IoT Big Data Analytics
1Lakehead University, Thunder Bay, Canada
2University of Prince Edward Island, Charlottetown, Canada
3Muroran Institute of Technology, Hokkaido, Japan
4St. Francis Xavier University, Antigonish, Canada
IoT Big Data Analytics
Description
A successful Internet of Things (IoT) environment requires standardization that contains interoperability, compatibility, reliability, and effectiveness of the operations on a global scale. The rapid growth of the IoT causes a sharp growth of data. Enormous amounts of networking sensors are continuously collecting and transmitting data to be stored and processed in the cloud. Such data can be environmental data, geographical data, astronomical data, logistic data, and so on. Mobile devices, transportation facilities, public facilities, and home appliances are the primary data acquisition equipment in IoT. The volume of such data will surpass the capacities of the IT architectures and infrastructure of existing enterprises and, due to real time analysis character, will also greatly impact the computing capacity. Management of these increasingly growing data is a challenge for the community in general. Due to the generation of Big Data by IoT, the existing data processing capacity of IoT is becoming ineffective and it is imperative to incorporate Big Data technologies to promote the development of IoT. It is important to understand that the success of IoT lies upon the effective incorporation of Big Data analytics. The widespread deployment of IoT also given a challenge to Big Data community to propose that newer techniques as Big Data and IoT are interdependent. On one hand, the widespread deployment of IoT produces data both in quantity and category, thus providing the opportunity for the application and development of Big Data; on the other hand, the incorporation of Big Data analytics in IoT simultaneously accelerates the research advances and business models of IoT. The purpose of this special issue is to provide recent results and related surveys on IoT and big data analytics (also its applications).
Potential topics include but are not limited to the following:
- Architecture and Protocols for IoT and Big Data
- Energy efficient IoT Big Data acquisition techniques
- IoT Big Data storage and processing techniques
- IoT and Big Data programming model
- IoT Big Data analytic tools
- Mining IoT Big Data
- Privacy and Security related to IoT and Big Data