Cloud Internet Of Things Research Articles

Incorporating the Internet of Things (IoT) Learning Module into the Smart Building Course

The utilization of Internet of Things (IoT) devices is increasing very rapidly, creating a demand for skilled professionals who can navigate the complexities of this technology. It is a crucial educational challenge to prepare undergraduates to become contributors in this emerging field. The curriculum needs to be updated to offer students both theoretical learning and practical application so they can benefit from it. This paper introduces a comprehensive IoT learning module to the final-year students of civil engineering in an elective course, Smart Building. The proposed learning module includes an introduction to IoT concepts and applications, IoT devices, communication protocols, cloud platforms, user interfaces, and IoT risk management and security, all of which can be easily integrated into existing courses. Results from the module are presented using direct and indirect assessments, including assignments, hands-on practical, examinations, and surveys. These assessments are designed to evaluate the students’ understanding and the challenges they encountered in their IoT learning process. The findings indicate that a significant majority, surpassing 90% of the students, have demonstrated understanding of a minimum of three essential components of the fundamental IoT architecture, despite their limited background knowledge at the beginning of the course. Additionally, this learning module can serve as a valuable resource for other educators who intend to deliver IoT-related courses.

Integrating IoT Sensors, Cloud AI, and Satellite Broadband for Enhanced ESG Governance in Smart Cities: A Tripartite Approach to Real-Time Environmental Monitoring

Urbanization is accelerating at an unprecedented rate, pushing the limits of environmental sustainability and demanding innovative solutions for effective management. Traditional methods for monitoring the environment fall short, lacking the scalability, immediacy, and comprehensive coverage required for modern urban settings. Our paper introduces an innovative solution to bridge these gaps, harnessing the power of Internet of Things (IoT) sensors, advanced cloud-based AI through Claude 3 Sonnet models on Amazon Bedrock, and the extensive reach of Project Kuiper's satellite broadband. This threefold integration forms a cutting-edge approach to urban environmental governance. It enables the analysis of environmental data in real time, offers predictive insights, and achieves wide-ranging coverage. The system equips city leaders, policymakers, and citizens with precise, actionable information, fostering decisions that align with Environmental Social Governance (ESG) goals. More than just improving urban life and sustainability, our approach makes environmental data accessible to all, promoting a collaborative and inclusive model of urban environmental management.

Efficient and Secure Blockchain-Based Access Control for Fog-Assisted IoT Cloud in Electronic Medical Records Sharing

Fog-assisted IoT Cloud environments have emerged as a result of the rapid spread of Internet of Things (IoT) devices and the requirement for seamless data sharing in the healthcare industry. The effective and safe management of access control to sensitive data, such as Electronic Medical Records (EMRs), becomes crucial in such configurations. The Efficient and Secure Blockchain-Based Access Control (ESBAC) framework, designed specifically for Fog-assisted IoT Cloud scenarios in EMR sharing, is our creative approach to this problem. The ESBAC architecture makes use of the blockchain's openness and immutability to provide reliable access control and auditability. The implementation of a consortium blockchain includes participants including healthcare providers, patients, and authorised entities. Each EMR has a smart contract attached to it that specifies access restrictions and permissions to make sure that only authorised parties are able to access and edit the data. Because of the decentralisation provided by the blockchain, security and privacy are improved because no single party has overwhelming control. Proposed framework uses attribute-based access control and lightweight encryption methods to provide a fair trade-off between security and efficiency. As a result, there can be fine-grained access control policies with the least amount of computing overhead. Comprehensive simulations are used to evaluate the suggested solution and compare it to current methods. The outcomes show that ESBAC improves security in EMR sharing and greatly lowers access latency.

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.

