Real-Time Problems to Be Solved by the Combination of IoT, Big Data, and Cloud Technologies

2021 ◽  
pp. 296-307
Author(s):  
Shaila S. G. ◽  
Monish L. ◽  
Rajlaxmi Patil
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Real Time ◽  
Power Efficiency ◽  
Main Concern ◽  
Network Connection ◽  
Technical Issues ◽  
Data Source ◽  
Internet Revolution ◽  
Cloud Technologies

With the advancement of computation power and internet revolution, IoT, big data, and cloud computing have become the most prevalent technologies in present time. Convergence of these three technologies has led to the development of new opportunities and applications which solve the real time problems in the most efficient way. Though cloud computing and big data have an inherent connection between them, IoT plays a major role of a data source unit. With the explosion of data, cloud computing is playing a significant role in the storage and management. However, the main concern that accompanies IoT are the issues related to privacy, security, power efficiency, computational complexities, etc. Misinterpretation of data and security limitations are the bottlenecks of big data whereas the limitations of cloud computing involve network connection dependency, limited features, technical issues, and security. The chapter considers use cases to address their real time problems and discusses about how to solve these issues by combining these technologies.

scholarly journals Big data actionable intelligence architecture

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tian J. Ma ◽  
Rudy J. Garcia ◽  
Forest Danford ◽  
Laura Patrizi ◽  
Jennifer Galasso ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Digital Media ◽  
Decision Makers ◽  
Time Data ◽  
Human In The Loop ◽  
Course Of Action ◽  
Insufficient Time ◽  
Data Source ◽  
Different Sources

AbstractThe amount of data produced by sensors, social and digital media, and Internet of Things (IoTs) are rapidly increasing each day. Decision makers often need to sift through a sea of Big Data to utilize information from a variety of sources in order to determine a course of action. This can be a very difficult and time-consuming task. For each data source encountered, the information can be redundant, conflicting, and/or incomplete. For near-real-time application, there is insufficient time for a human to interpret all the information from different sources. In this project, we have developed a near-real-time, data-agnostic, software architecture that is capable of using several disparate sources to autonomously generate Actionable Intelligence with a human in the loop. We demonstrated our solution through a traffic prediction exemplar problem.

Cloud Computing Technologies for Open Connected Government

2016 ◽  
pp. 1-14 ◽  
Author(s):  
Zaigham Mahmood
Keyword(s):  
Social Media ◽  
Cloud Computing ◽  
Web 2.0 ◽  
Real Time ◽  
Distributed Applications ◽  
Mobile Technologies ◽  
Computing Technologies ◽  
Cloud Technologies ◽  
Cloud Resources

Cloud Computing is an attractive paradigm for organisations that have a requirement to process large scalable distributed applications. It allows for self-provisioning of cloud resources to develop and host applications as well as acquire storage and networking resources. Connected Government (c-government) is an area where cloud technologies can be effectively used to achieve the benefits that the cloud paradigm promises. Social Media, Web 2.0 and mobile technologies can all help to further enhance the connected government capabilities. Using such technologies, governments and citizens can engage in real time in the electronic participation of a government's functioning. In this chapter, we introduce the cloud paradigm and then discussing the requirements of c-government, we outline how cloud technologies can help to achieve an open and transparent c-government. The aim is to provide the basics of relationship between c-government and cloud computing to set the scene for other contributions in this volume.

Trends and Technologies in Big Data Processing

2020 ◽  
pp. 17-42
Author(s):  
Amitava Choudhury ◽  
Kalpana Rangra
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Computing Technology ◽  
Time Data ◽  
Big Data Processing ◽  
Real Time Data ◽  
Real Time Data Processing

Data type and amount in human society is growing at an amazing speed, which is caused by emerging new services such as cloud computing, internet of things, and location-based services. The era of big data has arrived. As data has been a fundamental resource, how to manage and utilize big data better has attracted much attention. Especially with the development of the internet of things, how to process a large amount of real-time data has become a great challenge in research and applications. Recently, cloud computing technology has attracted much attention to high performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied. In this chapter, various big data processing techniques are discussed.

scholarly journals Perlustration on Recent Research into Multi Clouds integrated with Multi Party Computation

2019 ◽  
Vol 8 (6) ◽  
pp. 587-592
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Short Note ◽  
Research Work ◽  
Cloud Security ◽  
Ease Of Use ◽  
Technological Advancement ◽  
Future Work ◽  
Cloud Technologies ◽  
Multi Cloud

