Internet Of Things Services Research Articles

Internet of Things and Cloud Computing-based Disease Diagnosis using Optimized Improved Generative Adversarial Network in Smart Healthcare System

ABSTRACT The integration of IoT and cloud services enhances communication and quality of life, while predictive analytics powered by AI and deep learning enables proactive healthcare. Deep learning, a subset of machine learning, efficiently analyzes vast datasets, offering rapid disease prediction. Leveraging recurrent neural networks on electronic health records improves accuracy for timely intervention and preventative care. In this manuscript, Internet of Things and Cloud Computing-based Disease Diagnosis using Optimized Improved Generative Adversarial Network in Smart Healthcare System (IOT-CC-DD-OICAN-SHS) is proposed. Initially, an Internet of Things (IoT) device collects diabetes, chronic kidney disease, and heart disease data from patients via wearable devices and intelligent sensors and then saves the patient’s large data in the cloud. These cloud data are pre-processed to turn them into a suitable format. The pre-processed dataset is sent into the Improved Generative Adversarial Network (IGAN), which reliably classifies the data as disease-free or diseased. Then, IGAN was optimized using the Flamingo Search optimization algorithm (FSOA). The proposed technique is implemented in Java using Cloud Sim and examined utilizing several performance metrics. The proposed method attains greater accuracy and specificity with lower execution time compared to existing methodologies, IoT-C-SHMS-HDP-DL, PPEDL‐MDTC and CSO-CLSTM-DD-SHS respectively.

A Crucible of IoT Applications and Services and the Gambia Revenue Generation

The study investigated the relationship between Internet of Things (IoT) applications and services and The Gambia revenue generation, tax evasion and tax compliance respectively. It identified the challenges impeding the adoption and implementation of IoT applications and services in the revenue generation processes by the Gambia Revenue Authority (GRA). Both primary and secondary data were used in the study. Questionnaire and interview were used to generate the primary data. The study population (2188) comprised senior and mid-tier management personnel from the Ministry of Finance and Economic Affairs (MoFEA), staff members of The Gambia Revenue Authority, and the corporate taxpayers. The sampling approach used in this study was multistage. Personnel from the MoFEA and GRA were purposively selected, while corporate taxpayers were randomly selected. Their populations were then divided into various strata to ensure fair representation. Respondents were randomly picked from each stratum. To ensure an appropriate sample size, 327 individuals were selected. Data obtained were analysed using multiple regression, correlation coefficient, and simple regression analysis. Qualitative analyses were done on the interview responses. The study revealed that connectivity, networking, and data collection improve revenue generation, while increased IoT adoption and higher data utilisation and infrastructure investment reduce tax evasion and boost tax compliance. However, implementation of the IoT has encountered challenges in the form of technological barriers, financial constraints, and skill gaps. In view of these, investment must be made in enhancing connectivity and networking infrastructure, training staff in data analytics and cyber-security to optimise IoT data usage and boost IoT adoption through inducements and awareness programs. The study concluded that IOT applications and services are in the right direction to revolutionise The Gambia revenue generation.

Quad-Band Rectifier Circuit Design for IoT Applications

In this work, a novel quad-band rectifier circuit is introduced for RF energy harvesting and Internet of Things (IoT) applications. The proposed rectifier operates in the Wi-Fi frequency band and can supply low-power sensors and systems used in IoT services. The circuit operates at 2.4, 3.5, 5, and 5.8 GHz. The proposed RF-to-DC rectifier is designed based on Delon theory and Greinacher topology on an RT/Duroid 5880 substrate. The results show that our proposed circuit can harvest RF energy from the environment, providing maximum power conversion efficiency (PCE) greater than 81% when the output load is 0.511 kΩ and the input power is 12 dBm. In this work, we provide a comprehensive design framework for an affordable RF-to-DC rectifier. Our circuit performs better than similar designs in the literature. This rectifier could be integrated into an IoT node to harvest RF energy, thereby proving a green energy source. The IoT node can operate at various frequencies.