DESIGN AND IMPLEMENTATION OF IOT BASED HOTEL ROOMS OCCUPANCY REPORTING SYSTEM

Advances in technology applications are becoming more visible in everyday activities as the globe becomes more technologically skilled and evolved. Being able to receive and execute control is critical in the age of Internet of Things technology and control. This pattern eventually led to the development of an Internet of Things (IoT) system for reporting room occupancy as a promising technique or system for determining the availability of rooms and buildings. This method lowers human mistake, mismanagement, and energy use, all of which directly translate to decrease hotel running costs, possibly to zero. As a result, it is conceivable that the number of guests staying in Nigerian hotels has risen significantly. If this is the case, good management will most certainly result in an immense rise in profits. However, sharp practices among hotel employees tend to divert the profit met for the owners, thus this project is designed to automatically report hotel occupancy easily accessible to owners. An infrared detection circuit was used detect human movement and a lithium-ion battery that supplied 3.7 volts was used as the power supply for the circuit while a GPRS module (a SIM 800L GSM module) was utilized to receive and transmit data messages via radio waves, and an LGT8F328P microcontroller controlled every other component in the circuit. An android application was developed and installed on a mobile phone to gather all of the real-time readings and events that take place throughout the project’s system. The programming of the microcontroller was done using the Arduino Integrated Development Environment (IDE) software and C++ programming language. When the project was turned on, I saw that all of the LEDs turned on automatically. The first two LEDs (brown and green) were automatically turned on, as were the corresponding room LEDs. After that, the green LED turned off. When the infrared LEDs’ visibility is obscured, their input to the phototransistors in each room and the LEDs attached to each room automatically turn on, indicating that the rooms are occupied and vice versa. The various room compartments’ check-ins and check-outs are tracked, managed, and updated using the proposed hardware. The data is then transmitted to the IoT cloud and eventually to the Android application

Optimizing the resource allocation in cyber physical energy systems based on cloud storage and IoT infrastructure

Given the prohibited operating zones, losses, and valve point effects in power systems, energy optimization analysis in such systems includes numerous non-convex and non-smooth parameters, such as economic dispatch problems. In addition, in this paper, to include all possible scenarios in economic dispatch problems, multi-fuel generators, and transmission losses are considered. However, these features make economic dispatch problems more complex from a non-convexity standpoint. In order to solve economic dispatch problems as an important consideration in power systems, this paper presents a modified robust, and effective optimization algorithm. Here, some modifications are carried out to tackle such a sophisticated problem and find the best solution, considering multiple fuels, valve point effect, large-scale systems, prohibited operating zones, and transmission losses. Moreover, a few complicated power systems including 6, 13, and 40 generators which are fed by one type of fuel, 10 generators with multiple fuels, and two large-scale cases comprised of 80 and 120 generators are analyzed by the proposed optimization algorithm. The effectiveness of the proposed method, in terms of accuracy, robustness, and convergence speed is evaluated, as well. Furthermore, this paper explores the integration of cloud storage and internet of things (IoT) to augment the adaptability of monitoring capabilities of the proposed method in handling non-convex energy resource management and allocation problems across various generator quantities and constraints. The results show the capability of the proposed algorithm for solving non-convex energy resource management and allocation problems irrespective of the number of generators and constraints. Based on the obtained results, the proposed method provides good results for both small and large systems. The proposed method, for example, always yields the best results for the system of 6 power plants with and without losses, which are $15,276.894 and $15,443.7967. Moreover, the improvements made in the proposed method have allowed the economic dispatch problem regarding multi-fuel power plants to be solved not only with optimal results ($623.83) but also in less than 35 iterations. Lastly, the difference between the best-obtained results ($121,412) and the worst-obtained results ($121,316.1992) for the system of 40 power plants is only about $4 which is quite acceptable.

Enhanced slime mould optimization with convolutional BLSTM autoencoder based malware classification in intelligent systems

AbstractAutonomous intelligent systems are artificial intelligence (AI) tools that act autonomously without direct human supervision. Cloud computing (CC) and Internet of Things (IoT) technologies find it challenging to deploy sufficient security defences because of the different structures, storage, and limited computing capabilities that make them more vulnerable to attacks. Security threats against IoT structures, devices, and applications are increasing with the demand for IoT technology. The training data available to AI models may be limited, which could impact their performance and generalizability. Adopting AI solutions in real‐world situations may be impeded by compatibility concerns and the requirement for flawless integration. Malware classification errors can occur due to a lack of contextual knowledge, particularly in cases where benign files behave identically to malicious. Various studies were carried out on detecting IoT malware to evade the menaces posed by malicious code. However, prevailing techniques of IoT malware classification supported particular platforms or demanded complicated methods for attaining higher accuracy. This study introduces an enhanced slime mould optimization with a convolutional BLSTM autoencoder‐based malware classification (ESMO‐CBLSTMAE) system in the IoT cloud platform. The projected ESMO‐CBLSTMAE system focuses on detecting and classifying malware in the IoT cloud platform. To achieve that, the ESMO‐CBLSTMAE algorithm employs a min–max normalization technique for scaling the input dataset. The ESMO‐CBLSTMAE method uses a convolutional bidirectional long short‐term memory autoencoder (CBLSTM‐AE) model for the malware detection process. Lastly, the ESMO method is executed for the optimum hyperparameter tuning of the CBLSTM‐AE technique, which boosts the malware classification results. The experimental analysis of the ESMO‐CBLSTMAE method is tested against a benchmark database, and the outcomes portray the greater efficacy of the ESMO‐CBLSTMAE approach over other existing techniques. The proposed malware classification model achieved an accuracy of 98.57 and F Score of 80.77 and outperformed the existing models.