Recent advances in multi cloud technologies and multi-party computations have improved State of art usage of Cloud computing in real time scenarios. Primary reason behind using any service offered by others is ease of use with lesser economics. Cloud Computing is technological advancement which is in usage for last two decades because of its Pay-per-Usage policy offering enormous benefits across the user community. In spite of its enormous benefits, single factor which is stepping it back from its wider adoption throughout the digital society is its Security. Tremendous research work was done across industry and academia in association with cloud security. This paper focuses on brief history, real time deployment of cloud, usage, benefits, risks associated and Surveys various studies done by national and international organizations related to cloud security concerns and dwell upon the advantages of integrating multi clouds and multi-party computation techniques and emphasizes on recent research done across multi cloud environment and give a short note of future work to enhance security paradigm.

scholarly journals Design and Implementation of a Framework for Multi-Cloud Service Broker

2021 ◽  
Author(s):  
Md Ahsan Ullah
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Service Providers ◽  
Cloud Service ◽  
Cloud Services ◽  
Location Awareness ◽  
Performance Measurements ◽  
Positioning Systems ◽  
Network Connection ◽  
Service Broker

Cloud service broker (CSB) as an emerging technology intermediates heterogeneous multiple cloud services for both the providers and consumers. Recently, Cloud computing & mobile cloud computing applications (MCA) have gained an enormous popularity, which has led to an increasing need for the development of platform independent Middleware/CSB to support all types of cloud service consumer applications including x86*x64 based standard OS & ARM based mobile applications, web browsers, etc. Developing Platform Independent Hybrid CSB, however, is not an easy task. Developers have to deal with difficulties inherent from the different cloud controllers, cloud service providers environments, clients’ application types, network connection types (wired, wireless), GPS (Global Positioning Systems) information of cloud resources and clients’ etc. In this thesis, the proposed design of a middleware/CSB that abstracts the real-time resources of various clouds (private, public, home, Local) and stores the resources in its own Database. It will also store clients requests then analyzes the request to find the nearest available servers which is running the appropriate applications. Then the CSB will forward the destination servers information to the clients. Thesis goal is to achieve context awareness, location awareness, platform independence, portability, efficiency, and usability. Portability is achieved by following the J2ME platform specifications. The middleware has been implemented and tested on a real time Openstack cloud using by our newly designed Android Clients and platform independent Mozilla Firefox browser. The performance measurements of the middleware show that it achieves its efficiency requirements. Furthermore, the middleware’s database can be used for resource algorithm, pattern analysis, and for future requirements.

scholarly journals Application of Big Data Fusion Based on Cloud Storage in Green Transportation: An Application of Healthcare

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Li Qin Hu ◽  
Amit Yadav ◽  
Asif Khan ◽  
Hong Liu ◽  
Amin Ul Haq
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Fusion ◽  
Real Time ◽  
Strong Support ◽  
Heterogeneous Data ◽  
Continuous Growth ◽  
New Energy ◽  
The Government ◽  
Vehicle Network

In the 21st century, transportation brought great convenience to people, but at the same time, automobile transportation is the major factor causing greenhouse gas emissions and climate change. Movements of the world towards green environments, there is hike in use and production of electric vehicles (energy vehicles). However, with the continuous growth in the number of energy vehicles, it is necessary for the government to provide strong support in the construction of charging piles. Real-time and effective management has become a practical problem for the relevant departments which needs to be solved. This paper uses the information research method to fuse the huge amount of heterogeneous data generated by the charging pile resultant to the new energy electric vehicle in the vehicle network and introduces cloud computing as its storage module to facilitate the storage and related expansion of the big data. This paper proposes a system scheme of heterogeneous data fusion based on cloud computing for the acquisition, storage, and fusion of heterogeneous data in the vehicle network. After testing the results, it shows that the system is stable and effective in practical application, which can meet the design requirements of the system. What is the significance of analyzing big data of charging point? Considering from the supply side, obtaining the user’s charging behaviour data is helpful to build a digital map of the charging pile of new energy vehicles, connect the service information between the vehicle enterprises and the charging pile enterprises, and provide the most comprehensive and effective real-time charging information covering the widest range of vehicles, which can solve many problems of information asymmetry in the current charging information service.

scholarly journals Designing Efficient Smart Home Management with IoT Smart Lighting: A Case Study

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Tran Anh Khoa ◽  
Le Mai Bao Nhu ◽  
Hoang Hai Son ◽  
Nguyen Minh Trong ◽  
Cao Hoang Phuc ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Information Security ◽  
Internet Of Things ◽  
Real Time ◽  
Mobile Networks ◽  
Smart Home ◽  
Smart Cities ◽  
Smart Homes ◽  
Authentication Mechanism