Comprehensive analysis of services towards Data Aggregation, Data Fusion and enhancing security in IoT-based smart home

Data aggregation and sensors data fusion would be very helpful in a number of developing fields, including deep learning, driverless cars, smart cities, and the Internet of Things (IoT). An advanced smart home application will test the upgraded Constrained Application Protocol (CoAP) using Contiki Cooja. Smart home can enhance people’s comfort. Secure authentication between the transmitter and recipient nodes is essential for providing IoT services. In many IoT applications, device data are critical. Current encryption techniques use complicated arithmetic for security. However, these arithmetic techniques waste power. Hash algorithms can authenticate these IoT applications. Mobile protection issues must be treated seriously, because smart systems are automatically regulated. CoAP lets sensors send and receive server data with an energy-efficient hash function to increase security and speed. SHA224, SHA-1, and SHA256 were tested by the CoAP protocol. Proposed model showed that SHA 224 starts secure sessions faster than SHA-256 and SHA-1. The ChaCha ci. This study proposed enhanced ChaCha, a stream cipher for low-duty-cycle IoT devices. For wireless connections between the IoT gateway and sensors with a maximum throughput of 1.5 Mbps, the proposed model employs a wireless error rate (WER) of 0.05; the throughput rises with an increase in the transmission data rate.

A service-recommendation method for the Internet of Things leveraging implicit social relationships

Integrating social-relationship information is widely considered an effective means to addressing the sparsity issue in Internet-of-Things (IoT) service recommendations. However, owing to platform variations and privacy concerns, acquiring explicit social-relationship information has become challenging. Therefore, researchers are gradually focusing on leveraging implicit social-relationship information to enhance recommendation effectiveness. Nevertheless, when using implicit social relationships, both user/service-implicit social-relationship and user-service interaction information rely on the same rating matrix, leading to nonindependence and coupling, which influence the recommendation model. To address this challenge, the present paper introduces a unique approach for IoT service recommendations, leveraging implicit social-relationship information (short for ISoc-IoTRec). First, we construct a user-service interaction graph, user-implicit social-relationship graph, and service-implicit social-relationship graph, learning their node embeddings through graph neural networks (GNNs). Subsequently, we introduce a cross information control module to achieve feature separation, ensuring that the user and service embeddings learned from different graphs remain independent in representation, thereby alleviating the nonindependence and coupling issues arising from the same data source. Following feature separation, the user and service embeddings are aggregated separately. Through an attention mechanism module, the model can selectively emphasize or attenuate the impact of each feature while considering the overall information, further addressing nonindependence and coupling issues. Extensive experiments conducted on three real-world datasets underscore the remarkable performance of ISoc-IoTRec, significantly outperforming existing recommendation algorithms.

Enhancing IoT connectivity through spectrum sharing in 5G networks

The integration of the Internet of Things (IoT) into high-performance devices and monitoring systems, spanning domains such as smart building, e-health care, and smart agriculture, necessitates a critical emphasis on advancing mobile communications through efficient spectrum utilization. This research addresses pivotal challenges within agricultural IoT applications, specifically focusing on the substantial decline in spectrum efficiency observed with the increasing escalation of network bandwidth. Acknowledging the absence of comprehensive reviews on 5G resource allocation strategies in the existing literature, our study aims to contribute to a nuanced understanding of their implications for service quality. The identified research gaps underscore an urgent need for heightened efforts to optimize resource allocation in 5G networks. This investigation delves into the intricacies of spectrum sharing and real-time analysis techniques within the 5G and beyond network, with a targeted focus on augmenting agricultural IoT services. Three distinct models, namely (i) Non-Priority Algorithm (NPA), (ii) Reserved Channel Algorithm (RCA) - No Permanent Channels, and (iii) Reserved Channel Algorithm (RCA) - Permanent Channels, were meticulously designed and simulated for Agricultural IoT application scenarios. The methodology encompasses the comprehensive evaluation of performance metrics, including call blocking, termination, and handover, to strategically identify and allocate spectrum resources effectively. The research endeavors to address ongoing challenges pertaining to effective communication, standardization, and data management for diverse 5G IoT devices. In light of these persisting concerns, the study not only seeks to enhance the overall efficiency of 5G IoT networks but also proposes innovative perspectives on intelligent and ingenious spectrum allocation techniques. The anticipated outcomes pledge to optimize the utilization of limited spectrum through novel spectrum-sharing strategies, thereby contributing to the advancement of 5G networks and bolstering agricultural IoT devices and services.

