn by the workers in the field and a remote cloud IoT platform, which is responsible for storing and efficiently sharing the gathered data in accordance with regulations, ethics, and GDPR rules. Extended experiments conducted to validate the IoT components both in the laboratory and in the field proved the effectiveness of the proposed solution in monitoring the real-time status of workers in mines.

DESIGN OF IOT BASED TRICYCLE

The rapid advancement in Internet of Things (IoT) technology has opened up numerous opportunities for innovation across various industries. In the transportation sector, IoT has the potential to revolutionize conventional vehicles into smart, connected modes of transportation. This project focuses on the design of an IoT-based electric tricycle, aimed at providing a sustainable and efficient mode of personal transportation.The electric tricycle integrates IoT technology to enhance its functionality and user experience. Key features include real-time monitoring of vehicle parameters such as speed, battery status, and location, enabling remote tracking and management through a dedicated mobile application. Additionally, the tricycle incorporates smart safety features such as collision detection and automatic emergency braking, enhancing rider safety on the road.The design phase involves the selection of appropriate components and the integration of IoT modules, sensors, and actuators into the tricycle framework. The fabrication process encompasses building the chassis, integrating the electric propulsion system, and assembling the IoT components. Emphasis is placed on ensuring durability, reliability, and energy efficiency throughout the construction process.Overall, the project aims to contribute to the advancement of sustainable transportation solutions by leveraging IoT technology to create an innovative, eco-friendly electric tricycle. Through this endeavor, we envision promoting the adoption of electric vehicles and facilitating a more connected and efficient urban mobility ecosystemTop of Form Keywords: IoT-based electric tricycle, Sustainable transportation, Connected vehicles, Electric vehicle innovation, Real-time monitoring, Safety features, Fabrication process, Integration of IoT technology, User experience, Smart mobility solution

Monitoring and Mitigating Climate-Induced Natural Disasters with Cloud IoT

Innovative solutions are necessary for efficient monitoring and mitigation techniques as climate change increases the frequency and severity of natural catastrophes. In this study, we investigate how Cloud Internet of Things (IoT) might help mitigate climate-related calamities. Through the use of cloud computing and sensor networks, Cloud IoT facilitates the capture, processing, and distribution of data in near-real time. This strategy promotes catastrophe planning, response, and recovery by offering early warnings, predictive insights, and simplified communication. The article emphasises the cumulative influence of Cloud IoT components such as sensor networks, data analytics, decision support systems, and remote control in the context of disaster management. Cloud IoT is useful in real-world scenarios, such as the tracking of floods in Bangladesh and the identification of wildfires in California. These cases show how the technology may prevent injuries and preserve property through early warnings and well-coordinated responses. Despite its potential, it faces obstacles including ensuring the safety of data and dealing with issues related to the necessary infrastructure. In conclusion, including Cloud IoT in disaster management provides a cost-effective, scalable, and efficient solution, greatly contributing to constructing resilient communities and creating a sustainable future in the face of climate-induced natural catastrophes.

An energy efficient fusing data gathering protocol in wireless sensor networks

The Internet of Things (IoT) is rapidly developing as a promising technology for today’s digital world. Wireless sensor networks (WSN) are a crucial component of IoT, and one of the major problems faced is energy constraints. The clustering protocol is an effective energy saving solution. In this paper, we propose an energy efficient fusing data gathering protocol via distributed second level clustering and deep learning-based data fusion (EEFDG) for WSN. EEFDG divides the WSN into several second-level clusters by a novel distributed cluster algorithm. The transmission distance and the number of neighbors are jointly considered to select an energy-efficient parent node in each second-level cluster. A new cluster head weight function is proposed for intra-cluster parent node rotation. To minimize redundant data transmission, a multivariate convolutional neural network and long short-term memory network (MVCLNet) are designed for data fusion. MVCLNet is deployed hierarchically into the second-level clustered structure to extract features of the multi-sensor data. The EEFDG is an effective solution to the energy limitation problem of WSN. The proposed second-level clustering and data fusion algorithm based on MVCLNet typically decreases communication distances and the amount of data, thereby minimizing communication overhead and energy consumption. Simulation results show that EEFDG significantly prolongs the network lifetime in terms of the first node dead, compared to existing protocols including MR-LEACH, BPDA, CNNDA, FDEAM, DCNN, and LPLL-LEACH, achieving improvements of 181.3%, 42.2%, 48.2%, 19.6%, 62.0%, and 30.2%, respectively. The EEFDG enables highly accurate data fusion and has superior performance in terms of remaining energy, network lifetime, and data collection.

