Journal of Cloud Computing and Internet of Things ISSN: 3107-4391 (Online)

Authors: Amit Singh, Pooja Mehta

Abstract: Managing the growing number of IoT devices in cloud environments is a significant challenge due to scalability issues. This paper presents a scalable architecture for efficient IoT device management in cloud systems, utilizing containerization and microservices to handle large-scale deployments. The proposed architecture ensures seamless device registration, monitoring, and control, reducing latency and improving system resilience. We explore several cloud-native tools and platforms that support scalability in IoT management and demonstrate their effectiveness in managing high device density. Our case study highlights the architecture's ability to maintain performance under increasing device loads.

Keywords: IoT Device Management Scalable Architecture Cloud Environments Microservices Containerization

Author: Dr. Priya Menon

Abstract: Secure data sharing is a paramount concern in cloud-IoT environments, where sensitive information is transmitted across potentially insecure networks. This paper investigates the use of homomorphic encryption to enable secure data sharing without compromising functionality. Homomorphic encryption allows computations to be performed on encrypted data, ensuring privacy even in the event of unauthorized access. We evaluate several encryption schemes and assess their performance in real-world cloud-IoT applications. The proposed framework demonstrates a balance between security and computational efficiency, making it a viable solution for secure data sharing in cloud-IoT systems.

Keywords: Homomorphic Encryption, Secure Data Sharing, Cloud-IoT Security, Privacy-preserving Computation, Data Encryption

Authors: Sneha Patel, Amit Joshi

Abstract: The proliferation of Internet of Things (IoT) devices in cloud environments has led to massive data generation, necessitating efficient data management strategies. This paper investigates the role of machine learning algorithms in optimizing data storage, processing, and retrieval in cloud-based IoT systems. Through predictive analytics and adaptive learning techniques, machine learning can improve data management efficiency, reducing latency and energy consumption. We analyze supervised, unsupervised, and reinforcement learning approaches for their applicability in cloud-IoT ecosystems and evaluate their performance in real-time data processing scenarios. The proposed solutions demonstrate significant improvements in system throughput and resource optimization.

Keywords: Machine Learning, Data Management, Cloud-IoT Systems, Predictive Analytics, Resource Optimization

Author: Prof. Neha Reddy

Abstract: Cloud computing has become a critical enabler of the Internet of Things (IoT), allowing for scalable and on-demand resource management. However, as more devices connect to the cloud, security challenges have escalated. This paper explores how block chain technology can enhance cloud security for IoT applications. By leveraging decentralized ledgers, block chain minimizes vulnerabilities such as data breaches and unauthorized access. The paper analyzes several block chain protocols and their impact on cloud-based IoT systems. Performance metrics are discussed, with a focus on scalability and transaction speed. Furthermore, a case study of a block chain-empowered IoT framework is presented, illustrating how distributed security measures can be implemented to safeguard cloud resources.

Keywords: block chain, Cloud Computing, Internet of Things (IoTs), Security, Decentralized Ledger

Authors: Karan Mehta, Neha Bhargav

Abstract: Edge computing has emerged as a transformative approach to managing the vast amounts of data generated by Itoh devices in cloud environments. By processing data closer to the source, edge computing minimizes latency, reduces bandwidth usage, and enhances real-time decision-making capabilities. This paper explores the integration of edge computing with cloud-IoT architectures, focusing on its benefits and potential challenges. We provide a detailed analysis of several edge computing frameworks and their ability to optimize cloud-IoT interactions. The paper further examines edge computing's role in improving data security, scalability, and device interoperability within cloud ecosystems.

Keywords: Edge Computing, Cloud-IoT Architecture, Real-time Processing, Data Optimization, Interoperability

Authors: Shiva Mishra, Pooja Verma

Abstract: The integration of Cloud Computing (CC) and the Internet of Things (IoT) has the potential to revolutionize various industries by enabling scalable and efficient solutions. This paper investigates the scalability and performance optimization challenges in Cloud-IoT architectures, focusing on key factors that influence system performance, such as data processing, storage, and network communication. We review state-of-the-art techniques and methodologies for optimizing scalability and performance in Cloud-IoT systems, including distributed computing, edge computing, and load balancing strategies. The paper also presents a comparative analysis of different optimization approaches, highlighting their advantages and limitations. Through case studies and real-world implementations, we demonstrate the impact of these optimization techniques on the efficiency and scalability of Cloud-IoT architectures. The paper concludes with recommendations for future research to further enhance the scalability and performance of Cloud-IoT systems.