Smart homes are an element of developing smart cities. In recent years, countries around the world have spared no effort in promoting smart cities. Smart homes are an interesting technological advancement that can make people’s lives much more convenient. The development of smart homes involves multiple technological aspects, which include big data, mobile networks, cloud computing, Internet of Things, and even artificial intelligence. Digital information is the main component of signal control and flow in a smart home, while information security is another important aspect. In the event of equipment failure, the task of safeguarding the system’s information is of the utmost importance. Since smart homes are automatically controlled, the problem of mobile network security must be taken seriously. To address these issues, this paper focuses on information security, big data, mobile networks, cloud computing, and the Internet of Things. Security efficiency can be enhanced by using a Secure Hash Algorithm 256 (SHA-256), which is an authentication mechanism that, with the help of the user, can authenticate each interaction of a given device with a WebServer by using an encrypted username, password, and token. This framework could be used for an automated burglar alarm system, guest attendance monitoring, and light switches, all of which are easily integrated with any smart city base. In this way, IoT solutions can allow real-time monitoring and connection with central systems for automated burglar alarms. The monitoring framework is developed on the strength of the web application to obtain real-time display, storage, and warning functions for local or remote monitoring control. The monitoring system is stable and reliable when applying SHA-256.

Big Data for flood management: Realising the benefits for developing countries

2020 ◽  
Author(s):  
Namrata Bhattacharya Mis
Keyword(s):  
Developing Countries ◽  
Big Data ◽  
Real Time ◽  
Disaster Management ◽  
Low Income ◽  
Developing Nations ◽  
Scoping Study ◽  
Urban Settlements ◽  
Data Source

<p>Agenda 2030 goal 11 commits towards making disaster risk reduction an integral part of sustainable social and economic development. Flooding poses some of the most serious challenges in front of developing nations by hitting hardest to the most vulnerable. Focussing on the urban poor, frequently at highest risk are characterised by inadequate housing, lack of services and infrastructure with high population growth and spatial expansion in dense, lower quality urban structures. Use of big data from within these low-quality urban settlement areas can be a useful step forward in generating information to have a better understanding of their vulnerabilities. Big data for resilience is a recent field of research which offers tremendous potential for increasing disaster resilience especially in the context of social resilience. This research focusses to unleash the unrealised opportunities of big data through the differential social and economic frames that can contribute towards better-targeted information generation in disaster management. The scoping study aims to contribute to the understanding of the potential of big data in developing particularly in low-income countries to empower the vulnerable population against natural hazards such as floods. Recognising the potential of providing real-time and long-term information for emergency management in flood-affected large urban settlements this research concentrates on flood hazard and use of remotely sensed data (NASA, TRMM, LANDSAT) as the big data source for quick disaster response (and recovery) in targeted areas. The research question for the scoping study is: Can big data source provide real-time and long- term information to improve emergency disaster management in urban settlements against floods in developing countries?  Previous research has identified several potentials that big data has on faster response to the affected population but few attempts have been made to integrate the factors to develop an aggregated conceptual output . An international review of multi-discipline research, grey literature, grass-root projects, and emerging online social discourse will appraise the concepts and scope of big data to highlight the four objectives of the research and answer the specific questions around existing and future potentials of big data, operationalising and capacity building by agencies, risk associated and prospects of maximising impact. The research proposes a concept design for undertaking a thematic review of existing secondary data sources which will  be used to provide a holistic picture of how big data can support in resilience through technological change within the specific scope of social and environmental contexts of developing countries. The implications of the study lie in the system integration and understanding of the socio-economics, political, legal and ethical contexts essential for investment decision making for strategic impact and resilience-building in developing nations.</p>

scholarly journals Architectural Framework for Cloud Reliability Model using Fault Tolerance Techniques

2019 ◽  
Vol 8 (11) ◽  
pp. 515-519
Keyword(s):  
Cloud Computing ◽  
Fault Tolerance ◽  
Real Time ◽  
Data Storage ◽  
Threshold Value ◽  
Fine Grained ◽  
Storage Servers ◽  
Architectural Framework ◽  
Cloud Technologies ◽  
Reliability And Availability

Cloud Computing is an essential podium for workable and impracticable users. It achieves high quality reliable services provided to the users via data storage servers. The key challenge in cloud architecture is to run the facilities without any hazel to the users. In today’s world of information technology, most of the applications are real time. The major constraint of the systems used in real time applications is that, they are prone to failure. The failure may be due to the following reasons: a) Failure to complete the task in prescribed time threshold value. b) Failure to achieve prescribed reliability value. Virtualization and Internet-based Cloud computing causes diverse types of failures to occur and so necessity for reliability and availability have turn out to be of vital concern. To ensure reliability and availability of cloud technologies, methods for fault tolerance need to be developed and deployed. The proposed work will focus on adaptive behavior during the assortment of replication and fine-grained check pointing methods for achieving a reliable cloud platform that can grip diverse client requests. In addition to this, the proposed work will also conclude the best suitable fault tolerance scheme to each chosen virtual machine.

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