Understanding integration issues in intelligent transportation systems with IoT platforms, cloud computing, and connected vehicles

<p>The fast improvements in information and communication technology have altered the transportation business, resulting in intelligent transportation systems (ITS) that aim to improve transportation network safety, efficiency, and sustainability. Cloud computing, networked automobiles, and IoT platforms are driving this shift. Infrastructure for current civilization must include transport. The effectiveness of transport infrastructure is vital for all countries’ individual mobility, trade, and economic development. Traffic congestion, fuel price increases, and CO<sub>2</sub> emissions have all increased in recent years, posing problems for modern civilization. It is crucial to improve transportation efficiency and safety. To develop a sustainable, intelligent transportation system, it is necessary to seamlessly integrate and interoperate with cutting-edge technologies like linked cars, the Internet of Things (IoT), and cloud computing. To respond to difficulties facing the transportation industry, which include rising fuel costs, high CO<sub>2</sub> emissions, expanding congestion in traffic, and greater dangers on the highway, integration concerns must be concluded.</p>

A smart decentralized identifiable distributed ledger technology‐based blockchain (DIDLT‐BC) model for cloud‐IoT security

AbstractThe most important and difficult challenge the digital society has recently faced is ensuring data privacy and security in cloud‐based Internet of Things (IoT) technologies. As a result, many researchers believe that the blockchain's Distributed Ledger Technology (DLT) is a good choice for various clever applications. Nevertheless, it encountered constraints and difficulties with elevated computing expenses, temporal demands, operational intricacy, and diminished security. Therefore, the proposed work aims to develop a Decentralized Identifiable Distributed Ledger Technology‐Blockchain (DIDLT‐BC) framework that is intelligent and effective, requiring the least amount of computing complexity to ensure cloud IoT system safety. In this case, the Rabin algorithm produces the digital signature needed to start the transaction. The public and private keys are then created to verify the transactions. The block is then built using the DIDLT model, which includes the block header information, hash code, timestamp, nonce message, and transaction list. The primary purpose of the Blockchain Consent Algorithm (BCA) is to find solutions for numerous unreliable nodes with varying hash values. The novel contribution of this work is to incorporate the operations of Rabin digital data signature generation, DIDLT‐based blockchain construction, and BCA algorithms for ensuring overall data security in IoT networks. With proper digital signature generation, key generation, blockchain construction and validation operations, secured data storage and retrieval are enabled in the cloud‐IoT systems. By using this integrated DIDLT‐BCA model, the security performance of the proposed system is greatly improved with 98% security, less execution time of up to 150 ms, and reduced mining time of up to 0.98 s.

Dynamic and Distributed Intelligence over Smart Devices, Internet of Things Edges, and Cloud Computing for Human Activity Recognition Using Wearable Sensors

A wide range of applications, including sports and healthcare, use human activity recognition (HAR). The Internet of Things (IoT), using cloud systems, offers enormous resources but produces high delays and huge amounts of traffic. This study proposes a distributed intelligence and dynamic HAR architecture using smart IoT devices, edge devices, and cloud computing. These systems were used to train models, store results, and process real-time predictions. Wearable sensors and smartphones were deployed on the human body to detect activities from three positions; accelerometer and gyroscope parameters were utilized to recognize activities. A dynamic selection of models was used, depending on the availability of the data and the mobility of the users. The results showed that this system could handle different scenarios dynamically according to the available features; its prediction accuracy was 99.23% using the LightGBM algorithm during the training stage, when 18 features were used. The prediction time was around 6.4 milliseconds per prediction on the smart end device and 1.6 milliseconds on the Raspberry Pi edge, which can serve more than 30 end devices simultaneously and reduce the need for the cloud. The cloud was used for storing users’ profiles and can be used for real-time prediction in 391 milliseconds per request.

