The Internet of Natural Things (IoNT) is the extension of the Internet of Things (IoT) concept to natural environments, with the ability to monitor the environment in real-time using integrated sensor networks, Artificial Intelligence (AI), and remote sensing. IoNT can offer solutions to the ecological crisis that is facing the world today, such as climate change, loss of biodiversity, and depletion of resources. The survey addresses the technology behind the IoNT and its different applications in disaster management, forest conservation, biodiversity monitoring, and agriculture, among others. IoNT has been successfully applied to monitor the quality of water in remote river ecosystems and to irrigate precision agriculture. Furthermore, pilot projects have demonstrated that IoNT can be used to make decisions based on real-time data analytics so that it is possible to manage resources sustainably. The other aspect discussed in the survey is the consistency between the IoNT sensors and the commercial sensors, according to some case studies presented in the literature. In those works, it is also identified that the processes are experimental in sensor testing, data synchronization, and the functioning of standard metrics, which ensure the reliability and strength of IoNT sensor systems. Despite the fact that the IoNT has great strengths, it is still possible to encounter issues concerning data security, energy efficiency, and interoperability. The survey addresses these issues and identifies new trends, such as blockchain and autonomous systems, that can make IoNT applications more scalable and efficient to manage the sustainable ecosystem.

The Internet of Everything (IoE) is accelerating the demand for intelligent, low-latency, and highly reliable communication systems to support automation and real-time decision-making. In dense urban and industrial environments, unmanned aerial vehicles (UAVs) are increasingly utilized to extend network coverage, improve connectivity, and enable dynamic data collection. However, managing RIS-assisted UAV-enabled IoE networks poses significant challenges, including accurate signal prediction, high computational complexity, and decentralized task assignment. To address these issues, we propose a novel RIS-empowered UAV-based Dynamic Area of Coverage (DAC) architecture. In this framework, UAVs equipped with reconfigurable intelligent surfaces (RIS) adaptively adjust the phase of reflected signals to optimize wireless channel conditions, suppress interference, and enhance signal quality.We formulate the Dynamic Area of Coverage with Location, Resource Allocation, and Trajectory Optimization (DAC-LRT) problem as a mixed-integer nonlinear programming (MINLP) model, aiming to jointly optimize UAV positioning, power distribution, and trajectory control to maximize real-time downlink capacity and ensure energy efficiency. To solve the DAC-LRT problem in dynamic and large-scale IoE environments, we design a Multi-Agent Distributed Deep Deterministic Policy Gradient (MAD3PG) algorithm. MAD3PG enables decentralized and adaptive policy learning by allowing UAVs to derive optimal actions directly from environmental observations. Simulation results demonstrate that our proposed approach significantly outperforms state-of-the-art methods, achieving improvements of 82.92%, 78.02%, and 71.9% in downlink capacity, coverage ratio, average throughput, and spectral efficiency over Deep Deterministic Policy Gradient (DDPG), Asynchronous Advantage Actor-Critic (A3C), and Greedy algorithms, respectively.

This paper studies the iterative learning control with prescribed performance (namely, prescribed iterative learning control, P-ILC) for switched systems, aiming to plan the dynamic and steady-state characteristics of the convergence process of iterative learning control. First, a performance function that converges exponentially with iteration is proposed to progressively constrain the tracking error. Then, a nonlinear system construction method is proposed that is applicable to any initial iteration error within the performance boundary. And the boundedness of the nonlinear system is equivalent to the error constraint of the prescribed performance. Furthermore, an iterative asymptotically stable controller is designed, which can ensure that the nonlinear system is always asymptotically stable under any sign of initial iteration error within the performance boundary. The designed iterative controller can achieve ondemand planning of tracking error accuracy and convergence speed in the iterative domain. Finally, the effectiveness of the proposed method is verified through simulations.

This work reports design problem of the memory sampled-data (SD) controller for interval type-2 fuzzy singular systems (SSs) with external disturbances. First, an improved free-weighting matrix inequality is introduced for concerning fuzzy SSs to reduce conservatism of the integral terms. Then, a novel looped-functional-based Lyapunov-Krasovskii functional (LKF) is constructed that incorporates the data from sampling interval $\mathbf {z}(t)$ to $\mathbf {z}(t_{k})$ . With the help of improved integral inequality and novel LKF, a new set of admissibility conditions is developed in the form of linear matrix inequalities (LMIs). The developed criteria based on the memory SD controller ensure that the proposed systems is admissible with an $H_{\infty }$ attenuation level. Finally, numerical simulations are given to illustrate the usefulness and benefit of the proposed methods.

