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Adaptive dynamic network slicing in LoRa networks

机译：LoRa网络中的自适应动态网络切片

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摘要

Knowing the heterogeneity of applications and services that need to be supported in the internet of things (IoT), network slicing came out as a potential solution that virtually isolates 5G networks with various service requirements over a common physical network infrastructure. The latter needs to virtualy isolate traffic that comes from machine services to provide each service with specific QoS guarantees. In this paper, network slicing is investigated in LoRa networks using various slicing strategies as well as different spreading factor distributions. We propose an adaptive dynamic inter-slicing resource reservation algorithm based on a maximum likelihood estimation that avoids resource starvation and prioritizes a slice over another depending on its urgency and reliability. Moreover, a novel intra-slicing strategy is evaluated that maximizes resource allocation efficiency in each slice with regard to its QoS requirements. Simulation results performed in realistic IoT scenarios highlight the utility of our proposition in improving IoT devices QoS and providing isolation between LoRa slices.
机译：知道物联网（IoT）需要支持的应用程序和服务的异构性，网络切片是一种潜在的解决方案，可以通过通用的物理网络基础架构将具有各种服务要求的5G网络虚拟地隔离开来。后者需要虚拟隔离来自机器服务的流量，以为每个服务提供特定的QoS保证。在本文中，研究了LoRa网络中的网络切片，其中使用了各种切片策略以及不同的扩展因子分布。我们提出了一种基于最大似然估计的自适应动态切片资源预留算法，该算法避免了资源匮乏，并根据切片的紧迫性和可靠性将切片优先于其他切片。此外，评估了一种新颖的内部切片策略，该策略针对每个切片的QoS要求最大化了资源分配效率。在现实的IoT场景中执行的仿真结果突显了我们的命题在改善IoT设备QoS并在LoRa切片之间提供隔离方面的效用。

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