In industrial automation, the state of process control could be monitored by spatially distributed sensors and 5G machine-type communication (MTC) enabled industrial Internet of things (IIoT). Thus, ultra-reliable MTC and high-accurate state estimation play important roles for ensuing system stabilization. However, it is challenging due to complex industrial wireless environments and limited communication resources. To address this issue, this paper first presents a transmission-estimation codesign framework to lay down the foundation for guaranteeing the prescribed estimation accuracy with limited communication resources. Under this framework, a hierarchical transmission-estimation approach is proposed to improve the transmission reliability and estimation accuracy according to system dynamics. The proposed approach is then optimized by formulating a constrained minimization problem, which is mixed integer nonlinear programming and solved efficiently with a block-coordinate-descent-based decomposition method. Finally, simulation results demonstrate that the proposed approach has superiorities in improving both the estimation accuracy and the energy efficiency.

This letter proposes a fast Unmanned Aerial Vehicle (UAV) trajectory planning algorithm based on the Fermat-point theory in maritime Internet of Things systems. Specifically, we first construct Delaunay triangles based on the deployment of unmanned surface vehicles. Then, the Fermat point of each Delaunay triangle is calculated and as a hovering point of UAVs to improve the channel condition. Finally, the trajectory planning problem can be transformed into a vehicle routing problem with pickup, which is solved by the proposed algorithm based on C-W saving method. Simulation results show the proposed algorithm can efficiently increase collected data volume of UAVs.

The wide application of Internet of Things (IoT) in industrial automation encourages the emergence of a new paradigm of industrial IoT systems, wireless control system (WCS), where the system and/or control information is delivered over wireless channels. In practical systems, WCSs would consist of multiple control-loops in general, the resource competition among which would seriously increase mutual interferences and transmission collisions, making it is difficult to provide the required transmission reliability for the control strategy. To address this issue, we design the control strategy together with the hybrid cooperative transmission scheme for multi-loop WCSs in a proactive way. We firstly define the overall system cost function to explore the impacts of standard linear quadratic regulator (LQR) control cost and wireless transmission reliability on the control performance. In order to further minimize the overall system cost while guaranteeing the control stability, we then propose a control performance aware cooperative transmission scheme, which is formulated as a constrained optimization problem. Decomposition method and heuristic algorithms are designed based on the feature of network structure to solve the formulated mixed integer nonlinear programming problem efficiently. Finally, simulation results demonstrate that by using the proposed strategy, the overall system cost is significantly reduced, decreasing by 78% and 82% compared to the cases without considerations of system dynamics and without cooperative transmission, respectively.

In smart ocean, unmanned surface vehicles (USVs) are deployed to monitor the marine environment in a coordinated manner. The ubiquitous situation awareness of marine environment can be achieved by state estimation with the sensory data collected by USVs. Therefore, the transmission performance in terms of packet loss and delay of sensory data plays an important role in the state estimation of marine IoT systems. However, it is challenging to achieve the high-reliable and low-latency transmission for sensory data due to the path loss, spectrum scarcity and transmit power limitation. In this paper, we introduce the Age of Information (AoI) to mathematically characterize the impacts of packet loss and transmission delay on the state estimation error. We first explore the relationship between the state estimation error and the AoI of sensory data. We then investigate the co-design of state estimation and sensory data transmission for marine IoT systems. Specifically, a mother-ship assisted cooperative transmission scheme is proposed to mitigate the impact of limited resources and path loss on the estimation performance. Then, the mother-ship location, channel allocation and transmit power are jointly optimized to minimize the mean square error of state estimation, which is achieved by formulating a constrained minimization problem and solving it with the decomposition method. Simulation results demonstrate that the proposed scheme has superiorities in reducing the estimation error and the power consumption.

The evolution of wireless communications and networking technologies has led significantly expansion of the dimensionality of network resources, which compels innovations in resource management. Graphs, a classic discrete mathematical tool, have long been widely used for resource management thanks to their capabilities to model complex relationships and interactions among elements in wireless networks. Recently, resource management over graphs embraces various advanced approaches of graph optimization and graph learning, aligned with evolving demands in future wireless networks. To better learn recent research landscape and explore important trends, this twopart survey provides a comprehensive overview for resource management via graph optimization and learning. Part I presents the fundamentals of graph optimization and provides a recent literature review of graph optimization for resource management in various wireless communication scenarios, including cellular networks, device-to-device communications, multi-hop networks, multi-antenna systems, edge caching and computing, and nonterrestrial networks. Part II gives the basics of graph learning and provides a state-of-the-art literature review of graph learning in wireless networks for addressing various resource management issues, covering power control, spectrum management, beamforming design, task scheduling, and aerial coverage planning. A discussion of technical challenges and future research directions is covered in Part II.

This two-part survey provides a comprehensive review of graph optimization and learning for resource management in wireless networks. In Part I, we introduced the fundamentals of graph optimization and provided a recent literature review of graph optimization for resource management in various wireless communication scenarios. In this part, we first present an overview of graph learning and introduce several modern graph neural network models. Then, a state-of-the-art literature review of graph learning for different resource management issues in wireless networks is provided, which covers power control, spectrum management, beamforming design, task scheduling, and aerial coverage planning. Furthermore, we discuss current technical challenges and future research directions of graph optimization and learning for resource management in future wireless networks.