Adaptive edge security framework for dynamic IoT security policies in diverse environments

The rapid expansion of Internet of Things (IoT) technologies has introduced significant cybersecurity challenges, particularly at the network edge where IoT devices operate. Traditional security policies designed for static environments fall short of addressing the dynamic, heterogeneous, and resource-constrained nature of IoT ecosystems. Existing dynamic security policy models lack versatility and fail to fully integrate comprehensive risk assessments, regulatory compliance, and AI/ML (artificial intelligence/machine learning)-driven adaptability. We develop a novel adaptive edge security framework that dynamically generates and adjusts security policies for IoT edge devices. Our framework integrates a dynamic security policy generator, a conflict detection and resolution in policy generator, a bias-aware risk assessment system, a regulatory compliance analysis system, and an AI-driven adaptability integration system. This approach produces tailored security policies that adapt to changes in the threat landscape, regulatory requirements, and device statuses. Our study identifies critical security challenges in diverse IoT environments and demonstrates the effectiveness of our framework through simulations and real-world scenarios. We found that our framework significantly enhances the adaptability and resilience of IoT security policies. Our results demonstrate the potential of AI/ML integration in creating responsive and robust security measures for IoT ecosystems. The implications of our findings suggest that dynamic and adaptive security frameworks are essential for protecting IoT devices against evolving cyber threats, ensuring compliance with regulatory standards, and maintaining the integrity and availability of IoT services across various applications.

Edge-Intelligence-Powered Joint Computation Offloading and Unmanned Aerial Vehicle Trajectory Optimization Strategy

UAV-based air-ground integrated networks offer a significant benefit in terms of providing ubiquitous communications and computing services for Internet of Things (IoT) devices. With the empowerment of edge intelligence (EI) technology, they can efficiently deploy various intelligent IoT applications. However, the trajectory of UAVs can significantly affect the quality of service (QoS) and resource optimization decisions. Joint computation offloading and UAV trajectory optimization bring many challenges, including coupled decision variables, information uncertainty, and long-term queue delay constraints. Therefore, this paper introduces an air-ground integrated architecture with EI and proposes a TD3-based joint computation offloading and UAV trajectory optimization (TCOTO) algorithm. Specifically, we use the principle of the TD3 algorithm to transform the original problem into a cumulative reward maximization problem in deep reinforcement learning (DRL) to obtain the UAV trajectory and offloading strategy. Additionally, the Lyapunov framework is used to convert the original long-term optimization problem into a deterministic short-term time-slot problem to ensure the long-term stability of the UAV queue. Based on the simulation results, it can be concluded that our novel TD3-based algorithm effectively solves the joint computation offloading and UAV trajectory optimization problems. The proposed algorithm improves the performance of the system energy efficiency by 3.77%, 22.90%, and 67.62%, respectively, compared to the other three benchmark schemes.

