Finnegan, Joseph (2020) Large Scale Analysis and Scalability Enhancements for Low Power Wide Area Networks. PhD thesis, National University of Ireland, Maynooth.

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Abstract

The developing Internet of Things is leading to a much broader range of connected devices across many different industry sectors. The new paradigm of Low Power Wide Area Networks (LPWAN) contributes to this by enabling the deployment of cheap lowpower wireless devices, allowing for pervasive smart city applications. However, research into LPWANs is in the preliminary stages, with much analysis to be done to quantify the actual limits of proposed protocols. The energy efficiency and scalability of these protocols are key if these technologies are to be deployed effectively in densely populated cities. The goal of this work is to quantify and improve the LPWAN paradigm in these two dimensions. The first contribution is an energy consumption analysis of the LoRaWAN protocol. A study of the comparative energy consumption rate of the primary LPWAN technologies is performed, based on the physical and MAC layer states of the protocols. Then, an analysis of the feasibility of the powering of LoRaWAN applications fully through the harvesting of ambient RF energy is presented. The next contribution is a quantification of the energy efficiency and scalability of bidirectional LPWANs, using LoRaWAN as a case study. This is performed through the implementation of a LoRaWAN energy model, LoRaWAN Class B, and the Adaptive Data Rate (ADR) of LoRaWAN in simulation. This work for the first time fully implements previously unsimulated sections of the protocol, enabling the simulation of realistic large scale LoRaWAN networks. Simulations of LoRaWAN networks under a variety of conditions are performed, with large networks of devices running applications with realistic traffic requirements. This analysis can be considered state-of-the-art as it constitutes the first time the analysed LoRaWAN features have been implemented in the used simulator. Through the previous analysis, bottlenecks to performance for LoRaWAN networks are identified. The next contribution consists of enhancements to the ADR of LoRaWAN, which increases the scalability of the network and fixes issues which currently can limit the network adaptability. The final contribution is a novel lightweight collision avoidance algorithm which mitigates the effects of collisions from predictive traffic, based on the properties of LPWAN traffic. The scheme increases the scalability of the network while maintaining the energy efficiency of the protocol. The work presented in this thesis enhances the sustainable operation of LPWANs in denser environments. This work is validated through analytical models and simulation. The applicability of this work to other network wireless protocols is examined and potential future research directions are discussed. The contributions presented in this thesis advance the evolution of LPWANs by enabling the analysis and enhancing the scalability of large scale deployments, further accelerating the acceptability and pervasiveness of LPWAN networks.

Item Type:	Thesis (PhD)
Keywords:	Large Scale Analysis; Scalability Enhancements; Low Power Wide Area Networks;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	14870
Depositing User:	IR eTheses
Date Deposited:	01 Oct 2021 10:17
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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