Autonomic Context-Aware Wireless Sensor Networks

Nídia G. S. Campos, Danielo G. Gomes, Flávia C. Delicato, Augusto J. V. Neto, Luci Pirmez, José Neuman de Souza
2015 Journal of Sensors  
Autonomic Computing allows systems like wireless sensor networks (WSN) to self-manage computing resources in order to extend their autonomy as much as possible. In addition, contextualization tasks can fuse two or more different sensor data into a more meaningful information. Since these tasks usually run in a single centralized context server (e.g., sink node), the massive volume of data generated by the wireless sensors can lead to a huge information overload in such server. Here we propose
more » ... IM, a distributed autonomic inference machine distributed which allows the sensor nodes to do self-management and contextualization tasks based on fuzzy logic. We have evaluated DAIM in a real sensor network taking into account other inference machines. Experimental results illustrate that DAIM is an energy-efficient contextualization method for WSN, reducing 48.8% of the number of messages sent to the context servers while saving 19.5% of the total amount of energy spent in the network.
doi:10.1155/2015/621326 fatcat:ihebvctlfrazrbynxth5vueabi