In next-generation networks, including the sixth generation (6G), a large number of computing devices can communicate with ultra-low latency. By implication, 6G capabilities present a massive benefit for the Internet of Things (IoT), considering a wide range of application domains. However, some security concerns in the IoT involving authentication and encryption protocols are currently under investigation. Thus, mechanisms implementing quantum communications in IoT devices have been explored to offer improved security. Algorithmic solutions that enable better quantum key distribution (QKD) selection for authentication and encryption have been developed, but having limited performance considering time requirements. Therefore, a new approach for selecting the best QKD protocol based on a Deep Convolutional Neural Network model, called Tree-CNN, is proposed using the Tanh Exponential Activation Function (TanhExp) that enables IoT devices to handle more secure quantum communications using the 6G network infrastructure. The proposed model is developed, and its performance is compared with classical Convolutional Neural Networks (CNN) and other machine learning methods. The results obtained are superior to the related works, with an Area Under the Curve (AUC) of 99.89% during testing and a time-cost performance of 0.65 s for predicting the best QKD protocol. In addition, we tested our proposal using different transmission distances and three QKD protocols to demonstrate that the prediction and actual results reached similar values. Hence, our proposed model obtained a fast, reliable, and precise solution to solve the challenges of performance and time consumption in selecting the best QKD protocol.

Traction applications, such as electric and hybrid vehicles, require the electrical motor to be operated at low load conditions during most of their driving cycle. Permanent magnet (PM) motors in this case may not be an optimal choice due to their poor efficiency under low load conditions despite the fact that they exhibit the best output characteristics at the rated load. However, the wound rotor synchronous motor (WRSM) demonstrates the best efficiency at low load conditions due to their controllable excitation. This paper presents an interesting comparison of WRSM and the PM motor in terms of their efficiency over the entire drive cycle of traction applications, which demonstrates that WRSMs are more suitable compared to the apparently best performance PM motor. A WRSM with similar dimensions to the 2010 Toyota Prius interior permanent synchronous motor (IPMSM) was designed and optimized in this regard. The performance comparison of the PM motor and the WRSM was presented using 2-D finite element analysis (FEA) for the WRSM and the performance analysis report of the Toyota Prius IPMSM published by the US department of energies. The FEA analysis shows that the WRSM can achieve wide torque speed characteristics. Moreover, the WRSM exhibits better efficiency under low load conditions at the higher speed or field weakening region.

For slotted random access systems with a single channel, the slotted ALOHA (S-ALOHA) protocol shows 0.368 (packets/slot) as the maximum throughput, whereas some splitting (or tree) algorithms exhibit 0.487 (packets/slot). The S-ALOHA protocol has been widely adopted even for multi-channel systems such as Long-Term Evolution (LTE), as it is more practically implementable. However, the throughput of each channel in multi-channel S-ALOHA is limited to 0.368. In order to overcome this limit and some implementational drawbacks of the existing splitting algorithms, this paper proposes a novel splitting algorithm for multi-channel slotted systems which can also adapt to dynamic system situations through estimating the number of users who have packet to transmit. We analyze the throughput of our proposed algorithm and show that the proposed algorithm is more practical than the first-come first-serve (FCFS) algorithm and shows smaller access delay than the FCFS algorithm even for a single channel system. For M-channel systems, the proposed algorithm yields the maximum throughput of 0.487M. Extensive simulations validate our analytical results.

Due to the emergence of Internet of Things (IoTs), it can be expected that the bandwidth provided by cellular systems might be consumed up soon. Some applications of them are delay-sensitive such that it would be critical to guarantee random access (RA) delay less than a threshold. Since the existing Long-Term Evolution-Advanced (LTE-A) RA procedure is a four-step signaling procedure, it may not be suitable for such delay-sensitive applications due to its time-consuming procedure. This work investigates a two-step RA procedure for 5G New Radio systems, where RA preamble and bandwidth request message are transmitted at the same time. First we show that the operating region of two-step RA procedure can be divided into three regions such as unsaturated stable, bistable, and saturated regions in terms of a packet generation probability, retransmission probability, the number of devices, and the number of RA preambles. To see whether RA delay requirement of delay-sensitive applications can be guaranteed, this work shows that the system should run under unsaturated region and derives RA delay distribution when IoT devices employ geometric probability backoff (GPB) or uniform window backoff (UWB) algorithm. We then examine the probability that the RA delay would be larger than some threshold depending on the operation regions.