Understanding complex traffic patterns has become more challenging in the context of rapidly growing city road networks, especially with the rise of Internet of Vehicles (IoV) systems that add further dynamics to traffic flow management. This involves understanding spatial relationships and nonlinear temporal associations. Accurately predicting traffic in these scenarios, particularly for long‐term sequences, is challenging due to the complexity of the data involved in smart city contexts. Traditional ways of predicting traffic flow use a single fixed graph structure based on the location. This structure does not consider possible correlations and cannot fully capture long‐term temporal relationships among traffic flow data, making predictions less accurate. We propose a novel traffic prediction framework called Multi‐scale Attention‐Based Spatio‐Temporal Graph Convolution Recurrent Network (MASTGCNet) to address this challenge. MASTGCNet records changing features of space and time by combining gated recurrent units (GRUs) and graph convolution networks (GCNs). Its design incorporates multiscale feature extraction and dual attention mechanisms, effectively capturing informative patterns at different levels of detail. Furthermore, MASTGCNet employs a resource allocation strategy within edge computing to reduce energy usage during prediction. The attention mechanism helps quickly decide which services are most important. Using this information, smart cities can assign tasks and allocate resources based on priority to ensure high‐quality service. We have tested this method on two different real‐world datasets and found that MASTGCNet predicts significantly better than other methods. This shows that MASTGCNet is a step forward in traffic prediction.

The Internet of Health Monitoring (IoHM) plays a vital role in Emergency Medical Package Delivery (EMPD) by enabling real-time monitoring and transmission of critical health data through interconnected devices in 6G networks. UAVs act as Aerial Base Stations (ABSs), facilitating data collection and transmission between GAI-IoHM devices and edge servers. This is crucial for efficient communication in 6G-enabled Space-Air-Ground Integrated Networks (SAGIN). However, UAVs supporting EMPD face challenges related to limited energy and computational capacity, especially during task offloading to edge servers. To address these constraints, this paper proposes the integration of Generative Artificial Intelligence (GAI) into UAVs for intelligent policy learning, enabling adaptive decision-making, real-time diagnostics, and efficient path planning under uncertainty. We present a novel multi-agent deep reinforcement learning (MADRL)-based joint optimization framework for cooperative task allocation, trajectory planning, and power management (CTATP) in a 6G-enabled SAGIN architecture. The problem is modeled as a Partially Observable Markov Decision Process (POMDP) to capture dynamic and uncertain operational conditions. To solve this, we introduce the GAI-based Deep Deterministic Double Policy Gradient (GAI-DD3PG) algorithm, which leverages a generative actor-network to learn adaptive, energy-efficient control policies from latent action spaces. Simulations in urban emergency scenarios with 10–20 UAVs demonstrate GAI-DD3PG’s efficacy. Compared to Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Multi-Agent Federated Reinforcement Learning (MAFRL), and Greedy heuristics, GAI-DD3PG achieves a 20% energy reduction, 15% higher delivery success rate,25% shorter trajectories, and 30% improved resource utilization. These results highlight its potential for reliable EMPD in complex 6G SAGIN environments.

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time.

A dual-mode model predictive control (MPC) strategy is tailored for a networked control system (NCS) operating in a constrained environment that is vulnerable to additive disturbances as well as denial-of-service (DoS) attacks. DoS attacks disrupt communication, affecting data and control signals. This article optimizes and improves the traditional MPC strategy to solve the problems caused by DoS attacks on the system. A robustness constraint with scaling parameters is introduced to efficiently handle uncertainty in the MPC algorithm. The control strategy proposed in this article enables a dynamic shift in control modes. When the system state enters a predefined set, the control strategy transitions from optimization to a state feedback control law. In order to analyze the recursive feasibility of the optimization problem and system stability, the relationship between the system errors, disturbances, and DoS attacks is derived. We also derive conditions on disturbance upper bound, DoS attack maximum allowable duration, and scaling parameters should be satisfied to ensure the recursive feasibility of the optimization problem and system stability.

