An Energy Efficient Cooperative Spectrum Sensing for Cognitive Radio-Internet of Things with Interference Constraints

Abstract

Spectrum sensing plays a very important role in Cognitive Radio based Internet of Things (CR-IoT) networks for utilization of the licensed spectrum accurately. However, the performance of the conventional Energy Detector (ED) method is compromised in a noise-uncertain environment owing to interference constraints, i.e. the CR-IoT user interference with the licensed Primary User (PU) on the same licensed band. To overcome this drawback, we proposed an energy efficient Cooperative Spectrum Sensing (CSS) for a CR-IoT network with interference constraints using a novel ED method. In this method, each CR-IoT user is capable of spectrum sensing that makes both the local decision and the weight factor based on the sequential approach; we calculate the weight factor against each CR-IoT user based on the Kullback Leibler Divergence award score. After the local decision and the weight factor are made, each CR-IoT user transmits its measured both the local decisions, and the weight factor to a Fusion Center (FC), which is made a final decision about the PU activities based on the hard fusion rule. The simulation results demonstrates that the proposed ED method obtains an improved detection performance, an enhanced sum rate, a spectral efficiency, an energy efficiency, and a lower global error probability when compared to other conventional ED methods under time varying environments.

Machine Learning-Based Malicious User Detection in Energy Harvested Cognitive Radio-Internet of Things

“…The PU traffic is modeled as a basic mechanism of ON and OFF involving two instantaneous states e.g. s t = 1 is for active and s t = 0 is for inactive at the time slot t [2].…”

“…For the CSS of the CR-IoT users, the Energy Detector (ED) technique is commonly used because it can be applied effectively without any previous PU signal information being acquired. The sensing result z i (t) obtained by the i th CR-IoT user transmitter is the signal power in the time domain at a given frequency; a band-pass filter is added to the received signal, then an analog-todigital converter (ADC) converts the output of this filter, which is independently averaged and squared using the conventional ED technique to determine its own calculated energy, E i as follows [2]:…”

“…When the classification is done through the ML algorithms, all normal CR-IoT users execute local decision independently based on the sequential manner, and then transmit their decisions to the FC during the reporting time slot. After that, the FC makes a global decision about the PU activities like the absence and presence of the PU signal using the DS evidence theory under the sequential manner [2] as follows:…”

“…I NTERNET of things (IoT) consists of objects or things that are meant to sense their surrounding environments, exchange, and communicate information or data with others devices (e.g., computers, mobiles, cordless phones, any wired or wireless devices) with different applications [1], [2]. The number of devices connected to either public or private networks via wired or wireless has recently increased significantly [3].…”

Interference management for coexisting Internet of Things networks over unlicensed spectrum

“…Due to the crucial impact of interference on IoT communications, especially as shown in [28,30], different studies have investigated approaches for modeling and mitigation of interference in different use-cases [33,34]. In [35], a cooperative spectrum sensing approach for interference-aware communications has been developed. Autonomous interference mapping for industrial IoT Networks over unlicensed bands has been proposed in [36].…”