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The proliferation of 5G technologies and the vast deployment of Internet of Things (IoT) devices have heightened the demand for optimal spectrum utilization, necessitating robust spectrum management strategies. In this context, an efficient energy detector employing wideband spectrum sensing within a 5G environment is essential for identifying underutilized frequency bands suitable for cognitive radio applications across multiple subbands. While cooperative spectrum sensing (CSS) can enhance the detection capabilities of energy detectors amidst noise uncertainty, its performance often deteriorates under low signal-to-noise ratio (SNR) conditions. This study proposes an improved CSS framework that combines Maximal Ratio Combining (MRC) with the K-out-of-N fusion rule to address noise uncertainty in a complex Gaussian environment across multiple sub-bands in cooperative wideband spectrum sensing. Comparative performance analysis confirms that this integrated approach enhances detection probability and maintains a low false alarm rate across various low SNR scenarios, significantly outperforming traditional cooperative and non-cooperative wideband spectrum sensing methods. These results highlight the potential for advancing cognitive radio technologies by optimizing detection algorithms to improve performance under challenging conditions. 

In this paper, we present the design and implementation of a smart irrigation system using Internet of Things (IoT) technology, which can be used for automating the irrigation process in agricultural fields. It is expected that this system would create a better opportunity for farmers to irrigate their fields efficiently, as well as eliminating the field's under-watering, which could stress the plants. The developed system is organized into three parts: sensing side, cloud side, and user side. We used Microsoft Azure IoT Hub as an underlying infrastructure to coordinate the interaction between the three sides. The sensing side uses a Raspberry Pi 3 device, which is a low-cost, credit-card sized computer device that is used to monitor in near real-time soil moisture, air temperature and relative humidity, and other weather parameters of the field of interest. Sensors readings are logged and transmitted to the cloud side. At the cloud side, the received sensing data is used by the irrigation scheduling model to determine when and for how long the water pump should be turned on based on a user-predefined threshold. The user side is developed as an Android mobile app, which is used to control the operations of the water pump with voice recognition capabilities. Finally, this system was evaluated using various performance metrics, such as latency and scalability.