The acceptance of wireless sensor networks (WSN) has increased greatly due to their comprehensive capabilities. Since WSNs are generally battery-powered networks, reducing energy consumption is critical to improve their lifetime and, in turn, their performance and reliability. Recently, smart processing, especially neural networks, has been employed to efficiently manage the power consumed by Wireless Sensor Networks (WSN). Data driven approaches and, in particular, data reduction schemes can reduce the energy spent for communication by judicious selection of the time in which specific sensors of the network are interrogated. In this paper, a multi-layer perceptron (MLP) is used to decide on the data samples required. To justify the usefulness of our idea, we conduct an experiment for effective monitoring of environmental conditions. Results show that our method reduces the number of required samples …

Wireless Sensor Networks (WSN) play an important role in functioning of various applications. However, technical difficulties, like shortages in power supply, may eventually narrow down WSN’s application range. Minimization of power supply thus can be an adequate mean of prolonging their lifetime. Most of the components of a sensor, including its radio, can be turned off most of the time without influencing the network functionalities it is responsible for. Computational intelligence and, in particular, data prediction methods, may ensure effective operation of the network by the selection of essential samples. In this paper, we apply a multi-layer perception to select the required samples from simulated and experimental meteorological data. The results show that it leads to a considerable reduction of the number of samples and consequently of the power consumption, still preserving the information content.

Current research and improvements in the field of wireless sensor networks are focused on decreasing the power consumption and miniaturization, improved smartness and better wearability of the sensor, and especially with their capability for environmental sensing. Today, the survival of these kinds of networks is a critical issue especially in order to keep environmental information updated. This paper presents, an improvement of the environmental sensing acquisition system shown in [1], by applying more sensors to gather data. It was found a novel method of reading sensor data using smartphones and also the structure of sensors themselves helps to decrease the power consumption of the network.

Mobile devices (in particular smartphones and tablets) can be used to monitor quality of life parameters. Today mobile devices use embedded sensors such as accelerometers, compasses, GPSs, microphones, and cameras without considering, for example, the air quality or the pollutants of the environment. This paper presents the possibility to use the smartphones capabilities to gather data from other phones or sensors. Nowadays, monitoring climate condition’s parameters such as temperature and humidity is a prominent factor to control the changes of the environmental condition of living or working places for the human being. This point can be obtained by using distributed devices in different environments that containing high-resolution sensors and a wireless transmission apparatus for transferring data to smartphones. The Bluetooth was chosen as a transmission tool since it is embedded in all smartphones …