Engineering Commons^™

Energy Efficiency And Fault Tolerance In Open Ran And Future Internet, Saish Urumkar, Byrav Ramamurthy, Sachin Sharma

Conference papers

Open Radio Access Networks (Open RAN) repre- sent a promising technological advancement within the realm of the future internet. Research efforts are currently directed towards enhancing energy efficiency and fault tolerance, which are critical aspects for both Open RAN and the future internet landscape. In the context of energy saving in Open RAN, there exists a spectrum of methods for achieving energy efficiency. These methods include the toggling of on/off states for different hardware resources such as base station units, distributed units, and radio units. Conversely, for enhancing fault tolerance in Open RAN, Software-Defined Networking (SDN) and OpenFlow based techniques …

Improving Energy Efficiency In Open Ran Through Dynamic Cpu Scheduling, Saish Urumkar, Byrav Ramamurthy, Sachin Sharma

Conference papers

Open RAN is a promising cellular technology that is currently undergoing extensive research for future wireless radio access networks. Achieving optimal energy efficiency in Open RAN poses a significant challenge. This paper introduces a CPU scheduling algorithm that specifically targets this chal- lenge by optimizing energy consumption at the base station while maintaining optimal performance levels. With the goal of minimizing energy consumption, the proposed algorithm dynamically adjusts the CPU core states, seamlessly switching between active and sleep modes based on the load conditions. To evaluate the algorithm’s effectiveness in terms of energy saving and performance, experimental testing is conducted …

Poster: Optimising Electric Vehicle Charging Infrastructure In Dublin Using Geecharge, Alexander Mutiso Mutua, Ruairí De Fréin, Ali Malik, Kibanza Eliel, Sahbane Marco, Pantel Maxime

Conference papers

Range anxiety is a significant challenge affecting electric vehicles use as drivers fear running out of charge without finding a charging point on time. We develop methods to optimise the distribution of charging points. EV portacharge and GEECharge solutions distribute charging points in a city by considering the population density and Points Of Interest (POI) or road traffic. This paper focuses on (1) developing and evaluating methods to distribute Charging Points (CPs) in Dublin city; (2) optimising CP allocation; (3) visualising paths in the graph network to show the most used roads and points of interest; (4) describing a way …

Digital Challenges And Opportunities Of Addressing On-Site Productivity And Safety On Construction Job Sites: An International Perspective, Ahmed Hassan, Ankur Mitra, Mark Mulville, Alan Hore

Conference papers

The slow pace of digital transformation in construction remains an impediment to the industry’s evolution. Despite rapid developments in digital technologies, the vast bulk of data generated on construction job sites remains underutilised due to the limited use of real-time data capture. The diffusion of digital solutions in construction depends on successfully capturing critical real-time data that can inform and contribute to more productive work patterns and safer job sites. Nevertheless, the construction industry’s fragmented nature has segregated the digital solutions offered by technology providers from the volatile challenges facing on-site construction practitioners.

This paper will present the early development …

Round Trip Time Measurement Over Microgrid Power Network, Yasin Emir Kutlu, Ruairí De Fréin, Malabika Basu, Ali Malik

Conference papers

A focus of the Power Systems and Networking communities is the design and deployment of Microgrid (MG) integration systems that ensure that quality of service targets are met for load sharing systems at different endpoints. This paper presents an integrated Microgrid testbed that allows Microgrids endpoints to share their current, voltage and power values using a Network Published Shared Variable (NPSV) approach. We present Round Trip Time (RTT) measurements for time sensitive Microgrid control traffic in the presence of varying background traffic as an example quality of service measurement. Numerical results are presented using a range of different background traffic …

Poster: Round Trip Time Measurement Over Microgrid Power Network, Yasin Emir Kutlu, Ruairí De Fréin, Malabika Basu, Ali Malik

Conference papers

A focus of the Power Systems and Networking communities is the design and deployment of Microgrid (MG) integration systems that ensure that quality of service targets are met for load sharing systems at different endpoints. This paper presents an integrated Microgrid testbed that allows Microgrids endpoints to share their current, voltage and power values using a Network Published Shared Variable (NPSV) approach. We present Round Trip Time (RTT) measurements for time sensitive Microgrid control traffic in the presence of varying background traffic as an example quality of service measurement. Numerical results are presented using a range of different background traffic …

