Dial It In: Rotating RF Sensors to Enhance Radio Tomography

A radio tomographic imaging (RTI) system uses the received signal strength (RSS) measured by RF sensors in a static wireless network to localize people in the deployment area, without having them to carry or wear an electronic device. This paper addresses the fact that small-scale changes in the position and orientation of the antenna of each RF sensor can dramatically affect imaging and localization performance of an RTI system. However, the best placement for a sensor is unknown at the time of deployment. Improving performance in a deployed RTI system requires the deployer to iteratively "guess-and-retest", i.e., pick a sensor to move and then re-run a calibration experiment to determine if the localization performance had improved or degraded. We present an RTI system of servo-nodes, RF sensors equipped with servo motors which autonomously "dial it in", i.e., change position and orientation to optimize the RSS on links of the network. By doing so, the localization accuracy of the RTI system is quickly improved, without requiring any calibration experiment from the deployer. Experiments conducted in three indoor environments demonstrate that the servo-nodes system reduces localization error on average by 32% compared to a standard RTI system composed of static RF sensors.Comment: 9 page

An adaptive weighting algorithm for accurate radio tomographic image in the environment with multipath and WiFi interference

Radio frequency device-free localization based on wireless sensor network has proved its feasibility in buildings. With this technique, a target can be located relying on the changes of received signal strengths caused by the moving object. However, the accuracy of many such systems deteriorates seriously in the environment with WiFi and the multipath interference. State-of-the-art methods do not efficiently solve the WiFi and multipath interference problems at the same time. In this article, we propose and evaluate an adaptive weighting radio tomography image algorithm to improve the accuracy of radio frequency device-free localization in the environment with multipath and different intensity of WiFi interference. Field experiments prove that our approach outperforms the state-of-the-art radio frequency device-free localization systems in the environment with multipath and WiFi interference

Langattomien anturiverkkojen sotilas-, agroteknologia- ja energiatutkimussovelluksia

The physical quantities nowadays are widely measured by using electronic sensors. Wireless sensor networks (WSNs) are low-cost, low-power electronic devices capable of collecting data using their onboard sensors. Some wireless sensor nodes are equipped with actuators, providing the possibility to change the state of the physical world. The ability to change the state of a physical system means that WSNs can be used in control and automation applications. This research focuses on appropriate system design for four different wireless measurement and control cases. The first case provides a hardware and software solution for camera integration to a wireless sensor node. The images are captured and processed inside the sensor node using low power computational techniques. In the second application, two different wireless sensor networks function in cooperation to overcome seeding problems in agricultural machinery. The third case focuses on indoor deployment of the wireless sensor nodes into an area of urban crisis, where the nodes supply localization information to friendly assets such as soldiers, firefighters and medical personnel. The last application focuses on a feasibility study for energy harvesting from asphalt surfaces in the form of heat.Fysikaaliset suureet mitataan nykyisin elektronisten anturien avulla. Langattomat anturiverkot ovat kustannustasoltaan edullisia, matalan tehonkulutuksen elektronisia laitteita, jotka kykenevät suorittamaan mittauksia niissä olevilla antureilla. Langattomat anturinoodit voidaan myös liittää toimilaitteisiin, jolloin ne voivat vaikuttaa fyysiseen ympäristöönsä. Koska langattomilla anturi- ja toimilaiteverkoilla voidaan vaikuttaa niiden fysikaalisen ympäristön tilaan, niiden avulla voidaan toteuttaa säätö- ja automaatiosovelluksia. Tässä väitöskirjaty össä suunnitellaan ja toteutetaan neljä erilaista langattomien anturi- ja toimilaiteverkkojen automaatiosovellusta. Ensimmäisenä tapauksena toteutetaan elektroniikka- ja ohjelmistosovellus, jolla integroidaan kamera langattomaan anturinoodiin. Kuvat tallennetaan ja prosessoidaan anturinoodissa vähän energiaa kuluttavia laskentamenetelmiä käyttäen. Toisessa sovelluksessa kahdesta erilaisesta langattomasta anturiverkosta koostuvalla järjestelmällä valvotaan siementen syöttöä kylvökoneessa. Kolmannessa sovelluksessa levitetään kaupunkiympäristössä kriisitilanteessa rakennuksen sisätiloihin langaton anturiverkko. Sen anturinoodit välittävät paikkatietoa rakennuksessa operoiville omille joukoille, jotka voivat tilanteesta riippuen olla esimerkiksi sotilaita, palomiehiä tai lääkintähenkilökuntaa. Neljännessä sovelluksessa toteutetaan langaton anturiverkko, jonka keräämää mittausdataa käytetään arvioitaessa lämpöenergian keräämismahdollisuuksia asfalttipinnoilta.fi=vertaisarvioitu|en=peerReviewed

Wireless sensor systems in indoor situation modeling II (WISM II)

fi=vertaisarvioimaton|en=nonPeerReviewed

Doctor of Philosophy

dissertationThis work seeks to improve upon existing methods for device-free localization (DFL) using radio frequency (RF) sensor networks. Device-free localization is the process of determining the location of a target object, typically a person, without the need for a device to be with the object to aid in localization. An RF sensor network measures changes to radio propagation caused by the presence of a person to locate that person. We show how existing methods which use either wideband or narrowband RF channels can be improved in ways including localization accuracy, energy efficiency, and system cost. We also show how wideband and narrowband systems can combine their information to improve localization. A common assumption in ultra-wideband research is that to estimate the bistatic delay or range, "background subtraction" is effective at removing clutter and must first be performed. Another assumption commonly made is that after background subtraction, each individual multipath component caused by a person's presence can be distinguished perfectly. We show that these assumptions are often not true and that ranging can still be performed even when these assumptions are not true. We propose modeling the difference between a current set of channel impulse responses (CIR) and a set of calibration CIRs as a hidden Markov model (HMM) and show the effectiveness of this model over background subtraction. The methods for performing device-free localization by using ultra-wideband (UWB) measurements and by using received signal strength (RSS) measurements are often considered separate topic of research and viewed only in isolation by two different communities of researchers. We consider both of these methods together and propose methods for combining the information obtained from UWB and RSS measurements. We show that using both methods in conjunction is more effective than either method on its own, especially in a setting where radio placement is constrained. It has been shown that for RSS-based DFL, measuring on multiple channels improves localization accuracy. We consider the trade-o s of measuring all radio links on all channels and the energy and latency expense of making the additional measurements required when sampling multiple channels. We also show the benefits of allowing multiple radios to transmit simultaneously, or in parallel, to better measure the available radio links