On the Effectiveness of an IOT - FOG - CLOUD Architecture for a real-world application

Fog Computing is an emerging computing paradigm that shifts certain processing closer to the Edge of a network, generally within one network hop, where latency is minimized, and results can be obtained the quickest. However, not a lot of research has been done on the effectiveness of Fog in real-world applications. The aim of this research is to show the effectiveness of the Fog Computing paradigm as the middle layer in a 3-tier architecture between the Internet of Things (IoT) and the Cloud. Two applications were developed: one utilizing Fog in a 3-tier architecture and another application using IoT and Cloud with no Fog. A quantitative and qualitative analysis followed the application development, with studies focused on application response time and walkthroughs for AWS Greengrass and Amazon Machine Learning. Furthermore, the application itself demonstrates an architecture which is of both business and research value, providing a real-life coffee shop use-case and utilizing a newly available Fog offering from Amazon known as Greengrass. At the Cloud level, the newly available Amazon Machine Learning API was used to perform predictive analytics on the data provided by the IoT devices. Results suggest that Fog-enabled applications have a much lower range of response times as well as lower response times overall. These results suggest Fog-enabled solutions are suitable for applications which require network stability and reliably lower latency

FOTE 2008 Conference Report

A report prepared by JA.Net and ULCC about the Future of Technology in Education (FOTE 2008) conference, Imperial College, 3rd October 2008. It covers the main speakers, themes and presentations: Cloud Computing, Second Life, Portability, Personalisation, Shared Services, Campus of the Future, Mobile Technology, Creativity and Media Production, Social Collaboration Tools for Staff and Students

Establishing a Need for a Protocol for the Interoperability of Heterogeneous IoT Home Devices

The Internet of Things (IoT) refers to the field of connecting devices consumers use every day to the internet. As the world relies on more and more internet-driven technological devices to control functions within the home, issues with compatibility of those devices are surfacing. This research was created to establish the need for standardization of IoT devices within the home

Can Large Language Models Be Good Companions? An LLM-Based Eyewear System with Conversational Common Ground

Developing chatbots as personal companions has long been a goal of artificial intelligence researchers. Recent advances in Large Language Models (LLMs) have delivered a practical solution for endowing chatbots with anthropomorphic language capabilities. However, it takes more than LLMs to enable chatbots that can act as companions. Humans use their understanding of individual personalities to drive conversations. Chatbots also require this capability to enable human-like companionship. They should act based on personalized, real-time, and time-evolving knowledge of their owner. We define such essential knowledge as the \textit{common ground} between chatbots and their owners, and we propose to build a common-ground-aware dialogue system from an LLM-based module, named \textit{OS-1}, to enable chatbot companionship. Hosted by eyewear, OS-1 can sense the visual and audio signals the user receives and extract real-time contextual semantics. Those semantics are categorized and recorded to formulate historical contexts from which the user's profile is distilled and evolves over time, i.e., OS-1 gradually learns about its user. OS-1 combines knowledge from real-time semantics, historical contexts, and user-specific profiles to produce a common-ground-aware prompt input into the LLM module. The LLM's output is converted to audio, spoken to the wearer when appropriate.We conduct laboratory and in-field studies to assess OS-1's ability to build common ground between the chatbot and its user. The technical feasibility and capabilities of the system are also evaluated. OS-1, with its common-ground awareness, can significantly improve user satisfaction and potentially lead to downstream tasks such as personal emotional support and assistance.Comment: 36 pages, 25 figures, Under review at ACM IMWU

Applied Technology Group Project Documentation for NLS-Project

Loyalty systems are commonly used to build and improve relationships between business and customers. Throughout history they evolved and became almost essential for small businesses such as coffee shops or small local stores. In this paper we will analyze existing loyalty systems that are presented in the world and Irish market, how they are used in the coffee shop industry and how new technologies can change and shape this industry. We examined multiple coffee shops brands present in Ireland and their rewards systems. We compared their loyalty systems using alternative methodologies and theoretical lenses in order to find new ways to approach the subject of customer retention and loyalty. Our main goal was to make a brand new system which will offer a turnkey solution for the industry. Another goal was to simplify interaction with loyalty systems in such a way that customers will participate in it without even noticing any discomfort. This process can be achieved by using Near Field Communication chips that can be found in any smart-devices such as phones and watches, or bank cards that have the contactless payment option. We also considered ethical and data storing risks that the new system might bring. The product that was built during this research project represents only a Minimal Viable Candidate, and still needs to pass a long way before it can be implemented in the real market. On the other hand we hope it can start a movement in the right direction and impact the industry in the near future

Managing a Fleet of Autonomous Mobile Robots (AMR) using Cloud Robotics Platform

In this paper, we provide details of implementing a system for managing a fleet of autonomous mobile robots (AMR) operating in a factory or a warehouse premise. While the robots are themselves autonomous in its motion and obstacle avoidance capability, the target destination for each robot is provided by a global planner. The global planner and the ground vehicles (robots) constitute a multi agent system (MAS) which communicate with each other over a wireless network. Three different approaches are explored for implementation. The first two approaches make use of the distributed computing based Networked Robotics architecture and communication framework of Robot Operating System (ROS) itself while the third approach uses Rapyuta Cloud Robotics framework for this implementation. The comparative performance of these approaches are analyzed through simulation as well as real world experiment with actual robots. These analyses provide an in-depth understanding of the inner working of the Cloud Robotics Platform in contrast to the usual ROS framework. The insight gained through this exercise will be valuable for students as well as practicing engineers interested in implementing similar systems else where. In the process, we also identify few critical limitations of the current Rapyuta platform and provide suggestions to overcome them.Comment: 14 pages, 15 figures, journal pape

Queue-priority optimized algorithm: a novel task scheduling for runtime systems of application integration platforms

The need for integration of applications and services in business processes from enterprises has increased with the advancement of cloud and mobile applications. Enterprises started dealing with high volumes of data from the cloud and from mobile applications, besides their own. This is the reason why integration tools must adapt themselves to handle with high volumes of data, and to exploit the scalability of cloud computational resources without increasing enterprise operations costs. Integration platforms are tools that integrate enterprises’ applications through integration processes, which are nothing but workflows composed of a set of atomic tasks connected through communication channels. Many integration platforms schedule tasks to be executed by computational resources through the First-in-first-out heuristic. This article proposes a Queue-priority algorithm that uses a novel heuristic and tackles high volumes of data in the task scheduling of integration processes. This heuristic is optimized by the Particle Swarm Optimization computational method. The results of our experiments were confirmed by statistical tests, and validated the proposal as a feasible alternative to improve integration platforms in the execution of integration processes under a high volume of data.info:eu-repo/semantics/acceptedVersio