Conventional Internet Of Things Research Articles

Evaluation of machine learning and deep learning methods for early detection of internet of things botnets

The internet of things (IoT) represents a rapidly expanding sector within computing, facilitating the interconnection of myriad smart devices autonomously. However, the complex interplay of IoT systems and their interdisciplinary nature has presented novel security concerns (e.g. privacy risks, device vulnerabilities, Botnets). In response, there has been a growing reliance on machine learning and deep learning methodologies to transition from conventional connectivity-centric IoT security paradigms to intelligence-driven security frameworks. This paper undertakes a comprehensive comparative analysis of recent advancements in the creation of IoT botnets. It introduces a novel taxonomy of attacks structured around the attack life-cycle, aiming to enhance the understanding and mitigation of IoT botnet threats. Furthermore, the paper surveys contemporary techniques employed for early-stage detection of IoT botnets, with a primary emphasis on machine learning and deep learning approaches. This elucidates the current landscape of the issue, existing mitigation strategies, and potential avenues for future research.

Advancing the Social Internet of Things (SIoT): Challenges, Innovations, and Future Perspectives

This study conducts an in-depth review of the Social Internet of Things (SIoT), a significant advancement from the conventional Internet of Things (IoT) via the integration of socialization principles akin to human interactions. We explore the architecture, trust management, relationship dynamics, and other crucial aspects of SIoT, with a particular focus on the relatively neglected areas of fault tolerance, cloud–fog computing, and clustering. Our systematic literature analysis, spanning research from 2011 to April 2023, uncovers critical gaps and establishes a detailed taxonomy of emerging SIoT themes. This paper not only sheds light on the current state of SIoT research but also charts a course for future exploration and development in this burgeoning field.

Optimized LoRaWAN Architectures: Enhancing Energy Efficiency and Long-Range Connectivity in IoT Networks for Sustainable, Low-Power Solutions and Future Integrations with Edge Computing and 5G

The Internet of Things (IoT) has expanded rapidly, allowing for a network of sensors and gadgets to collect and share information to make people's lives easier and more convenient. As the Internet of Things (IoT) grows, however, energy efficiency becomes a major issue, especially for portable and wireless gadgets. Low-power, long-range communication capabilities are needed, and Long-Range Wide Area Network (LoRaWAN) has emerged as a viable solution to meet this need. This study provides an in-depth analysis of the LoRaWAN-based, low-power Internet of Things. The suggested network architecture is optimized for low power consumption and high connectivity for numerous Internet of Things (IoT) use cases. This low-power Internet of Things network relies on LoRaWAN gateways, end devices, and a server to function. LoRaWAN is a technology that enables the low-power, long-range transmission of data packets. The results show that the optimized case and non-optimized case have a delivery ratio of 0.85 to 0.73 from node 100 to 500. LoRaWAN significantly reduces energy usage compared to conventional IoT connectivity alternatives, making it a fantastic option for battery-powered devices in far-flung or limited-resource locations. Finally, the adoption of LoRaWAN provides a viable solution to address the energy efficiency concerns in IoT networks, hence allowing for sustainable, long-lasting IoT installations and enabling a wide variety of new applications within the IoT ecosystem. Furthermore, addresses the potential applications of this technology in the future, including upgrades and integration with other technologies like edge computing and 5G networks.

Optimal Deep-Learning-Based Cyberattack Detection in a Blockchain-Assisted IoT Environment

The Internet of Things (IoT) is the most extensively utilized technology nowadays that is simple and has the advantage of replacing the data with other devices by employing cloud or wireless networks. However, cyber-threats and cyber-attacks significantly affect smart applications on these IoT platforms. The effects of these intrusions lead to economic and physical damage. The conventional IoT security approaches are unable to handle the current security problems since the threats and attacks are continuously evolving. In this background, employing Artificial Intelligence (AI) knowledge, particularly Machine Learning (ML) and Deep Learning (DL) solutions, remains the key to delivering a dynamically improved and modern security system for next-generation IoT systems. Therefore, the current manuscript designs the Honey Badger Algorithm with an Optimal Hybrid Deep Belief Network (HBA-OHDBN) technique for cyberattack detection in a blockchain (BC)-assisted IoT environment. The purpose of the proposed HBA-OHDBN algorithm lies in its accurate recognition and classification of cyberattacks in the BC-assisted IoT platform. In the proposed HBA-OHDBN technique, feature selection using the HBA is implemented to choose an optimal set of features. For intrusion detection, the HBA-OHDBN technique applies the HDBN model. In order to adjust the hyperparameter values of the HDBN model, the Dung Beetle Optimization (DBO) algorithm is utilized. Moreover, BC technology is also applied to improve network security. The performance of the HBA-OHDBN algorithm was validated using the benchmark NSLKDD dataset. The extensive results indicate that the HBA-OHDBN model outperforms recent models, with a maximum accuracy of 99.21%.

