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publications

A Comparative Survey of LPWA Networking

Published in Zhejiang University International Doctoral Students Conference, 2017

Motivated by the increasing variance of suggested Internet of Things (IoT) applications and the lack of suitability of current wireless technologies in scalable, long range deployments, a number of diverging Low Power Wide Area (LPWA) technologies have been developed. These technologies promise to enable a scalable high range network on cheap low power devices, facilitating the development of a ubiquitous IoT. This paper provides a definition of this new LPWA paradigm, presents a systematic approach to defined suitable use cases, and undertakes a detailed comparison of current LPWA standards, including the primary technologies, upcoming cellular options, and remaining proprietary solutions.

Recommended citation: J. Finnegan. (2017). "A Comparative Survey of LPWA Networking" Zhejiang University International Doctural Students Conference. https://arxiv.org/pdf/1802.04222.pdf

An Analysis of the Energy Consumption of LPWA-based IoT Devices

Published in IEEE International Symposium on Networks, Computers and Communications (ISNCC), 2018

The unique challenges posed by the breadth of Internet of Things applications have resulted in the development of a number of different Low Power Wide Area wireless solutions. These technologies enable scalable long range networks on cheap low power devices, facilitating the development of a ubiquitous Internet of Things. The energy efficiency of these wireless technologies have a significant impact on battery lifetime. In this paper we propose an approach to energy efficiency calculations suited to this new paradigm by focusing on daily throughput. We present a set of deployment cases, develop energy models to represent each of the technologies studied, and use these models to provide a thorough comparison in terms of predicted device lifetime for a range of daily throughputs. This quantitative analysis of network device efficiency vs. daily throughput enables identification of the changeover point between optimal solutions. Our contributions are the integration of different energy models that have not been previously compared into a common framework, and the identification of the energy-efficiency crossover points between these models. This enables the selection of the most efficient wireless solution for specific Internet of Things applications, which is a key factor in optimising device lifetime.

Recommended citation: J. Finnegan. (2018). "An Analysis of the Energy Consumption of LPWA-based IoT Devices" IEEE International Symposium on Networks, Computers and Communications (ISNCC). https://ieeexplore.ieee.org/document/8531068

Modeling the Energy Consumption of LoRaWAN in ns-3 Based on Real World Measurements

Published in Global Information Infrastructure and Networking Symposium (GIIS), 2018

LPWAN technologies are defined by their focus on extended coverage while maintaining energy efficiency, at the expense of data throughput. In this research we enable the analysis of LoRa, a key LPWAN technology, in terms of energy efficiency. We perform real-world measurements of a standard LoRa chip and use the results to develop an energy consumption module in ns-3. Our contributions are an analysis of the energy consumption of different states in a LoRa transmission by the SX1272, the LoRa transceiver that is used in most common LoRaWAN devices, beyond what is provided in the datasheet, and an energy consumption module for use in three of the LoRaWAN ns-3 modules described in research, enabling more accurate energy consumption analysis of LoRa-based systems.

Recommended citation: J. Finnegan. (2018). "Modeling the Energy Consumption of LoRaWAN in ns-3 Based on Real World Measurements" Global Information Infrastructure and Networking Symposium (GIIS). https://ieeexplore.ieee.org/document/8635786

Evaluating the Scalability of LoRaWAN Gateways for Class B Communication in ns-3

Published in IEEE Conference on Standards for Communications and Networking (CSCN), 2018

New wireless technologies have been developed in recent years which enable applications that require the transmission of small amounts of data over long distances in an energy efficient manner. One of these technologies, LoRaWAN, includes a server-initiated communication mode named Class B which provides a deterministic latency for downlink communications. In this paper, we model Class B of LoRaWAN in ns-3 to explore the limits of scale at which this form of bi-directional communication remains feasible in large networks. The simulation results show that the principle restriction on scalability is caused by the duty cycle limits that the gateway must adhere to. In addition, we identify a limitation in the protocol which in certain configurations allows a gateway node to block the future transmission of its own beacon frames. Our contributions are the development of the first implementation and simulation of LoRaWAN Class B in ns-3, and an evaluation of the scalability limits of Class B.

