Data aggregation mechanisms in the Internet of things: A systematic review of the literature and recommendations for future research

• Published in: Journal of network and computer applications Vol. 97; pp. 23 - 34
• Main Authors: Pourghebleh, Behrouz, Navimipour, Nima Jafari
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2017
• Subjects: Data aggregation; Internet; Internet of things; IoT; Review; SLR; Data aggregation; SLR; Internet of things; Internet; Review; IoT
• ISSN: 1084-8045; 1095-8592
• DOI: 10.1016/j.jnca.2017.08.006

Internet of Things (IoT) as a new interesting subject in information technology enables data receiving and sending for each creature through the communication network. Also, network lifetime is the critical challenge in the IoT, which tries to lengthen the lifetime of the IoT. In this regard, data aggregation as an efficient method is used to decrease the number of transmissions among objects. The loss of data redundancy leads to lengthen the network lifetime and decrease the energy consumption. Although the data aggregation has a vital role in the IoT, there is not any systematic and comprehensive study about analyzing its important mechanisms. Therefore, this paper aims to study and review the present data aggregation mechanisms in the IoT systematically. The data aggregation mechanisms are categorized into three main groups, including tree-based, cluster-based and centralized. Also, the detailed comparison of the significant techniques in each class brings a recommendation for further studies. •Presenting a systematic review and studying the existing methods for data aggregation in the IoT.•Exploring the primary challenges and issues about the data aggregation in the IoT.•Dividing the data aggregation methods into three main classes, including tree-based, cluster-based and centralized.•Providing an accurate assessment using some important metrics such as energy efficiency and latency.•Describing the important areas to increase the efficiency of the IoT through an efficient data aggregator.