Recent studies have revealed that Split Federated Learning (SFL) is vulnerable to Model Inversion (MI) attacks, where the attacker can reconstruct clients’ raw data by exploiting collected features. Though achieving results, current defenses are unsatisfactory due to the limited ability to suppress the sensitive information while preserving task-conducive information within features. Since such limited ability can be attributed to insufficient disentanglement of data-feature and feature-task dependencies, we propose a Dual Dependency Disentangling framework for SFL (D3SFL) to strengthen defense ability against MI attacks while maintaining the utility. Specifically, we first propose a variable-structure data-feature dependency decoupling module, which produces privacy-preserving features by learning input-specific sub-networks, therefore enhancing the disentanglement of data-feature dependencies to hide sensitive information. Then, we propose a stochastic feature-task dependency separating module that adopts sparse binary masks to preserve the target-task-critical features and reduce sensitive information, resulting in effective disentanglement of feature-task dependencies for lower privacy leakage and better utility maintenance. Extensive experiments on image-classification datasets (CIFAR-100 and FaceScrub) and the time-series dataset (METR-LA) show that D3SFL outperforms the comparisons, achieving remarkable defense ability against MI attacks (with up to 54×, 17×, and 18× reconstruction MSE on average, respectively) while maintaining better utility (with only 0.13% and 0.06% Accuracy drops over the standard SFL on CIFAR-100 and FaceScrub, respectively, and only a 0.03 MAE increase on METR-LA over CNFGNN). Our code is available at https://github.com/Shawn-CT/D3SFL.

In the context of rapidly growing city road networks, understanding complex traffic patterns and implementing effective safety monitoring through advanced Transportation Cyber-Physical Systems (T-CPS) has become increasingly challenging. This involves understanding spatial relationships and non-linear temporal associations. Accurately predicting traffic in such scenarios, particularly for long-term sequences, is challenging due to the complexity of the data. Traditional ways of predicting traffic flow use a single fixed graph structure based on location. This structure does not consider possible correlations and cannot fully capture long-term temporal relationships among traffic flow data, thereby limiting the system ability to ensure safety and reliability. To address this challenge, we propose a novel traffic prediction framework called Attention-based Spatio-temporal Multi-scale Graph Convolutional Recurrent Network (ASTMGCNet). This study introduces a novel framework designed to improve prediction accuracy in dynamic urban traffic systems by effectively capturing complex spatio-temporal correlations through multi-scale feature extraction and attention mechanisms. ASTMGCNet records changing features of space and time by combining Gated Recurrent Units (GRU) and Graph Convolutional Networks (GCN). Its design incorporates multi-scale feature extraction and dual attention mechanisms, effectively capturing informative patterns at different levels of detail. This strategic design allows ASTMGCNet to effectively capture complex spatio-temporal correlations within traffic sequences, enhancing prediction accuracy. We have tested this method on two different real-world datasets and found that ASTMGCNet predicts significantly better than other methods, demonstrating its potential to advance traffic flow prediction and improve safety and reliability in T-CPS applications.

The Internet of Vehicles (IoV) enables technology that allows IoV and vehicles to connect everything. IoV has become an essential component of modern life. This exponential growth of IoV technology has introduced significant security and privacy issues, which pose potential threats to different types of attacks and cause different threats to the normal operation of vehicles. To prevent intelligent vehicle accidents and identify malicious attacks within IoV networks, various researchers have focused on machine learning (ML)-based methods to detect attacks. Intrusion detection systems (IDS) are a prominent solution for cyber attacks in IoV using ensemble learning. To achieve higher accuracy and detection rate, designing an improved detection framework using ensemble learning is a challenging task. The design of an ensemble-based IDS depends on two main challenges: selecting base classifiers and their combination methods. Therefore, in this study, we propose a hybrid ML model to detect various attacks in IoV. We have used different ML algorithms to develop an enhanced algorithm that can efficiently detect attacks in IoV networks. To evaluate the performance of the proposed system, we have used two well-known datasets, (CIC-IDS2017) and (UNSW-NB15). The proposed algorithm shows outstanding performance from the performance results, with an average attack detection accuracy of 99.75% and 100% and an F1 score of 99.74% and 100%, respectively, for both datasets. Further performance scores, that is, recall, precision, and F1 score metrics, validate the exceptional effectiveness of the proposed framework.

As the demand for efficient parcel delivery continues to grow in the logistics industry, optimizing multi-robot task assignment has become crucial for enhancing overall delivery performance. This paper addresses the precedence-constrained multi-truck multi-drone package delivery task assignment problem, where each truck coordinates with a drone to serve multiple dispersed customers under precedence constraints that specify the required order of service. While trucks deliver packages to designated customers, drones can simultaneously serve other customers, subject to their limited flight endurance and payload capacity. To tackle this challenge, a three-phase heuristic algorithm is proposed to minimize the total delivery time required to serve the last customer while ensuring all precedence constraints are satisfied. In the first phase, an extended minimum marginal cost algorithm is applied to quickly construct truck-only routes that comply with precedence constraints. In the second phase, a splitting algorithm combined with an endurance checking procedure is employed to generate hybrid truck–drone routes considering drone limitations. In the final phase, a variable neighborhood descent approach is introduced to further improve the solution by strategically perturbing the truck-only routes. Extensive simulations and experiments demonstrate that the proposed three-phase heuristic algorithm consistently achieves higher-quality solutions with reduced computation time compared with the widely used adaptive large neighborhood search method.

