Understanding packet loss for sound monitoring in a smart stadium IoT testbed

The Smart Stadium for Smarter Living project provides an end-to-end testbed for IoT innovation through a collaboration between Croke Park Stadium in Dublin, Ireland and Dublin City University, Intel and Microsoft. This enables practical evaluations of IoT solutions in a controlled environment that is small enough to conduct trials but large enough to prove and challenge the technologies. An evaluation of sound monitoring capabilities during the 2016 sporting finals was used to test the capture, transfer, storage and analysis of decibel level sound monitoring. The purpose of the evaluation was to use existing sound level microphones to measure crowd response to pre-determined events for display on big screens at half-time and to test the end-to-end performance of the testbed. While this is not the specific original purpose of the sound level microphones, it provided a useful test case and produced engaging content for the project. Analysis of the data streams showed significant packet loss during the events and further investigations were conducted to understand where and how this loss occurred. This paper describes the smart stadium testbed configuration using Intel gateways linking with the Azure cloud platform and analyses the performance of the system during the sound monitoring evaluation

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

Failures from the Environment, a Report on the First FAILSAFE workshop

This document presents the views expressed in the submissions and discussions at the FAILSAFE workshop about the common problems that plague embedded sensor system deployments in the wild. We present analysis gathered from the submissions and the panel session of the FAILSAFE 2017 workshop held at the SenSys 2017 conference. The FAILSAFE call for papers specifically asked for descriptions of wireless sensor network (WSN) deployments and their problems and failures. The submissions, the questions raised at the presentations, and the panel discussion give us a sufficient body of work to review, and draw conclusions regarding the effect that the environment has as the most common cause of embedded sensor system failures

Study, Measurements and Characterisation of a 5G system using a Mobile Network Operator Testbed

The goals for 5G are aggressive. It promises to deliver enhanced end-user experience by offering new applications and services through gigabit speeds, and significantly improved performance and reliability. The enhanced mobile broadband (eMBB) 5G use case, for instance, targets peak data rates as high as 20 Gbps in the downlink (DL) and 10 Gbps in the uplink (UL). While there are different ways to improve data rates, spectrum is at the core of enabling higher mobile broadband data rates. 5G New Radio (NR) specifies new frequency bands below 6 GHz and also extends into mmWave frequencies where more contiguous bandwidth is available for sending lots of data. However, at mmWave frequencies, signals are more susceptible to impairments. Hence, extra consideration is needed to determine test approaches that provide the precision required to accurately evaluate 5G components and devices. Therefore, the aim of the thesis is to provide a deep dive into 5G technology, explore its testing and validation, and thereafter present the OTE (Hellenic Telecommunications Organisation) 5G testbed, including measurement results obtained and its characterisation based on key performance indicators (KPIs)

Inducing sparsity in deep neural networks through unstructured pruning for lower computational footprint

Deep learning has revolutionised the way we deal with media analytics, opening up and improving many fields such as machine language translation, autonomous driver assistant systems, smart cities and medical imaging to only cite a few. But to handle complex decision making, neural networks are getting bigger and bigger resulting in heavy compute loads. This has significant implications for universal accessibility of the technology with high costs, the potential environmental impact of increasing energy consumption and the inability to use the models on low-power devices. A simple way to cut down the size of a neural network is to remove parameters that are not useful to the model prediction. In unstructured pruning, the goal is to remove parameters (ie. set them to 0) based on some importance heuristic while maintaining good prediction accuracy, resulting in a high-performing network with a smaller computational footprint. Many pruning methods seek to find the optimal capacity for which the network is the most compute efficient while reaching better generalisation. The action of inducing sparsity – setting zero-weights – in a neural network greatly contributes to reducing over-parametrisation, lowering the cost for running inference, but also leveraging complexity at training time. Moreover, it can help us better understand what parts of the network account the most for learning, to design more efficient architectures and training procedures. This thesis assesses the integrity of unstructured pruning criteria. After presenting a use-case application for the deployment of an AI application in a real-world setting, this thesis demonstrates that unstructured pruning criteria are ill-defined and not adapted to large scale networks due to the over-parametrisation regime during training, resulting in sparse networks lacking regularisation. Furthermore, beyond solely looking at the performance accuracy, the fairness of different unstructured pruning networks is evaluated highlighting the need to rethink how we design unstructured pruning

Getting smarter about smart cities: Improving data privacy and data security

Abstract included in text