Integrated Dc Microgrid Testbed For Qos Evaluation, Yasin Emir Kutlu, Ruairí De Fréin, Malabika Basu, Ali Malik

Conference papers

This poster describes the development of an integrated Direct Current (DC) Microgrid (MG) testbed, consisting of power system and computer networking hardware, e.g. Cisco 4331 Integrated Services Routers (ISR) and 500W AC/DC power supplies, to test the Quality of Service (QoS) of MG control packets which are delivered over Layer-2 and Layer-3 networking topologies. It facilitates an examination of control packets between MG end-points, that enable real-time power load sharing, in the presence of background computer network traffic such as video streaming. The effects of difference levels of background traffic on the round trip time of control packets are demonstrated.

Poster: Integrated Dc Microgrid Testbed For Quality Of Service Evaluation, Yasin Emir Kutlu, Ruairí De Fréin, Malabika Basu, Ali Malik

Conference papers

This study demonstrates a testbed for evaluating the effects of background network traffic on the Round-Trip Times (RTTs) of community-based integrated Direct Current (DC) Microgrid (MG) testbed using real hardware and different networking topologies.

The effects of difference levels of background traffic on the RTT of control packets are demonstrated.

The Suitability Of Demand-Controlled Sensor Based Ventilation Systems In Retrofit Dwellings - A Longitudinal Study., Seamus Harrington, Mark Mulville

Conference papers

A fabric-first approach to dwelling retrofit results in increased airtightness, therefore there is an obligation to ensure that the upgrades do not lead to poor indoor air quality (IAQ) resulting from inadequate ventilation. The sensor-based demand-controlled ventilation (SBDCV) under review seeks to provide fresh air for breathing and to dilute and exhaust pollutants and odours. This system modulates the ventilation rate over time based on relative humidity levels and/or presence detection and considers that the level of ventilation provided is sufficient to control the concentration of all other indoor air pollutants, including those that are not a result of human …

Comparing The Iwl-Mvm-Rs And Minstrel-Ht Rate Adaptation Algorithms Under Different Local Error Conditions, Luhan Wang, Mark Davis

Conference papers

WLAN technology has grown rapidly and now provides increasingly reliable and fast wireless connectivity. A number of Rate Adaptation Algorithms (RAAs) which are designed to select the optimal line rate according to the channel conditions have been proposed. Iwl-Mvm-Rs RAA which is implemented in the Intel wireless chips and Minstrel-HT RAA which is implemented in the Linux kernels are two such well known algorithms. Many existing papers evaluate these two algorithms with regard to device mobility and signal fading. However, the causes of frame errors can be divided into two categories: weak signal reception and collisions. Therefore, in this paper, …

Analysis Of Attention Mechanisms In Box-Embedding Systems, Jeffrey Sardina Jeffrey Sardina, Callie Sardina, John Kelleher, Declan O’Sullivan

Conference papers

Large-scale Knowledge Graphs (KGs) have recently gained considerable research attention for their ability to model the inter- and intra- relationships of data. However, the huge scale of KGs has necessitated the use of querying methods to facilitate human use. Question Answering (QA) systems have shown much promise in breaking down this human-machine barrier. A recent QA model that achieved state-of-the-art performance, Query2box, modelled queries on a KG using box embeddings with an attention mechanism backend to compute the intersections of boxes for query resolution. In this paper, we introduce a new model, Query2Geom, which replaces the Query2box attention mechanism with …

Printed-Dipole End-Fire Array For Mm-Wave Applications, Neeraj Kumar Maurya, Max Ammann

Conference papers

A novel curved printed-dipole element in an end-fire array for mmWave communication is presented. The curved dipole element is printed on both sides of a RO5880 substrate of 0.5mm thickness. The proposed antenna operates in the frequency bands n257, n258, and n261 (22.79 GHz - 29.7 GHz) and the 4-element array has a peak gain of 8.85 dBi.