Traffic state estimation (TSE) is attracting significant attention due to its importance to the Internet of Vehicles (IoV) for various applications, such as vehicle path planning. In classic IoV, the real-time traffic data collected by road side units requires transferring to the cloud server for processing. Such a centralized manner may raise privacy leakage issues. Split federated learning (SFL) has emerged as one of the prevalent methods to solve these issues. However, recent studies have shown that the existing SFL frameworks are vulnerable to the model inversion (MI) attacks, leading to private raw data leakage. To this end, in this article, we propose ShieldTSE, a privacy-enhanced SFL framework for TSE in IoV. To protect privacy and maintain utility, a variational encoder-decoder-based privacy-preserving feature extraction module with adversarial learning is first proposed to generate better privacy-preserved intermediate activations with a lower-dimensional feature space. Then, a hard attention-based feature selection module is designed to select partial yet crucial features from the intermediate activations by removing redundant sensitive features to further reduce the data privacy leakage. Experimental results demonstrate that ShieldTSE achieves superior privacy-preserving ability when against training-based and optimization-based MI attacks with an average reconstruction mean-square error (MSE) improvement of$18\times $and$35\times $on METR-LA and PEMS-BAY compared to the baseline without the privacy-preserving strategy, respectively. ShieldTSE also successfully maintains better model utility compared to the privacy protection baselines.

This paper studies the multi-robot task assignment problem in which a fleet of dispersed robots needs to efficiently transport a set of dynamically appearing packages from their initial locations to corresponding destinations within prescribed time-windows. Each robot can carry multiple packages simultaneously within its capacity. Given a sufficiently large robot fleet, the objective is to minimize the robots’ total travel time to transport the packages within their respective time-window constraints. The problem is shown to be NP-hard, and we design two group-based distributed auction algorithms to solve this task assignment problem. Guided by the auction algorithms, robots first distributively calculate feasible package groups that they can serve, and then communicate to find an assignment of package groups. We quantify the potential of the algorithms with respect to the number of employed robots and the capacity of the robots by considering the robots’ total travel time to transport all packages. Simulation results show that the designed algorithms are competitive compared with an exact centralized Integer Linear Program representation solved with the commercial solver Gurobi, and superior to popular greedy algorithms and a heuristic distributed task allocation method. Note to Practitioners—This work presents two group-based distributed auction algorithms for a sufficiently large fleet of robots to efficiently transport a set of dynamically appearing dispersed packages from their initial locations to corresponding destinations within prescribed time-windows. Each robot can carry multiple packages simultaneously within its capacity, and the objective is to minimize the robots’ total travel time to transport all the packages within the prescribed time-windows. The paper’s practical contributions are threefold: First, the multi-robot task assignment problem is formulated through a robot-group assignment strategy, which enables complex logistic scheduling for tasks grouped according to their distributions and time-windows. Second, we theoretically show that the multi-robot task assignment problem is an NP-hard problem, which implies the necessity for designing approximate task assignment algorithms. Third, the proposed group-based distributed auction algorithms are efficient and can be adapted for real scenarios.

This paper investigates the task assignment problem for one truck and one drone to deliver packages to a group of customer locations. The truck, carrying a large number of packages, can only travel between a group of prescribed street-stopping/parking locations to replenish the drone with both packages and batteries. The drone can carry multiple packages simultaneously to serve customers sequentially within its limited operation range. The objective is to reduce the amount of time it takes the drone to deliver the necessary package to the last customer while taking into account its operation range and loading capacity. First, the package delivery task assignment problem is shown to be an NP-hard problem, which guides us to design heuristic task assignment algorithms. Secondly, based on graph theory, a lower bound on the minimum time for the drone to serve the last customer is achieved to approximately evaluate the performance of a task assignment algorithm. Third, several decoupled heuristic algorithms are designed to sequentially plan the routes for the drone and the truck. Two coupled heuristic algorithms, namely the improved nearest inserting algorithm and the improved minimum marginal-cost algorithm, are proposed to simultaneously plan the routes for the drone and the truck. Numerical simulations demonstrate that the improved minimum marginal-cost algorithm reduces the total service time by 14.93% and 14.06% on average compared with the existing decoupled two-phase algorithm TPA and the coupled greedy algorithm, respectively. In the best case, it reduces the total service time by 41.71% and 40.11% compared with the TPA and the coupled greedy algorithm, respectively.

