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Managing Alarming Situations with Mobile Crowdsensing Systems and Wearable Devices

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Managing Alarming Situations with Mobile Crowdsensing Systems and Wearable Devices DEGREE PROJECT IN INFORMATION AND COMMUNICATION TECHNOLOGY, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2020 Managing Alarming Situations with Mobile Crowdsensing Systems and Wearable Devices VIKTORIYA KUTSAROVA KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Managing Alarming Situations with Mobile Crowdsensing Systems and Wearable Devices VIKTORIYA KUTSAROVA Master in Computer Science ICT Innovation, Cloud Computing and Services Date: August 18, 2020 Supervisor: Shatha Jaradat Examiner: Mihhail Matskin School of Electrical Engineering and Computer Science Host company: RISE Swedish title: Hantering av farliga situationer med Mobile Crowdsensing Systems och bärbara enheter iii Abstract Dangerous events such as accidental falls, allergic reactions or even severe panic attacks can occur spontaneously and within seconds. People experienc- ing alarming situations like these often require assistance. On the one hand, wearable devices such as smartphones or smartwatches can be used to detect these situations by utilising the plethora of sensors built into them. On the other hand, mobile crowdsensing systems (MCS) might be used to manage the detection and mitigation of alarming situations. To be able to handle these events, an MCS requires integration with mobile sensory devices, as well as the voluntary participation of people willing to help. This thesis investigates how to incorporate wearables into an MCS. Furthermore, it explores how to utilise the gathered data and the participants in the system to manage alarming situations. The contributions of this thesis are twofold. First, we propose the exten- sion of a mobile crowdsensing system for managing alarming situations that allows integration of wearables. We base our work on CrowdS - an MCS that facilitates the distributed interactions between people and sensory devices. We integrate a commodity smartwatch into CrowdS using different techniques (i.e. Internet and Bluetooth). The smartwatch’s sensors enable the detection of various alarming situations and their transmission to the MCS. The mobile crowdsensing system then relays the data and finds volunteers willing to help. Our solution can be adapted to handle various types of dangerous situations. Moreover, the system can easily be integrated with other types of wearables. Second, to test the usefulness of an MCS without actually deploying it in real life, we create a simulation that models different scenarios that rep- resent dangerous events. It allows us to represent the event visually and to parametrise various factors that influence the effectiveness of the system. The simulation helps to identify how different parameters might affect the outcome of the alarming situation. Our results show that important attributes include but are not limited to the coverage of the system, the number of participants and their density, as well as distribution and means of transportation. We enhance the capabilities of CrowdS by enabling the integration of var- ious Bluetooth wearable devices. Thus we expand CrowdS into a prototype of a system for managing alarming situations. Moreover, through the MCS simulation, we identify essential parameters that need to be considered when building such a system. The simulation is a tool that can also be used to find the optimal configuration of the MCS. iv Sammanfattning Farliga händelser som till exempel oavsiktliga fall, allergiska reaktioner eller till och med panikångest kan inträffa utan förvarning och inom några sekun- der. Människor som upplever livsfarliga situationer som dessa behöver ofta hjälp. Bärbara enheter som smartphones eller smartwatches användas för att upptäcka dessa situationer genom att använda en mängd sensorer som är in- byggda i dem. Mobile Crowdsensing Systems (MCS) användas för att hantera upptäckten av dessa situationer och hjälpa människor få de hjälp som behövs. För att kunna hantera dessa situationer kräver en MCS integration mellan mo- bila sensoriska enheter, såväl som ett deltagande av människor som är villiga att hjälpa. Denna avhandling undersöker hur man kan integrera wearables i en MCS. Man undersöker även hur man kan samla in data och deltagare i systemet för att hantera farliga situationer. Bidragen i denna avhandling är tvåfaldiga. För det första föreslår vi utök- ning av en MCS för att hantera farliga situationer som möjliggör integrationen av bärbara enheter. Vi baserar vårt arbete på CrowdS - ett MCS som under- lättar distribuerade interaktioner mellan människor och sensoriska apparater. Vi integrerar en smartwatch med CrowdS med hjälp av olika metoder (exem- pelvis. Internet och Bluetooth). Smartwatch-sensorerna möjliggör upptäckt av olika farliga situationer och deras överföring till MCS. MCS vidarebefordrar sedan datan och försöker hitta frivilliga som är villiga att hjälpa. Vår lösning kan anpassas för att hantera olika typer av farliga situationer. Dessutom