Optimized Dynamic Service Placement for Enhanced Scheduling in Fog-Edge Computing Environments

The traditional cloud computing model struggles to efficiently handle the vast number of Internet of Things (IoT) services due to its centralized nature and physical distance from end-users. In contrast, edge and fog computing have emerged as promising solutions for supporting latency-sensitive IoT applications by distributing computational resources closer to the data source. However, these technologies are limited by their size and computational capacities, making optimal service placement a critical challenge. This paper addresses this challenge by introducing a dynamic and distributed service placement policy tailored for edge and fog environments. By leveraging the inherent advantages of proximity in fog and edge nodes, the proposed policy seeks to enhance service delivery efficiency, reduce latency, and improve resource utilization. The proposed method focuses on optimizing the placement of high-demand services closer to the data generation sources to enhance scheduling efficiency in fog computing environments. Our method is divided into three interconnected modules. The first module is the service type estimator, which is responsible for distributing services to appropriate nodes. Here, we use the Political Optimizer (PO) as a new metaheuristic algorithm for deploying IoT services. The second module is service dependency estimator, which manages service dependencies. Here, we load dependent services near the data using a path matrix based on the Warshall algorithm. Finally, the third module is resource demand scheduling, which estimates resource demand to facilitate optimal scheduling. Here, we use a popularity-based policy to manage resource demand and service execution scheduling. Implementation results demonstrate significant improvements over existing state-of-the-art policies, highlighting the efficacy of the proposed policy in enhancing service delivery within fog-edge computing frameworks.

Securing the IoT-enabled smart healthcare system: A PUF-based resource-efficient authentication mechanism

As the Internet of Things (IoT) continues its rapid expansion, cloud computing has become integral to various smart healthcare applications. However, the proliferation of digital health services raises significant concerns regarding security and data privacy, making the protection of sensitive medical information paramount. To effectively tackle these challenges, it is crucial to establish resilient network infrastructure and data storage systems capable of defending against malicious entities and permitting access exclusively to authorized users. This requires the deployment of a robust authentication mechanism, wherein medical IoT devices, users (such as doctors or nurses), and servers undergo registration with a trusted authority. The process entails users retrieving data from the cloud server, while IoT devices collect patient data. Before granting access to data retrieval or storage, the cloud server verifies the authenticity of both the user and the IoT device, ensuring secure and authorized interactions within the system. With millions of interconnected smart medical IoT devices autonomously gathering and analyzing vital patient data, the importance of robust security measures becomes increasingly evident. Standard security protocols are fundamental in fortifying smart healthcare applications against potential threats. To confront these issues, this paper introduces a secure and resource-efficient cloud-enabled authentication mechanism. Through empirical analysis, it is demonstrated that our authentication mechanism effectively reduces computational and communication overheads, thereby improving overall system efficiency. Furthermore, both informal and formal analyses affirm the mechanism's resilience against potential cyberattacks, highlighting its effectiveness in safeguarding smart healthcare applications.

Study to integrate Delay-Tolerant Network protocols in IoT LEO constellations for flood prevention

In the era of the Internet of Things (IoT) the development of Direct-to-Satellite IoT (DtS-IoT) applications are becoming increasingly relevant. These applications are based on enabling IoT devices to communicate directly with satellites. In this scenario, LEO satellites can provide global IoT service coverage, becoming essential to connect devices in remote areas. As an example, the deployment of NarrowBand-IoT (NB-IoT) service is being actively investigated with the apparition of Non-Terrestrial Network (NTN) and DtS-IoT concepts. Although some initiatives propose seamless and ubiquitous service, these features may not be required for IoT applications. This relaxes the requirements in the satellite constellation architecture, enabling it to be more sparse. The connection between sensors, satellites and ground segment becomes intermittent and discontinuous in these non-dense architectures. Delay/Disruptive-tolerant protocols are essential to coexist with this network characteristics. Unfortunately, traditional IoT protocols have not been designed with this delay-tolerant feature. This work tackles this challenge by integrating Delay-Tolerant Network protocols with NB-IoT architecture. Specifically, the integration is based on interconnecting the Bundle Protocol and IP traffic through a novel interface. This development has been divided in three phases: (1) The definition of an architecture and protocol stack to tackle NTN IoT scenarios with discontinuities, (2) the analysis from simulated data of the resulting protocol stack in a realistic scenario based on flooding prevention, and (3) the implementation and validation of it in a laboratory testbed. The proposed case study uses a Direct-to-Satellite IoT architecture to create an early warning flooding detection system. The simulation results provide insights of the achieved performance with these architectures when servicing hundreds of sensing nodes.

