This thesis presents a feasibility study on unobtrusive localization of GSM en- abled cellphones using a Fake Base Station (FBS). An FBS is a radio transceiver that emulates the behaviour of a legitimate GSM Base Station (BS) to fool unal- tered cellphones to connect with it. This feasibility study investigates how an FBS can be utilized to estimate positions of connected cellphones in an area of interest. We present a proof of concept system that consists of a mobile FBS that measures the Time Of Arrival (TOA) and Received Signal Strength (RSS) to a cell- phone. The positions of the mobile FBS are determined with GPS. We employ calibration-free localization algorithms as we assume unknown environments and unknown hardware. Our experiments in an outdoor 180x100 m2 Line-Of- Sight (LOS) environment show that our calibration-free localization algorithms provide an average localization error less than 10 meters, which is sufficient for most applications of interest. In addition, our experiments show that RSS-based localization outperforms TOA-based localization when the average distance be- tween the FBS and cellphone is roughly 50 meters. Our experiments show that TOA-based localization outperforms RSS-based localization when the average dis- tance increases to roughly 75 meters. This research is part of the Smart Savannah project in which a wide range of different surveillance systems are developed to protect rhinos from poachers. We envision that our localization system can be used to detect and localize these poachers in an unobtrusive way. In addition, we envision that our localization sys- tem can be used in Search And Rescue (SAR) operations to estimate the positions of cellphones of missing persons. / Detta examensarbete undersöker möjligheten att lokalisera mobiltelefoner med GSM teknologi genom att använda en Falsk Basstation (FBS). En FBS är en radio transceiver som emulerar beteendet hos en legitim GSM basstation för att lura omodifierade mobiltelefoner att ansluta till den. Undersökningen tar reda på hur en FBS kan användas för att estimera positionerna av anslutna mobiltelefoner inom ett målområde. För att undersöka detta har ett Proof-Of-Concept-system ta- gits fram. Systemet består av en mobil FBS som som mäter propageringstid (TOA) och mottagen signalstyrka (RSS). FBS:ens positioner bestäms med GPS. Systemet använder kalibreringsfria algoritmer för lokalisering, då vi antar att miljön och mobiltelefonernas hårdvara är okänd. Tester av systemet har utförts utomhus i ett 180x100 m2 Line-Of-Sight-område. Dessa tester visar att lokaliseringsalgorit- merna ger ett genomsnittligt fel på mindre än 10 meter. Detta anses vara till- räckligt för de flesta tillämpningar av intresse. Utöver detta visar även testerna att RSS-baserad lokalisering ger bättre resultat än TOA-baserad lokalisering när medelavståndet mellan FBS och mobiltelefon är omkring 50 meter. TOA-baserad lokalisering ger däremot ett bättre resultat än RSS-baserad lokalisering när me- delavståndet ökar till omkring 75 meter. Denna undersökning är en del av Smart Savannah projektet som innefattar flera olika övervakningssystem, utvecklade för att skydda noshörningar från tjuv- skyttar. Målet med vårt lokaliseringssystem är att det ska kunna användas för att upptäcka och lokalisera tjuvskyttar utan deras vetskap. Vi tror även att lokalise- ringssystemet kan användas vid eftersökning- och räddnings-operationer för att lokalisera försvunna personers mobiltelefoner. / Project Ngulia

This thesis describes the development of an offline, cooperative, range-based localization algorithm for use in settings where there is limited or no access to a positioning infrastructure. Motivating applications include underground animal tracking and indoor pedestrian localization. It is assumed that each sensor performs dead reckoning to estimate its current position, relative to a starting point. Each measurement adds error, causing the position estimate to drift further from the truth with time. The key idea behind the proposed algorithm is to use opportunistic radio contacts to mitigate this drift, and hence localize with greater accuracy. The proposed algorithm first fuses radio and motion measurements into a compact graph. This graph encodes key positions along sensor trajectories as vertices, and distance measurements as edges. In so doing, localization is cast as the graph realization problem: assigning coordinates to vertices, in such a way that satisfies the observed distance measurements. The graph is first analysed to certify whether it defines a localization problem with a unique solution. Then, several algorithms are used to estimate the vertex coordinates. These vertex coordinates are then used to apply piecewise corrections to each sensor's dead reckoning trajectory to mitigate drift. Finally, if sufficient anchors are available, the corrected trajectories are then projected into a global coordinate frame. The proposed algorithm is evaluated in simulation for the problem of indoor pedestrian tracking, using realistic error models. The results show firstly that 2D and 3D problems become provably more localizable as more anchors are used, and as the experiment duration increases. Secondly, it is shown that widely-used graph realization algorithms cannot be used for localization, as the complexity of these algorithms scales polynomially or greater with graph vertex count. Thirdly, it is shown a novel piecewise drift correction algorithm typically works well compared to a competing approach from the literature, but rare and identifiable graph configurations may cause the method to underperform.

Searching for objects in physical space is one of the most important tasks for humans. Mobile sensor networks can be great tools for the task. Transient targets refer to a class of objects which are not identifiable unless momentary sensing and signaling conditions are satisfied. The transient property is often introduced by target attributes, privacy concerns, environment constraints, and sensing limitations. Transient target localization problems are challenging because the transient property is often coupled with factors such as sensing range limits, various coverage functions, constrained mobility, signal correspondence, limited number of searchers, and a vast searching region. To tackle these challenge tasks, we gradually increase complexity of the transient target localization problem such as Single Robot Single Target (SRST), Multiple Robots Single Target (MRST), Single Robot Multiple Targets (SRMT) and Multiple Robots Multiple Targets (MRMT). We propose the expected searching time (EST) as a primary metric to assess the searching ability of a single robot and the spatiotemporal probability occupancy grid (SPOG) method that captures transient characteristics of multiple targets and tracks the spatiotemporal posterior probability distribution of the target transmissions. Besides, we introduce a team of multiple robots and develop a sensor fusion model using the signal strength ratio from the paired robots in centralized and decentralized manners. We have implemented and validated the algorithms under a hardware-driven simulation and physical experiments.