AN ARCHITECTURAL DESIGN FOR CLOUD OF THINGS

In recent times the world has seen an exponential rise in the number of devices connected to the internet. This widespread expansion of the internet and growth in the number of interconnected devices has lead to the rise of many new age technologies. Internet of Things being one of them allows devices to communicate with one another that are connected through the internet. It provides a new way of looking towards pervasive computing wherein "things" be it sensors, embedded devices, actuators or humans interact with one another. But currently IoT is facing a number of challenges related to scalability, interoperability, storage capacity, processing power and security act as a deterrent for its practical implementation. Cloud computing, the buzzword of the IT industry, suits best to handle all these challenges, thus leading towards the integration of cloud and IoT. In this paper, we present a layered architecture for Cloud of Things i.e. the amalgamation of cloud computing and internet of things. The architecture provides a scalable approach for IoT as it allows dynamic addition of n-number of "things". Moreover the architecture allows the end users to host their applications onto the cloud and access there IoT systems remotely.  Towards the end, the paper discusses a use case that proves the correctness of the proposed architecture

Blockchain: Future of e-Governance in Smart Cities

In recent times, Blockchain has emerged as a transformational technology with the ability to disrupt and evolve multiple domains. As a decentralized, immutable distributed ledger, Blockchain technology is one of the most recent entrants to the comprehensive ideology of Smart Cities. The rise of urbanization and increased citizen participation have led to various technology integrations in our present-day cities. For cities to become smart, we need standard frameworks and procedures for integrating technology, citizens and governments. In this paper, we explore the potential of Blockchain technology as an enabler for e-governance in smart cities. We examine the daily challenges of citizens and compare them with the benefits being offered by Blockchain integration. On the basis of a comprehensive literature review, we identified four key areas of e-governance wherein Blockchain can provide monumental advantages. In the context of Blockchain integration for e-governance, the paper presents a survey of prominent published works discussing various urban applications

Blockchain–Cloud Integration: A Survey

Over the last couple of years, Blockchain technology has emerged as a game-changer for various industry domains, ranging from FinTech and the supply chain to healthcare and education, thereby enabling them to meet the competitive market demands and end-user requirements. Blockchain technology gained its popularity after the massive success of Bitcoin, of which it constitutes the backbone technology. While blockchain is still emerging and finding its foothold across domains, Cloud computing is comparatively well defined and established. Organizations such as Amazon, IBM, Google, and Microsoft have extensively invested in Cloud and continue to provide a plethora of related services to a wide range of customers. The pay-per-use policy and easy access to resources are some of the biggest advantages of Cloud, but it continues to face challenges like data security, compliance, interoperability, and data management. In this article, we present the advantages of integrating Cloud and blockchain technology along with applications of Blockchain-as-a-Service. The article presents itself with a detailed survey illustrating recent works combining the amalgamation of both technologies. The survey also talks about blockchain–cloud services being offered by existing Cloud Service providers

A Social Network Analysis Approach to COVID-19 Community Detection Techniques

Machine learning techniques facilitate efficient analysis of complex networks, and can be used to discover communities. This study aimed use such approaches to raise awareness of the COVID-19. In this regard, social network analysis describes the clustering and classification processes for detecting communities. The background of this paper analyzed the geographical distribution of Tambaram, Chennai, and its public health care units. This study assessed the spatial distribution and presence of spatiotemporal clustering of public health care units in different geographical settings over four months in the Tambaram zone. To partition a homophily synthetic network of 100 nodes into clusters, an empirical evaluation of two search strategies was conducted for all IDs centrality of linkage is same. First, we analyzed the spatial information between the nodes for segmenting the sparse graph of the groups. Bipartite The structure of the sociograms 1–50 and 51–100 was taken into account while segmentation and divide them is based on the clustering coefficient values. The result of the cohesive block yielded 5.86 density values for cluster two, which received a percentage of 74.2. This research objective indicates that sub-communities have better access to influence, which might be leveraged to appropriately share information with the public could be used in the sharing of information accurately with the public

Biodiesel Production from Jatropha: A Computational Approach by Means of Artificial Intelligence and Genetic Algorithm

In the past couple of years, the world has come to realize the importance of renewable sources of energy and the disadvantages of excessive use of fossil fuels. Numerous studies have been conducted to implicate the benefits of artificial intelligence in areas of green energy production. Artificial intelligence (AI) and machine learning algorithms are believed to be the driving forces behind the fourth industrial revolution and possess capabilities for interpreting non-linear relationships that exist in complex problems. Sustainable biofuels are derived from renewable resources such as plants, crops, and waste materials other than food crops. Unlike traditional fossil fuels such as coal and oil, biofuels are considered to be more sustainable and environmentally friendly. The work discusses the transesterification of jatropha oil into biodiesel using KOH and NaOH as alkaline catalysts. This research aims to examine and optimize the nonlinear relationship between transesterification process parameters (molar ratio, temperature, reaction time, and catalyst concentration) and biodiesel properties. The methodology employed in this study utilizes AI and machine learning algorithms to predict biodiesel properties and improve the yield and quality of biodiesel. Deep neural networks, linear regression, polynomial regression, and K-nearest neighbors are the algorithms implemented for prediction purposes. The research comprehensively examines the impact of individual transesterification process parameters on biodiesel properties, including yield, viscosity, and density. Furthermore, this research introduces the use of genetic algorithms for optimizing biodiesel production. The genetic algorithm (GA) generates optimal values for transesterification process parameters based on the desired biodiesel properties, such as yield, viscosity, and density. The results section presents the transesterification process parameters required for obtaining 72%, 85%, and 98% biodiesel yields. By leveraging AI and machine learning, this research aims to enhance the efficiency and sustainability of biodiesel production processes