Optimized and efficient computation of big data in heterogonous internet of things

Abstract

The development of information technology, distributed computing, hardware, wireless communication and intelligent technology has been increased in Internet of Things (IoT) heterogonous filed to improve the limitations of cloud computing in big data processing. Computation of data over wireless communication based distributed computing face different challenges in term of off-loading decision, data delay in heterogonous IoT devices. Optimization of caching, data computation and load maintenance of different edge clouds is still a challenging task in heterogonous IOT for effective processing of big data. So that this paper presents Novel Optimized and Sorted Positional Index List (OSPIL) approach on big data processing to reduce and optimize delay, I/O cost, CPU usage and memory overhead significantly. In this approach sorted index is used to build attributes are (data consists different attributes) arranged in ascending order. This approach consists two phases in data processing, in Phase 1, scan the depth of all the sorted list and schedule the processing data. In phase 2, explore the sorted list and then give results in sequential order on hash table. Experimental results of proposed approach give better and significant data processing results to optimize delay, I/O cost, CPU usage and memory overhead on real world data sets relates wireless communication.