Abstract
Large Scale Wireless Sensor Networks (LS-WSNs) are Wireless Sensor Networks (WSNs) composed of an impressive number of sensors, with inherent detection and processing capabilities, to be deployed over large areas of interest. The deployment of a very large number of diverse or similar sensors is certainly a common practice that aims to overcome frequent sensor failures and avoid any human intervention to replace them or recharge their batteries, to ensure the reliability of the network. However, in practice, the complexity of LS-WSNs pose significant challenges to ensuring quality communications in terms of symmetry of radio links and maximizing network life. In recent years, most of the proposed LS-WSN deployment techniques aim either to maximize network connectivity, increase coverage of the area of interest or, of course, extend network life. Few studies have considered the choice of a good LS-WSN deployment strategy as a solution for both connectivity and energy consumption efficiency. In this paper, we designed a LS-WSN as a tool for collecting big data generated by smart cities. The intrinsic characteristics of big data require the use of heterogeneous sensors. Furthermore, in order to build a heterogeneous LS-WSN, our scientific contributions include a model of quantifying the kinds of sensors in the network and the multi-level architecture for LS-WSN deployment, which relies on clustering for the big data collection. The results simulations show that our proposed LS-WSN architecture is better than some well known WSN protocols in the literature including Low Energy Adaptive Clustering Hierarchy (LEACH), E-LEACH, SEP, DEEC, EECDA, DSCHE and BEENISH.
Highlights
The last decade has undeniably been the decade of the rapid growth of wireless communication technologies [1,2]
There has been a lot of interest concerning Large Scale Wireless Sensor Networks (LS-Wireless Sensor Networks (WSNs)) issues in recent years
The issue of energy consumption remains very critical as the number of sensors becomes more and more important, which requires the development of new sensor energy conservation techniques to extend the life of these networks
Summary
The last decade has undeniably been the decade of the rapid growth of wireless communication technologies [1,2]. The number of application perspectives of WSNs, including precision agriculture, forest monitoring for fire detection, patient monitoring, natural disaster management, etc., makes it possible to consider the use of these networks for collecting big data generated by smart cities [7,8,9]. In such contexts, these WSNs are consisting of an almost large number of sensors to be deployed over large areas [1]. In [24], for processing large data while saving the energy consumption in a distributed wireless sensor network, the authors designed a data aggregation technique based on the Hadoop framework with simple/multi-cluster architectures. Hierarchy (LEACH) algorithm proposed in [32]
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