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One of the interesting areas in Wireless SensorNetworks (WSN) is an environmental monitoring. WSN applications in environmental monitoring are characterized mainly by the need to ensure their long-term operation with minimal energy resources. Therefore, this contribution is dedicated to the energy-saving methods for indirect monitoring of water flow or water level. The proposed methods are based on processing of the acoustic signal emitted by the water flow. The system uses the principles known from the theory of learning systems. Before deploying each sensor into operation, the parameters of the model have to be set during the learning phase. After setting of these parameters the sensing element can work independently. Individual sensors are communicating over the network using the communication subsystem working in the ISM band.

Abstract-Global warming is big issue of this time. It is caused by producing emissions, mainly carbon dioxide. Many organizations tries to establish restrictions to limit CO 2 emissions. Our aim is to monitor underground parking lots and detect level of carbon dioxide using wireless sensor network. Gained results are drawn in the map of pollution of monitored area.

Abstract-The paper is focused on the use of methods of compressed sensing (CS) in energy efficient monitoring of signals. CS allows to minimize the number of data that need to be transmitted to the sink node in the WSN environment. As a case study, we use compressed sensing for monitoring of mains voltage deformation. In this case we can assume that the measured signal is sparse in frequency domain and using of methods of compressed sensing is meaningful. Computational complexity imposed on the sensor node is minimized. On the other hand, reconstruction of the original signal in the sink node requires relatively high computing power.

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