Localization and security algorithms for wireless sensor networks and the usage of signals of opportunity

In this dissertation we consider the problem of localization of wireless devices in environments and applications where GPS (Global Positioning System) is not a viable option. The _x000C_rst part of the dissertation studies a novel positioning system based on narrowband radio frequency (RF) signals of opportunity, and develops near optimum estimation algorithms for localization of a mobile receiver. It is assumed that a reference receiver (RR) with known position is available to aid with the positioning of the mobile receiver (MR). The new positioning system is reminiscent of GPS and involves two similar estimation problems. The _x000C_rst is localization using estimates of time-di_x000B_erence of arrival (TDOA). The second is TDOA estimation based on the received narrowband signals at the RR and the MR. In both cases near optimum estimation algorithms are developed in the sense of maximum likelihood estimation (MLE) under some mild assumptions, and both algorithms compute approximate MLEs in the form of a weighted least-squares (WLS) solution. The proposed positioning system is illustrated with simulation studies based on FM radio signals. The numerical results show that the position errors are comparable to those of other positioning systems, including GPS. Next, we present a novel algorithm for localization of wireless sensor networks (WSNs) called distributed randomized gradient descent (DRGD), and prove that in the case of noise-free distance measurements, the algorithm converges and provides the true location of the nodes. For noisy distance measurements, the convergence properties of DRGD are discussed and an error bound on the location estimation error is obtained. In contrast to several recently proposed methods, DRGD does not require that blind nodes be contained in the convex hull of the anchor nodes, and can accurately localize the network with only a few anchors. Performance of DRGD is evaluated through extensive simulations and compared with three other algorithms, namely the relaxation-based second order cone programming (SOCP), the simulated annealing (SA), and the semi-de_x000C_nite programing (SDP) procedures. Similar to DRGD, SOCP and SA are distributed algorithms, whereas SDP is centralized. The results show that DRGD successfully localizes the nodes in all the cases, whereas in many cases SOCP and SA fail. We also present a modi_x000C_cation of DRGD for mobile WSNs and demonstrate the e_x000E_cacy of DRGD for localization of mobile networks with several simulation results. We then extend this method for secure localization in the presence of outlier distance measurements or distance spoo_x000C_ng attacks. In this case we present a centralized algorithm to estimate the position of the nodes in WSNs, where outlier distance measurements may be present

Metoda za določanje položaja v prostoru na osnovi signalov WiFi in modela zgradbe