Green IoT Based on Tropical Weather: The Impact of Energy Harvesting in Wireless Sensor Network

Wireless Sensor Networks (WSNs) are a key component of Green IoT, as they play a critical role in many applications. However, a major challenge faced by WSNs is their limited energy capacity, which can impede their effectiveness. To address this issue, energy harvesting techniques are used to harness ambient energy and power the nodes, eliminating the need for frequent battery replacements or recharging. This study proposes a solar energy harvesting technique to prolong the lifespan of each wireless sensor node in a network. The aim of the research is to assess the impact of energy harvesting on wireless sensor networks with a focus on solar ambient light energy, as it is a critical component of achieving a Green Internet of Things (IoT). The study is adapted to the tropical weather of Malaysia and its forecasted conditions. Through the use of NetSim simulations, the energy efficiency and reliability of the network are evaluated using solar energy harvesting. The recharge rate for energy harvesting in a wireless sensor network is calculated to be 2.34W per day using a 55x70mm solar photovoltaic panel based on Malaysia weather forecasts and sun hours. This proposed method is successfully executed using NetSim, which utilizes real device parameters for a WSN in a simulated agriculture environment. The results show an overall improvement of 22.4 percent in performance parameters in all areas, including energy consumption, network layer, routing performance, and IEEE 802.15.4 metrics, representing a significant improvement. With the implementation of solar energy harvesting, traditional energy of WSNs can benefit from developing smart agricultural monitoring systems. This efficient method and calculation for solar energy harvesting can help to sustain the infinite lifetime of WSN by optimizing the operational time to 18% of the duty cycle. Results demonstrate that the proposed method can effectively increase the energy efficiency and reliability of the network, making it a promising solution for WSNs operating in tropical regions. This research contributes to the development of sustainable and energy-efficient WSNs, promoting the Green IoT concept.

Firefly algorithm based WSN-IoT security enhancement with machine learning for intrusion detection

A Wireless Sensor Network (WSN) aided by the Internet of Things (IoT) is a collaborative system of WSN systems and IoT networks are work to exchange, gather, and handle data. The primary objective of this collaboration is to enhance data analysis and automation to facilitate improved decision-making. Securing IoT with the assistance of WSN necessitates the implementation of protective measures to confirm the safety and reliability of the interconnected WSN and IoT components. This research significantly advances the current state of the art in IoT and WSN security by synergistically harnessing the potential of machine learning and the Firefly Algorithm. The contributions of this work are twofold: firstly, the proposed FA-ML technique exhibits an exceptional capability to enhance intrusion detection accuracy within the WSN-IoT landscape. Secondly, the amalgamation of the Firefly Algorithm and machine learning introduces a novel dimension to the domain of security-oriented optimization techniques. The implications of this research resonate across various sectors, ranging from critical infrastructure protection to industrial automation and beyond, where safeguarding the integrity of interconnected systems are of paramount importance. The amalgamation of cutting-edge machine learning and bio-inspired algorithms marks a pivotal step forward in crafting robust and intelligent security measures for the evolving landscape of IoT-driven technologies. For intrusion detection in the WSN-IoT, the FA-ML method employs a support vector machine (SVM) machine model for classification with parameter tuning accomplished using a Grey Wolf Optimizer (GWO) algorithm. The experimental evaluation is simulated using NSL-KDD Dataset, revealing the remarkable enhancement of the FA-ML technique, achieving a maximum accuracy of 99.34%. In comparison, the KNN-PSO and XGBoost models achieved lower accuracies of 96.42% and 95.36%, respectively. The findings validate the potential of the FA-ML technique as an active security solution for WSN-IoT systems, harnessing the power of machine learning and the Firefly Algorithm to bolster intrusion detection capabilities.

Internet of Things Driven Automated Production Systems using Machine Learning

With the advent of Industry 4.0, integrating Cyber-Physical Systems (CPS) and the Internet of Things (IoT) has become crucial in automated production. This study examines the interplay between technological advancements and human factors, particularly the social aspects within this dynamic environment. In the context of Industry 4.0, where CPS and IoT intersect, human factors significantly impact the design, implementation, and operation of automated production systems. This research highlights the mutually beneficial interactions among CPS, IoT, and human-centric components, focusing on how these technologies influence social interactions in automated industrial settings. The integration of intelligent robots and human operators fosters new collaboration and communication models. Understanding these social interactions is key to enhancing the effectiveness and productivity of Industry 4.0 manufacturing. Additionally, the study examines the role of social networks, where CPS and IoT technologies facilitate essential connections between humans and machines. In summary, this research emphasizes the need to incorporate social aspects into developing and implementing Industry 4.0 technologies. Recognizing the impact of CPS, IoT, and human factors on social interactions and networks enables a more comprehensive and efficient technological integration in Industry 4.0.

Solution-processed colloidal quantum dots for internet of things.

Colloidal quantum dots (CQDs) have been a hot research topic ever since they were successfully fabricated in 1993 via the hot injection method. The Nobel Prize in Chemistry 2023 was awarded to Moungi G. Bawendi, Louis E. Brus and Alexei I. Ekimov for the discovery and synthesis of quantum dots. The Internet of Things (IoT) has also attracted a lot of attention due to the technological advancements and digitalisation of the world. This review first aims to give the basics behind QD physics. After that, the history behind CQD synthesis and the different methods used to synthesize most widely researched CQD materials (CdSe, PbS and InP) are revisited. A brief introduction to what IoT is and how it works is also mentioned. Then, the most widely researched CQD devices that can be used for the main IoT components are reviewed, where the history, physics, the figures of merit (FoMs) and the state-of-the-art are discussed. Finally, the challenges and different methods for integrating CQDs into IoT devices are discussed, mentioning the future possibilities that await CQDs.

