基于lora的物联网系统qos感知自适应移动性处理方法

Berrevoets Robbe, Weyns Danny
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引用次数: 10

摘要

物联网(IoT)是一种新兴模式,在智慧城市中得到越来越多的应用。在物联网中使用的一种流行技术是支持远程无线通信的LoRa。在本研究中,我们研究了基于lora的物联网系统,该系统具有电池供电的端节点,可收集数据并将数据传输到网关以进行进一步处理。这种物联网系统中的现有方法通常只考虑固定的终端节点。我们专注于具有移动终端节点的系统,为目标跟踪等新应用铺平道路。这些设置的关键服务质量(QoS)要求是通信的可靠性和能耗。对于移动终端节点,确保这些QoS具有挑战性,因为系统会受到持续变化的影响。在本文中,我们研究了移动端节点的设置如何影响可靠性和能耗的关键性能指标。基于从广泛的现场实验中获得的见解,我们设计了一种算法,该算法可以自动调整移动端节点的设置,以确保其具有单个网关的设置的QoS要求。然后,我们将该算法扩展到具有多个网关的设置。我们演示了这些算法如何在具体的物联网部署中实现移动端节点的QoS要求。
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A QoS-Aware Adaptive Mobility Handling Approach for LoRa-Based IoT Systems
Internet-of-Things (IoT) is an emergent paradigm that is increasingly applied in smart cities. A popular technology used in IoT is LoRa that supports long-range wireless communication. In this research, we study LoRa-based IoT systems with battery-powered end nodes that collect and communicate data to a gateway for further processing. Existing approaches in such IoT systems usually only consider stationary end nodes. We focus on systems with mobile end nodes, paving the way to new applications such as target tracking. Key Quality of Service (QoS) requirements for these settings are the reliability of the communication and energy consumption. With mobile end nodes, ensuring these QoS is challenging as the system is subject to continuous changes. In this paper, we investigate how the settings of a mobile end node impact key performance indicators for reliability and energy consumption. Based on insights obtained from extensive field experiments, we devise an algorithm that automatically adapts the settings of a mobile end node to ensure its QoS requirements for a setup with a single gateway. We then extend the algorithm to a setup with multiple gateways. We demonstrate how the algorithms achieve the QoS requirements of a mobile end node in a concrete IoT deployment.
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Self-Organized Resource Allocation for Reconfigurable Robot Ensembles [Copyright notice] A QoS-Aware Adaptive Mobility Handling Approach for LoRa-Based IoT Systems SASO 2018 Subreviewers Self-Adaptation of Coordination in Imperfectly Known Task Environments
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