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.

Towards Event-Driven Context-Aware Web Services

Web services provide a successful way to communicate distributed applications, in a platform-independent and loosely coupled manner. Even though there are examples of good practice for the design, development, and management of web services, getting services to be context-aware is still under investigation. Current proposals require communication with an external context server or manager, slowing down service performance. In this work, the authors propose an architecture which utilizes complex event processing for detecting context events relevant to the services in question and an aspect-oriented adaptation, maintaining a loosely coupled service implementation as well as keeping its main functionality structure without adding any context-related intrusive code.

Applying SIP VoIP to Context-Aware Telephony

The simple, efficient Session Initial Protocol (SIP) is widely used to integrate context-awareness computing techniques. We propose a SIP-based research framework that integrates automatic speech generation to make communication between users more convenient. Based on the proposed approach, we built and tested a SIP context server and several SIP agents hosted on PCs. If the called party is absent or busy, a context-aware message is automatically replied to the caller via synthesized natural speech. Experiments conducted by 50 college students using ADSL connected to the SIP server showed encouraging results, especially in user satisfaction in message transfer rates and service waits.