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.

中文翻译：

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用于大数据收集的大规模无线传感器网络建模框架

大规模无线传感器网络 (LS-WSN) 是由数量惊人的传感器组成的无线传感器网络 (WSN)，具有固有的检测和处理能力，可部署在大范围的感兴趣区域。部署大量不同或相似的传感器当然是一种常见的做法，旨在克服传感器频繁故障并避免任何人为干预来更换它们或给电池充电，以确保网络的可靠性。然而，在实践中，LS-WSN 的复杂性对确保无线电链路对称性和最大化网络寿命方面的质量通信提出了重大挑战。近年来，大多数提议的 LS-WSN 部署技术旨在最大化网络连接性，增加感兴趣区域的覆盖范围，或者当然是延长网络寿命。很少有研究考虑选择一个好的 LS-WSN 部署策略作为连接性和能源消耗效率的解决方案。在本文中，我们设计了一个 LS-WSN 作为收集智慧城市产生的大数据的工具。大数据的内在特性需要使用异构传感器。此外，为了构建异构 LS-WSN，我们的科学贡献包括量化网络中传感器种类的模型和 LS-WSN 部署的多级架构，该架构依赖于大数据收集的集群。结果模拟表明，我们提出的 LS-WSN 架构优于文献中一些众所周知的 WSN 协议，包括低能量自适应聚类层次结构 (LEACH)、E-LEACH、SEP、DEEC、EECDA、DSCHE 和 BEENISH。