Abstract
Wireless Sensor Networks (WSN) consist of small, low-cost, resource-constrained embedded computers equipped with low-power radios and various sensors. When deployed, they form an ad-hoc wireless network and sense the environment cooperatively. Application areas of WSNs include environmental monitoring, healthcare, structural monitoring and the military among others. Many WSN applications rely on the location of sensor nodes in order to make consistent spatio-temporal observations. Sensor node localization, therefore, has been an active research area for the past decade. Consequently, a wide variety of localization methods has been proposed for WSNs. Among the most popular are acoustic and radio signal-based approaches. The limited range of acoustic methods make them impractical for anything but small scale deployments. The best known radio-based method is the Global Positioning System (GPS). For outdoor applications that require absolute localization accuracy in the order of meters, GPS is generally a good solution. However, its price and power requirement restrict its applicability to high-end WSNs. In indoor and urban environments, heavily affected by multipath effects, Ultra Wide band (UWB) methods offer high accuracy by measuring the Time of Flight (TOF) or Time Difference of Arrival (TDOA) of a short pulse-waveform to deduce ranges Tuchler et al. (2005), Fontana et al. (2003). The UWB-based techniques typically achieve sub-meter ranging error where the accuracy depends mainly on the available bandwidth, which in turn is constrained by emission regulations. UWB has not become popular in WSNs primarily due to its high cost. Techniques based on received radio signal strength (RSS) gained significant popularity in WSNs as they require no specialized hardware; they use the radio chip readily available on WSN nodes. RSS-based ranging measurements are also simple as they typically consist of averaging the value of the radio chip RSS signal. The operating range is limited to the communication range of the radio with a precision of a few meters Taubenheim et al. (2005).
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CITATION STYLE
Szilvasi, S., Volgyesi, P., Sallai, J., Ledeczi, A., & Maroti, M. (2012). Interferometry in Wireless Sensor Networks. In Interferometry - Research and Applications in Science and Technology. InTech. https://doi.org/10.5772/36492
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