Recognition of Sleep Disorders using IoT-Based Wearables and Neutrosophic Data Analytics

In the dynamic landscape of healthcare technology, the amalgamation of Internet of Things (IoT) systems and Neutrosophic Data Analytics has heralded a paradigm shift. This study delves deep into this transformative synergy by presenting an innovative IoT-based wearable system design for the recognition of sleep disorders. Our meticulously crafted multilayer cellular system seamlessly integrates IoT devices, data acquisition, cloud computing, and machine learning to unlock a wealth of insights into sleep patterns, their anomalies, and the presence of sleep disorders. Through fair and rigorous experimental comparisons, we unveil the prowess of Long Short-Term Memory (LSTM) within the machine learning realm, showcasing its superior performance over baseline models. The results affirm LSTM's ability to detect sleep disorders with remarkable accuracy, precision, and recall, revolutionizing sleep medicine and healthcare practices. This research, at the crossroads of innovation and healthcare, not only illuminates the path to advanced sleep disorder diagnosis but also heralds a new era of personalized healthcare interventions and remote monitoring solutions. As we navigate the realm of IoT and data-driven healthcare, our findings hold the promise of improving the quality of life for countless individuals, reaffirming the pivotal role of technology in safeguarding one of the most fundamental aspects of human well-being – a peaceful and restorative night's sleep.

BUDGET-BASED SMART CITY IMPLEMENTATION FOR TIER III CITIES

In the present day's world the Internet of Things (IoT) is gaining popularity as a field of research in various engineering applications. IoT includes various elements like communication technologies, communication protocols, identification technologies, sensing, computation, and actuation. Today, almost every home is equipped with smart IoT home appliances. As cities grow and expand, smart and innovative solutions are crucial for improving productivity, increasing operational efficiencies, reducing management costs, and enhancing the lifestyle of human beings. A smart city consists of a number of applications. In this paper, budget-based implementation of smart cities with specific applications like smart parking, smart dustbins, smart streetlights, and smart traffic monitoring and management systems are done using various IoT boards, various sensors, actuators, and various IoT cloud platforms. In the smart city application, several heterogeneous devices are connected through the internet for communication, and huge amounts of data are collected from various applications and stored in databases. Data are classified, analyzed, and visualized on both web and mobile dashboards.

IOT-BASED SMART SALINE LEVEL AND HEALTH MONITORING SYSTEM

The many uses of saline solution include as providing temporary fluids when there is under or no hydration among the trauma cases. In addition, they participate in the control of salt and the levels of electrolytes in the body. To maintain the level of a saline bottle, hospital nurses or aides prime responsibility is to check it on a regular basis. The failure to replace them in time may result in a reversal of blood flow, compromising the patient’s life. An Internet of Things (IoT)-based Smart Saline Level was developed to solve this issue and to provide an automated and efficient solution for remote monitoring. The Proposed system employs Internet of Things (IoT) technology, integrating NodeMCU, a load cell amplifier (HX711), Arduino UNO, Pulse oximeter (MAX 30100) sensor, DHT11 Sensor and a voltage regulator(L7805). The load cell, in combination with the HX711 amplifier, accurately measures the weight of the saline bottle. MAX30100 sensor measures Heart Rate & Oxygen saturation (SPo2) levels in blood. DHT11 Sensor detects the Humidity and Temperature levels. All the data is transmitted to the Arduino UNO, which serves as a master, the NodeMCU facilitates wireless communication, relaying the collected data from the Arduino UNO to the designated IoT cloud platform for remote monitoring.

Towards Sustainable Smart Living: Cloud-Based IoT Solutions for Home Automation

In recent times, the realm of home automation systems has garnered significant attention, thanks to the ever-evolving landscape of communication technology. The concept of a smart home, essentially an application of the Internet of Things (IoT), leverages the power of the internet to oversee and employ household appliances through a sophisticated automation infrastructure. Nevertheless, challenges persist within the existing home automation systems, such as constrained wireless transmission reach, a deficiency in backup power management, and the substantial financial outlay involved. Addressing these limitations, our study introduces an economical and resilient solution that combines cloud based IoT with an uninterrupted power management system, making a cutting-edge home automation prototype. This system relies on a microcontroller unit, specifically the ESP-32, which functions as a Wi-Fi-enabled gateway for connecting a variety of sensors and transmitting their data to the Blynk IoT cloud server. The data assembled from a multitude of sensors, including vibration sensors and voltage detectors, becomes readily accessible on users' devices, be it smartphones or laptops, irrespective of their geographical location. The system is further strengthened by a set of relays that link the ESP-32 with household appliances, allowing for centralized control. Structurally, the design uses a control box that can be seamlessly integrated into a real home environment, offering the means to both monitor and govern an array of household devices. This IoT-based home automation solution not only efficiently manages internet-connected appliances but also provides an effective emergency power management system, enabling remote initiation and deactivation of backup generators. It represents a innovative leap in the evolution of home automation systems, steering in convenience, efficiency, and cost-effectiveness.