The detection of DGA domain name has become a central topic in network security in recent years. With DGA (Domain Generation Algorithm), a large number of pseudo-random domain names could be generated and used in DDoS (Distributed Denial of Service) attacks, reflection amplification attacks and other network attacks. Traditional deep learning algorithms based on character feature have the advantages of automatic feature extraction and shorter training time in DGA domain name detection. But for wordlist-based DGA domain name, the accuracy is lower. In order to improve the detection accuracy, semantic features of domain name are extracted to extend character features for the first time. Experiment results show that using extended character feature in Bi-LSTM (Bi-Directional Long Short-Term Memory) could improve the detection performance. In multi-classification, micro average of F1 score reaches up to 98.72%.

This article investigates the task assignment problem in which multiple dispersed robots need to visit a set of target locations while trying to minimize the robots’ total travel distance. Each robot initially has the position information of all the targets and of those robots that are within its limited communication range, and each target demands a robot with some specified capability to visit it. We propose a decentralized auction algorithm which first employs an information consensus procedure to merge the local information carried by each communication-connected (CC) robot subnetwork. Then, we apply a marginal-cost-based strategy to construct conflict-free target assignments for the CC robots. When the communication network of the robots is not connected, we demonstrate that the robots’ total travel distance might in fact increase when their communication range grows, and more importantly, such a somewhat counterintuitive fact holds for a range of algorithms. Furthermore, the proposed algorithm guarantees that the total travel distance of the robots is at most twice of the optimal when the communication network is initially connected. Finally, Monte Carlo simulation results demonstrate the satisfying performance of the proposed algorithm.

This article studies the task assignment problem for a fleet of dispersed vehicles to efficiently visit a set of target locations where some target locations might be unreachable for one or several vehicles. The objectives are to visit as many target locations as possible by using the minimum number of vehicles while minimizing the vehicles' total travel time. We first propose a target merging strategy to deal with the optimization problem, which is in general NP-hard, and show that for the special case of a single vehicle, it requires linear time to calculate the maximum number of targets to be visited. Second, we design a longest path-based algorithm and analyze the cases in which the objective to visit the maximum number of targets by using the minimum number of vehicles can be obtained through the proposed algorithm within linear running time. Once the targets to be visited and the corresponding employed vehicles are determined, the marginal-cost-based target inserting principle to be discussed guarantees that the chosen targets will be visited within a computable finite maximal travel time, which is at most twice of the optimal when the cost matrix is symmetric. Integrating the longest path-based algorithm with two target inserting principles used to minimize the vehicles' total travel time, we design two two-phase task assignment algorithms. Furthermore, we propose a one-phase algorithm to optimize the multiple objectives simultaneously by improving a co-evolutionary multipopulation genetic algorithm. Numerical simulations show that the proposed task assignment algorithms can lead to satisfying solutions against popular genetic algorithms.

This article investigates the task planning problem where one vehicle needs to visit a set of target locations while respecting the precedence constraints that specify the sequence orders to visit the targets. The objective is to minimize the vehicle's total travel distance to visit all the targets while satisfying all the precedence constraints. We show that the optimization problem is NP-hard, and consequently, to measure the proximity of a suboptimal solution from the optimal, a lower bound on the optimal solution is constructed based on the graph theory. Then, inspired by the existing topological sorting techniques, a new topological sorting strategy is proposed; in addition, facilitated by the sorting, we propose several heuristic algorithms to solve the task planning problem. The numerical experiments show that the designed algorithms can quickly lead to satisfying solutions and have better performance in comparison with popular genetic algorithms.

This paper studies the precedence-constrained task assignment problem for a team of heterogeneous vehicles to deliver packages to a set of dispersed customers subject to precedence constraints that specify which customers need to be visited before which other customers. A truck and a micro drone with complementary capabilities are employed where the truck is restricted to travel in a street network and the micro drone, restricted by its loading capacity and operation range, can fly from the truck to perform the last-mile package deliveries. The objective is to minimize the time to serve all the customers respecting every precedence constraint. The problem is shown to be NP-hard, and a lower bound on the optimal time to serve all the customers is constructed by using tools from graph theory. Then, integrating with a topological sorting technique, several heuristic task assignment algorithms are proposed to solve the task assignment problem. Numerical simulations show the superior performances of the proposed algorithms compared with popular genetic algorithms.