On the design of software and hardware for a WSN transmitter

Software defined radios (SDR) are booming. However, for a final breakthrough these systems need to be versatile, inexpensive and easy to program. In this paper a next step is taken to meet all these requirements. Our hardware consists of a computer with an affordable data acquisition (DAQ) card and a cheap self-made single-stage up-converter. The software is written in the slow learning-curve graphical programming environment LabVIEW. To prove the versatility of our SDR transmitter concept, we send packets with the wireless sensor networks (WSN) protocol IEEE 802.15.4, which are received by an existing packet sniffer

A linear regression based cost function for WSN localization

Localization with Wireless Sensor Networks (WSN) creates new opportunities for location-based consumer communication applications. There is a great need for cost functions of maximum likelihood localization algorithms that are not only accurate but also lack local minima. In this paper we present Linear Regression based Cost Function for Localization (LiReCoFuL), a new cost function based on regression tools that fulfills these requirements. With empirical test results on a real-life test bed, we show that our cost function outperforms the accuracy of a minimum mean square error cost function. Furthermore we show that LiReCoFuL is as accurate as relative location estimation error cost functions and has very few local extremes

Automated linear regression tools improve RSSI WSN localization in multipath indoor environment

Received signal strength indication (RSSI)-based localization is emerging in wireless sensor networks (WSNs). Localization algorithms need to include the physical and hardware limitations of RSSI measurements in order to give more accurate results in dynamic real-life indoor environments. In this study, we use the Interdisciplinary Institute for Broadband Technology real-life test bed and present an automated method to optimize and calibrate the experimental data before offering them to a positioning engine. In a preprocessing localization step, we introduce a new method to provide bounds for the range, thereby further improving the accuracy of our simple and fast 2D localization algorithm based on corrected distance circles. A maximum likelihood algorithm with a mean square error cost function has a higher position error median than our algorithm. Our experiments further show that the complete proposed algorithm eliminates outliers and avoids any manual calibration procedure