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Received signal strength (RSS) changes of static wireless nodes can be used for device-free localization and tracking (DFLT). Most RSS-based DFLT systems require access to calibration data, either RSS measurements from a time period when the area was not occupied by people, or measurements while a person stands in known locations. Such calibration periods can be very expensive in terms of time and effort, making system deployment and maintenance challenging. This paper develops an Expectation-Maximization (EM) algorithm based on Gaussian smoothing for estimating the unknown RSS model parameters, liberating the system from supervised training and calibration periods. To fully use the EM algorithm's potential, a novel localization-and-tracking system is presented to estimate a target's arbitrary trajectory. To demonstrate the effectiveness of the proposed approach, it is shown that: (i) the system requires no calibration period; (ii) the EM algorithm improves the accuracy of existing DFLT methods; (iii) it is computationally very efficient; and (iv) the system outperforms a state-of-the-art adaptive DFLT system in terms of tracking accuracy.

Low data rate wireless networks can be deployed for physical intrusion detection and localization purposes. The intrusion of a physical object (or human) will disrupt the radio frequency magnetic field, and can be detected by observing the change of radio attenuation. This gives the basis for the radio tomographic imaging technology which has been recently developed for passively monitoring and tracking objects. Due to noise and the lack of knowledge about the number and the sizes of intruding objects, multi-object intrusion detection and localization is a challenging issue. This article proposes an extended VB-GMM (i.e. variational Bayesian Gaussian mixture model) algorithm in treating this problem. The extended VBGMM algorithm applies a Gaussian mixture model to model the changed radio attenuation in a monitored field due to the intrusion of an unknown number of objects, and uses a modified version of the variational Bayesian approach for model estimation. Real world data from both outdoor and indoor experiments (using the radio tomographic imaging technology) have been used to verify the high accuracy and the robustness of the proposed multi-object localization algorithm.

In this thesis, hardware implementation of an energy adaptive resource-scalable wireless sensor node is presented. The node is designed by using commercial off-the-shelf components. Special attention is paid on minimizing the total power consumption in order to find out what levels are achievable with current state-of-the-art devices. The designed node holds many advantages over conventional wireless sensor nodes which are often strictly application-specific and require extensive hardware and software modifications when used for other applications. Re-usability of the node hardware reduces the required effort and cost when developing new applications. Sensors or additional resources can be easily attached to the node as separate boards. Part of energy adaptivity is the possibility for energy autonomous operation. The node can harvest ambient energy through specific elements that convert physical phenomena into electricity. Required conditions for energy autonomous operation are studied and analysed in the thesis. Power consumption of the node is characterized by performing measurements with different clock and power -related settings. The results show that the minimum power consumption of the node is approximately 140 microW while the maximum power consumption is approximately 85 mW. Measurement results are presented in the thesis and are used to estimate required size for the harvesting element., Tässä työssä esitellään kaupallisesti saatavilla oleviin komponentteihin perustuva langattoman, energia-adaptiivisen anturilaitteen toteutus. Laitteen laskentaresurssit ovat määriteltävissä laskentatarpeen mukaan. Keskeinen optimoitava suure suunnittelussa on laitteen kokonaistehonkulutus, mikä on pyritty minimoimaan, jotta laite voisi toimia autonomisesti keräten tarvitsemansa energian sisätilavalosta. Työssä on myös selvitetty laitteen eri osien vaikutus tehonkulutukseen. Suunnitellulla anturilaitteella on monia etuja tavanomaiseen langattomaan anturilaitteeseen nähden. Tyypilliset toteutukset ovat sovelluskohtaisia ja niiden muuntaminen toiseen käyttötarkoitukseen edellyttää suuria muutoksia laitteistossa ja ohjelmistoissa. Tässä työssä toteutettua anturilaitetta voidaan käyttää ilman laitteistomuutoksia moniin eri tarkoituksiin ja sen ominaisuuksia voidaan laajentaa yksinkertaisilla laajennusmoduuleilla. Olennainen osa energia-adaptiivisuutta on kyky toimia autonomisesti. Anturilaite kykenee keräämään ympäristöstään energiaa käyttäen elementtejä, jotka muuntavat valosähköisen, mekaanisen tai termisen energian sähköiseksi tehoksi. Tämän työn tavoitteena on määritellä laiteen minimienergiankulutusvaatimukset ja energiantuotantokapasiteettivaatimukset energiakeräimelle autonomisen toiminnan mahdollistamiseksi. Laitteen tehonkulutus on mitattu useilla kellotaajuus- ja toimintatila-asetuksilla. Tulokset osoittavat, että pienin saavutettavissa oleva tehonkulutus on noin 140 microW ja maksimitehonkulutus noin 85 mW. Työssä esiteltyjen mittausten perusteella voidaan määritellä laitteen tarvitseman energiankeräimen koko.

In this paper, we discuss fault diagnosis for wireless sensor systems. Fault diagnosis is only possible if comprehensive system monitoring is in place. We thus also present architecture for in-depth system monitoring.