IoT Enabled Boat for Cleaning Water Surface

This project introduces an IoT-enabled boat designed for the purpose of cleaning water surfaces. The system utilizes two gear motors to drive a conveyor belt for efficient collection of floating debris. An Arduino Uno, in conjunction with two IR sensors, provides precise control over the conveyor system, ensuring effective garbage collection. Additionally, the boat's overall movement is managed through a controller employing two gear motors and an Arduino IoT Cloud platform. The conveyor system, powered by the gear motors, facilitates the seamless collection of debris present on the water surface. The Arduino Uno, acting as the central processing unit, interprets signals from two IR sensors strategically placed to optimize the efficiency of the garbage collection process. This intelligent control system allows for the autonomous navigation of the boat and precise maneuvering around obstacles. Key Words: IoT (Internet of Things), Water Surface Cleaning, Gear Motors, Conveyor System, Arduino Uno, IR Sensors, Boat Controller, Arduino IoT Cloud, Garbage Collection, Autonomous Navigation, Remote Monitoring, Environmental Cleanup, Sustainability, Pollution Control, Scalable Solution, Real-time Feedback, Smart Technologies, Automation, Water Pollution, Adaptive Systems.

An Analytical Study on the Implementation of a Healthcare App to Assist People with Disabilities Using Cloud Computing and IoT

This study targets a group of people who require care, that is, people with special needs. The significance of this study lies in addressing the main problem that this group suffers from, which is the lack of awareness and information that leads to the acceptance of that group in society. This work aims to create a mobile application that contributes to spreading knowledge among people with special needs and enhancing their skills to help them become accepted by community members. This application supports people with special needs with training resources, education, suitable jobs, and other services helping them in developing their experiences and knowledge to be active in society. In addition, an evaluation questionnaire has been developed to collect data from both the private and public sectors to classify the building blocks necessary for KSA to incorporate the Internet of Things (IoT) and cloud computing into the healthcare sector. As a result, most respondents acknowledge the importance of a streamlined data-gathering process, the IoT, and cloud-based computing to meet their healthcare needs. Lastly, six main blocks for checking suppliers and the public to accept IoT and cloud healthcare applications are then acknowledged in this paper.

Securing Iot Data Transmission: A Comprehensive Approach Integrating Two-Fish Encryption With Wireless Smart Energy Systems And Iot Cloud Services

This work addresses the challenges posed by the interconnected nature of the Internet of Things (IoT). This paradigm, facilitating the seamless connectivity of diverse devices, gives rise to issues such as addressability, confidentiality, and data management. The proposed solution advocates for the integration of the robust Twofish encryption algorithm into wireless smart energy systems, creating a holistic approach that capitalizes on the capabilities of IoT cloud services. Twofish, recognized for its strong encryption capabilities, becomes an integral part of the IoT framework to ensure the security of communication and data confidentiality. Through the amalgamation of Twofish encryption with wireless smart energy systems and IoT cloud services, the approach provides a comprehensive solution for the secure transmission and management of data. This integration enhances system scalability, flexibility, and accessibility, thereby safeguarding the confidentiality of information exchanged over the internet. The paper establishes the superiority of Twofish over lightweight cryptography for low-power devices within IoT systems through a thorough analysis of critical factors. These factors encompass the robust security strength of Twofish, its effective encryption capabilities guaranteeing data confidentiality, the algorithm's maturity validated by extensive scrutiny from the cryptographic community, compatibility and interoperability with diverse platforms, resource efficiency in maintaining a balance between security and device capabilities, and the crucial long-term security assurance required for the extended operational lifetimes of IoT devices. The research findings affirm that the incorporation of Twofish into wireless smart energy systems and IoT cloud services offers a secure and efficient solution for safeguarding sensitive information within IoT environments. This consolidated approach contributes significantly to advancing secure communication and data management in the continually evolving landscape of the Internet of Things.

