ABSTRACT
Internet of Things (IoT) is the latest technology where a set of devices will communicate through internet and among themselves for forming interconnections, and provide smart services to consumers at any instance. A huge number of these types of devices generate large amount of data but these devices were not sufficient for storing and processing normal data. Big data provides components of its framework to deal with enormous amount of data. Therefore, mapping of these big data components with IoT reference architecture layers was made to meet their requirements in terms of storage or processing. The major problems during communication of IoT devices are their connectivity with internet and their connectivity through gateway where different devices establish communication and transfer the data for big data analytics. The IoT device storage has much impact while gathering data from real-time environments; their collection for processing for big data needs to be managed with data center for reliable storage; analytics for future scope of applications needs to be performed through IoT. The identity of IoT devices plays a prominent role while securing data communication among a large number of IoT devices. Thing identifiers need to follow specific standards so that their significance in terms of utilization of storage and processing data will happen in secure manner so that their loss of data will not cause any major effect in analytics. Registry of IoT devices and storing their state information play a prominent role in giving unique identity for communication of devices through internet and data center storage for further processing inside big data for analytics. Registry of IoT devices varies depending on the application framework where they are hosted. Semantic study of IoT was analyzed for easy registration, and IoT data management life cycle with architecture layers was highlighted. Device provisioning was a critical task to ensure which set of IoT devices are involved while gathering information from the real-time environment and sending for big data for analytics. Application framework mechanisms for device provisioning were dealt here to have a clear idea how IoT devices are getting provisioned to do their relevant task and further processing at data center. Processing of IoT data at big data was understood by streaming analytics, and the steps for performing streaming process were highlighted in perspective of their analysis for IoT data with streaming architectures. The managing of data center happens by studying high-scale computation models for IoT. The advantages of distributed middleware services, big data support for computation, and cloud storage for IoT data were highlighted, thus making the computation more realistic for future needs of IoT device data management for large-scale computations. Finally, Corton Intelligence Suite use cases are studied with respect to big data analytics. General idea of Corton Intelligence Suite was first highlighted, and further specific intelligent use cases are studied for more understanding of big data analytics for IoT.
