mqtt驱动节点发现集成物联网雾设置重访:广告动态的影响

Riccardo Venanzi, B. Kantarci, L. Foschini, P. Bellavista
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引用次数: 16

摘要

最近电信技术的进步、强大且始终连接的智能设备的广泛可用性以及云计算服务的广泛采用正在为雾计算的概念铺平道路。该领域的一个突出问题是,在物联网(IoT)层面,服务寻求者和提供商之间有效的应用层协议的可用性,因为大多数物联网节点都是靠电池运行的。因此,物联网雾发现服务必须节能,以延长智能对象的使用寿命。本文对我们之前提出的mqtt驱动的物联网雾集成,即节能节点发现(PEND)进行了深入研究,并研究了动态到达模式对其性能的影响。MQTT代理充当雾节点,通过监视周围对象的轨迹来触发打开/关闭蓝牙低功耗(BLE)接口。此外,它利用这种额外的位置感知来显著降低移动设备的设备发现过程的功耗。基于这一动机,我们对PEND在各种设置下的性能进行了详细的研究,并就可获得的节能和我们的增强型BLE设备发现解决方案的有效性进行了深入的讨论。我们提出的结果对于雾社区设计新的优化和完善整个物联网设备发现过程具有价值,以提高效率和可扩展性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MQTT-Driven Node Discovery for Integrated IoT-Fog Settings Revisited: The Impact of Advertiser Dynamicity
The recent advances in telecommunications, wide availability of powerful and always-connected smart devices, and the wide adoption of cloud computing services are paving the way towards the concept of fog computing. An outstanding problem in this area is the availability of effective application-layer protocols between service seekers and providers at the Internet of Things (IoT) level because most IoT nodes run on batteries. Hence, IoT-Fog discovery service must be energy efficient to prolong the lifetime of smart objects. This paper presents a thorough study of our previously proposed MQTT-driven IoT-fog integration, namely the Power Efficient Node Discovery (PEND), and investigates the impact of dynamic arrival patterns on its performance. The MQTT broker serves as a fog node to trigger turning on/off the Bluetooth Low Energy (BLE) interfaces of the surrounding objects by monitoring their trajectories. Furthermore it leverages this additional location awareness to significantly reduce the power consumption of the device discovery process for mobile devices. With this motivation, we present a detailed performance study of PEND under various settings and provide in-depth discussions of some lessons about the obtainable energy saving and the effectiveness of the our enhanced BLE device discovery solution. The results we present are valuable for the fog community to design new optimizations and to refine the whole IoT device discovery process to the purpose of better efficiency and scalability.
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