Keywords: Cloud Computing, Internet of Things (IoT), Edge Computing, Scalability, Performance Optimization, Data Security, Edge AI

Authors: Kavita Joshi, Keshav Kumar

Abstract: Healthcare systems worldwide are increasingly adopting advanced technologies to improve patient care and operational efficiency. This paper surveys the current landscape of cloud computing and IoT integration in healthcare. It examines various applications such as remote patient monitoring, telemedicine, and electronic health records management. The survey highlights the benefits of this integration, including improved patient outcomes, cost reduction, and enhanced accessibility to healthcare services. The paper also discusses the technical and regulatory challenges that need to be addressed to ensure the successful deployment of cloud-IoT solutions in healthcare.

Keywords: Healthcare, Cloud Computing, Internet of Things, Remote Monitoring, Telemedicine

Authors: Ritu Sharma, Kapil Kumar

Abstract: The integration of Cloud Computing (CC) and the Internet of Things (IoT) has generated an unprecedented amount of data, necessitating efficient data management and analytics solutions. This paper examines the challenges and solutions related to data management and analytics in Cloud-IoT environments. We discuss the key components of data management, including data collection, storage, processing, and analytics, and explore various techniques and tools for managing and analyzing large-scale IoT data. The paper also highlights the role of machine learning and artificial intelligence in enhancing data analytics capabilities. We present case studies and real-world examples to illustrate the practical applications and benefits of effective data management and analytics in Cloud-IoT environments. The paper concludes with recommendations for future research to address the evolving challenges and develop advanced solutions for data management and analytics in Cloud-IoT ecosystems.

Keywords: Cloud Computing, Internet of Things, Data Management, Analytics, Machine Learning

Authors: Rinku Kumar, Niharika Gupta

Abstract: The integration of Cloud Computing (CC) and the Internet of Things (IoT) has revolutionized the concept of smart cities, enabling efficient and scalable solutions for urban management. This paper explores the synergies between CC and IoT in the context of smart city applications, highlighting the advantages, challenges, and future directions of this convergence. We examine various case studies and real-world implementations to understand the impact of CC-IoT integration on urban infrastructure, including traffic management, energy optimization, and public safety. The findings suggest that leveraging cloud services for IoT data processing and storage significantly enhances the capabilities of smart city solutions, offering improved efficiency, scalability, and cost-effectiveness. However, the integration also presents challenges such as data security, privacy concerns, and the need for robust communication networks. The paper concludes with recommendations for addressing these challenges and outlines future research directions to further enhance the CC-IoT integration in smart city applications.

Keywords: Cloud Computing, Internet of Things, Smart Cities, Urban Management, Data Security

Authors: Asha Gupta, Sonu Kumar

Abstract: Cloud computing and the Internet of Things (IoT) are two of the most transformative technologies of the 21st century. Their integration presents significant opportunities for innovation and efficiency across various sectors, including healthcare, transportation, smart cities, and agriculture. This paper provides a comprehensive review of the current state of research on the integration of cloud computing and IoT. It explores the technical challenges and solutions, the benefits of such integration, and the potential future directions. The review highlights the importance of cloud computing in providing scalable storage and computational resources for IoT devices, which often have limited capabilities. The paper also discusses the security and privacy concerns associated with this integration and proposes potential mitigation strategies.

Keywords: Cloud Computing, Internet of Things, IoT Integration, Data Security, Smart Cities

Author:Akansha Sharma

Abstract:Serverless computing has emerged as a paradigm shift in cloud computing, offering a new model for developing and deploying applications without the need to manage underlying infrastructure. This paper explores the advantages, challenges, and future trends of serverless computing, providing insights into its impact on the development and deployment of applications.

