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.

Receiver Strength Signal Indication based Wireless Sensor Networks offer a cheap solution for location-aware applications. For a final breakthrough these systems need fast deployment and easy auto-configuration. In this study, we use the real-life iMinds test bed to expand a two-dimensional localization algorithm to the pseudo third dimension with very low additional computational time. Our experiments show that this fast three-dimensional algorithm has no outliers and avoids manual calibration. Our algorithm has lower position errors than a maximum likelihood algorithm with a mean square error cost function. Furthermore, with non-parametric statistical tests, we show that our previously designed twodimensional preprocessing performs equally well in pseudo-three dimensions: the preprocessing reduces the position error in a statistically significant way.

In this paper interference of WiFi on wireless sensor networks (WSN) is studied. Indeed, these networks share the 2.4GHz industrial, scientific and medical (ISM) band with Bluetooth, WiFi, wireless USB, cordless phone and microwave ovens. How large is the interference between these devices? In all these standards different modulation techniques like frequency hopping, direct-sequence spread spectrum (DSSS) and orthogonal frequency-division multiplexing (OFDM) are involved. The frequency spectrum of a WiFi interferer is measured. We will show that the frequency overlap of 802.11g on WSN is greater than that of a 802.11b channel. Next the effect of a WiFi interferer on a WSN will be studied. We will present experimental graphs for minimal separation of a 802.11b/g interferer and a ZigBee receiver. For the same packet error rate some transmitters need to be further away from ZigBee receiver than others. In this paper it is shown with measurements that lowering the throughput of 802.11g (OFDM) to span a longer distance will have a negative effect on the quality of the ZigBee link. Based on these interference results it is possible to develop a method for co-operative and/or non co-operative interference suppression.