Empowering IoT connectivity: unveiling the potential of IoT.Js for enhanced interconnectivity

ABSTRACT The concept of a global Internet of Things (IoT), as proposed by Bart, revolutionizes connectivity by assigning unique digital identities to everyday objects, thereby enabling the development of smarter devices. However, the advancement and expansion of the IoT face significant obstacles and constraints, including energy efficiency and memory usage, which require further investigation. To address these challenges, this study presents a novel framework named IoT.js. This framework aims to facilitate the interconnection of lightweight devices within the web-based IoT ecosystem. These devices, ranging from microcontrollers to those with limited memory capacities, stand to benefit from IoT.js’s utilization of JavaScript for IoT application development. By promoting collaboration among lightweight devices with small memory sizes, IoT.js offers a promising solution to the resource constraints encountered in IoT deployments. This study contributes to the advancement of the IoT landscape by introducing a new approach that not only enables the efficient use of limited resources but also facilitates the seamless interconnection of lightweight devices within the IoT network. Additionally, the study conducts performance analysis to measure the efficacy of the proposed framework. Through rigorous evaluation of key metrics, such as energy efficiency and memory usage, the research findings validate the effectiveness of IoT.js in addressing the challenges associated with IoT deployment. Overall, the proposed IoT.js based approach represents a significant step forward in enhancing interconnectivity within the IoT ecosystem, thereby unlocking new possibilities for the development of innovative IoT applications and services.

IoT Security Using Machine Learning Methods with Features Correlation

The Internet of Things (IoT) is an innovative technology that makes our environment smarter, with IoT devices as an integral part of home automation. Smart home systems are becoming increasingly popular as an IoT service in the home that connects via a network. Due to the security weakness of many devices, the malware is targeting IoT devices. After being infected with malicious attacks on smart devices, they act like bots that the intruders can control. Machine learning methods can assist in improving the attack detection process for these devices. However, the irrelevant features raise the computation time as well as affect the detection accuracy in the processing with many features. We proposed a machine learning-based IoT security framework using feature correlation. The feature extraction scheme, one-hot feature encoding, correlation feature selection, and attack detection implement an active detection mechanism. The results show that the implemented framework is not only for effective detection but also for lightweight performance. The proposed system outperforms the results with the selected features, which have almost 100% detection accuracy. It is also approved that the proposed system using CART is more suitable in terms of processing time and detection accuracy.

APPS: Authentication-enabled privacy protection scheme for secure data transfer in Internet of Things

The Internet of Things (IoT) is an emerging field that encompasses several heterogeneous devices and smart objects that are integrated with the network. In open platforms, these objects are deployed to present advanced services in numerous applications. Innumerable security-sensitive data is generated by the IoT device and therefore, the security of these devices is an important task. This work formulates a secure data transfer technique in IoT, named Authentication enabled Privacy Protection (APPS) scheme for resource-constrained IoT devices. The proposed scheme demonstrates resilience against various attacks; such as resisting reply attacks, device anonymity, untracebility, session key establishment, quantum attacks and resisting MITM attacks. For the privacy protection scheme, the secured data transfer is initiated between the entities, like IoT devices, servers, and registration centers, by using various phases, namely registration phase, key generation phase, data encryption, authentication, verification, and data retrieval phase. Here, a mathematical model is designed for protecting data privacy using hashing, encryption, secret keys, etc. Finally, performance of proposed APPS model is analyzed; wherein the outcomes reveal that the proposed APPS model attained the maximum detection rate of 0.85, minimal memory usage of 0.497MB, and minimal computational time of 112. 79 sec and minimal turnaround time 131.91 sec.

