Affiliations: [a] ISMAI, University of Kairouan, Higher Institute of Applied Mathematics and Computer Science of Kairouan, Tunisia | [b] Lab-STA, University of Sfax, National Engeneering School of Sfax, Tunisia
Correspondence:
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Corresponding author: Omnia Mezghani, ISMAI, University of Kairouan, Higher Institute of Applied Mathematics and Computer Science of Kairouan, Tunisia. E-mail: [email protected].
Note: [1] These authors contributed equally to this work.
Abstract: Nowadays, using mobile computing devices and the Internet of Things (IoT) in networks have posed several challenges to match up the forthcoming technological requirements. Wireless Sensors Network (WSN) is considered as an important component of the IoT which produces a massive amount of data (big data). However, dealing with limited capacities of the elementary components of a network in an IoT enabled WSN, is a key challenge. The existing approaches in the literature are inadequate for large networks and cannot be applied to IoT platform without adaptation. Data Centric Network (DCN) is an important notion for the future Internet architecture to resolve the problems related to big data manipulation. In fact, using a DCN strategy for the resource limited capacities WSN enabled IoT networks is beneficial to manage densely deployed and mobile components to enhance the data gathering mechanism. In this context, this paper proposes an IoT cluster based routing protocol for data centric mobile wireless sensors networks named ICMWSN. The proposed algorithm fits with a WSN belonging a large number of mobile sensors as well as a mobile sink. It is based on a clustering technique to form multi-hops clusters around fixed pre-elected CHs. Besides, an effective tour construction method is involved for the mobile sink to collect data from the eventual cluster heads. The extensive simulation results proved that ICMWSN outperformed the compared methods in terms of energy consumption, network lifetime and data delivery rate.
Keywords: IoT, wireless sensors network, clustering, data gathering, mobile sensors, mobile sink
