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Probabilistic Position Estimation and Model Checking for Resource-Constrained IoT Devices

机译:资源受限的物联网设备的概率位置估计和模型检查

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The Internet of Things (IoT) has been applied to home/office, healthcare, intelligent transportation and agriculture systems. The new IoT technology are growing rapidly and will play an essential role in our future societal lifestyle, economy and business. Currently, power hungry and radio wave interference are two big challenges hindering the IoT development. In this study, we propose a Markov localization algorithm to estimate positions of IoT devices considering various gateway allocation scenarios in a widespread and boundaryless field. We adopt the model-checking technique to validate the convergence of positions of the IoT devices. Our approach can accurately identify positions of IoT devices and connect each IoT node to its nearest gateways for sending sensing data and receiving commands from the cloud computing resources. We use a cattle-breeding IoT network as a case study to validate the proposed approach,which reduces IoT power consumption while enhancing the connectivity of the network. Additionally, we also discuss how to apply this simple approach to real-world IoT networks such as smart home and vehicular ad-hoc networks.
机译:物联网(IoT)已应用于家庭/办公室,医疗保健,智能交通和农业系统。新型物联网技术正在迅速发展,并将在我们未来的社会生活方式,经济和商业中发挥至关重要的作用。当前,电力消耗和无线电波干扰是阻碍物联网发展的两个重大挑战。在这项研究中,我们提出了一种马尔可夫定位算法来估计物联网设备的位置,其中考虑了在广阔而无边界的领域中各种网关分配方案。我们采用模型检查技术来验证IoT设备位置的融合。我们的方法可以准确识别IoT设备的位置,并将每个IoT节点连接到其最近的网关,以发送感测数据并从云计算资源接收命令。我们以繁育的物联网网络为例,对所提出的方法进行了验证,该方法在降低物联网功耗的同时增强了网络的连通性。此外,我们还将讨论如何将此简单方法应用于现实世界的物联网网络,例如智能家居和车辆自组织网络。

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