In this paper, we propose a hierarchical data collection scheme, towards the realization of unmanned aerial vehicles (UAV)-aided industrial wireless sensor networks (IWSN). The particular application is that of agricultural monitoring. For that, we propose the use of hybrid compressed sampling (CS) through exact and greedy approaches. With exact approach - to model the energy-optimal formulation - it is utilized an improved linear programming formulation of the minimum cost flow problem. The greedy approach is based on a proposed balance factor parameter, consisting of data sparsity and distance from cluster head to normal nodes. To improve node clustering efficiency, a hierarchical data collection scheme is implemented, by which, nodes in different layers are adaptively clustered, and the UAV can be scheduled to perform energy-efficient data collection. Simulation results show that our method can effectively collect the data and plan the path for the UAV at a low energy cost.

Unmanned Aerial Vehicles (UAVs) have attacted much attention in the field of wireless communication due to its agility and altitude. UAVs can be used as low-altitude aerial base stations (UAV-BSs) to provide communication services for ground devices (GDs) in various scenarios, such as emergency communication and traffic offloading in hotspots. However, due to the limited communication ranges and high prices of commercial UAV-BSs, covering a target area all the time with sufficient UAVs is quite challenging, especially under dynamic environment. We need to design the trajectory of the UAV-BSs to optimize system performance. Most existing works focus on the energy-efficient coverage and throughput maximization but ignore the fairness of communication service, especially the fairness at user-level. Besides, reinforcement learning is suitable for solving decision problems in dynamic environments. However, most existing works use centralized deep reinforcement learning (DRL) approaches. Due to the scalability and low time complexity, a distributed DRL approach is more suitable for multiple UAV-BSs communication system in dynamic environment. Unlike previous works, we characterize the fairness at user-level based on proportional fairness scheduling and formulate a weighted-throughput maximization problem via designing UAV-BSs’ trajectory. Then we model the dynamic deploymentproblem of UAV-BSs as a Markov game and propose a multi-agent deep reinforcement learning-based distributed UAV-BSs control approach named MAUC. MAUC approach adopts the framework of centralized training with distributed execution. Simulation results show that the MAUC can improve fairness of communication service by sacrificing a small amount of throughput.

As an emergent Internet of Underwater Things (IoUT) system, the Underwater Wireless Networks (UWNs), especially the Autonomous Underwater Vehicle (AUV)-based UWNs are considered to be future of deep-sea exploration. Instead of underwater exploring or data collection based on an independent AUV, the multi-AUVs cooperative system or the AUV flock-based UWNs perform more efficiently and accurately in some particular underwater exploring tasks. In this paper, we focus on improving the scalability or controllability of the AUV flock-based UWNs, and utilize the paradigm of Software Defined Networking (SDN) to improve the flexibility and controllability of the AUV flock-based UWNs. With the proposed SDN-enabled architecture for the AUV flock-based UWNs, the UWNs are divided into three layers, and the data transmission, synchronization, collection among the AUVs are implemented by the proposed software-defined beacon and control frameworks. By the centralized management feature of SDN, we define the concept of AUV flock and the united control model based on artificial potential field theory. Then, we propose an exact path planning scheme for the AUV flock, especially when potential underwater obstacles or ’no-go’ areas are taken into account. We will show how an SDN controller can be a director/leader for the AUV flock-based UWNs to perform exact underwater path planning mission. Simulation results show that our proposal is efficient in managing the operation of the AUV flock, especially the proposal SDN controller-guided path planning scheme performs more efficiently than the normal distributed path planning scheme.

The exploitation/utilization of marine resources and the rapid development of urbanization along coastal cities result in serious marine pollution, especially underwater diffusion pollution. It is a non-trivial task to detect the source of diffusion pollution, such that the disadvantageous effect of the pollution can be reduced. With the vision of 6G framework, we employ Autonomous Underwater Vehicle (AUV) flock and introduce the concept of AUV-based network. In particular, we utilize the Software-Defined Networking (SDN) technique to update the controllability of the AUV-based network, leading to the paradigm of SDN-enabled multi-AUVs network Intelligent Transportation Systems (SDNA-ITS). For SDNA-ITS, we utilize artificial potential field theories to model the control model. To optimize the system output, we introduce the graph-based Soft Actor-Critic (SAC) algorithm, i.e., a category of Multi-Agent Reinforcement Learning (MARL) mechanism where each AUV can be regarded as a node in a graph. In particular, we improve the optimization model based on Centralized Training Decentralized Execution (CTDE) architecture with the assistance of the SDN controller, by which each AUV can efficiently adjust its speed towards the diffusion source. Further, to achieve exact path planning for detecting the diffusion source, a dynamic detection scheme is proposed to output the united control policy to schedule the SDNA-ITS dynamically. Simulation results demonstrate that our approaches are available to detect the underwater diffusion source when the actual scenario is taken into account and perform better than some recent research products.

In smart cities, urban Intelligent Transportation Systems (ITSs) are highly anticipated to improve transportation efficiency, decrease traffic-congestion, and promote sustainable transportation development. However, the ITS-based transportation network may fail as a result of a traffic-congestion which is considered as one of the challenging issues in large-scale smart cities. The possible traffic-congestion on the link is with spatio-temporal features and may vary over time. Nevertheless, the continuous or delay-sensitive traffic flow is always requested in smart cities even under serious traffic-congestion. In this paper, we will prove such spatio-temporal features of traffic-congestion can be forecasted, and the path for the delay-sensitive urban traffic can be accurately planed before the traffic is started. Our main contributions can be summarized as follows: 1) We employ Software-Defined Networking (SDN) technology to improve the scalability of ITS in smart cities and propose a grid-based model to quantify the traffic-congestion probability of the transportation network; 2) We propose a polynomial-time solvable algorithm to recognize the grids that affect the traffic-congestion probability of the network links or paths; 3) We utilize time-expanded network technology to expand the time slots in the spatial dimension and propose a polynomial-time path planning algorithm that can seek for a congestion-aware path to schedule the traffic within a given time threshold.

The rapid development of Internet of Things technologies (such as edge computing) has promoted the development of numerous emerging urban applications, particularly smart transportation. As an anticipated aspect of smart transportation, bike sharing systems have recently been deployed in many cities, and are considered an efficient way to address the issue of ’the last mile’. In a bike sharing system, the supply and demand of shared bikes at each bike station frequently change over time. Consequently, one of the most challenging issues of a bike sharing system is predicting the required number of shared bikes at each station. In this study, we take the real aspects of a bike sharing system into account, e.g., the high complexity, nonlinearity, and uncertainty of the traffic flow, and propose a hybrid edge-computing based machine learning model. Notably, our proposed model, which combines a self-organizing mapping network with a regression tree, is applied to predict the bicycle demand of a certain station through the following steps: 1) First, the proposed model adopts self-organization mapping to assemble the original samples in the form of clusters, and 2) each cluster is then built as a regression tree to forecast the required number of bikes at each station. Experiments based on real data from the Washington and London bike-sharing systems show that our proposed method achieves a higher prediction accuracy and better generalization than previous approaches.