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Application of IoT in the Food Processing Industry
Published in Sam Goundar, Archana Purwar, Ajmer Singh, Applications of Artificial Intelligence, Big Data and Internet of Things in Sustainable Development, 2023
Himanshi Garg, Vasudha Sharma, Soumya Ranjan Purohit
It is a web-based computing platform that enables the sharing and on-demand access to various computing devices (computers, networks, storage, software, and so on). Since IoT devices generate heaps of data and are analyzed with high-speed processing computers to provide real-time and efficient decision-making, cloud computing is necessary for IoT deployment (Lee & Lee, 2015). Numerous IoT cloud platforms in the market perform the same function as middleware. However, cloud computing aims to connect IoT devices and their applications. It helps decision-makers transmit and secure data from IoT devices to enterprise resource planning (ERP) systems and business intelligence by providing real-time information.
Exploring the Scope of Policy Issues Influencing IoT Health and Big Data: A Structured Review
Published in Adarsh Garg, D. P. Goyal, Global Healthcare Disasters, 2023
Cloud computing involves the remote use of servers, computing platforms, and software systems over the Internet. In resource-constrained settings, cloud technologies can be an effective way of lowering infrastructure cost while also facilitating technical robustness (Wang et al., 2016). Nevertheless, there are concerns about using public clouds to host health data, including technical setbacks such as Internet bandwidth limitations (Auffray et al., 2016; Wahl et al., 2018).
A scalable cyberinfrastructure and cloud computing platform for forest aboveground biomass estimation based on the Google Earth Engine
Published in International Journal of Digital Earth, 2019
Zelong Yang, Wenwen Li, Qi Chen, Sheng Wu, Shanjun Liu, Jianya Gong
Rapid advancement of spatial data acquisition methods (e.g. Earth Observation, unmanned aerial vehicles, LiDAR scanner) results in the production of a massive amount of geospatial data (Yang et al. 2010; Romero et al. 2011), requiring therefore a new way to store, organize and process these resources. Cloud computing, a computing paradigm that adopts the concept of elastic resource allocation and virtualization has been proposed to directly address these computing needs for big data analytics (Li et al. 2016). Cloud computing platforms provide computing resources by service-oriented abstraction, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) (Mell and Grance 2011). Several open source cloud computing software, such as CloudStack (Kumar et al. 2014), and OpenStack (Sabharwal and Shankar 2013; Lamourine 2014) have been widely used to establish the institutional or private cloud (Wuhib, Stadler, and Lindgren 2012, Tan et al. 2015). There are also many popular commercial cloud platforms, i.e. Microsoft Azure, Google Cloud Platform, Amazon Web Services (AWS), available to allow researchers to rapidly deploy applications and conduct experiments. Although readily available, these platforms only provide a computing environment, all the configurations, data and applications need to be built by researchers. It also requires an in-depth understanding in computing in order to optimize resource allocation and system performance. To further provide an integrated and easy-to-use environment for scientific analysis, Google has released its GEE platform, where scientists are allowed to have ‘local’ access to various datasets, and achieve parallel programing using GEE built-in library without a steep learning curve on parallelism, resource scheduling and allocation. GEE also provides supporting cloud services for data storage, exchange and indexing, further facilitating problem solving especially when big data is used. These advantages make GEE an ideal platform for most users, especially scientists in different disciplines to rapidly construct their own cloud-based high-performance applications.