Keywords:  Serverless Computing, Function as a Service (FaaS), Cloud Computing, Advantages, Challenges, Multi-Cloud Serverless, Edge Computing Integration, Serverless Machine Learning

Authors:Rajat Kapoor, Shubham Bansal

Abstract:The advent of cloud computing has revolutionized the way organizations deploy and manage their IT infrastructure. Scalability and elasticity are two critical features that contribute to the success of cloud services, enabling businesses to efficiently handle varying workloads and ensuring optimal resource utilization. This paper provides an in-depth exploration of scalability and elasticity in cloud services, discussing their definitions, importance, challenges, and key technologies. Additionally, the paper presents practical examples and case studies to illustrate the implementation of these concepts in real-world scenarios.

Keywords:  Cloud Computing, Scalability, Elasticity, Containerization, Kubernetes, Serverless Computing, Load Balancing, Auto-scaling, Resource Provisioning

Authors:Sameer Joshi, Akash Gupta

Abstract:We live in an Internet-based world. On-demand information or data is required wherever and whenever it is wanted. Big Data is defined as a large volume of data in many formats that cannot be handled using typical methods such as a database management system. Hadoop's MapReduce is one of the computational techniques used in Big Data Analytics. MapReduce as a Service is provided by the cloud. We examine and analyse the problems and requirements of MapReduce integrity in the cloud in this work. The capabilities of some of the most major integrity assurance frameworks are also examined, as well as future research directions. Algorithms for detecting collusive and non-collusive personnel were discussed.

Keywords: Cloud Computing, Hadoop, MapReduce, Security, Integrity

Author:Lecturer

Abstract:Hybrid cloud architectures have emerged as a versatile solution to address the dynamic needs of modern businesses, combining the benefits of both public and private clouds. This paper explores the implementation strategies and best practices associated with hybrid cloud architectures, offering insights into key considerations, challenges, and recommendations for organizations seeking to adopt this hybrid approach. The paper also includes relevant tables and figures to illustrate key concepts and comparisons.

Keywords:  Hybrid Cloud Architecture, Cloud Computing, On-Premises Infrastructure, Private Cloud, Public Cloud, Data Integration, Interoperability

Authors:Kavita Sharma, Mohan Kumar

Abstract:Privacy concerns have become increasingly critical in the era of big data and machine learning. Federated learning has emerged as a promising solution for privacy-preserving machine learning, allowing multiple parties to collaboratively train a model while keeping their data decentralized and secure. This paper provides an in-depth overview of federated learning, its applications, advantages, challenges, and potential future directions. We also include illustrative figures and tables to enhance the understanding of the concepts discussed.

Keywords: Federated Learning, Privacy-Preserving Machine Learning, Decentralized Model Training, Collaborative Model Training, Data Privacy Secure Aggregation, Data Heterogeneity, Communication Efficiency, Edge Computing, Differential Privacy

Authors:Dr. Jaswant Singh Rao, Anurag Kashyap

Abstract:With the increasing adoption of cloud computing, organizations are leveraging multi-cloud environments to achieve enhanced scalability, flexibility, and cost efficiency. However, the utilization of multiple cloud service providers simultaneously introduces unique security challenges that must be carefully addressed. This article explores the security risks associated with multi-cloud environments and presents effective mitigation strategies to safeguard sensitive information. By implementing these strategies, organizations can ensure data confidentiality, integrity, and availability while harnessing the benefits of cloud computing.

Keywords:Multi-cloud environments, cloud computing, security challenges, mitigation strategies, data confidentiality, data integrity, access control, encryption, multi-factor authentication, security monitoring, vendor evaluation.

Authors: Sarswati Apsingekar, Chaitrali Nakhate, Swamini Tarange, Siddharth Payghan

Abstract: An essential component of the cloud storage environment is data security. Preserving very important user data in the cloud is critical. The Interplanetary File System (IPFS) offers a content-addressable block storage format for storing and distributing files in a distributed environment. It is a version-controlled file system in a peer-to-peer architecture. We have developed a block chain-based architecture to share the file utilizing content-addressable block storage in the peer-to-peer paradigm in accordance with an IPFS feature. We've developed a distributed storage architecture based on block chain technology and IPFS that secures the resource's immutability, integrity, and availability in order to circumvent the availability, dependability, storage overhead, and other problems associated with centralized service providers. In this system, we store the file on IPFS and the transaction-level addressable content (hash) on the block chain. The issue of a centralized service provider's availability, dependability, and storage can be successfully resolved by our suggested plan.

