GeoFog4Health: a fog-based SDI framework for geospatial health big data analysis

Spatial Data Infrastructure (SDI) is an important framework for sharing geospatial big data using the web. Integration of SDI with cloud computing led to emergence of Cloud-SDI as a tool for transmission, processing and analysis of geospatial data. Fog computing is a paradigm where embedded computers are employed to increase the throughput and reduce latency at the edge of the network. In this study, we developed and evaluated a Fog-based SDI framework named GeoFog4Health for mining analytics from geo-health big data. We built prototypes using Intel Edison and Raspberry Pi for studying the comparative performance. We conducted a case study on Malaria vector-borne disease positive maps of Maharastra state in India. The proposed framework had provision of lossless data compression for reduced data transfer. Also, overlay analysis of geospatial data was implemented. In addition, we discussed energy savings, cost analysis and scalability of the proposed framework with respect to efficient data processing. We compared the performance of the proposed framework with the state-of-the-art Cloud-SDI in terms of analysis time. Results and discussions showed the efficacy of the proposed system for enhanced analysis of geo-health big data generated from a variety of sensing frameworks

TOWARDS DATA STORAGE SCHEME IN BLOCKCHAIN BASED SERVERLESS ENVIRONMENT: AES ENCRYPTION AND DECRYPTION ALGORITHM APPROACH

In today's digital age, data storage has become an increasingly important issue. The exponential growth of data has led to the need for secure and private storage solutions. Unfortunately, without a standardized protocol, the security and privacy of sensitive data can be a challenge. However, blockchain technology has emerged as a promising solution for secure data storage. Blockchain's decentralized and immutable nature provides a comprehensive solution for the security and privacy of all types of data. In this research, we propose an innovative framework that leverages the benefits of blockchain technology to securely handle and store data in a serverless environment of distributed nodes. To ensure the highest level of security, we evaluated four encryption algorithms - Blowfish, RC4, DES, and AES - for storing data in a permissioned blockchain network. We found that AES encryption and decryption algorithms provide the best solution for creating a decentralized, immutable coordinate system. Our proposed framework is based on a permissioned blockchain network that enables multiple users to join the network through suitable identity verification processes, and each user is assigned certain special and designated permissions to perform actions. This framework provides a secure and efficient solution for the storage of all types of data, ensuring privacy and security. Our proposed framework offers an innovative solution for secure data storage and management in a decentralized environment. This research has practical implications for organizations that need to store sensitive data securely, and it also contributes to the ongoing development of blockchain technology

<i>DeepFog:</i> Fog Computing-Based Deep Neural Architecture for Prediction of Stress Types, Diabetes and Hypertension Attacks

The use of wearable and Internet-of-Things (IoT) for smart and affordable healthcare is trending. In traditional setups, the cloud backend receives the healthcare data and performs monitoring and prediction for diseases, diagnosis, and wellness prediction. Fog computing (FC) is a distributed computing paradigm that leverages low-power embedded processors in an intermediary node between the client layer and cloud layer. The diagnosis for wellness and fitness monitoring could be transferred to the fog layer from the cloud layer. Such a paradigm leads to a reduction in latency at an increased throughput. This paper processes a fog-based deep learning model, DeepFog that collects the data from individuals and predicts the wellness stats using a deep neural network model that can handle heterogeneous and multidimensional data. The three important abnormalities in wellness namely, (i) diabetes; (ii) hypertension attacks and (iii) stress type classification were chosen for experimental studies. We performed a detailed analysis of proposed models&#8217; accuracy on standard datasets. The results validated the efficacy of the proposed system and architecture for accurate monitoring of these critical wellness and fitness criteria. We used standard datasets and open source software tools for our experiments

DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

In today&rsquo;s digital world healthcare is one core area of the medical domain. A healthcare system is required to analyze a large amount of patient data which helps to derive insights and assist the prediction of diseases. This system should be intelligent in order to predict a health condition by analyzing a patient&rsquo;s lifestyle, physical health records and social activities. The health recommender system (HRS) is becoming an important platform for healthcare services. In this context, health intelligent systems have become indispensable tools in decision making processes in the healthcare sector. Their main objective is to ensure the availability of the valuable information at the right time by ensuring information quality, trustworthiness, authentication and privacy concerns. As people use social networks to understand their health condition, so the health recommender system is very important to derive outcomes such as recommending diagnoses, health insurance, clinical pathway-based treatment methods and alternative medicines based on the patient&rsquo;s health profile. Recent research which targets the utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed which reduces the workload and cost in health care. In the healthcare sector, big data analytics using recommender systems have an important role in terms of decision-making processes with respect to a patient&rsquo;s health. This paper gives a proposed intelligent HRS using Restricted Boltzmann Machine (RBM)-Convolutional Neural Network (CNN) deep learning method, which provides an insight into how big data analytics can be used for the implementation of an effective health recommender engine, and illustrates an opportunity for the health care industry to transition from a traditional scenario to a more personalized paradigm in a tele-health environment. By considering Root Square Mean Error (RSME) and Mean Absolute Error (MAE) values, the proposed deep learning method (RBM-CNN) presents fewer errors compared to other approaches