Improving quality of service for Internet of Things(IoT) in real life application: A novel adaptation based Hybrid Evolutionary Algorithm

In this paper, we address critical challenges in IoT sensor lifespan, service latency, and coverage area, all impacting energy consumption in smart agriculture applications. To enhance the quality of service (QoS) while prolonging the energy efficiency of smart sensors, a novel optimization algorithm is introduced. Referred to as the ”Adaptation-Based Hybrid Evolutionary Algorithm,” this innovative approach combines the strengths of Grey Wolf Optimizers (GWO) and Differential Evolution (DE) algorithms. The methodology involves a new adaptation-based strategy and incorporates a hybrid algorithm that synergizes the exploratory and exploitative capabilities of both GWO and DE algorithms. This hybrid approach is leveraged to meticulously select optimal mutation new adaptation services, drawing from the GWO and DE algorithm frameworks. Notably, the algorithm’s control parameters autonomously adjust through insights gained from prior evolutionary searches. Furthermore, we enhance the DE-based crossover technique by integrating the proficient search capabilities of the GWO algorithm, renowned for tackling continuous global optimization problems. To validate our approach, we apply it to IoT scenarios and optimize QoS through a fitness function that comprehensively accounts for energy consumption, coverage rate, lifespan, and latency. Comparative evaluations against standard algorithms underscore the superior performance of our proposed methodology, particularly evident in its application to IoT-smart agriculture settings.

Development and implementation of a raspberry Pi-based IoT system for real-time performance monitoring of an instrumented tractor

The tractor serves as a crucial power source in agricultural operations. However, the tractor's power often remains underutilized due to a mismatch between the tractor and implement, considering specific field conditions. To enhance system output, it becomes vital to acquire data on performance-related parameters for the tractor-implement combination. In this study we develop a real-time Instrumented Tractor Performance Monitoring System (ITPMS) using the Internet-of-Things (IoT). This system consists of a Raspberry Pi, a GPS sensor, a proximity sensor, a rotary potentiometer, and a three-point hitch dynamometer. The rotary potentiometer measures tillage depth, while the three-point hitch dynamometer used to measure data on draft force. Proximity sensors are installed on a two-wheel drive (2WD) tractor to measure forward speed and drive-wheel slip. We establish a dedicated web server using a Google® Firebase® project to store data from all sensors through Raspberry Pi. Additionally, we design a web interface and a mobile application to provide real-time data generated from the sensors. Field experiments were done to evaluate and monitor the performance parameters of the tractor-implement combination utilising the developed ITPMS. The results demonstrate that the system effectively monitors the performance parameters necessary for tractor-implement combination. Furthermore, the system's capability to update data to the IoT server in real-time is validated. Overall, the development and implementation of this Raspberry Pi based IoT system, provides a reliable and efficient solution for real-time performance monitoring of instrumented tractors.

Organized Optimization Integration Validation Model for Internet of Things (IoT)-Based Real-Time Applications

Emerging technology like the Internet of Things (IoT) has great potential for use in real time in many areas, including healthcare, agriculture, logistics, manufacturing, and environmental surveillance. Many obstacles exist alongside the most popular IoT applications and services. The quality of representation, modeling, and resource projection is enhanced through interactive devices/interfaces when IoT is integrated with real-time applications. The architecture has become the most significant obstacle due to the absence of standards for IoT technology. Essential considerations while building IoT architecture include safety, capacity, privacy, data processing, variation, and resource management. High levels of complexity minimization necessitate active application pursuits with variable execution times and resource management demands. This article introduces the Organized Optimization Integration Validation Model (O2IVM) to address these issues. This model exploits k-means clustering to identify complexities over different IoT application integrations. The harmonized service levels are grouped as a single entity to prevent additional complexity demands. In this clustering, the centroids avoid lags of validation due to non-optimized classifications. Organized integration cases are managed using centroid deviation knowledge to reduce complexity lags. This clustering balances integration levels, non-complex processing, and time-lagging integrations from different real-time levels. Therefore, the cluster is dissolved and reformed for further integration-level improvements. The volatile (non-clustered/grouped) integrations are utilized in the consecutive centroid changes for learning. The proposed model’s performance is validated using the metrics of execution time, complexity, and time lag.

