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Satellite networks can provide Internet of Things (IoT) devices in remote areas with seamless coverage and downlink multicast transmissions. However, the large transmission latency, serious path loss, as well as the energy and resource constraints of IoT terminals challenge the stringent service requirements for throughput and latency in the 6G era. To address these problems, technologies including space-air-ground integrated networks (SAGINs), machine learning, edge computing, and energy harvesting are highly expected in 6G IoT. In this article, we consider the unmanned aerial vehicles (UAVs) and satellites to offer wireless-powered IoT devices edge computing and cloud computing services, respectively. To accelerate the communications, Terahertz frequency bands are utilized for communications between UAVs and IoT devices. Since the tasks generated by terrestrial IoT devices can be conducted locally, offloaded to the UAV-based edge servers or remote cloud servers through satellites, we focus on the computation offloading problem and consider deep learning techniques to optimize the task success rate considering the energy dynamics and channel conditions. A deep-learning-based offloading policy optimization strategy is given where the long short-term memory model is considered to address the dynamics of energy harvesting performance. Through the theoretical explanation and performance analysis, we discover the importance of emerging technologies including SAGIN, energy harvesting, and artificial intelligence techniques for 6G IoT. [ABSTRACT FROM AUTHOR]

IEEE 802.15.4 protocol defines multiple security specifications to provide different levels of safety protection in the link layer. How to choose the security level for harvesting enabled Internet of Things (IoT) nodes is an important problem as it decides the security protection and also impacts on network performance. Considering the dynamics of network threats and harvesting performance, in this letter, a harvesting and threat aware security configuration strategy is proposed. Simulation results illustrate that the proposal can choose the suitable security configuration according to the network security threats, service requirements, and the variable harvested energy. Comparison with conventional methods, the proposed strategy can extend the working time, resulting in improved network performance. Moreover, the proposed security configuration method can also meet the diversified service requirements. [ABSTRACT FROM AUTHOR]

In the context of the IoT, mMTC technology in 5G enables a range of new applications. The 3GPP NB-IoT introduced in Release 13 is a major enhancement toward mMTC to support devices requiring low data rate, low cost, and long battery life. Previous works suggested that NB-IoT physical channel characteristics have potential to fulfill design requirements. From the system point of view, the interaction among UEs and application data transmission procedures have to be further evaluated considering radio resource scheduling. In this article, we identify radio resource scheduling issues for NB-IoT systems and provide a comprehensive performance evaluation. We consider control plane optimization procedures with a massive number of UEs in mixed CE levels. Using a scheduling architecture verified with derived effective data rates, we show the effects of essential parameters on control channel allocation and data scheduling. Given our scheduling settings in anchor carriers, the number of transmitted data packets achieves about 65,000 per cell per hour with mixed CE levels. The results satisfy the design goal of NB-IoT data transmission in 3GPP TR 45.820. [ABSTRACT FROM AUTHOR]