Internet of Things (IoT) is one of the evolutionary directions of the Internet. This paper focuses on the low earth orbit (LEO) satellite constellation-based IoT services for their irreplaceable functions. In many cases, IoT devices are distributed in remote areas (e.g., desert, ocean, and forest) in some special applications, they are placed in some extreme topography, where are unable to have direct terrestrial network accesses and can only be covered by satellite. Comparing with the traditional geostationary earth orbit (GEO) systems, LEO satellite constellation has the advantages of low propagation delay, small propagation loss and global coverage. Furthermore, revision of existing IoT protocol are necessary to enhance the compatibility of the LEO satellite constellation-based IoT with terrestrial IoT systems. In this paper, we provide an overview of the architecture of the LEO satellite constellation-based IoT including the following topics: LEO satellite constellation structure, efficient spectrum allocation, heterogeneous networks compatibility, and access and routing protocols.

When using blockchain in mobile systems, computation intensive mining tasks pose great challenges to the processing capabilities of mobile miner equipment. Mobile edge computing (MEC) is an effective solution to alleviating the problem via task offloading. In the mining process, miners compete for rewards through puzzle solving, where only the miner that first completes the process will be rewarded. Thus, miners may wish to pay higher price and use more communication resources in task offloading and more computation resources in task processing for latency reduction. However, there are risks for the miners not profiting from consuming more resources or paying a higher price, so miners are rational in blockchain systems. In order to maximize the rational total profit of all miners, we use an asynchronous advantage actor-critic (A3C) deep reinforcement learning algorithm to obtain the resource pricing and allocation, considering the stochastic properties of wireless channels, and the prospect theory is employed to strike a good balance between risks and rewards. Numerical results show that our proposed A3C based joint optimization algorithm converges fast and outperforms the baseline algorithms in terms of the total reward.

Network Virtualization (NV) is a promising approach to remove the ossification of current Internet. Virtual Network Embedding (VNE) is the key issue in NV which efficiently and effectively maps various of virtual networks, with different node and link resource requests, onto the shared substrate network(s) with finite underlying resources. Previous VNE algorithms in the literature are mostly heuristic. Single network topology attribute and each node’s local resources are assisted to rank nodes in most heuristic algorithms, leading to inefficient resource utilization of substrate network in the long run. To deal with this issue, we propose the Network Topology Attribute and Network Resource-Considered algorithm (VNE-NTANRC). The VNE-NTANRC algorithm adopts a novel node-ranking approach to rank all substrate and virtual nodes before embedding each given Virtual Network (VN). The novel node-ranking approach has two sub-approaches and considers five important network topology attributes and global network resources altogether. One sub-approach is able to calculate all node values directly. The other sub-approach, stimulating from the Google PageRank website algorithm, enables to calculate node values in a stable state. Simulation results reveal that VNE-NTANRC algorithm outperforms typical and latest heuristic algorithms, only considering single network topology attribute and local resources.

AI-enabled logistics unmanned aerial vehicles (UAVs) are progressively revealing their unique advantages for future smart cities. Nevertheless, the existing research on logistics UAV path planning lacks to simultaneously consider the UAV energy consumption constraints, the customer time windows, the impacts of wind speed and direction. This omission renders the existing models inappropriate for real-world transportation systems. Besides, the UAVs are still constrained by the limited payload and battery due to the highly automatic delivery process. Consequently, we investigate the effect of wind speed and direction on UAV flight states, establishes pertinent parameters and their resolution methods impacted by wind conditions, and delves into the logistics UAV path planning issue that concurrently considers the UAV energy consumption constraints, the customer time windows, and the impact of wind conditions. To resolve the proposed trajectory optimization issue, the large-scale neighborhood search algorithm (LNS) is amalgamated with the genetic algorithm (GA), forming the GA-LNS, to address the static problem, while dynamic planning concepts are employed in the decoding process of GA-LNS to solve the dynamic trajectory optimization problem. Simulation results demonstrate that the devised algorithms yield superior solutions within a plausible timeframe, reducing distribution costs by approximately 9% in comparison to the conventional GA. Unlike the no-wind and static scenarios, path planning that incorporates dynamic wind conditions circumvents issues related to energy constraints and customer satisfaction bias evident in the prior cases. Furthermore, the proposed algorithm can provide a high-efficiency, low-energy-consumption, and low-delay UAV planning strategy in the scenario of UAV-assisted data collection.

While 5G is being deployed all around the world, the industry and academia start the investigation of 6G. Network function virtualization (NFV) is seen as the key enabler towards the flexible resource management and sharing of 6G networks. The main idea of NFV is to decouple the physical devices from the specific functions that run on them. In NFV, virtual network service (NS) is modeled as a service function chain (SFC). The challenges in SFC description, composition, embedding, and scheduling are key issues in NFV. In this paper, we focus on the joint SFC embedding and scheduling for NFV in 6G networks. With the assistance of sensing the physical node resource abilities, we propose a novel SFC embedding and scheduling algorithm, which is named as RA-SFC-6G. A new service is first composed as an SFC which is an ordered sequence of virtual network functions (VNFs). Afterwards, our RA-SFC-6G algorithm will select the physical nodes with stronger resource abilities and abundant resources to accommodate the SFC. In addition, the NS is guaranteed to be implemented without violating the NS maximum allowed scheduling time. If violated, our RA-SFC-6G can re-embed and re-schedule the violated VNFs to suitable positions. In order to validate our RA-SFC-6G performance, we do the experiment work. Experiment results show that our RA-SFC-6G achieves at least 10% higher SFC acceptance ratio than the previous counterparts and the typical heuristics.

This paper considers an uplink cell-free massive multi-input multi-output (mMIMO) system with multi-antenna access points (APs) and users, assuming low-resolution analog-to-digital converter (ADC) architecture is employed at the APs. Leveraging on the additive quantization noise model (AQNM), we derive a tight approximate expression for uplink spectral efficiency (SE). This trackable finding provides us with a tool for easily quantifying the impacts of the number of antenna arrays and the number of quantization bit of low-resolution ADCs. We find 5-bit is required in a cell-free mMIMO with low-resolution ADCs to achieve the same SE as a cell-free mMIMO with full-precision ADCs. Besides, when the number of antennas of the user is small, deploying more antennas at the users can boost the sum SE. Then, to further highlight the potential of low-resolution ADCs architecture, we also investigate the tradeoff between the SE and energy efficiency (EE) with design issues surrounding the quantization bit of low-resolution ADCs and the number of antenna arrays. The resulting observations reveal that by choosing a proper quantization bit, the cell-free mMIMO with low-resolution ADCs has the capability to enjoy a better SE-EE tradeoff compared to the perfect ADCs counterpart.