Fog Data Analytics Research Articles

An Overview of Fog Data Analytics for IoT Applications.

With the rapid growth in the data and processing over the cloud, it has become easier to access those data. On the other hand, it poses many technical and security challenges to the users of those provisions. Fog computing makes these technical issues manageable to some extent. Fog computing is one of the promising solutions for handling the big data produced by the IoT, which are often security-critical and time-sensitive. Massive IoT data analytics by a fog computing structure is emerging and requires extensive research for more proficient knowledge and smart decisions. Though an advancement in big data analytics is taking place, it does not consider fog data analytics. However, there are many challenges, including heterogeneity, security, accessibility, resource sharing, network communication overhead, the real-time data processing of complex data, etc. This paper explores various research challenges and their solution using the next-generation fog data analytics and IoT networks. We also performed an experimental analysis based on fog computing and cloud architecture. The result shows that fog computing outperforms the cloud in terms of network utilization and latency. Finally, the paper is concluded with future trends.

A systematic survey on internet of things: Energy efficiency and interoperability perspective

AbstractWith the provision of rapid advancement in smart devices used in various technological fields exponentially increases the heterogeneity and energy consumption in internet of things (IoT). The technological leap in information and communication technology instigates the heterogeneity in smart devices, frameworks, architectures, communication technologies, and various industrial and nonindustrial applications. Therefore, a detailed taxonomy of IoT is proposed covering the diverse aspect of IoT lacking interoperability and energy efficiency. Existing research lacks the root causes of heterogeneity and energy consumption at the industrial and technological level. Keeping this in view, our research identified industrial integration and technological challenges. Moreover, we explore the effect on IoT devices when different types of energy harvesters are connected with IoT devices. Our comprehensive research addresses the various issues such as resource management, fog data analytics, energy consumption, heterogeneity, scalability, and the role of quality of service, data science, machine learning to accomplish interoperability and energy efficiency in IoT.

Fog data analytics: A taxonomy and process model

Through the exponential growth of sensors and smart gadgets (collectively referred to as smart devices or Internet of Things (IoT) devices), significant amount of heterogeneous and multi-modal data, termed as Big Data (BD), is being generated. To deal with such BD, we require efficient and effective solutions such as data mining, analytics, and reduction to be deployed at the edge of fog devices on a cloud. Existing research and development efforts generally focus on performing BD analytics overlook the difficulty of facilitating fog data analytics (FDA). In this paper, we discuss the unique nature and complexity of fog data analytics. A detailed taxonomy for FDA is abstracted into a process model. The proposed model addresses various research challenges, such as accessibility, scalability, fog nodes communication, nodal collaboration, heterogeneity, reliability, and quality of service (QoS) requirements. To demonstrate the proposed process model, we present two case studies.