Enabling Efficient IoT Device Connectivity and Dynamic Network Management through SDN: A Weighted Sum Method Approach

SDN-Enabled IoT Networks bring about a transformative shift in conventional network models by integrating the core principles of Software-Defined Networking (SDN) into the realm of the Internet of Things (IoT). This integration empowers the agile and effective management of IoT devices, facilitating smooth connectivity, optimized distribution of resources, and flexible network setups. Through the consolidation of control and the utilization of virtualization methods, SDN-Enabled IoT Networks amplify scalability, security, and real-time responsiveness. This addresses the obstacles presented by the extensive proliferation of IoT devices. This paradigm transition heralds a fresh era of interconnectedness, where SDN assumes a central role in harmonizing the intricate interplay of IoT devices and services. The rapid expansion of Internet of Things (IoT) devices has introduced unparalleled complexities in overseeing networks and establishing connections. This compels the need for inventive strategies to effectively manage the substantial surge of IoT devices and their ever-changing connectivity prerequisites. Software-Defined Networking (SDN) emerges as a promising approach to tackle these issues by enabling the flexible management of networks and allocation of resources. This study investigates the amalgamation of SDN within the realm of IoT, aiming to streamline device connections, optimize data transmission efficiency, and accommodate adaptable network setups. Introducing an innovative weighted sum technique for resource allocation optimization, this work lays the foundation for a comprehensive framework that bolsters IoT network performance and expandability. Four different SDN implementations are examined, including the Conventional IoT Network, SDN-enabled IoT utilizing Centralized Control, SDN-enabled IoT employing Distributed Control, SDN-enabled IoT with Hierarchical Control, and SDN-enabled IoT utilizing Hybrid Control. The assessment considers various aspects such as Enhanced Scalability, Enhanced Traffic Engineering, Heightened Security, Implementation Complexity, Difficulty of Migration, and Reliance on Vendors. The Conventional IoT Network secures a moderate 3rd position with a Preference Score of 0.56030, while the SDN-enabled IoT with Centralized Control holds the 5th rank at 0.49732, despite excelling in specific domains. The SDN-enabled IoT with Distributed Control achieves the top rank with a notable Preference Score of 0.79414 due to comprehensive performance, followed by the SDN-enabled IoT with Hierarchical Control securing the 2nd spot (Preference Score: 0.57022), and the SDN-enabled IoT with Hybrid Control taking the 4th position (Preference Score: 0.51300), particularly excelling in Traffic Engineering.

Harmonic Distortions in Smart Devices: A Comprehensive Discussion on Challenges of Conventional and Future Smart IoT Devices

The extensive utility of sensor technology and M2M networking are the future of industries for the production of commercial and non-commercial applications. One such application, the Internet of Things (IoT), was the universally accepted technology of sensors and wide-area networking. This causes severe effects of low power quality and results in the device malfunctioning. The increase in the use of smart devices will be a future challenge for power quality and its effect on associative devices. The main contribution of this survey is to analyze harmonic distortions due to non-linear load characteristics in the power management unit of traditional systems & advanced smart IoT devices. It helps to understand the utility of conventional harmonic mitigation techniques and the need for adaptive features for the latest technology. The conventional compensation techniques are incorporated with machine learning approaches to deal with the uncertainty of load variation on real-time signals. This survey gives a brief insight into the present scope of conventional compensation techniques and futuristic soft computing techniques to mitigate harmonic distortions. Additionally, the survey is intended to call the attention of the researchers to the future perspective of smart devices and to make the academic community aware of the existing practices.

