Autonomic Context-Aware Wireless Sensor Networks

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 daim,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. © 2015 nídia g. s. campos et al.