Preprint Article Version 1 This version is not peer-reviewed

An Enhanced Distributed Data Aggregation Method in the Internet of Things

Version 1 : Received: 9 May 2019 / Approved: 10 May 2019 / Online: 10 May 2019 (14:49:18 CEST)
Version 2 : Received: 13 May 2019 / Approved: 15 May 2019 / Online: 15 May 2019 (12:28:23 CEST)

How to cite: Shahab, S.; Homaei, M. An Enhanced Distributed Data Aggregation Method in the Internet of Things. Preprints 2019, 2019050134 (doi: 10.20944/preprints201905.0134.v1). Shahab, S.; Homaei, M. An Enhanced Distributed Data Aggregation Method in the Internet of Things. Preprints 2019, 2019050134 (doi: 10.20944/preprints201905.0134.v1).

Abstract

As a novel concept in technology and communication world, “Internet of Things (IoT)” has been emerged. In such modern technology, the capability to transmit data through data communication networks (such as Internet or Intranet) is provided for each organism (e.g. human being, animals, things, and so forth). Due to the limited hardware and communication operational capability as well as small dimensions, IoT undergoes quite a few challenges. Such inherent challenges not only cause fundamental restrictions in the efficiency of aggregation, transmission, and communication between nodes; but they also degrade routing performance. To cope with the reduced avail- ability time and unstable communications among nodes, data aggregation and transmission approaches in such networks are designed more intelligently. In this paper, a distributed method is proposed to set child balance among nodes. In this method, the height of the network graph is increased through restricting the degree; and network congestion is reduced as a result. Besides, a dynamic data aggregation approach -named as LA-RPL- is proposed for RPL networks. More specifically, each node is equipped with learning automata in order to perform data aggregation and transmissions. Simulation results demonstrate that the proposed approach outperforms previously suggested base approaches in terms of energy consumption, network control overhead, and packet loss rate.

Subject Areas

Data aggregation methods, Learning automata, RPL, Routing, Internet of Thing (IoT).

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