Data Aggregation Mechanisms in the Internet of Things: A Study, Qualitative and Quantitative Analysis

Abstract

Internet of Things (IoT), a paradigm added to the ever-growing technological arena in recent times acts like a bridge between the things in the physical world and their representation within the digital world. The basic "things" in the IoT are sensor devices, which gather as well as monitor all types of data on physical machines and human social life. IoT enables data sending and receiving for each "thing" through the communication network. The purpose of Data Aggregation is to decrease the number of communications/transmissions among the objects/things in the Internet of Things framework. The effectiveness of the data aggregation technique employed is a key factor in the success of IoT systems in terms of data freshness and efficiency. Different data aggregation techniques have been proposed in recent past, which include - Tree-Based, Cluster-Based and Centralized data aggregation techniques. The paper aims at detailed study and analysis of data aggregation schemes employed in the Internet of Things in terms of the working and time complexity. Lowest Common Ancestor (LCA) aided Tree-Based Data Aggregation algorithm is designed. In addition, the Cluster-Based data aggregation algorithm incorporated with β-dominating set and Centralized Data Aggregation algorithm incorporated with the SUM() aggregation function are proposed. The algorithms are supported by well-formed flowcharts describing the flow and working of the data aggregation mechanisms designed. The results are obtained on a system consisting of 60 nodes with all the three aggregation algorithms being evaluated against each other. The centralized data aggregation algorithm is better when the number of nodes in the network is lesser. However, as the number of nodes increases, the cluster-based and tree-based algorithms produce better results as compared to the centralized data aggregation algorithm.