WiFi indoor localization is a difficult task due to the variability of the WiFi signal. Consequently, there have been many attempts to develop WiFi-based methods which were aided by some other means to provide accurate indoor localization. Technologies like dead reckoning and IMU sensors, crowd utilization and pattern matching, specialized Li-Fi hardware and directional antennas, etc. were used to aid the WiFi in order to develop more accurate and stable methods. The main disadvantage of such methods lies in difficult deployments due to technologies and requirements: Dead-reckoning-aided methods are not suitable for stationary objects, methods leveraging groups of people and many individuals are not best suited for home environment, Li-Fi assisted methods require mobile terminals to provide Li-Fi connectivity and therefore rule out mobile phones as the most common terminal. In the past, many fingerprinting methods were proposedthese require a survey in the area of localization during the setup phase. Unfortunately, the majority of fingerprinting-based methods do not address issues of long-term stability of the WiFi signals. Thus, they face accuracy issues a few days after the calibrationfrequent, costly and time-consuming recalibration procedures are used to address these issues. Model-based methods try to eliminate calibration procedures by simulating signal propagation. Many of the methods assume at least some parameters of propagation as fixed and therefore poorly address the issues of WiFi’s variability and long-term stability. A pure WiFi model-based method that successfully addresses these issues and requires a mobile terminal only for emitting or receiving the WiFi signals is the ultimate goal of the WiFi indoor localization. This thesis presents a novel indoor localization method, with the main intent of addressing the issues of real-world applicability. Therefore, we focused on developing a method with accuracy comparable to the state-of-the-art methods, while reducing the complexity of deployment and minimizing the required maintenance for long-term deployments. The presented method is a model-based method, implementing self-adaptive operability, i.e. it does not require any human intervention. The thesis discusses in detail the topics of the long-term stability of the WiFi signal, receiving vs. transmitting methods, the future WiFi standards, comparability of the methods and architectural aspects with respect to real-world applicability of the localization methods. Our presented method estimates the parameters of signal propagation, by knowing the positions of the access points, the architectural floor plan with the dividing walls and by monitoring power of the packets travelling between the access points. From this data propagation parameters defined in propagation model are inferred in an online manner. A device trying to define its position captures power information of the packets sent by the access points. Devices’ information on the observed power is used to determine its position by an algorithm run on the localization server. The presented WiFi method is primarily developed and evaluated in single- and multi-room office environments. The method’s ability to be easily applicable in any environment is emphasized by its evaluation in two different environments – office and residential. Between the two, no parameters were modified, thus evaluations indicate universality of the method. Furthermore, we provide evaluation also in narrow hallway because in the field of indoor localization such evaluation environments are common practice. During the evaluation of our proposed method in the office environment, we obtained an average error of 2.63 m and 3.22 m for the single- and multi-room environments respectively. Second evaluation was performed in the residential environment, for which the method or any of the parameters were not modified. Our method achieved an average evaluation error of 2.65 m with standard deviation of 1.51 m, during the four independent evaluations, each consisting of 17 localization points. High accuracy of localization, with acknowledgement to the intricate and realistic multi-room floor plan with different types of walls, realistic furniture and real-world signal interference from the neighboring apartments, proves the method’s applicability to the real-world environment. Evaluation accuracy can be compared to the state-of-the-art methods, while our easily-applicable method requires far less complicated setup procedures and/or hardware requirements. In the second part of the thesis, we generalize the WiFi method to be applicable to the frequencies other than 2.4 GHz WiFi. By defining a fusion algorithm which considers accuracy of the individual frequencies, we have defined the MFAM method: Multiple Frequency Adaptive Model-Based Indoor Localization Method. The MFAM is one of the first purely model-based approaches capable of utilizing multiple frequencies simultaneously. The MFAM method was evaluated in residential environment on two frequency bands: 868 MHz and 2.4 GHz. The method retained positive properties of our WiFi approach (e.g. pure model-based, self-adaptive operability, wide applicability on affordable hardware), while improving the accuracy due to multi-frequency fusion. The usage of multiple frequencies improved the average error of localization from 2.65 m, while using only the WiFi, down to 2.16 m, in the case of multi-frequency fusion, thus improving localization accuracy for 18%. Similar improvements were observed also for the standard deviation. Although the accuracy of the presented WiFi and MFAM methods is comparable if not better than the state-of-the-art methods, one of the most important achievements of our work is the applicability of the method to the real-world situations and its long-term stability. The definition of our method ensures that the accuracy of the method will be the same at the time it is initialized, as well as days later, without any human interaction.Določanje lokacije znotraj prostorov na podlagi WiFi signalov je zaradi variabilnosti signala WiFi težka naloga. Posledično je bilo v preteklosti veliko poizkusov razvoja WiFi metod, ki uporabljajo dodatne informacije za natančno lokalizacijo. Ocena prehojene poti in inercijski senzorji, uporaba množice ljudi in ujemanje vzorcev, tehnologija Li-Fi in usmerjene antene itd. je le nekaj v preteklosti uporabljenih načinov za dopolnitev WiFi