H2020 Auto-Dan Project: Enhance The Participation Of The Community To Demand Response By Providing The State-Of-The-Art Technological And Policy Solution, Rene Peeren, Dharmesh Dabhi, John Dalton

Conference papers

The growing demand for electricity in Europe has increased the need for a more flexible and sustainable power system. In recent years, Demand Response (DR) has emerged as a promising solution to meet this need, by providing an opportunity for residential and smaller commercial consumers to actively participate in the electricity market. This research paper investigates the potential for DR among the residential community and small commercial electricity consumers in Europe and identifies the technological barriers and drivers that impact consumer engagement with DR programs in Europe. The different DR opportunities are identified and validated at the six different demo …

Fully 3d-Printed Hemispherical Dielectric Resonator Antenna For C-Band Applications, Jakub Przepiorowski, Irina Munina, Max Ammann, Daniel Trimble

Conference papers

This paper investigates the 3D printing of a hemispherical dielectric resonator antenna (DRA) on a ground plane made from a 3D printed conductive material. The DRA is designed to operate in the C-band (3700 – 4200 MHz) and is intended for satellite communication (SATCOM) applications. The proposed antenna prototype achieved a -10 dB bandwidth of 12.2% with an average and peak gain of 4.69 dBi and peak gain of 5.39 dBi respectively.

A Strong Scattering Solution With Applications To Remote Sensing Systems Modelling, Jonathan Blackledge

Conference papers

Although not always fully appreciated as such, the linear stationary convolution model for a signal and/or image, generated by the interaction of an incident wave field with an inhomogeneous medium, is based on the ‘weak scattering condition’, commonly referred to as the ‘Born approximation’. This approximation forms the basis for signal processing and analysis associated with applications over a broad range of frequencies. The Born approximation assumes that a scattering interaction is based on single scattering events alone and that the scattered field is therefore a weak field in comparison to the incident field. The physical limitations of such a …

Energy-Aware Ai-Driven Framework For Edge-Computing-Based Iot Applications, Muhammad Zawish, Nouman Ashraf, Rafay Iqbal Ansari, Steven Davy

Conference papers

The significant growth in the number of Internet of Things (IoT) devices has given impetus to the idea of edge computing for several applications. In addition, energy harvestable or wireless-powered wearable devices are envisioned to empower the edge intelligence in IoT applications. However, the intermittent energy supply and network connectivity of such devices in scenarios including remote areas and hard-to-reach regions such as in-body applications can limit the performance of edge computing-based IoT applications. Hence, deploying state-of-the-art convolutional neural networks (CNNs) on such energy-constrained devices is not feasible due to their computational cost. Existing model compression methods, such as network …

Quantum Classifiers For Video Quality Delivery, Tautvydas Lisas, Ruairí De Fréin

Conference papers

Classical classifiers such as the Support Vector Classifier (SVC) struggle to accurately classify video Quality of Delivery (QoD) time-series due to the challenge in constructing suitable decision boundaries using small amounts of training data. We develop a technique that takes advantage of a quantum-classical hybrid infrastructure called Quantum-Enhanced Codecs (QEC). We evaluate a (1) purely classical, (2) hybrid kernel, and (3) purely quantum classifier for video QoD congestion classification, where congestion is either low, medium or high, using QoD measurements from a real networking test-bed. Findings show that the SVC performs the classification task 4% better in the low congestion …

Optimising Electric Vehicle Charging Infrastructure In Dublin Using Geecharge, Alexander Mutua Mutiso, Ruairí De Fréin, Ali Malik, Eliel Kibanza, Marco Sahbane, Maxime Pantel

Conference papers

Range anxiety poses a hurdle to the adoption of Electric Vehicles (EVs), as drivers worry about running out of charge without timely access to a Charging Point (CP). We present novel methods for optimising the distribution of CPs, namely, EV portacharge and GEECharge. These solutions distribute CPs in Dublin, in this paper, by considering the population density and Points Of Interest (POIs) or road traffic. The object of this paper is to (1) develop and evaluate methods to distribute CPs in Dublin city; (2) optimise CP allocation; (3) visualise paths in the graph network to show the most used roads …

Synergising Bim And Real-Time Data For Improved Efficiency: An Irish Case Study, Ahmed Hassan, Ankur Mitra, Alan Hore, Mark Mulville

Conference papers

The evolution of 3D visualisation and the Internet of Things (IoT) presents a substantial opportunity for integrating real-time data with Building Information Models (BIM) to improve its functionality and construction workflow efficiency. Integrating real-time data with BIM can enhance the digital representation of construction buildings’ physical and functional characteristics and provide recordable status of on-site operations. Nevertheless, the integration between Visualisation and IoT technologies with BIM remains in its preliminary stages and faces a myriad of technical and operational challenges. Furthermore, developing advanced solutions to facilitate this complex integration requires a considerable understanding of the viability and feasibility of merging …

