The ongoing deployment of 5G network involves the Internet of Things (IoT) as a new technology for the development of mobile communication, where the Internet of Everything (IoE) as the expansion of IoT has catalyzed the explosion of data and can trigger new eras. However, the fundamental and key component of the IoE depends on the computational intelligence (CI), which may be utilized in the sixth generation mobile communication system (6G). The motivation of this article presents the 6G enabled network in box (NIB) architecture as a powerful integrated solution that can support comprehensive network management and operations. The 6G enabled NIB can be used as an alternative method to meet the needs of next-generation mobile networks by dynamically reconfiguring the deployment of network functions, providing a high degree of flexibility for connection services in various situations. Especially the CI technology such as evolutionary computing, neural computing and fuzzy systems utilized as a part of NIB have inherent capabilities to handle various uncertainties, which have unique advantages in processing the variability and diversity of large amounts of data. Finally, CI technology for NIB, which is widely used is also introduced such as distributed computing, fog computing, and mobile edge computing in order to achieve different levels of sustainable computing infrastructure. This article discusses the key technologies, advantages, industrial scenario applications of CI technology as NIB, typical use cases and development trends based on IoE, which provides directional guidance for the development of CI technology as NIB for 6G.

The unmanned aerial vehicle (UAV) assisted transmission can significantly improve the spectrum efficiency and coverage of the wireless communication network. The paper proposed a cooperative secrecy transmission mechanism, which can take advantage of transmitter signal of primary user (PU) as a dedicated radio frequency (RF) source with decode and forward (DF) UAV selection and energy harvesting (EH) under cognitive network. Specifically, the transmitting signal of PU in the cognitive network can be viewed and used as energy to drive secondary user (SU) nodes and multi-relay nodes to send signals. It was worth noting that the DF UAV relay may bring about the unexpected noise and decrease the received signal to noise ratio (SNR), therefore, the multiple UAV assisted relays selected with secrecy capacity maximization criteria under cognitive network was proposed and investigated in detail, where the destination node equipped with multiple antennas and adopted the optimal antenna selection receiver. Moreover, By using the function analysis method, the closed expressions of the system secrecy outage probability (SOP) and the probability of non-zero secrecy capacity were calculated and derived accurately. Furthermore, the asymptotic expressions of the UAV assisted relay system under the large SNR cases were analyzed in detail, where the expressions of secrecy diversity order and secrecy diversity gain of the UAV assisted relay cognitive system were obtained thoroughly. Simulation results verified the effectiveness of the proposed mechanism and the correctness of the calculation.

This paper investigated the multiple Unmanned Aerial Vehicle (UAV) relays assisted network in Internet of Things (IoT) systems enhanced with energy harvesting in order to overcome the large scale fading between source and sink as well as achieve the green cooperative communications, where time switch (TS) and power splitting (PS) strategies were typically applied for UAV relays to implement energy harvesting transmission, which were also selected via signal to noise ratio (SNR) maximization criterion so that the terminal node can obtain the optimal signal. Meanwhile, it was worth noting that the terminal node may be disturbed by aggregated interference caused from dense network signaling interaction in the future 5G/B5G systems. Therefore, after TS and PS protocols designing and utilizing, the closed form expressions of outage probability and bit error rate (BER) for UAV relay assisted IoT systems suffered with aggregated interference were derived in detail. Additionally, the throughput and delay limited state of UAV relay assisted transmission were also analyzed thoroughly. The derivations and analysis results showed that the proposed multi-parameter joint optimization of transmitting power, scaling factor and UAV relay selection could effectively improve the system throughput and reduce the system outage probability and BER. Simulation experiments verified the effectiveness of the proposed schemes and the correctness of theoretical analysis.

As for fuel cell hybrid electric vehicle equipped with battery (BAT) and ultracapacitor (UC), its dynamic topology structure is complex and different characteristics of three power sources induce challenges in energy management for fuel economy, power sources lifespan, and dynamic performance of the vehicle. In this paper, an energy management strategy (EMS) based on a hierarchical power splitting structure and deep reinforcement learning (DRL) is proposed. In the higher layer strategy of the proposed EMS, the UC is employed to supply peak power and recover braking energy through the adaptive filter based on fuzzy control. Then, the integrated DRL and equivalent consumption minimization strategy framework is proposed to optimize the power allocation of fuel cell (FC) and BAT in the lower layer, to ensure the highly efficient operation of FC and reduce hydrogen consumption. And the action trimming based on heuristic technique is proposed to further restrain the adverse effect of sudden peak power on FC lifespan. The simulation results show the proposed EMS can make the output of FC smoother, improve its working efficiency to alleviate the stress of BAT, and increase by 14.8% compared with the Q-learning strategy in fuel economy under WLTP driving cycle. Meanwhile, the obtained results under UDDSHDV show fuel economy of the proposed EMS can reach dynamic programming (DP) benchmark level of 89.7 $\%$ .

According to the limited communication distance and the low transmission efficiency for massive electronic tags/sensors of traditional or bistatic backscatter technology, the BackFi transmission protocol design with energy harvesting of massive smart multi-hop electronic tags/sensors were investigated in amibient backscatter communication systems. The paper proposed the large scale tags/sensors multi-hop communication protocols with energy harvesting under Wi-Fi architecture, where the ”CTS-to-self” frame can be used to set the tradtional AP/STA to the sensing status without sending, then the new defined frame ”Multi-RTS” may send trigger signal to massive tags or sensors with it’s frame field as ”M-RTS” strategy, compressed bitmap, tags/sensors number and other message. The tags/sensors shall respond the sounding signal and give the backscatter signal to AP using energy harvesting. It can be mentioned that the Wi- Fi standard in the paper is based on IEEE 802.11ah. Finally, AP can collect and modulate the backscatter signal after massive tags/sensors response to the reader. It was worth noting that the multi-hop relay adopt the power splitting way to implement communication process, where the object function was energy efficiency maximization with the parameters optimization of transmit power and relay scaling factor. In order to solve the non-convex problem, the paper derived the approximate expression in high SNR case and achieved the optimal solution via Lambert W function. The simulation and analysis verified the effectiveness of the proposed schemes and correctness of the derivations.