Automatic Crop Protection from Rain Fall Using IOT and IBM Cloud

With the ongoing fourth revolution, technology has been growing rapidly day by day. One of the most significant and efficient uses of this has been evident in the farming sector. From a basic water sprinkler system to the requirement of Fertilizer for specific crops, the likes of Artificial intelligence and the Internet of things have brought in great changes. One of the issues which need to be addressed is the protection of crops from heavy rainfall which causes significant damage to the crop production and soil yield, also causing huge mental distress to a farmer. We in our proposed model have given a solution to this problem by giving automated as well as manual control. The motivation behind our paper is to keep the crops protected from these heavy precipitations and preserve the same rainwater for future purposes when water is scarce. We use a sensor with a NodeMCU module to ensure the covering of the estimated field and LCD display to show status of Field. This project addresses the critical issue of protecting crops from heavy rainfall through the integration of Internet of Things (IoT) technology and the IBM Cloud platform. By deploying a network of sensors capable of measuring rainfall intensity, soil moisture, temperature, and humidity, real-time data is collected and transmitted to the IBM Cloud for analysis. These measures may include adjusting protective structures, or activating drainage systems. The project aims to improve crop yield and reduce losses caused by adverse weather conditions, making agriculture more sustainable and efficient. The combination of IoT and IBM Cloud empowers farmers with real-time monitoring and control, enhancing their ability to protect valuable crops. This project not only demonstrates the practical application of advanced technologies in agriculture but also provides a foundation for future research and development in this field.

Provably Secure Lightweight Mutual Authentication and Key Agreement Scheme for Cloud-Based IoT Environments.

A paradigm that combines cloud computing and the Internet of Things (IoT) allows for more impressive services to be provided to users while addressing storage and computational resource issues in the IoT environments. This cloud-based IoT environment has been used in various industries, including public services, for quite some time, and has been researched in academia. However, various security issues can arise during the communication between IoT devices and cloud servers, because communication between devices occurs in open channels. Moreover, issues such as theft of a user's IoT device or extraction of key parameters from the user's device in a remote location can arise. Researchers interested in these issues have proposed lightweight mutual authentication key agreement protocols that are safe and suitable for IoT environments. Recently, a lightweight authentication scheme between IoT devices and cloud servers has been presented. However, we found out their scheme had various security vulnerabilities, vulnerable to insider, impersonation, verification table leakage, and privileged insider attacks, and did not provide users with untraceability. To address these flaws, we propose a provably secure lightweight authentication scheme. The proposed scheme uses the user's biometric information and the cloud server's secret key to prevent the exposure of key parameters. Additionally, it ensures low computational costs for providing users with real-time and fast services using only exclusive OR operations and hash functions in the IoT environments. To analyze the safety of the proposed scheme, we use informal security analysis, Burrows-Abadi-Needham (BAN) logic and a Real-or-Random (RoR) model. The analysis results confirm that our scheme is secure against insider attacks, impersonation attacks, stolen verifier attacks, and so on; furthermore, it provides additional security elements. Simultaneously, it has been verified to possess enhanced communication costs, and total bit size has been shortened to 3776 bits, which is improved by almost 6% compared to Wu et al.'s scheme. Therefore, we demonstrate that the proposed scheme is suitable for cloud-based IoT environments.

Secure data access using blockchain technology through IoT cloud and fabric environment

AbstractIn the current landscape, staying abreast of the latest technological advancements is a formidable challenge, especially given the deluge of data inundating the internet. The realization has dawned that effectively managing the surge in emerging data necessitates the integration of multiple technologies. In pursuit of this objective, the Internet of Things (IoT), renowned for its sensor‐based data capture capabilities, is frequently coupled with blockchain technology to ensure secure data storage and access. This amalgamation, in turn, leverages the cloud environment when data volume surpasses a machine's processing capacity, thus mitigating infrastructure and maintenance costs. This study endeavors to optimize data storage within the blocks of the blockchain (BCT) by storing an index that points to the actual data. This innovative approach not only conserves storage space but also enhances operational efficiency. Furthermore, it simplifies the task of identifying malicious or faulty nodes deployed at different locations for data capture within the prescribed time frame. To exemplify this implementation, a case study is presented, focusing on securing user votes through the creation of contracts. The results showcased underscore the preference for a permissioned blockchain, such as Fabric, over a permissionless one, like Ethereum, particularly in the context of security considerations. The findings reveal that as the number of operations (in this case, votes cast) increases, Ethereum's performance deteriorates, while Fabric exhibits exceptional robustness. Additionally, the study analyzes sensor data simulated via IoT nodes before and after the application of security algorithms to underscore the significance of the proposed Secure Cloud‐Based Blockchain (SCB2) model. The analysis encompasses various facets, including the creation, validation, and computation times of transactions and blocks within a node, and positions the model favorably in comparison to existing literature.