A Polynomial Series-based data aggregation and spectrum aware clustering technique for a combined model of WSN and cognitive radios in IoT applications

Abstract

Currently, the demand for wireless communication has increased drastically due to the emergence of IoT based applications. In this field, wireless sensor networks have acquired huge attention from the research community, academia, and industries. Generally, these sensor networks and IoTs operate in the license-free spectrum where numerous other devices also operate which leads to generating interference and impacts the Quality of Service (QoS) of these communication systems. To handle this issue, cognitive radio communication technology is a viable solution which facilitates opportunistic access to the spectrum which allows sensor networks to operate in licensed bands. This approach helps to mitigate the interference and improves the performance of the communication system. However, incorporating cognitive radio network with IoT and sensor networks is an encouraging method to proliferate the performance of wireless communication but CRs perform several tasks which consume excessive energy as the sensor networks are equipped with limited power and computation resources. Moreover, continuous data collection leads to gather huge amount of data which suffers from redundancy. Thus, the research community has suggested incorporating the aggregation method to discard the data redundancy. On the other hand, energy aware routing is also considered as novel solution to abate energy depletion during data exchange. In this work, we have focused on a combined model of WSN and cognitive radios for IoT based applications and presented a polynomial series-based data aggregation and spectrum aware clustering technique. The proposed approach achieves overall packet delivery as 95.47%, energy consumption as 5.216J and delay as 0.554 s, which shows better performance when compared with existing schemes in CRSNs.