Definition and implementation of the Cloud Infrastructure for the integration of the Human Digital Twin in the Social Internet of Things

With the integration of virtualization technologies, the Internet of Things (IoT) is expanding its capabilities and quickly becoming a complex ecosystem of networked devices. The Social Internet of Things (SIoT), where intelligent things include social properties that improve functioning and user engagement, is the result of this progress. The SIoT still has issues with scalability, data management, and user-centric operations, despite tremendous progress. In order to overcome these obstacles, a strong architecture is needed that can handle the enormous number of IoT devices while simultaneously streamlining the user interface.This study provides a unique architecture for the IoT that uses containerization to efficiently deploy and manage services while integrating Virtual Users (VUs) and Social Virtual Objects (SVOs) into a scalable Cloud/Edge infrastructure. These innovative aspects collectively advance previous works presented in literature and focused on novel SIoT architectures and implementations, by addressing key challenges in scalability, efficiency, and automation within the SIoT. The proposed method presents an extensible, modular architecture that lets VUs self-manage IoT services, making user administration easier and improving system security and scalability. Important parts of the design include a host controller for container orchestration, a deployer for automated service deployment, and user clusters for aggregating VUs, SVOs, and apps to provide secured and efficient data sharing. We show through experimental assessment that the architecture can manage high-volume installations and operating needs, exceeding the conventional platform based on Google App Engine in terms of system overhead and deployment timeframes. The obtained results highlight how our suggested architecture, which provides an easy-to-use, scalable, and secure foundation for IoT deployments, has the potential to advance the SIoT landscape.

A Real-Time Intrusion Detection System for DoS/DDoS Attack Classification in IoT Networks Using KNN-Neural Network Hybrid Technique

As more devices are connected to the Internet through the Internet of Things (IoT), there are huge security challenges. One of the major problems is Distributed Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. These attacks floods networks with useless traffic, disrupting IoT services. There is a need for better security measures to handle this. Intrusion Detection Systems (IDS) is used to find suspicious activities, but many of them can't keep up with new types of attacks in real time. This study focuses on creating an efficient real time hybrid framework that uses the Kth Nearest Neighbor (KNN) algorithm and dense neural networks. This proposed framework aims to identify and categorize DoS/DDoS attacks in real time through the utilization of a simulation model and MQTT-IoT-IDS2020 dataset and compared with existing frameworks, our proposed framework excels in accuracy, precision, and recall.

Integrated analysis of reliability, power, and performance for IoT devices and servers

The Internet of Things (IoT) is currently widely used in various sectors and spaces. IoT devices are becoming small yet powerful servers and perform server-like functions. Reliability is a critical aspect in both IoT devices and servers, as they work together to create a robust and dependable IoT ecosystem. Power and performance are two other major considerations of an IoT system. Modeling, analysis, evaluation, and optimization of reliability, power, and performance for IoT devices and servers are major components in IoT systems development and deployment. In this paper, we conduct an integrated study of reliability, power, and performance for IoT devices and servers by mathematically rigorous modeling and analysis. The contributions of the paper can be summarized as follows. We establish a continuous-time Markov chain (CTMC) model that incorporates server failure rate, server repair rate, task arrival rate, and task processing rate. Using such an analytical model, we can calculate the server availability, the average task response time, and the average power consumption. We point out that there is an optimal server speed that minimizes the power-time product and a combined cost-performance metric of power, performance, and reliability. We show the impact of server reliability on response time, power consumption, server utilization, and the power-performance tradeoff. To the best of the author’s knowledge, this is the first paper that takes a combined approach to modeling and analysis of reliability, power, and performance for IoT devices and servers. It has been noticed that there has been little such theoretically solid investigation in the existing literature. Therefore, this paper has made tangible contributions and significant advances in the joint understanding of reliability, power, performance, and their interplay in IoT devices and servers quantitatively and mathematically.