Machine Learning Approach for Cloud Data Analytics in IoT

In this era of IoT, edge devices generate gigantic data during every fraction of a second. The main aim of these networks is to infer some meaningful information from the collected data. For the same, the huge data is transmitted to the cloud which is highly expensive and time-consuming. Hence, it needs to devise some efficient mechanism to handle this huge data, thus necessitating efficient data handling techniques. Sustainable computing paradigms like cloud and fog are expedient to capably handle the issues of performance, capabilities allied to storage and processing, maintenance, security, efficiency, integration, cost, energy and latency. However, it requires sophisticated analytics tools so as to address the queries in an optimized time. Hence, rigorous research is taking place in the direction of devising effective and efficient framework to garner utmost advantage.

Machine learning has gained unmatched popularity for handling massive amounts of data and has applications in a wide variety of disciplines, including social media.

Machine Learning Approach for Cloud Data Analytics in IoT details and integrates all aspects of IoT, cloud computing and data analytics from diversified perspectives. It reports on the state-of-the-art research and advanced topics, thereby bringing readers up to date and giving them a means to understand and explore the spectrum of applications of IoT, cloud computing and data analytics.

Nyckelord: Cloud Computing; Data Analytics; Fog Computing; Internet of Things; Machine Learning; Artificial Intelligence; Data Analytics in Cloud Computing; Data Analytics using Machine Learning; Cloud Computing and Machine Learning; Challenges in Cloud Data Analytics; Research issues in Cloud Analytics; Challenges and issues in IoT; Cloud computing in IoT; Issues and challenges in Cloud computing; Cloud computing in Indsutry 4.0; Data analytics in IoT; Predictive data analytics; IOT enabled Cloud Security; Data analytics in healthcare Industry; Data analytics in agriculture domain; Data analytics in transport; Data analytics in weather monitoring; Data Privacy Issues; Development of Smart cities and Smart home; Energy efficiency in cloud computing. Practical Internet of Things Security, Artificial Intelligence, Grid & Cloud Computing, Intelligent Systems & Agents, Artificial Intelligence, Grid & Cloud Computing, Intelligent Systems & Agents