Acceptance of IoT by Students in Universities of Sindh, Pakistan: A Proposed Framework

The Internet of Things (IoT) encompasses a convergence of physical and virtual technologies, connecting devices through networks to enhance the overall quality of life. These technologies offer diverse interactions, featuring unique user interfaces and functionalities. Beyond its technological prowess, IoT presents contemporary methodologies and tools, catering to both modern and traditional learning approaches. While IoT components have received considerable attention, there exists a notable gap in exploring factors influencing students' receptiveness to these services. This research centers on the Unified Theory of Acceptance and Use of Technology (UTAUT 2), a widely acknowledged framework for assessing user willingness to adopt new technologies. The primary objective of our pilot study is to establish a conceptual framework based on UTAUT 2 and assess its reliability and validity. This framework sets the stage for future investigations into IoT acceptance among higher education students in Pakistan. To achieve this, an online pilot research questionnaire was distributed via Google Forms to students across various institutions, including the University of Karachi, University of Sindh Jamshoro, University of Sufism and Modern Sciences Bhitshah, Government College University Hyderabad, and SALU Khairpur. Fifty participants completed the questionnaire, and the ensuing data attests to the questionnaire's high reliability (ranging from .795 to .961), affirming its suitability for broader surveys.

Enterprise Architecture Design and Implementation for IoT Integration in Manufacturing Electrical Panels

Internet of Things technology has transformed manufacturing efficiency and optimization. Electrical panel manufacturing benefits from Internet of Things for better functionality, predictive maintenance, and smoother operations. This study examines the design and implementation of an Enterprise Architecture strategy for seamless Internet of Things integration in electrical panel manufacturing. This research aims to explain Enterprise Architecture and use it as a framework for Internet of Things integration in electrical panel manufacturing. This study examines the complex relationships between Internet of Things components, their connectivity, and a broad Enterprise Architecture framework needed to organize their functionality. This integration uses Enterprise Architecture principles to optimize resource use, reduce downtime, and improve manufacturing efficiency. This effort involves analyzing existing infrastructure, identifying Internet of Things deployment points, and creating an Enterprise Architecture plan that meets business goals. This research emphasizes the need for close IT-operations collaboration to achieve a unified vision and smooth Internet of Things integration. This research addresses Internet of Things implementation challenges in manufacturing, including security, data interoperability, and scalability. Strong governance and adaptable architecture are stressed to address these challenges within an Enterprise Architecture framework. This research aims to help electrical panel manufacturers harness the transformative power of the Internet of Things. Strategic Enterprise Architecture helps businesses navigate complexity, leverage Internet of Things, and create a more agile, connected, and optimized manufacturing landscape.

Evaluating dynamics, security, and performance metrics for smart manufacturing

The role of the Internet of Things (IoT) in Smart Manufacturing, aimed to illuminate its transformative impact on operational efficiency, responsiveness, and environmental sustainability. The aim of the investigation was to explore IoT's pivotal significance in reshaping manufacturing processes towards heightened efficiency, responsiveness, and environmental consciousness. The study presents results from performance metric assessments, visualizations, and data simulations. It contains information about way IoT data is shown in manufacturing environments, the clear relationship between temperature and pressure, the distribution of security risks and related safety measures, and the dynamic behaviour of important IoT components. The importance of IoT in real-time environmental management, process optimization, and security enhancement within paradigms of smart manufacturing are the key points of observation. The comparative analysis of Conventional Analytical Models and Composite Models highlights the choice between stability and adaptability, providing crucial insights for modeling approaches tailored to distinct manufacturing requirements. IoT's transformative potential within Smart Manufacturing, emphasizing data integrity, security, sensor dynamics, analytics, and sustainability.

A comprehensive survey on IoT attacks: Taxonomy, detection mechanisms and challenges

The Internet of Things (IoT) has set the way for the continuing digitalization of society in various manners during the past decade. The IoT is a vast network of intelligent devices exchanging data online. The security component of IoT is crucial given its rapid expansion as a new technology paradigm since it may entail safety-critical procedures and the online storage of sensitive data. Unfortunately, security is the primary challenge when adopting Internet of Things (IoT) technologies. As a result, manufacturers’ and academics’ top priority now is improving the security of IoT devices. A substantial body of literature on the subject encompasses several issues and potential remedies. However, most existing research fails to offer a comprehensive perspective on attacks inside the IoT. Hence, this survey aims to establish a structure to guide researchers by categorizing attacks in the taxonomy according to various factors such as attack domains, attack threat type, attack executions, software surfaces, IoT protocols, attacks based on device property, attacks based on adversary location and attacks based on information damage level. This is followed by a comprehensive analysis of the countermeasures offered in academic literature. In this discourse, the countermeasures proposed for the most significant security attacks in the IoT are investigated. Following this, a comprehensive classification system for the various domains of security research in the IoT and Industrial Internet of Things (IIoT) is developed, accompanied by their respective remedies. In conclusion, the study has revealed several open research areas pertinent to the subject matter.