Internet of Things: A Comprehensive Overview on Protocols, Architectures, Technologies, Simulation Tools, and Future Directions

The Internet of Things (IoT) is a global network of interconnected computing, sensing, and networking devices that can exchange data and information via various network protocols. It can connect numerous smart devices thanks to recent advances in wired, wireless, and hybrid technologies. Lightweight IoT protocols can compensate for IoT devices with restricted hardware characteristics in terms of storage, Central Processing Unit (CPU), energy, etc. Hence, it is critical to identify the optimal communication protocol for system architects. This necessitates an evaluation of next-generation networks with improved characteristics for connectivity. This paper highlights significant wireless and wired IoT technologies and their applications, offering a new categorization for conventional IoT network protocols. It provides an in-depth analysis of IoT communication protocols with detailed technical information about their stacks, limitations, and applications. The study further compares industrial IoT-compliant devices and software simulation tools. Finally, the study provides a summary of the current challenges, along with a broad overview of the future directions to tackle the challenges, in the next IoT generation. This study aims to provide a comprehensive primer on IoT concepts, protocols, and future insights that academics and professionals can use in various contexts.

IRS-Assisted RF-Powered IoT Networks: System Modeling and Performance Analysis

Emerged as a promising solution for future wireless communication systems, intelligent reflecting surface (IRS) is capable of reconfiguring the wireless propagation environment by adjusting the phase-shift of a large number of reflecting elements. To quantify the gain achieved by IRSs in the radio frequency (RF) powered Internet of Things (IoT) networks, in this work, we consider an IRS-assisted cellular-based RFpowered IoT network, where the cellular base stations (BSs) broadcast energy signal to IoT devices for energy harvesting (EH) in the charging stage, which is utilized to support the uplink (UL) transmissions in the subsequent UL stage. With tools from stochastic geometry, we first derive the distributions of the average signal power and interference power which are then used to obtain the energy coverage probability, UL coverage probability, overall coverage probability, spatial throughput and power efficiency, respectively. With the proposed analytical framework, we finally evaluate the effect on network performance of key system parameters, such as IRS density, IRS reflecting element number, charging stage ratio, etc. Compared with the conventional RF-powered IoT network, IRS passive beamforming brings the same level of enhancement in both energy coverage and UL coverage, leading to the unchanged optimal charging stage ratio when maximizing spatial throughput.

Development, Service-Oriented Architecture, and Security of Blockchain Technology for Industry 4.0 IoT Application

The Internet of Things (IoT) paradigm is laying the groundwork for a world in which many of our everyday devices will be connected and will interact with their surroundings to gather data and automate some operations. Among other things, such a concept necessitates seamless authentication, data privacy, security, attack resilience, simplicity of deployment, and self-maintenance. Blockchain, a technology created with the Bitcoin cryptocurrency, can provide such advantages. To create blockchain-based IoT (BIoT) applications, a full discussion of how to modify blockchain to meet the unique requirements of IoT is offered in this paper. The most important BIoT applications are detailed after a brief introduction to blockchain, with the goal of highlighting how blockchain can affect conventional cloud-based IoT applications. Then, several factors that have an impact on the design, development, and deployment of a BIoT application are covered, along with present obstacles and potential improvements. Lastly, a list of recommendations is provided to help future BIoT researchers and developers understand some of the problems that need to be solved before deploying the upcoming generation of BIoT applications. Doi: 10.28991/HIJ-2023-04-01-010 Full Text: PDF

Nx-IoT: Improvement of Conventional IoT Framework by Incorporating SDN Infrastructure

The rapid advancement of the Internet of Things (IoT) in real-world applications has attracted immense research endeavors in the last few years. It has got tremendous potential in industrial automation and various other fields. The use of IoT has changed the perspective of general applications in today’s world. In this article, a next-generation architecture, Nx-IoT, is proposed for conventional 6LoWPAN-based IoT. The proposed Nx-IoT architecture works in two modes: 1) single controller-based (6SSDx) and 2) multicontroller-based (6MSDx). The Nx-IoT architecture uses new algorithms for routing management and load distribution among the SDN controllers. The experimentation is carried out in a prototype testbed environment. The result shows improved performances in terms of round trip and packet drop, compared to the conventional 6LoWPAN and cloud system. The proposed Nx-IoT architecture reduces the latency and shows better throughput performances as compared to the existing state of the art.

