Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

Vasteajan mittausjärjestelmän suunnittelu, toteutus ja testaus

A touchscreen is a commonly used medium for the interaction between a user and a device. Response to user's action is often indicated visually on the screen after a certain delay. This interface latency is inherent in any computer system. Studies indicate that the latency has a major contribution on how users perceive the interaction with the device. While modern commercial touchscreen devices manifest latencies ranging between 50 ms and 200 ms, research indicates that the user performance for tapping tasks deteriorates at considerably lower levels and users are able to discern the latency as low as 3 ms. In this Thesis we present a novel solution for Android operated mobile devices to expose factors behind the feedback latency of a tap event. We start by reviewing the main components of the Android operating system. Next we describe the internal system elements which partake in the interaction between the user's touch input event and its corresponding visual presentation on the screen of the device. Propelled by the obtained information, we implement an affordable, fully automated system that is capable of collecting both temporal and environmental data. The constructed measurement system provided revealing results. We discovered that most of the feedback latency on a mobile device is accumulated by the internal components which are involved in presenting the visual feedback to the user. We also identified two main user action patterns which impose a huge effect upon system's responsiveness. Firstly, the location of touch is reflected in the amount of feedback latency. Secondly, the interval between two consecutive touch events might cause even unexpected results. Our study demonstrated that the latency can vary a lot between different devices by ranging from no effect on one device to a five-fold difference on another device. The study concludes that, despite the feedback latency is affected by multiple factors, the latency can be measured very precisely with the system that can be built even by an average Joe.Kosketusnäyttö on yleisesti käytetty kanava käyttäjän ja laitteen välisessä vuorovaikutuksessa. Järjestelmän palaute käyttäjän antamaan syötteeseen esitetään usein visuaalisesti laitteen näytöllä. Vasteen tuottamisessa syntyy kuitenkin jonkin verran viivettä eli latenssia. Tutkimusten mukaan viiveellä on suuri vaikutus käyttäjäkokemukseen. Nykyisten kosketuslaitteiden latenssi vaihtelee yleensä 50 ja 200 millisekunnin välillä. Kosketuspohjaisten tapahtumien suorittamisen on todettu heikentyvät jo huomattavasti pienemmän viiveen johdosta ja jopa alle kolme millisekuntia kestävä viive on vielä havaittavissa. Tässä diplomityössä esitetään Android-pohjaisille mobiililaitteille luotu edullinen järjestelmä, jonka avulla pystytään mittaamaan käyttäjän näytölle luoman kosketuksen ja sitä vastaavan järjestelmän antaman visuaalisen palautteen välistä viivettä. Työssä esitetellään ensin Android-käyttöjärjestelmän komponentit, jotka osallistuvat tämän tapahtumaketjun suorittamiseksi vaadittaviin toimintoihin. Tietojen pohjalta luodaan järjestelmä, jolla voidaan kerätä automaattisesti dataa viiveen eri syntykohdista ja sen ympäristöön littyvistä seikoista. Datan avulla pystytään aiempaa paremmin arvioimaan viiveen syntyyn vaikuttavia tekijöitä. Saatua tietoa voidaan hyödyntää yleisesti viiveen hallitsemiseen tähtääviin toimenpiteisiin ja siten lopulta käyttäjäkokemuksen parantamiseen. Järjestelmällä mitatuista tuloksista selviää, että suurin osa tapahtumaketjun latenssista syntyy käyttäjälle esitettävän visuaalisen palautteen vaatimiin toimenpiteisiin. Lisäksi työ tuo esille kaksi käyttäjän syötteen antamiseen liittyvää toimintatapaa, joilla on suuri vaikutus latenssiin. Kosketuksen sijainti ruudulla ja kahden peräkkäisen kosketuksen välinen aika vaikuttavat vasteaikaan. Latenssi ei aina muodostu suoraviivaisesti ja se voi ilmentää jopa yllättäviä piirteitä eri laitteiden välillä: toimintatapa yhdessä laitteessa ei vaikuta tulokseen, mutta saattaa toisessa laitteessa näkyä moninkertaisena erona. Vaikka latenssin syntyyn vaikuttaa monta eri tekijää, sitä voidaan onneksi mitata erittäin tarkasti järjestelmällä, jonka jopa Matti Meikäläinen pystyy rakentamaan

