Distributed Streaming Big Data Analytics for Internet of Things (IoT)

In this chapter, a discussion on the integration of distributed streaming Big Data Analytics with the Internet of Things is presented. The chapter begins with the introduction of these two technologies by discussing their features and characteristics. Discussion on how the integration of these two technologies benefit in efficient processing of IoT device generated sensor data follows next. Such data centric processing of IoT data powered by cloud, services and other enablers will be the architecture of most of the realtime systems involving sensors and real-time monitoring and actuation. The Volume, Variety and Velocity of sensor generated data make it a Big Data scenario. In addition, the data is real time and requires decisions or actuations immediately. This chapter discusses how IoT data can be processed using distributed, scalable stream processing systems. The chapter is concluded with future directions of such real time Big Data Analytics in IoT.

Research on the Communication Transformation and Its Risk Prevention Method in the Background of Big Data

The communication based on big data technology is an organized communication form built with data and algorithms as the core, which has a very serious risk of liquefaction in the practical development. Therefore, based on the understanding of the transformation of communication ontology and the uncertainty of the subject under the background of big data technology, this paper studies and analyzes the risk prevention countermeasures based on streaming big data analysis according to the alienated risks it presents.

SBIoT: Scalable Broker Design for Real Time Streaming Big Data in the Internet of Things Environment

Thanks to the recent development in the technology number of IoT devices increased dramatically. Therefore,industries have been started to use IoT devices for their business processes. Many systems can be done automatically thanks to them. For this purpose, there is a server to process sensors data. Transferring these data to the server without any loss has crucial importance for the accuracy of IoT applications. Therefore, in this thesis a scalable broker for real time streaming data is proposed. Open source technologies, which are NoSql and in-memory databases, queueing, fulltext index search, virtualization and container management orchestration algorithms, are used to increase efficiency of the broker. Firstly, it is planned to be used for the biggest airport in Turkey to determine the staff location. Considering the experiment analysis, proposed system is good enough to transfer data produced by devices in that airport. In addition to this, the system can adapt to device increase, which means if number of devices increasing in time, number of nodes can be increased to capture more data.

Complicity of High-End SOC-FPGA’s for Data Centers

As the network traffic increasing significantly due to increase in Data streaming, Big Data Analytics, Cloud Computing, Increasing the load on Data Centers, Which leads to demand for high computational capabilities, low latency, high-bandwidth, power efficient data accelerators. As Re-Configurability of FPGA’s are more flexible for developing customized applications, so the FPGA hardware based data accelerators are the potential devices to achieve low latency and power efficient requirements. The modern FPGA’s are coming up with the embedded communication hard IP’s like PCIe, Ethernet, & DDR based memory controllers, which makes easy for the deployment of network attached FPGA’s in data centers. This paper presents the role of FPGA’s in datacenters and analysis of high-end FPGA’s by various vendors, which are suitable for deployment in data centers.