A Sub-mW Cortex-M4 Microcontroller Design for IoT Software-Defined Radios

We present an Internet-of-Things (IoT) software-defined radio platform based on an ultra low-power microcontroller. Whereas conventional wireless IoT radios often implement a single protocol, we demonstrate that general-purpose microcontrollers running software implementations of wireless physical layers are a promising solution to increase interoperability of IoT devices. Yet, since IoT devices are often energy-constrained, the underlying challenge is to implement the digital signal processing of the radio in software while maintaining an overall very low power consumption. To overcome this problem, we propose an ultra low-power microcontroller architecture with an ARM Cortex-M4 processor for the protocol-specific computations and a hardware digital front-end for the generic signal processing. The proposed architecture has been prototyped in 28nm FDSOI and the physical layers of the well-known LoRa and Sigfox protocols have been implemented in software. Thanks to the efficient hardware/software partitioning and an ultra-low power digital implementation, experimental evaluations of the microcontroller prototype show sub-mW power consumptions (32 – 332 μW) for the digital signal processing of the software-defined radios.

Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues.

Internet of Vehicles (IoV) technology has been attracting great interest from both academia and industry due to its huge potential impact on improving driving experiences and enabling better transportation systems. While a large number of interesting IoV applications are expected, it is more challenging to design an efficient IoV system compared with conventional Internet of Things (IoT) applications due to the mobility of vehicles and complex road conditions. We discuss existing studies about enabling collaborative intelligence in IoV systems by focusing on collaborative communications, collaborative computing, and collaborative machine learning approaches. Based on comparison and discussion about the advantages and disadvantages of recent studies, we point out open research issues and future research directions.

Mean-Field Artificial Noise Assistance and Uplink Power Control in Covert IoT Systems

In this paper, we study a covert Internet of Things (IoT) system. Compared with conventional IoT systems that apply cryptography and information-theoretic secrecy approaches to secure the transmission, our considered IoT system adopts the covertness technique and intends to hide the legitimate transmission from the observant adversaries. In the IoT system, the IoT devices randomly transmit the collected data to their associated IoT gateways (GWs). In the meantime, the adversaries attempt to detect the existence of legitimate transmission based on their received signal power and launch hostile attacks accordingly. To avoid being detected by the adversaries, the IoT system applies uplink power control to achieve covert legitimate transmission. Moreover, to distort the observation of the adversaries so as to mislead their decisions, we propose an artificial noise (AN)-assisted covert communication design, where the AN is transmitted by in-band full-duplex (IBFD) IoT GWs as a jamming operation. We formulate a Stackelberg game to study the interaction between the adversaries and the legitimate entities including the IoT GWs and IoT devices, where the legitimate entities, as the leaders, decide on the powers of legitimate and AN transmissions at the upper level and the adversaries, as the followers, aim to minimize their detection errors at the lower level. Thereafter, considering the large scale of IoT system, we further cast the Stackelberg game into a mean-field Stackelberg game and incorporate the stochastic geometry and statistical channel model to capture the location heterogeneity and channel dynamics among and of the system entities, respectively. In the performance evaluation, we verify the practicability of the mean-field Stackelberg game. Moreover, we demonstrate the effectiveness of AN in improving the transmission covertness.

An Adaptive Modeling and Performance Evaluation Framework for Edge-Enabled Green IoT Systems

The enormous growth in Internet of Things (IoT) has caused large-scale transformation in data acquisition and communication mechanism for conventional IoT systems. The continuously increasing requirements for delay-tolerant delivery of services in IoT applications has led to the emergence of more scalable and energy-efficient computing platforms like edge computing. However, the massive growth in volume of data being offloaded from low-powered IoT devices to the edge has imposed challenges on edge servers in terms of traffic bottlenecks, latency, and wastage of energy. In this view, a Local Data Reduction (LDR) framework is proposed which addresses the latency issues and cost constraints to facilitate energy-efficient processing of IoT data. We exploit the Markovian birth-death process to model edge-based IoT systems and derive performance metrics for the proposed LDR model. We also provide explicit analytical solution for the total expected cost function incurred pertaining to the LDR and without LDR (WLDR) models. Through extensive numerical illustrations we validate our findings and observe that the proposed LDR model outperforms the WLDR model. Hence, the LDR model operates well to meet the Quality of Service (QoS) requirements for real-time IoT systems by favouring green computing paradigms.

