Data Summarization in Internet of Things

• Published in: SN computer science Vol. 3; no. 4; p. 304
• Main Authors: Deevi, Sri Aditya, Manoj, B. S.
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.07.2022; Springer Nature B.V
• Subjects: Algorithms; Clustering; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data compression; Data storage; Data Structures and Information Theory; Energy consumption; Energy storage; Information Systems and Communication Service; Internet of Things; Levels; Pattern Recognition and Graphics; Sensors; Software Engineering/Programming and Operating Systems; Survey Article; Vision; Wireless sensor networks; Big data; Internet of Things; Clustering; Artificial intelligence; Summarization
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-022-01188-9

With recent advances in the field of Internet of Things (IoT), the quantity of data being generated by various sensors and Internet users has increased dramatically which in turn has skyrocketed the need for efficient data compression methods. Data summarization is an efficient and effective technique for data compression that can generate a brief and succinct summary from typically larger quantities of data in an intelligent and highly useful manner, which can be done at various levels of abstraction. The impact of using such a technique in large IoT networks can be significantly advantageous in terms of reduction in the processing time, overall computation, data storage-transmission requirements, energy consumption, and possible workload on IoT users. In this work, a review of existing methods for data summarization techniques at various levels of abstraction of typical IoT networks are discussed. The levels of categorization that are considered are Low-level and High-level. Under each abstraction level, various techniques are further classified while briefly describing their essential characters.