In this paper, a two‐way relay‐aided cognitive satellite terrestrial network (TR‐CSTN) model is proposed, where primary users are located at the edge of the base station. In the TR‐CSTN, one of satellite terminal users (STUs) is selected by the fusion center as the TR to forward information between two edge primary users with power of the TR. Meanwhile, these edge primary users share the licensed frequency band with the selected TR to send information to the satellite. Then, given the limited spectrum utilization and energy efficiency (EE) of the communication system, the cooperative spectrum sensing is employed to realize green communication. Specifically, the fusion center threshold, energy detection threshold, sensing duration and number of STUs are jointly optimized to enhance EE. Furthermore, considering that the node's energy shortage results in a short network lifetime, absolute EE gets improved. In detail, a power allocation scheme named normalized power aided Lévy flight trajectory‐based whale optimization algorithm (NP‐LWOA) is provided, which fulfills effective energy compensation among STUs to prolong the network lifetime notably. Finally, numerical results confirm the theoretical analysis and show the effectiveness of the TR‐CSTN and the NP‐LWOA in efficiently achieving the concept of green communication compared with other methods.

A two‐way relay‐aided cognitive satellite terrestrial network (TR‐CSTN) model, which can share the terrestrial licensed frequency band with the selected satellite terminal user to send information, and help the edge primary users communicate, is proposed. Given the limited spectrum utilization and energy efficiency (EE) of the communication system, in the TR‐CSTN, the optimization of the EE and the absolute EE is focused on to realize the concept of green communication.image

High frequency systems (HF) are still used in many communication applications. Data can be sent for hundreds of kilometers at a low power cost with only one drawback, low bitrate. To increase the bitrate, several methods were reported using the single‐Tone modulation with 24 kHz bandwidth, but still not practical in real‐time applications because, the 24 kHz is unavailable due to the instability of the ionosphere layer. In this work, we propose a structured method built on the Non‐Orthogonal Multiple Access technique (NOMA) to increase the bit rate per HF narrowband channel. To increase the data rate, two parts of the data are divided and sent to one user over two different power levels. We use the NOMA technique on the receiver's side to recognize the two parts of data. The results show a significant improvement (up to 45%) using NOMA in narrowband 3 kHz channels. The bit rate at the receiver has increased up to 200 Kb/s concerning, while the maximum bit rate recorded by conventional methods used in previous studies is 138 Kb/s. The system is built on the SDR platform and tested in multiple channels, weather, and power conditions in real‐time experiments to validate its reliability.

The novelty of this research is to increase the bit rate on high frequency (HF) systems. Non‐orthogonal multiple access technique (NOMA) technology is used over HF at narrow band 3 kHz ranges. In order to have real experiments SDR Hack RF systems are used, and the results show improvement in bit rate up to 45%. image

With the development of the drying rack system, users with limited time tend to use the fully functional drying rack system to realize various intelligent control functions. However, the existing control methods for drying rack system has some defects such as, low intelligence and system delay, which are unsuitable for most users. In this paper, an efficient intelligent control algorithm based on the back‐propagation (BP) neural network is proposed. In this system, STM32F103 is first utilized as the central controller and multiple sensors are used to collect environmental information. Then, the remote control, voice keywords, and buttons can be used to achieve intelligent control. Subsequently, a motor drive intelligence control algorithm based on the BP neural network (MCBP) is proposed to improve the accuracy of the intelligent control. Next, an Application (APP) that can display environmental data such as wind speed, temperature, and humidity is developed. The APP can realize various intelligent control functions such as lifting, panning, rotating, and harvesting. Finally, MCBP is compared with normal control and programmable logic controller control. The accuracy of the MCBP is higher than other two control methods. The final extensive experiments confirm the accuracy and efficiency of the proposed intelligent control algorithm.

The designed system uses multiple sensors to collect environmental data, and the remote control, voice keywords, and buttons can be used to achieve intelligent control. The APP can also be used to achieve functions such as moving up, panning, rotating, and harvesting. Therefore, the efficient intelligent control algorithm for drying rack system can meet the additional requirements of people.image

This paper investigates the unmanned aerial vehicle (UAV)‐assisted wireless communication network that collects the data information of Internet of things (IoT) devices deployed in the region, where the cellular networks cannot cover. Due to the numerous variety and number of IoT devices, a large amount of data generated by IoT networks needs to be collected by UAV. The goal of this paper is to minimize the UAV's cruise time with the joint optimization of IoT devices communication scheduling, UAV trajectory, and transmit bandwidth allocation. To facilitate data collection by UAVs, the data‐distance‐k‐means (d2‐k‐means) algorithm is proposed to divide IoT devices into multiple initial clusters. However, the formulated problem is mixed‐integer joint non‐convex, so it is difficult to solve directly. Since it may be with relatively high computational complexity, as an alternative, a block coordinate descent (BCD)‐based method is designed. To tackle the non‐convex problem, a successive convex approximation (SCA)‐based algorithm is also proposed. Numerical results demonstrate that the proposed scheme is able to achieve significant performance over other schemes for scenarios of UAV‐assisted wireless IoT networks to collect massive amount of data.

This paper investigates the unmanned aerial vehicle (UAV)‐assisted wireless communication network that collects the data information of Internet of things (IoT) devices deployed in the region, where the cellular networks cannot cover. The goal of this paper is to minimize the UAV's cruise time with the joint optimization of IoT devices communication scheduling, UAV trajectory, and transmit bandwidth allocation.image

In order to solve the problems of spectrum resource shortage, low spectrum utilization and shadow effect, a regional autonomous security cooperative spectrum sensing (CSS) method based on trust value is proposed to address the vulnerability of CSS technology in cognitive radio to malicious node attacks. Considering the difference in node position, the vulnerability of CSS to malicious node attacks, the large amount of centralized sensing data processing, and the large energy consumption of distributed sensing, based on the trust value update mechanism of ‘slow growth and fast recovery’, the perception area is divided and managed, and the final perception result is determined by majority voting based on the centralized sensing. The convergence value of the time difference of the perceptual process trust value is used to detect the anomaly of the node, and the cumulative difference in trust values between the nodes can detect and locate the malicious node. Experimental simulation results show that the proposed model is superior to traditional algorithms in spectral perception accuracy and malicious node detection.

Aiming at the shortcomings of large data processing capacity and large energy consumption of distributed spectrum sensing, a regional autonomous cooperative spectrum sensing (CSS) method based on trust value is proposed. Regional autonomy is used to control the differences of individuals, detect node anomalies by detecting node anomalies before and after the update of trust values, and detect and locate malicious nodes by considering the cumulative differences in node trust values during the sensing period.image