Indoor Localization Using Accidental Infrastructure
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Delft University of Technology Master’s Thesis in Embedded Systems Indoor Localization using Accidental Infrastructure Zsolt Kocsi-Horvath´ Indoor Localization using Accidental Infrastructure Master’s Thesis in Embedded Systems Embedded Software Section Faculty of Electrical Engineering, Mathematics and Computer Science Delft University of Technology Mekelweg 4, 2628 CD Delft, The Netherlands Zsolt Kocsi-Horvath´ [email protected] 4th January 2013 Author Zsolt Kocsi-Horvath´ ([email protected]) Title Indoor Localization using Accidental Infrastructure MSc presentation 15. January 2013 Graduation Committee prof. dr. K. G. Langendoen (chair) Delft University of Technology dr. ir. A. Phillips ARM R&D, Cambridge ir. H. Vincent ARM R&D, Cambridge dr. S.O. Dulman Delft University of Technology dr. A. Iosup Delft University of Technology Abstract The level of the technological development of embedded devices is at a constant rise. We can foresee a near-future scenario where a huge number of semi-intelligent devices are part of our everyday environment, our homes, the public places and the office as well. The intelligent thermostat uploads the temperature readings to an on- line database; the fridge sends a tweet when we are out of milk; the coffee machine texts us when the coffee is ready. Each device has a unique and individual purpose. But what if they could be grouped together as a so-called accidental infrastructure to serve a more advanced cause? We have set out to demonstrate the possibilities of such an accidental infrastructure in the field of indoor localization. An ambient device in itself is not intentionally prepared for localization purposes, but using many of them together and combining the collected data can surpass the devices’ limited individual capabilities. Our approach was to build a prototype system based on a homogeneous array of radio-connected nodes and an additional entity with a higher magnitude of com- puting power. This central entity then controls the data collection from the nodes and executes a custom localization algorithm, based on probabilistic methods and a Kalman filter. We have evaluated our system both by simulations with ideal input data and by real-world measurements. The results show that the system is able to track and update the location estimates, but due to the heavy multipath effect it is only capable of very moderate improvements. iv Preface I would like to express my deepest gratitude to everyone who helped me during these past months. First, to my supervisors at ARM, Amyas Phillips and Hugo Vin- cent; and to my professor, Koen Langendoen. Thank you for all your help, insight, but most importantly, for your infinite patience. I would like to thank my family and my friends in Budapest, in Cambridge, and in Delft for their great support. Es- pecially Beus, Reka,´ Shanti, Szadd´ am,´ Balint,´ Krisz and Tomi – the people of dorm rooms SCH1514 and SCH408, where I spent most of my days and nights typing out the words of my thesis, until the Sun came up. I would also like to thank Paul´ına, Andrea, Adel´ and Gabi for polishing my writings on a regular basis. Finally, a word of thanks to all the great writers and musicians that provided some sort of inspira- tion or a peace of mind during the long hours of writing. Zsolt Kocsi-Horvath´ Delft, The Netherlands 4th January 2013 v vi Contents Contents 1 Introduction1 1.1 Project description..............................2 1.2 Problem Statement.............................3 1.3 Approach...................................3 1.4 Outline....................................4 2 Background5 2.1 Wireless networks..............................5 2.2 Radio Protocols...............................6 2.3 Localization methods............................7 2.4 Location and probability.......................... 11 2.5 Briefly on the Kalman filter......................... 13 2.6 Example systems and applications.................... 15 3 Design 17 3.1 Main thesis scenario............................. 17 Introducing the central entity....................... 18 Radio transmission............................. 20 Initial coordinates and reliability values................. 20 3.2 Localization process............................. 21 Representation of the current system state................ 21 Converting signal information to distance................ 22 Custom localization algorithm with Kalman filter............ 24 vii Contents 4 Implementation 29 4.1 Nodes hardware............................... 29 mbed LPC1768............................... 30 Radio module................................ 31 4.2 Node software................................ 32 Signal survey using the Chibi stack.................... 32 Modular software development...................... 33 Commands and actions........................... 34 Real Time OS and threads......................... 35 4.3 Central entity software........................... 36 Generating an alternate database..................... 37 Kalman subsystem............................. 37 5 Evaluation 41 5.1 Configuration and methodology...................... 41 Node arrangement.............................. 41 System parameters............................. 43 Selected scenarios.............................. 45 5.2 Analysing results.............................. 46 Location updates.............................. 47 Configuring the Kalman filter....................... 48 Sequences.................................. 49 Convergence................................. 50 The advantage of diverse input...................... 51 Comparing measured and theoretical cases............... 52 A note on errors............................... 53 6 Conclusion 55 Future Work................................. 56 Appendix 58 A.1 Localization system output figures, full size............... 60 viii Contents Configuring the Kalman filter....................... 60 Sequences.................................. 63 The advantage of diverse input...................... 65 Comparing measured and theoretical cases............... 67 Bibliography 71 ix Contents x Chapter 1 Introduction The term Embedded wireless networks is very loosely defined, but in general it refers to a network of small devices, where each of them has at least a central pro- cessing unit and some means of radio communication. Wireless sensor networks and Mobile Ad-hoc Networks fall under this category, and so does the Internet of Things. When the devices themselves are being deployed in large numbers, the manufactur- ing cost is significantly reduced. Low cost, small size and radio connectivity makes embedded wireless networks ideal for localization and tracking purposes. Some devices incorporate extra circuitry for positioning (e.g. magnetometer, accelero- meter, GPS chip), while others rely solely on their radio chips (i.e. signal strength, triangulation). These options are further explored in the Background chapter. Beside the nodes that are deliberately developed for measurement and localization, there are numerous semi-intelligent general purpose devices that can be modified to serve a similar goal (e.g. a WiFi router with programmable firmware or an intelli- gent thermostat, also shown in Figure 1.1a). This accidental infrastructure of smart devices is predicted to have a huge increase both in numbers and in capabilities in the following decade. This scenario, part present and existing, part futuristic and hypothetical, provides the setting for this thesis. 1 Chapter 1. Introduction 1.1 Project description An obstacle to widespread deployment of indoor localisation systems has been the provision of suitable supporting Internet of Things infrastructure at a cost which is justified by the value of the applications it enables. It is possible to imagine a near-future scenario in which a building contains a variety of smart objects in fixed locations, such as room temperature sensors, door locks and window controls. Some are battery powered and must spend much of their time asleep. Others have mains power and function as gateways to the internet. These fixed nodes can potentially become an infrastructure for indoor positioning services. Nodes both mobile and fixed might benefit from knowing their location relative to one another or a map. This thesis aims to study how this accidental infrastructure can be used to provide indoor positioning services. An example system is being developed and tested, that serves as an prototype of a future network of nodes deployed in a general work environment (office building). Project context The Master thesis is being carried out at ARM Cambridge, Research & Development division, Internet of Things workgroup. The example system and the surrounding theoretical research are to benefit both TU Delft and ARM R&D. (a) Intelligent thermostat (b) Smartphone guidance system Figure 1.1: Internet of Things in everyday use 2 1.2. Problem Statement 1.2 Problem Statement There is a hypothetical future environment of an office building augmented with radio-enabled intelligent devices. Each node’s location is defined with a reliability ranging from exact to unknown, represented on a scale of [0 .. 1000]. One or mul- tiple so-called central entities with a higher order of computing power are added to this environment. These have a room floorplan stored with the nodes’ rough initial location and the reliability values. They can communicate with the nodes via their own radio interface and measure the signal strength of messages. There is also a reliability value on each signal strength measurement