QoS Aware Load Balancing in Cognitive Wireless Sensor Networks using Machine Learning Concepts

As energy is limited resource in Wireless Sensor Networks, it is required to utilize the resource carefully to maximize the life time of the network. The dramatic increase in data traffic leads to consume more energy in WSNs. Hence, to achieve an excellent Quality of Service (QoS), it is necessary to reduce the energy consumption and optimize the resource utilization efficiently to improve network performance. To attain this, WSNs often adapt Machine Learning techniques which provide practical solutions for many issues, maximize resource utilization, and prolong lifespan of the network. Cognition technique is adapted for efficient utilization of the spectrum. In this research article,we propose an algorithm that enhance end-to-end throughput in Cognitive Wireless Sensor Networks by exploiting available resources using machine learning techniques. With the increase in the throughput, the proposed algorithm proved there 62% improvement in the energy efficiency.