Keywords: IPFS, Cloud Storage Environment, block chain, peer-to-peer

Authors: Ashish Akare, Sayali Mahajan, Gouri Choudhar, Monika Dahale

Abstract: Internet of Things is one of the appear techniques that help in extend over the gap between the physical and cyber world. In the Internet of Things, the different smart objects connected, communicate with each other, data is gathered from the smart objects and based on the need of the users, and the data collection are queried and setback to the user. IOT helps in monitoring electrical and physical parameters. The main goal of this project is to implement green technologies for society to reduce the fuel gobble up costs. Green technology is an alternative energy but inexpensive, efficient and effective. This can open fuel supplies, cost of capital and release. In addition, the aim of this project is to generate electricity by developing a Pico-hydro-generation proto type system that produces low capacity to be used in rural communities. This project has primarily focused on the design and manufacture of a Pico-hydro device that can be used for low capacity equipment such as motor and bulb. In addition, this project will evaluate the generator production based on turbine rotation. In the high-PVC tubing, water flow has the ability to move the turbine where it is attached to a generator to transform mechanical energy into electrical energy. Within this project the pulley system can be seen to improve the turbine’s output. The turbine connecting to the pulley system needed lower speed compared to the turbine directly connecting to the generator.

Keywords: Smart Appliance Control, Electricity Generation, high-PVC tubing, Pico-hydro, IOT Electricity Generation

Authors: A.P. Devmore, Payal P. Thombare , Nikita S. Killedar, Prerana A. kamble  ^,Sakshi A. Koli

Abstract:Physical keys are the most natural way to lock or open a door, and everyone is familiar with it. Although the physical key is a well-proven and well-known technology, it is not without faults. For a lock, there can only be one unique key. Different keys are required for various locks. Carrying a big number of keys is also inconvenient. Smart locks are key-less door locks that let you unlock your door without having to use areal key. A smart lock is an electromagnetically lock that is meant to lock and unlock a door when it receives instructions from an authorized device and executes the authorization procedure using a cryptography key. Keywords—door lock, Arduino, Smart home, Home automation, Security. Conceptual Security has consistently been a significant worry to the general public either in the family units or the workplace condition. There are different methodologies set up to address these issues. This venture is proposed to build up a savvy locking framework utilizing the Internet of Things. Utilizing conventional keyed locks is basic since the start of humankind, anyway there is a high risk of keys being lost or getting into inappropriate hands. Subsequently, many individuals prefer biometric locks over traditional keyed locks to improve the security of their homes or workplaces. In contrast to the conventional lock, a cutting-edge biometric lock requires no key to bolt or open the door and instead uses a biometric sensor. Our project is an Arduinonano based adaptable working device that provides physical security utilizing the biometric sensor which is available in a smartphone.

Keywords:  internet of things (IOT), sensors, microcontroller, ESP8266, cloud computing with thing Speak, Arduino.

Authors : Sanket Patil, Satish Patil, Kshitij Kamble, Shubham Patil, Z.Z.Makandar

Abstract: Heart disease like arrhythmia need continual long-term monitoring. For example, in emergency at home, where the patient is unable help themselves or seek help, there is a need for long distance health monitoring for early and faster assessment for treatment. This project presents a remote monitoring system for monitoring the irregularity in heart rate, which enables real-time monitoring for the cardiovascular diseases (CVDs) patient. The sensor is non-invasive where pulse reading is taken from the finger.

The microcontroller is used to receive and process the signal. When irregularity in heart rate is detected, the microcontroller will send data to a smartphone using Bluetooth. A mobile application is developed to receive the data and to send out an alert in the form of a text message to another mobile phone. The alert is successfully sent to the specified recipient such as medical doctors or next of kin in the emergency which contain the details such as heart rate information and GPS coordinate. Evaluation on the functionality of the device shows that the developed device can reach accuracy of 97.50%, precision of 96.55%, sensitivity of 99.29% and specificity of 91.67%.

Keywords:  Heart Rate, IOT, GSM module, ESP32 Microcontroller, ECG Sensor AD8232.