Cognitive Energy-Aware Spectrum Sensing With Improved Throughput for Medical Sensor Networks

Abstract

Internet of Things (IoT) based medical devices equipped with biosensors are installed at the patient's location to collect sensitive information, such as body temperature, heart rate, blood pressure, and saturated oxygen levels. With the rapid escalation in the number of wearable devices and other applications, the radio spectrum which was once plentiful is now overcrowded and became scarce. Data security, packet loss, and throughput are the common hurdles in these networks. Identification of spectrum holes in cognitive radio networks is done using spectrum sensing, and this process consumes time and power. This letter proposes a cooperative spectrum sensing using a slotted ALOHA scheme for medical sensor networks with the goal to improve the overall throughput. The channel is divided into slots to achieve efficient spectrum sensing. The modeled network reduces interference with primary users and substantially increases throughput up to 70% with increasing secondary users. Comparative analysis with the existing similar models shows that the proposed model is superior in terms of false detection probability, average network throughput, and energy efficiency.