Throughput optimization of interference limited cognitive radio-based Internet of Things (CR-IoT) network

The Internet of Things (IoT) technology allows massive devices to connect to the internet for data exchange. It is anticipated that in near future, trillions of IoT devices will be connected to the internet. To deploy these devices, the requirement of the spectrum is increasing day by day. Most of these devices transmit data over unlicensed frequency bands that cause severe interference to each other while exchanging the data as these bands are becoming overcrowded. Therefore, to overcome spectrum scarcity and interference problems among these devices, a novel communication paradigm called cognitive radio-based internet of things (CR-IoT) is evolving at a very fast pace that integrates cognitive radio technology into the IoT devices. The technology has the potential to overcome the spectrum scarcity and interference problem by allowing dynamic spectrum access to conventional IoT networks. Such devices continuously monitor spectrum availability to transmit the data by incorporating an intelligent sensing mechanism into the devices. However, the performance of the sensing unit in terms of the probability of detection and the probability of false alarm, significantly deteriorates due to the noise uncertainties, especially in low signal-to-noise ratio environments. For the efficient utilization of the spectrum, the probability of detection and the probability of false alarm of the spectrum sensor should be high and low, respectively. Both sensing parameters are greatly influenced by the selection of the sensing threshold. Moreover, these IoT devices deal with short packet transmissions, the optimum sensing time is another crucial parameter that governs the performance of these devices.While addressing these two important issues, the convex optimization problem is formed over sensing time and sensing threshold, and the concavity on sensing threshold is proved. Further, an iterative algorithm is proposed for the CR-IoT system that intelligently adapts the sensing threshold to meet desired sensing performance in terms of Pd and Pf especially, in low SNR regions, and also optimize the sensing time to overcome the sensing throughput tradeoff. The simulation results are presented to validate the effectiveness of the proposed algorithm. It is demonstrated that the proposed algorithm increases the CR-IoT system throughput by 95% as compared to the conventional scheme at the received signal to noise ratio equals −20 dB while satisfying the requirement of Pd and Pf as per IEEE 802.22 standard.

An Efficient Secure Cryptography Scheme for New ML-based RPL Routing Protocol in Mobile IoT Environment

Internet of Things (IoT) offers reliable and seamless communication for the heterogeneous dynamic lowpower and lossy network (LLNs). To perform effective routing in IoT communication, LLN Routing Protocol (RPL) is developed for the tiny nodes to establish connection by using deflaut objective functions: OF0, MRHOF, for which resources are constraints like battery power, computation capacity, memory communication link impacts on varying traffic scenarios in terms of QoS metrics like packet delivery ratio, delay, secure communication channel. At present, conventional Internet of Things (IoT) are having secure communication channels issue for transmission of data between nodes. To withstand those issues, it is necessary to balance resource constraints of nodes in the network. In this paper, we developed a security algorithm for IoT networks with RPL routing. Initially, the constructed network in corporates optimizationbased deep learning (reinforcement learning) for route establishment in IoT. Upon the establishment of the route, the ClonQlearn based security algorithm is implemented for improving security which is based onaECC scheme for encryption and decryption of data. The proposed security technique incorporates reinforcement learning-based ClonQlearnintegrated with ECC (ClonQlearn+ECC) for random key generation. The proposed ClonQlearn+ECCexhibits secure data transmission with improved network performance when compared with the earlier works in simulation. The performance of network expressed that the proposed ClonQlearn+ECC increased the PDR of approximately 8% - 10%, throughput of 7% - 13%, end-to-end delay of 5% - 10% and power consumption variation of 3% - 7%.

Smart Digital Forensic Readiness Model for Shadow IoT Devices

Internet of Things (IoT) is the network of physical objects for communication and data sharing. However, these devices can become shadow IoT devices when they connect to an existing network without the knowledge of the organization’s Information Technology team. More often than not, when shadow devices connect to a network, their inherent vulnerabilities are easily exploited by an adversary and all traces are removed after the attack or criminal activity. Hence, shadow connections pose a challenge for both security and forensic investigations. In this respect, a forensic readiness model for shadow device-inclusive networks is sorely needed for the purposes of forensic evidence gathering and preparedness, should a security or privacy breach occur. However, the hidden nature of shadow IoT devices does not facilitate the effective adoption of the most conventional digital and IoT forensic methods for capturing and preserving potential forensic evidence that might emanate from shadow devices in a network. Therefore, this paper aims to develop a conceptual model for smart digital forensic readiness of organizations with shadow IoT devices. This model will serve as a prototype for IoT device identification, IoT device monitoring, as well as digital potential evidence capturing and preservation for forensic readiness.

