An Adaptive Fuzzy-Based Clustering Model for Healthcare Wireless Sensor Networks

Healthcare wireless sensor networks (WSNs) have made their way into a wide range of applications and technologies with significantly different requirements and features in recent years. The integration of sensor nodes into intelligent sensing, information processing and information exchange infrastructures form healthcare WSNs will have a significant impact on a variety of applications, including telemedicine, habitat monitoring, structure health monitoring, human-centric applications and medical applications, among others. Monitoring, identification of events and responding to an event which requires a continuous access to real-time information either partial or fully distributed environment is a challenging issue. In order to overcome the challenges of healthcare wireless sensor networks (H-WSN), fuzzy-based clustering provides a cost-effective and efficient solution. Most of the problems in WSN are real time based that require fast computation, real-time optimal solution and need to be adaptive to the situation of the events and data traffic to achieve desired goals. Hence, neural networks and fuzzy sets would form appropriate candidates for implementing most of the computations involved in the issues of resource management in sensor networks. A real-time event detection is simulated and implemented on Crossbow mote (sensor node) using TinyOS.