Challenges And Opportunities For Automating Physical Compliance On Construction Sites, Ankur Mitra, Mark Mulville

Conference papers

The construction project lifecycle includes several compliance requirements that need to be checked at multiple levels and at different phases of the project. Inability to comply with these regulations due to lack of time and resources or human oversight can affect the project throughout its service lifecycle with the potential for severe outcomes. Following a number of high-profile failings and owing to the high stakes nature of compliance, digitalisation has been introduced in this field of construction over the past few decades to reduce mistakes and neglect. Although the compliance checking process in the design phase has seen significant digital …

Medical Concept Mention Identification In Social Media Posts Using A Small Number Of Sample References, Vasudevan Nedumpozhimana, Sneha Rautmare, Meegan Gower, Maja Popovic, Nishtha Jain, Patricia Buffini, John Kelleher

Conference papers

Identification of mentions of medical concepts in social media text can provide useful information for caseload prediction of diseases like Covid-19 and Measles. We propose a simple model for the automatic identification of the medical concept mentions in the social media text. We validate the effectiveness of the proposed model on Twitter, Reddit, and News/Media datasets.

Work In Progress: A Virtual Educational Robotics Coding Club Framework To Improve K-6 Students Emotional Engagement In Stem, Kate Carmody, Julie Booth, Jospehine Bleach, Pramod Pathak, Paul Styles

Conference papers

The growing popularity and deployment of Internet of Things (IoT) devices has led to serious security concerns. The integration of a security operations center (SOC) becomes increasingly important in this situation to ensure the security of IoT devices. In this article, we will present a summary of IoT device security issues, their vulnerabilities, a review of current challenges to keep these devices secure, and discuss the role that SOC can bring in protecting IoT devices while considering the challenges encountered and the directions to consider when implementing a reliable SOC for IoT monitoring.

Queer In Ai: A Case Study In Community-Led Participatory Ai, Anaelia Ovalle, Arjun Subramonian, Ashwiin Singh, Claas Voelcker, Danica Sutherland, Davide Locatelli, Eva Breznik, Felip Klubicka, Hang Yuan, Hetvi J, Huan Zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Melind Agarwal, Nyx Mclean, Pan Xu, A. Pranav, Raj Korpan, Ruchira Ray, Sarah Mathew, Sarthak Arora, S.T. John, Tanvi Anand, Vishakha Agrawal, William Agnew, Yanan Long, Zijie J. Wang, Zeerak Talat, Avijit Ghosh, Nathaniel Dennler, Michael Noseworthy, Sharvani Jha, Emi Baylor, Aditya Joshi, Natalia Y. Bilenko, Andrew Mcnamara, Raphael Gontijo-Lopes, Alex Markham, Evyn Dong, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark

Conference papers

Queerness and queer people face an uncertain future in the face of ever more widely deployed and invasive artificial intelligence (AI). These technologies have caused numerous harms to queer people, including privacy violations, censoring and downranking queer content, exposing queer people and spaces to harassment by making them hypervisible, deadnaming and outing queer people. More broadly, they have violated core tenets of queerness by classifying and controlling queer identities. In response to this, the queer community in AI has organized Queer in AI, a global, decentralized, volunteer-run grassroots organization that employs intersectional and community-led participatory design to build an inclusive …

Capturing The Behaviour Of Volunteer Pedestrians In A Newly-Developed University Campus Using A Distributed Array Of Bluetooth Low Energy Devices, Ahlam Al Anbouri, Mayank Parmar, David Powell, Paula Kelly, Niall Holmes Dr., Damon Berry, Lorraine D'Arcy

Conference papers

Contemporary public infrastructure projects emphasise sustainable options that integrate pedestrian routes, leisure facilities and convenient access to public transport systems. It is important to understand the effectiveness of these contemporary designs. In the age of the General Data Protection Regulation (GDPR), there is a need to develop technologybased solutions that collect information about the behaviour of pedestrians in public spaces as they commute and engage in leisure pursuits while simultaneously preserving the privacy of these citizens. Bluetooth Low Energy (BLE), has privacypreserving features that make it worth considering as part of a technology solution for studies of this type. This …