Reliable Data Transmission Scheme for Perception Layer of Internet of Underwater Things (IoUT)

Internet of Underwater things (IoUT) is growing into one of the most important research interests since the last few decades. The primary objective of IoUT is to develop a worldwide network of smart underwater-interconnected objects. It has wide-ranging aquatic applications, including surveillance, pollution reduction, monitoring offshore oil and gas pipelines, disaster prevention, and navigation assistance, making it as important as a terrestrial communication system. The underwater communication channel poses unique acoustic communication challenges that suffer from long propagation delay, low bandwidth, multipath, and fading. IoUT significantly differs from the conventional Internet of Things (IoT) due to disparate environmental conditions under the ocean that require re-designing the underwater communication channel model. In addition to the challenges mentioned above, end-to-end delay and efficient energy utilization are also of great concern. Therefore, in this research paper, diverse attributes of underwater acoustic communication, including the speed of sound, transmission loss, absorption, and ambient noise, are analyzed to design a channel model for underwater communication. These environmental conditions also make underwater acoustic channels highly variable, so efficient resources, including bandwidth and power, are required. Adaptive modulation is proposed to make the communications system efficient by considering the distance between nodes and the signal-to-noise ratio as channel state information. The proposed channel model critically analyzed the CSI factors, results show efficient bandwidth utilization and appropriate power consumption for the well-suited route. The proposed research also aims to reduce end-to-end propagation delay by considering vertical angle-based shortest path (efficient route) in the Multilevel rotating priority MRP-routing algorithm.

Toward Ubiquitous Connectivity via LoRaWAN: An Overview of Signal Collision Resolving Solutions

The ever increasing demand for energy-efficient, long-range, and license-free Internet of Things (IoT) communication networks has spurred great interest in unlicensed low-power wide area networks (LPWANs). Such a revolutionary technology represents a paradigm shift in the development of wireless networks, as it can enable low-power (milliwatts) IoT devices to transmit at low data rates (kilobits per second) over long distances (several kilometers), which is clearly different from conventional IoT networking technologies (e.g., Wi-Fi and ZigBee) that only support short-range communications. Specifically, long-range WAN (LoRaWAN) is a leading and representative LPWAN technology that has been gathering remarkable attention from both academia and industry because of its open source software and installation ease. Despite this interest, however, current LoRaWAN is not yet a mature communication platform to provide ubiquitous connectivity for a wide variety of IoT applications. This is mainly because LoRaWAN typically serves a large volume of coexisting end devices among which signal collisions frequently occur at the physical layer (LoRa) of LoRaWAN, thereby leading to severe degradation of overall network capacity. In this context, the aim of this article is to provide a comprehensive overview of up-to-date physical-layer solutions for LoRa collision resolution. We particularly investigate different types of LoRa collisions resulting from the spreading factor network parameter. Moreover, the potential visions of physical-layer design to resolve LoRa collisions that might be considered for future research are also discussed.

AIoT Used for COVID-19 Pandemic Prevention and Control.

The pandemic of COVID-19 is continuing to wreak havoc in 2021, with at least 170 million victims around the world. Healthcare systems are overwhelmed by the large-scale virus infection. Luckily, Internet of Things (IoT) is one of the most effective paradigms in the intelligent world, in which the technology of artificial intelligence (AI), like cloud computing and big data analysis, is playing a vital role in preventing the spread of the pandemic of COVID-19. AI and 5G technologies are advancing by leaps and bounds, further strengthening the intelligence and connectivity of IoT applications, and conventional IoT has been gradually upgraded to be more powerful AI + IoT (AIoT). For example, in terms of remote screening and diagnosis of COVID-19 patients, AI technology based on machine learning and deep learning has recently upgraded medical equipment significantly and has reshaped the workflow with minimal contact with patients, so medical specialists can make clinical decisions more efficiently, providing the best protection not only to patients but also to specialists themselves. This paper reviews the latest progress made in combating COVID-19 with both IoT and AI and also provides comprehensive details on how to combat the pandemic of COVID-19 as well as the technologies that may be applied in the future.