kan systemet enkelt integreras med andra typer av bärbara enheter. För att testa nyttan av MCS utan att distribuera den i verkliga livet, skapar vi en simulering av olika scenarier som representerar farliga händelser. I si- muleringen kan vi ändra parametrar och faktorer under händelseförloppet för att se hur det påverkar systemets effektivitet. Simuleringen hjälper till att iden- tifiera hur olika parametrar kan påverka resultatet av den farliga situationen. Våra resultat visar att en del viktiga attribut inkluderar men inte är begränsa- de till området som täcks av systemet, antalet deltagare och deras täthet, samt distribution av människor samt tillgång till transportmedel. Vi förbättrar förmågan hos CrowdS genom att möjliggöra integrationen av olika Bluetooth-bärbara enheter. Vi har utvecklat CrowdS till en prototyp av ett system för att hantera farliga situationer. Genom MCS-simuleringen iden- tifierar vi dessutom viktiga parametrar som måste uppmärksamma när man bygger ett sådant system. Simuleringen är ett verktyg som kan användas för att hitta den optimala konfigurationen av MCS. v Acknowledgement I am incredibly grateful to my examiner prof. Mihhail Matskin for the invalu- able insights and practical suggestions on how to tackle the work during the creation of this master thesis. I am grateful to my supervisor Shatha Jaradat and to Ronja Jösch for the thor- ough feedback and advice on how to improve my work, as well as the relentless support. Special thanks to Mikael Bengtsson for the vast amount of assistance when providing me with the necessary resources to finish this work. I’d like to recognise the effort that I received from Niklas Fürderer from Nec- tarine Health for our great discussions on how their product works. I gratefully acknowledge the support of my friends and family that allowed me to pursue my higher education. Last, but not least, I express my deepest gratitude to Milko Mitropolitsky who is my cornerstone in every aspect of life. Contents 1 Introduction 1 1.1 Motivation . .1 1.2 Problem statement . .2 1.3 Research questions . .3 1.4 Purpose . .3 1.5 Goals . .4 1.6 Thesis contributions . .4 1.7 Thesis limitations . .5 1.8 Research methodology . .5 1.9 Ethics and Sustainability . .6 1.10 Outline of the document . .6 2 Background 8 2.1 Risk management . .9 2.1.1 Health risk situations . .9 2.1.2 Fall detection and prevention . .9 2.2 Sensing wearables . 10 2.3 Crowdsourcing and crowdsensing . 11 2.3.1 Crowdsourcing . 11 2.3.2 Crowdsensing . 12 2.3.3 Mobile crowdsoucring and crowdsensing . 12 2.4 Mobile crowdsensing taxonomy . 13 2.4.1 Sensing scale . 13 2.4.2 User involvement and responsiveness . 13 2.4.3 Sampling rate . 14 2.4.4 Network infrastructure . 14 2.5 MCS for health care, emergency, safety . 14 2.5.1 Emergency management and prevention . 15 2.5.2 Healthcare and wellbeing . 15 vi CONTENTS vii 2.5.3 Mobile social networks . 16 2.6 Platform: Crowds . 16 3 Design and Implementation 19 3.1 Extending CrowdS Android application . 19 3.2 Smartwatch Application . 23 3.3 Smartwatch integration with CrowdS via Internet . 25 3.4 Smartwatch integration with CrowdS using Bluetooth . 25 3.5 Extended CrowdS sample scenario . 26 3.6 Nectarine’s platform . 29 4 Experiments 31 4.1 Motivation . 31 4.2 Experiments setup . 32 4.3 Parameters selection . 34 5 Results and Discussion 38 5.1 Smartwatch integration with CrowdS . 38 5.1.1 Communication protocols comparison . 38 5.1.2 Comparison between smartwatch-oriented and smartphone- oriented approaches . 40 5.2 Simulation experiments . 40 5.2.1 Scenario 1: Different density . 40 5.2.2 Scenario 2: Different transportation methods . 42 5.2.3 Scenario 3: Different probability of participation . 43 5.2.4 Scenario 4: Different distribution of participants . 44 5.2.5 Scenario 5: Constantly increasing density . 45 5.2.6 Scenario 6: Different radius of MCS . 46 5.2.7 Discussions . 48 6 Conclusion and Future Work 49 6.1 Conclusion . 49 6.2 Future work . 50 6.2.1 Smartwatch application . 50 6.2.2 CrowdS platform . 51 6.2.3 Simulation . 52 6.2.4 Ethics . 52 6.2.5 Discover opinions regarding participation in risk de- tection system . 52 viii CONTENTS Bibliography 53 A Calculating the number of visitors and density for simulation 64 List of Tables 4.1 Difference between use cases used to derive parameters of an MCS for risky situation detection . 34 4.2 Parameters and default values used in experiments with a sys- tem for handling emergencies . 37 5.1 Advantages and disadvantages of integrating smartwatch with CrowdS directly via Internet . 39 5.2 Advantages and disadvantages of connecting smartwatch to a smartphone through Bluetooth . 40 5.3 Parameters used in Scenario 1: Different density . 41 5.4 Parameters used in Scenario 3: Different probability of par- ticipation . 43 ix List of Figures 3.1 CrowdS System Overview. Source: (V. Granfors et al. 2018) . 20 3.2 Device overview. Adapted from source: (V. Granfors et al. 2018) 21 3.3 Extended client side overview. Adapted from source: (V.Gran- fors et al. 2018) . 22 3.4 CrowdS platfrom emergency detection dataflow . 22 3.5 Basic Architecture of Fall Detection System.
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