இன்டர்நேஷனல் ஜர்னல் ஆஃப் சென்சார் நெட்வொர்க்ஸ் மற்றும் டேட்டா கம்யூனிகேஷன்ஸ்

This paper presents a method for utilizing sensor networks to predict human’s thermal comfort and sensation. A neural network is dynamically organized on the basis of correlations between the thermal sensation of office occupants and a number of values measured by the sensor network, and the structure of the neural network is cyclically updated. By way of an example, the air-conditioning system in an office is used. We place a number of temperature sensors and participants in this indoor environment, and conduct an experiment where the sensor readings and the thermal sensation of the participants are monitored concurrently. From the experimental results it can be seen that the various sensors are selected correctly and can be used to predict the desired system behavior.

In the near future, it is likely that wireless sensor networks (WSNs) become a major technology for the sensing in different application areas. One of the main challenges in WSNs is the secure routing of data through the network. This is resulting from the fact that WSNs are normally deployed in unattended or even hostile environments. While in last few years the routing approaches were mainly focussing on metrics such as robustness, energy preservation, etc., recently, different security solutions came to the fore that were taking also the security issues in WSNs into account. In this paper, different types of attacks on the routing layer of WSNs are investigated. Subsequently, measures for secure routing; including cryptography, key establishment, trust & reputation and secure localization; are reviewed, which were proposed by researchers in this area. Based on these findings, future prospects are discussed and final conclusions will be drawn.