TCloud: Cloud SDI model for tourism information infrastructure management

This chapter proposes and develops a cloud-computing-based SDI model named as TCloud for sharing, analysis, and processing of spatial data particularly in the Temple City of India, Bhubaneswar. The main purpose of TCloud is to integrate all the spatial information such as tourism sites which include various temples, mosques, churches, monuments, lakes, mountains, rivers, forests, etc. TCloud can help the decision maker or planner or common users to get enough information for their further research and studies. It has used open source GIS quantum GIS for the development of spatial database whereas QGIS plugin has been linked with quantum GIS for invoking cloud computing environment. It has also discussed the various spatial overlay analysis in TCloud environment

Smart Healthcare System in Server-Less Environment: Concepts, Architecture, Challenges, Future Directions

Server-less computing is a novel cloud-based paradigm that is gaining popularity today for running widely distributed applications. When it comes to server-less computing, features are available via subscription. Server-less computing is advantageous to developers since it lets them install and run programs without worrying about the underlying architecture. A common choice for code deployment these days, server-less design is preferred because of its independence, affordability, and simplicity. The healthcare industry is one excellent setting in which server-less computing can shine. In the existing literature, we can see that fewer studies have been put forward or explored in the area of server-less computing with respect to smart healthcare systems. A cloud infrastructure can help deliver services to both users and healthcare providers. The main aim of our research is to cover various topics on the implementation of server-less computing in the current healthcare sector. We have carried out our studies, which are adopted in the healthcare domain and reported on an in-depth analysis in this article. We have listed various issues and challenges, and various recommendations to adopt server-less computing in the healthcare sector

Steady-state and laser flash photolysis studies of 1-aziridinyl-1,2-dibenzoylalkenes

This article does not have an abstract

Hybrid mist-cloud systems for large scale geospatial big data analytics and processing: opportunities and challenges

The cloud and fog computing paradigms are developing area for storing, processing, and analysis of geospatial big data. Latest trend is mist computing which boost fog and cloud concepts for computing process where edge devices are used to help increase throughput and reduce latency to support at client edge. The present research article discussed the mist computing emergence for geospatial analysis of data from various geospatial applications. It also created a framework based on mist computing, i.e., MistGIS for analytics in mining domain from geospatial big data. The developed MistGIS platform is used in Tourism Information Infrastructure Management and Faculty Information Retrial System. Tourism Information Infrastructure Management is to assimilate entire geospatial data in context to travel/tourism places constitute of various lakes, mountains, rivers, forests, temples, mosques, churches, monuments, etc. It can aid all the stakeholders or users to acquire sufficient data in subsequent research studies. In this study, it has taken the Temple City of India, Bhubaneswar as the case study. Whereas Faculty Information Retrial System facilitated many functionalities with respect to finding the detail information of faculties according to their research area, contact details, and email ids, etc in all 31 National Institutes of Technology (NITs) in India. The framework is built with the Raspberry Pi microprocessor. The MistGIS platform has been confirmed by prelude analysis which includes cluster and overlay. The outcome show that mist computing assist cloud and fog computing to provide the analysis of geospatial big data

FogGrid: Leveraging Fog Computing for Enhanced Smart Grid Network

The present manuscript concentrates on the application of Fog computing to a Smart Grid Network that comprises of a Distribution Generation System known as a Microgrid. It addresses features and advantages of a smart grid. Two computational methods for on-demand processing based on shared information resources is discussed. Fog Computing acts as an additional layer of computational and/or communication nodes that offload the Cloud backend from multi-tasking while dealing with large amounts of data. Both Fog computing and Cloud computing hierarchical architecture is compared with respect to efficient utilization of resources. To alleviate the advantages of Fog computing, a Fog computing framework based on Intel Edison is proposed. The proposed architecture has been hardware implemented for a microgrid system. The results obtained show the efficacy of Fog Computing for smart grid network in terms of low power consumption, reduced storage requirement and overlay analysis capabilities

BCGeo: Blockchain-Assisted Geospatial Web Service for Smart Healthcare System

Most recent research on healthcare systems has focused on integrating the Internet of Things (IoT), Blockchain technology, and cloud computing to enhance the performance of IoT devices with limited resource availability, create smart healthcare platforms, and offer patients the best possible healthcare service. Modern healthcare systems use large-scale sensor devices to address many challenges brought on by the conventional delivery of healthcare services. Most studies have lately identified data collection, massive data processing, geolocating, access management, device prioritization, and storing as primary issues in most IoT healthcare systems. Decentralization, privacy, security, scalability, trust, anonymity, and building geospatial-based intelligent healthcare systems for patient care are significant difficulties that most healthcare systems today must overcome. Blockchain technology in healthcare platforms is noteworthy and innovative since it opens platforms for data privacy, anonymity, and validity through the consensus process. In this work, we proposed a novel decentralized Blockchain-enabled geospatial service architecture for smart healthcare systems called BCGeo. The proposed framework offers an online geospatial healthcare service for residents of Bhubaneswar, a city in India, who are newcomers to the city and are less familiar with its local healthcare organizations. An analytical queueing method prioritizes serving Critical patients more than other patients. In contrast to previously proposed frameworks, the proposed framework includes immutability, scalability, geospatial mapping, patient prioritizing, and decentralized privacy protection policies for addressing the technical challenges in most of the current healthcare systems. Additionally, it explains the performance analysis of BCGeo. It includes graphs showing the various possible outcomes of arithmetic operations, performance measurement, and experimental results on the proposed architecture