Autonomic Context-Aware Wireless Sensor Networks

摘要: Autonomic Computing allows systems like wireless sensor networks (WSN) to self-manage computing resources in order extend their autonomy as much possible. In addition, contextualization tasks can fuse two or more different data into a meaningful information. Since these usually run single centralized context server (e.g., sink node), the massive volume of generated by sensors lead huge information overload such server. Here we propose DAIM, distributed autonomic inference machine which nodes do self-management and based on fuzzy logic. We have evaluated DAIM real network taking account other machines. Experimental results illustrate that is an energy-efficient method for WSN, reducing 48.8% number messages sent servers while saving 19.5% total amount energy spent network.