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Locating the nodes of outdoor wireless sensor networks (WSNs) using (tri)lateration with a low-cost ranging technique, such as the received signal strength indicator (RSSI), often results in inaccurate location estimates. This can mostly be explained by the combined effect of distance estimate errors and localization geometry, both of which are subject to the reference nodes used. To develop techniques for reducing localization error, the distance estimate errors and localization geometry must be analyzed and taken into account. To address these challenges, this paper aims to seek ways to improve the quality of range-based trilateration localization for WSN nodes in varying outdoor conditions. Based on simulations, we analyze the effects of ranging error and localization geometry on localization error. For that purpose, we introduce a simple measure to evaluate the geometry of reference triangle (GRT). To improve localization accuracy and precision, we propose an adaptive range-based localization (ARBL) algorithm that is based on trilateration and reference node selection. In ARBL, the GRT values are calculated for each 3-combination of preselected reference node set, based on which the combinations are selected. The algorithm exploits these reference node 3-combinations aiming to find the best ones at a given time using a selection criteria that is based on ranging error and localization geometry. The simulation and experimental results indicate that the proposed algorithm reduces localization error considerably. This shows that it is possible to achieve sufficient localization accuracy using range-based trilateration localization, even based on the RSSI in challenging outdoor conditions, by employing applicable techniques and information. peerReviewed

Most localization algorithms in wireless sensor networks rely on a few reference nodes with known locations to estimate the locations of unknown nodes. The locations of reference nodes can be either manually configured or, more practically, obtained by means of some satellite-based positioning system(s). However, satellite-based locations may be inaccurate and imprecise, which results in reduced location accuracy of localization algorithms. This paper proposes a peer-to-peer cooperative GNSS-based localization algorithm for stationary reference nodes to improve their relative location accuracy and precision. The algorithm applies simple statistical methods and GNSS-based information from multiple reference nodes within a WSN in a peer-to-peer fashion to achieve the improvement. The results of the experiments indicate that both location precision and relative location accuracy are clearly increased due to the algorithm. peerReviewed

Environmental noise is considered as one of the big issues in environmental pollutions. Typical noise sources like traffic can have great influence on wellbeing and healthy. In this paper, we report the sensor function design and implementation of a wireless sensor network application for measuring environmental acoustic noise. The sensing system is built on Atmega128 and CC2420 platform. The protocol stack is based on CiNet stack with a global synchronization scheme and supports multi-hop communications. Strict filtering function specified by ITU-R 468 (namely A-weighting) is followed. Both the indoor and outdoor test results were compared with a standard sound level meter and showed a less than ± 2.1 dB mean differences in both short-term and long-term measurement. Power consumption has been measured that a single AA-type battery can sustain the application. Comparing to the traditional noise measurement method, the designed wireless sensor network solution is much lower in cost.

Many wireless sensor networks operating outdoors are exposed to changing weather conditions, which may cause severe degradation in system performance. Therefore, it is essential to explore the factors affecting radio link quality in order to mitigate their impact and to adapt to varying conditions. In this paper, we study the effects of temperature and humidity on radio signal strength in outdoor wireless sensor networks. Experimental measurements were performed using Atmel ZigBit 2.4GHz wireless modules, both in summer and wintertime. We employed all the radio channels specified by IEEE 802.15.4 for 2.4GHz ISM frequency band with two transmit power levels. The results show that changes in weather conditions affect received signal strength. Of the studied weather variables, variation in signal strength can be best explained by the variation in temperature. We also show that frequency diversity can reduce the effects of channel-specific variation, and the difference between the transmit power levels. peerReviewed