Investigating optimal internet data collection in low resource networks

Community networks have been proposed by many networking experts and researchers as a way to bridge the connectivity gaps in rural and remote areas of the world. Many community networks are built with low-capacity computing devices and low-capacity links. Such community networks are examples of low resource networks. The design and implementation of computer networks using limited hardware and software resources has been studied extensively in the past, but scheduling strategies for conducting measurements on these networks remains an important area to be explored. In this study, the design of a Quality of Service monitoring system is proposed, focusing on performance of scheduling of network measurement jobs in different topologies of a low-resource network. We also propose a virtual network testbed and perform evaluations of the system under varying measurement specifications. Our results show that the system is capable of completing almost 100% of the measurements that are launched by users. Additionally, we found that the error due to contention for network resources among measurements stays constant at approximately 34% with increasing number of measurement nodes

Event Detection and Estimation Using Distributed Population Owned Sensors

Smart phones are an indispensable tool in modern day-to-day life. Their widespread use has spawned numerous applications targeting diverse domains such as bio-medical, environment sensing and infrastructure monitoring. In such applications, the accuracy of the sensors at the core of the system is still questionable, since these devices are not originally designed for high accuracy sensing purposes. In this thesis, we investigate the accuracy limits of one of the commonly used sensors, namely, a smart phone accelerometer. As a use case, we focus on utilizing smart phone accelerometers in structural health monitoring (SHM). Using the already deployed network of distributed citizen-owned sensors is considered a cheap alternative to standalone sensors. These devices can capture floors vibration during disasters, and consequently compute the instantaneous displacement of each floor. Hence, damage indicators defined by government standards such as peak relative displacement can be estimated. In this work, we study the displacement estimation accuracy and propose a zero-velocity update (ZUPT) method for noise cancellation. Theoretical derivation and experimental validation are presented, and we discuss the impact of sensor error on the achieved building classification accuracy. Moreover, in spite of the presence of sensor error, SHM systems can be resilient by adopting machine learning. Several algorithms such as support vector machine (SVM), K-nearest neighbor (KNN) and convolutional neural network (CNN) are adopted and compared. Techniques for addressing noise levels are proposed and the results are compared to regular noise cancellation techniques such as filtering.Finally, since most previous work focused on modelling the sensor chip error itself, we study other sources of error such as sampling time uncertainty which is introduced by the device operating system (OS). That type of error can be considered a major contributor to the overall error, specially for sufficiently large signals. Hence, we propose a novel smart device accelerometer error model that includes the traditional additive noise as well as sampling time uncertainty errors. The model is validated experimentally using shake table experiments, and maximum likely-hood estimation (MLE) is used to estimate the model parameters. Moreover, we derive the Cramer-Rao lower bound (CRLB) of acceleration estimation based on the proposed model

Prime: A framework for co-located multi-device apps

Even though mobile devices are ubiquitous, the conceptually simple endeavor of using co-located devices for multi-user experiences is cumbersome. It may not even be possible when certain apps are not widely available. We introduce Prime, a thin-client framework for colocated multi-device apps (MDAs). It leverages wellestablished remote display protocols to enable spontaneous use of MDAs. One device acts as a host, executing the app on behalf of connected clients. The key challenges is dynamic scalability: providing high framerates, low latency and fairness across clients. Therefore, we have developed: An online scheduling algorithm that provides frame rate, latency and fairness guarantees; a modified 802.11 MAC protocol that provides low-latency and fairness; and an efficient video encoder pipeline that offers up to fourteen times higher framerates. We show that Prime can scale a host up to seven concurrent players for a commercially released open source action game, achieving touch-To-pixel latency below 100ms for all clients