Combinedeepnet: A Deep Network For Multistep Prediction Of Near-Surface Pm2.5 Concentration, Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan

Conference papers

PM2.5 is a type of air pollutant that can cause respiratory and cardiovascular problems. Precise PM2.5 ( μg/m3 ) concentration prediction may help reduce health concerns and provide early warnings. To better understand air pollution, a number of approaches have been presented for predicting PM2.5 concentrations. Previous research used deep learning models for hourly predictions of air pollutants due to their success in pattern recognition, however, these models were unsuitable for multisite, long-term predictions, particularly in regard to the correlation between pollutants and meteorological data. This article proposes the combine deep network (CombineDeepNet), which combines multiple deep networks, including a …

Action Classification In Human Robot Interaction Cells In Manufacturing, Shakra S.M. Mehak, Maria Chiara Leva, John Kelleher, Michael Guilfoyle

Conference papers

Action recognition has become a prerequisite approach to fluent Human-Robot Interaction (HRI) due to a high degree of movement flexibility. With the improvements in machine learning algorithms, robots are gradually transitioning into more human-populated areas. However, HRI systems demand the need for robots to possess enough cognition. The action recognition algorithms require massive training datasets, structural information of objects in the environment, and less expensive models in terms of computational complexity. In addition, many such algorithms are trained on datasets derived from daily activities. The algorithms trained on non-industrial datasets may have an unfavorable impact on implementing models and validating …

Understanding And Quantifying Human Factors In Programming From Demonstration: A User Study Proposal, Shakra Mehak, Aayush Jain, John D. Kelleher, Philip Long, Michael Guilfoyle, Maria Chiara Leva

Conference papers

Programming by demonstration (PbD) is a promising method for robots to learn from direct, non-expert human interaction. This approach enables the interactive transfer of human skills to the robot. As the non-expert user is at the center of PbD, the efficacy of the learned skill is largely dependent on the demonstrations provided. Although PbD methods have been extensively developed and validated in the field of robotics, there has been inadequate confirmation of their effectiveness from the perspective of human teachability. To address this gap, we propose to experimentally investigate the impact of communicating robot learning process on the efficacy of …

Impact Of Character N-Grams Attention Scores For English And Russian News Articles Authorship Attribution, Liliya Mukhmutova, Robert J. Ross, Giancarlo Salton

Conference papers

Language embeddings are often used as black-box word-level tools that provide powerful language analysis across many tasks, but yet for many tasks such as Authorship Attribution access to feature level information on character n-grams can provide insights to help with model refinement and development. In this paper we investigate and evaluate the importance of character n-grams within an embeddings context in authorship attribution through the use of attention scores. We perform this investigation both for English (Reuters_50_50) and Russian (Taiga) news authorship datasets. Our analysis show that character n-grams attention score is higher for n-grams that are considered to be …

Show, Prefer And Tell: Incorporating User Preferences Into Image Captioning, Annika Lindh, Robert J. Ross, John Kelleher

Conference papers

Image Captioning (IC) is the task of generating natural language descriptions for images. Models encode the image using a convolutional neural network (CNN) and generate the caption via a recurrent model or a multi-modal transformer. Success is measured by the similarity between generated captions and human-written “ground-truth” captions, using the CIDEr [14], SPICE [1] and METEOR [2] metrics. While incremental gains have been made on these metrics, there is a lack of focus on end-user opinions on the amount of content in captions. Studies with blind and low-vision participants have found that lack of detail is a problem [6, 13, …

Meme Sentiment Analysis Enhanced With Multimodal Spatial Encoding And Face Embedding, Muzhaffar Hazman, Susan Mckeever, Josephine Griffith

Conference papers

Internet memes are characterised by the interspersing of text amongst visual elements. State-of-the-art multimodal meme classifiers do not account for the relative positions of these elements across the two modalities, despite the latent meaning associated with where text and visual elements are placed. Against two meme sentiment classification datasets, we systematically show performance gains from incorporating the spatial position of visual objects, faces, and text clusters extracted from memes. In addition, we also present facial embedding as an impactful enhancement to image representation in a multimodal meme classifier. Finally, we show that incorporating this spatial information allows our fully automated …