Recommended citation: J. Finnegan. (2020). "Evaluating the Scalability of LoRaWAN Gateways for Class B Communication in ns-3" IEEE Conference on Standards for Communications and Networking (CSCN). https://ieeexplore.ieee.org/document/8581759

Analysis and Enhancement of the LoRaWAN Adaptive Data Rate Scheme

Published in IEEE Internet of Things Journal, 2020

The adaptive data rate (ADR) algorithm is a key component of the LoRaWAN protocol which controls the performance of a LoRaWAN Network. This modifies the data rate parameter of a device based on the current wireless conditions. In this article, we present substantive enhancements for the End Device and Network Server which reduce the convergence time for LoRaWAN devices to reach their optimal data rate. We extend the LoRaWAN module in ns-3 by adding ADR, enabling the simulation of realistic LoRaWAN networks, and add the implementation of the new enhancements in this module. The simulations show that these modifications can result in a significant reduction of the data rate convergence time for LoRaWAN devices and lead to an increased overall packet delivery rate for the network in a dynamic network environment. Our contributions are a publicly available implementation of ADR in ns-3, an analysis of the original algorithm behavior, and a novel version of the algorithm with enhancements that improve performance in every case while remaining easily integrable into an existing LoRaWAN system.

Recommended citation: J. Finnegan. (2020). "Analysis and Enhancement of the LoRaWAN Adaptive Data Rate Scheme" IEEE Internet of Things Journal. https://ieeexplore.ieee.org/document/9044872

Lightweight Timeslot Scheduling Through Periodicity Detection for Increased Scalability of LoRaWAN

Published in IEEE International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM), 2020

Massive Machine Type Communications is a wireless paradigm which focuses on traffic that is transmitted by a huge number of low cost, low power, infrequently transmitting devices. LoRaWAN, a Low Power Wide Area Network technology, is particularly suited to contribute to coverage of this form of traffic. In this work, we introduce a novel, lightweight timeslot scheduling scheme that supports the requirements for massive Machine Type Communications on LoRaWAN networks (based on traffic periodicity, and the multiple channels and quasiorthogonal data rates in LoRaWAN). Our novel approach does not require extended downlink transmissions from the gateways, and does not require time synchronisation between the devices and the LoRaWAN Network Server. We implement our scheme in a publicly available LoRaWAN ns-3 module. Our results show that the scheme doubles the number of frequently transmitting mMTC devices that can be handled by a single LoRaWAN gateway while providing the same level of performance in terms of successful packet deliveries, and maintaining a reasonable delay for mMTC use cases, without impacting the ability of the network to send downlink frames or acknowledge high priority packets. Our contribution is a novel approach to TDMA that is suited for mMTC networks.

Recommended citation: J. Finnegan. (2020). "Lightweight Timeslot Scheduling Through Periodicity Detection for Increased Scalability of LoRaWAN" IEEE International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM). https://ieeexplore.ieee.org/document/9217770

Exploring the Boundaries of Ambient RF Energy Harvesting with LoRaWAN

Published in IEEE Internet of Things Journal, 2020

Environmental monitoring is an important application for wireless sensing devices. Battery power requires in-the-field replacement, and the chemicals involved are environmentally harmful, so harvested energy is a useful alternative. Previous research has shown the feasibility of powering LoRaWAN sensors using high-energy ambient or wireless transfer power sources. This paper extends this work by exploring the boundaries of using low-energy RF ambient sources. Ambient RF energy harvesting is an attractive option, but it is more challenging due to the low levels of energy density typically available. Using an analytical LoRaWAN device model and RF energy data collected from around the world, a systematic investigation of the design and environmental space is performed. The main contribution of this paper is to identify the boundaries of feasibility for powering LoRaWAN sensor nodes from ambient RF energy. These boundaries include design and environmental factors.

Recommended citation: J. Finnegan. (2020). "Exploring the Boundaries of Ambient RF Energy Harvesting with LoRaWAN" IEEE Internet of Things Journal. https://ieeexplore.ieee.org/document/9222182

talks

teaching

CS130 Databases (Demonstrator)

Undergraduate Course, Maynooth University, Department of Computer Science, 2014

Working as a demonstrator in a lab for the 2nd year Databases module. Correcting completed lab assignments and helping and guiding students during the sessions.

Hardware Intern (Co-Supervisor)

Supervision, Maynooth University, Department of Electronic Engineering, 2017

Working as a co-supervisor for a student performing an internship on embedded hardware development.

Software Intern (Co-Supervisor)

Supervision, Maynooth University, Department of Electronic Engineering, 2017

Working as a co-supervisor for a student performing an internship on embedded software development.

CS320 Computer Networks (Demonstrator)

Undergraduate Course, Maynooth University, Department of Computer Science, 2017

Working as a demonstrator in a lab for the 3rd year Computer Networks module. Correcting completed lab assignments and helping and guiding students during the sessions.

CS380 Multimedia & Mobile Communications (Head Demonstrator)

Undergraduate Course, Maynooth University, Department of Computer Science, 2017

Working as a demonstrator in a lab for the 3rd year Multimedia and Mobile Communications module. Correcting completed lab assignments and helping and guiding students during the sessions.