signalov pri razvoju natančnih in stabilnih metod. Glavna slabost takih metod se kaže v zahtevnem uvajanju zaradi uporabljenih tehnologij in zahtev: metode ocene prehojene poti niso primerne za stacionarne predmete, metode, ki uporabljajo množice ljudi, niso primerne za domače okolje, Li-Fi metode zahtevajo, da so mobilni terminali opremljeni z ustreznimi sprejemniki in tako izključijo mobilne telefone kot terminale. V preteklosti so bile predlagane številne metode, ki bazirajo na prstnih odtisih signalov. Te metode zahtevajo kalibracijske meritve v prostoru v fazi implementacije metode. Večina teh metod ne naslovi vprašanj dolgoročne stabilnosti WiFi signalov, posledično se soočajo s težavami zaradi natančnosti nekaj dni po kalibraciji. Pogoste, drage in časovno potratne ponovne kalibracije so potrebne za reševanje teh težav. Metode, temelječe na matematičnih modelih, poskušajo eliminirati kalibracijske postopke s simulacijo širjenja signala. Večina teh metod vseeno privzame vsaj nekatere parametre propagacije kot fiksne in tako slabo naslovi variabilnost WiFi signalov in dolgoročno stabilnost. Izključno WiFi modelna metoda, ki uspešno naslovi te težave in zahteva, da mobilni terminal samo oddaja ali sprejema WiFi signale, je končni cilj WiFi metod za določanje položaja v zaprtih prostorih. Ta doktorska dizertacija predstavlja novo metodo za določanje pozicije znotraj prostorov, z glavnim ciljem, da naslovi težave pri realni uporabi. Zato smo se osredotočili na razvoj metode z natančnostjo, ki je primerljiva z najsodobnejšimi metodami, hkrati pa je cilj zmanjšati kompleksnost implementacije in vzdrževanje za dolgoročno uporabnost. Predstavljena metoda je modelnega tipa in implementira prilagodljivo delovanje, zato ne zahteva nobenega človeškega posredovanja. Dizertacija podrobno razpravlja o temah dolgoročne stabilnosti WiFi signalov, o metodah, temelječih na sprejemanju in oddajanju signalov, prihodnjih standardih WiFi, primerljivosti sorodnih metod in arhitekturnih vplivih z ozirom na realno uporabnost. Naša metoda predstavljena v tej nalogi oceni prametre propagacije signala iz poznavanja pozicije dostopnih točk, arhitekturnega načrta z informacijami o predelnih stenah in s pomočjo opazovanja moči paketov, ki potujejo med dostopnimi točkami. Iz teh podatkov se propagacijski parametri definirani v modelu določijo v realnem času. Naprava, ki želi določiti pozicijo zajame informacijo o moči paketov, ki jih pošiljajo dostopne točke. Te meritve so uporabljene v algoritmu za določanje pozicije naprave, ki teče na strežniku. Predstavljena metoda je bila primarno razvita in evalvirana v enosobni in večsobni postavitvi pisarniškega okolja. Sposobnost metode, da se enostavno prilagodi vsakemu okolju, je poudarjena z evalvacijo v dveh okoljih – pisarniškem in stanovanjskem. Med obema evalvacijama nismo spremenili nobenega parametra metode, kar indicira njeno univerzalnost. V nadaljevanju predstavimo tudi evalvacijo metode v dolgem hodniku, ker je v raziskovalnem področju lokalizacije znotraj prostorov tako okolje pogosto uporabljeno. Evalvacija predlagane metode v pisarniškem okolju je rezultirala v povprečni napaki 2,63 m in 3,22 m za enosobno in večsobno postavitev. Druga evalvacija je bila opravljena v stanovanjskem okolju, za katerega nismo spreminjali metode ali njenih parametrov. Naša metoda je tekom evalvacije štirih neodvisnih setov meritev, od katerih je vsak sestavljen iz 17 lokalizacijskih točk, dosegla povprečno napako lokalizacije 2,65 m s standardno deviacijo 1,51 m. Visoka natančnost lokalizacije ob upoštevanju zapletenega in realističnega večsobnega tlorisa, ki vsebuje več vrst sten, realistično pohištvo in motnje signalov iz sosednjih stanovanj, dokazuje uporabnost metode v praksi. Natančnost je primerljiva z najsodobnejšimi metodami, medtem ko naša metoda zahteva veliko manj zapletene postopke namestitve in/ali strojne zahteve. V drugem delu teze posplošimo WiFi metodo, da lahko uporablja tudi druge frekvence poleg 2,4 GHz WiFi. Z definicijo fuzijskega algoritma, ki upošteva natančnost posameznih frekvenc, smo definirali MFAM metodo – večfrekvenčno prilagodljivo modelno metodo za določanje lokacije znotraj stavb (ang. multiple frequency adaptive model-based indoor localization method). MFAM metoda predstavlja eno prvih modelnih metod, ki lahko hkrati uporablja več frekvenc. MFAM metoda je bila evalvirana v stanovanjskem okolju na dveh frekvenčnih pasovih: 868 MHz in 2,4 GHz. Metoda je ohranila pozitivne lastnosti predlagane WiFi metode (tj. izključno modelni pristop, prilagodljivo delovanje, možnost široke uporabe na dosegljivi strojni opremi), hkrati pa rezultira v boljši natančnosti zaradi fuzije signalov več frekvenc. Uporaba več frekvenc je izboljšala povprečno napako iz 2,65 m pri uporabi WiFi na 2,16 m, s čimer se izboljša natančnost lokalizacije za 18%podobne izboljšave smo opazili tudi pri standardnemu odklonu. Čeprav je natančnost predstavljenih WiFi in MFAM metod primerljiva, če ne boljša, kot trenutno najsodobnejše metode, je eden najpomembnejših dosežkov našega dela uporabnost metode v realnih situacijah in njena dolgoročna stabilnost. Definicija naše metode zagotavlja, da bo natančnost metode ob času postavitve enaka kot dneve kasneje brez človeške interakcije

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Effects of the troposphere upon radio communications and implications for weather measurement

The aim of this research is to use observations of Very High Frequency (VHF) radio wave propagation to estimate the refractive index of air over a propagation path and subsequently the pressure, temperature, and water vapour. This can be accomplished by transmitting a known signal from one spatial location to another, and accurately timing the signal. The research develops methods for accomplishing the required measurements by means of a passive bistatic radar setup with a cooperative target. Such an approach overcomes the timing problems that are normally associated with propagation measurements. A network of these measurements can then be assimilated into a numerical weather prediction model, such as that used by the Australia Bureau of Meteorology, to increase weather forecast capability. Importantly, this research develops novel techniques to relate the propagation of radio waves from weather data. These techniques are important in their own right to identify true propagation paths.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 201