The deployment of battery charging stations (BCS) has emerged as a critical bottleneck in robotic mobile fulfillment systems (RMFS), directly governing operational continuity and system-level throughput. Optimizing BCS deployment under multi-visit charging demands remains a significant challenge, particularly in dynamic environments with fluctuating workloads and spatiotemporal constraints.We propose CLAP-IGA (Integrated Genetic Algorithm Considering Charging Logic and Allocation Path), a constraint-embedded evolutionary framework that jointly optimizes BCS layout, robot routing, and multi-visit task scheduling. CLAP-IGA incorporates a rolling-horizon replanning mechanism and embeds real-time battery state tracking, time-varying order arrival dynamics, and spatiotemporal path conflict management directly into the evolutionary process. Two deployment strategies—internal and external BCS configurations—are systematically compared to characterize their trade-offs across varying operational scales. Experiments across three operational scales demonstrate that CLAP-IGA reduces total completion time by up to 30% and improves charging station utilization by 18% relative to standard GA, greedy, and DQN baselines. The findings further reveal a counterintuitive capacity-threshold interaction: naively expanding BCS capacity can paradoxically degrade throughput, underscoring the necessity of co-designing charging policy and infrastructure rather than optimizing either in isolation.

This paper presents our simulation of cyber-attacks and detection strategies on the traffic control system in Daytona Beach, FL. using Raspberry Pi virtual machines and the OPNSense firewall, along with traffic dynamics from SUMO and exploitation via the Metasploit framework. We try to answer the research questions: are we able to identify cyber attacks by only analyzing traffic flow patterns. In this research, the cyber attacks are focused particularly when lights are randomly turned all green or red at busy intersections by adversarial attackers. Despite challenges stemming from imbalanced data and overlapping traffic patterns, our best model shows 85% accuracy when detecting intrusions purely using traffic flow statistics. Key indicators for successful detection included occupancy, jam length, and halting durations. All implementation details and source code are publicly available on GitHub at: https://github.com/U1overground/Cybersummer

Centralized federated learning is being widely researched and applied. However, centralized federated learning is prone to problems such as single point of failure and privacy disclosure because it relies too much on the central server. Focusing on decentralized federated learning, this paper innovatively constructs a decentralized federated learning framework based on dynamic selection collaboration and topology optimization. Firstly, we propose a dynamic client selection algorithm based on node training quality. Then, a global network topology for data communication is constructed by us based on the Watts-Strogatz(WS) model. Finally, we design a temporary topology algorithm to realize synchronization and model update in training. In the process of decentralized federated learning, the global network topology based on WS model cooperates with the current network topology constructed by temporary topology algorithm. The two network topologies work together to realize a dynamic client selection algorithm based on node training quality. A large number of experiments verify that DFedMQ can accelerate the model convergence and improve the training effect under the premise of privacy protection.

With the continuous development of 6G technology and the Internet of Things, small terminal devices are gradually joining deep model training through wireless networks, leading to the evolution of federated learning. In comparison to traditional centralized learning, federated learning not only leverages the computational power of individual terminals but also ensures the security of terminal data. However, the increasing number of devices poses new requirements on resource utilization in federated learning at scale. In this paper, we aim to address these challenges by proposing an energy-efficient and adaptive resource allocation strategy for wireless heterogeneous layered federated learning model (HLFLM). Specifically, we deploy both macro base stations and multiple micro base stations to construct a HLFLM, and perform resource allocation for subcarriers and power optimization. This approach focuses on optimizing energy consumption in federated learning networks while enhancing scalability and real-time performance of wireless communication. Experimental results demonstrate the effectiveness of the proposed method in medium-sized scenarios.

In the context of rising demands for marine resource exploitation and scientific research, collaborative trajectory planning for multiple Autonomous Underwater Vehicles (AUVs) in complex underwater environments—marked by obstacles, ocean currents, and low visibility—remains a critical challenge. Although the Gray Wolf Optimization (GWO) algorithm has advanced multi-objective trajectory planning, it faces issues such as poor high-dimensional space adaptability, susceptibility to local optima, and insufficient constraint handling. To address these, this article proposes a multi-AUV trajectory planning algorithm (EA-GWO) based on evolutionary learning to improve GWO. The method optimizes multi-AUV trajectory planning by leveraging hierarchical population hunting behavior, integrating position update equations to prioritize population bootstrapping, and balancing exploration and exploitation via fitness-based population distribution. Experimental validation across general, ocean current, and threat environments compares EA-GWO with the traditional GWO and multiple population GWO (MP-GWO). For sailing time: in the general environment, EA-GWO reduces total time by 90.6% compared to GWO and 90.6% compared to MP-GWO; in the ocean current environment, it cuts time by 0.9% versus GWO and 2.4% versus MP-GWO; in the threat environment, it cuts time by 13.6% versus GWO and 14.9% versus MP-GWO. For sailing distance: in the general environment, EA-GWO shortens total distance by 9.8% compared to GWO and 3.4% compared to MP-GWO; in the ocean current environment, it reduces distance by 2.3% versus GWO and 4.9% versus MP-GWO; in the threat environment, it shortens distance by 5.5% versus GWO and 1.0% versus MP-GWO. In terms of convergence performance reflected by the fitness curve: across the three environments, EA-GWO demonstrates faster convergence speed. These results highlight that EA-GWO outperforms the other two algorithms in sailing time, distance, and convergence efficiency, verifying its effectiveness in real-time dynamic coordination and constraint handling for multi-AUV missions.

Deep reinforcement learning models have achieved promising results in the path planning problem for uncrewed aerial vehicles (UAVs). However, their update mechanisms can lead to overestimation and poor stability. This study addresses these issues by using a more realistic reward function, assigning priorities to experiences in the experience replay pool, and employing double decoupling of state and action values in Q-networks. We train the improved Deep Q-Network (DQN) algorithm for three-dimensional environment simulation experiments in a simulated environment. The 3D simulation experiments compare the algorithm with the A-star and unimproved DQN algorithms. The experimental results show that the algorithm has been improved, demonstrating its enhanced performance in the final path planning results. Moreover, the final testing results reveal that the UAV can safely reach the target point from the starting point.

Drone participation in truck delivery is a potential booster for the last-mile logistics system, which has been an emerging hot research field. Among that, how to arrange a fleet of drones from the truck and optimize the vehicle routing problem with drones (VRPDs) is a key issue. However, most existing studies fail to derive the feasible solutions due to unordered customer distributions and multivariant drone feature constraints. In this article, we propose a novel self-driven reinforcement learning structure, named constraint-based hybrid pointer network (CH-Ptr-Net) model, which is a hybrid pointer network approach composed of graph neural network (GNN) embedding and attention decoder. We go into developing the simpler embedding version for multiple drones-assisted truck delivery. The CH-Ptr-Net model tends to generate a set of optimal delivery sequence, after constructing the mixed-integer linear program (MILP) formulation. Extensive numerical testing indicates that the proposed method performs better than recent exact and heuristic approaches for collaborative delivery routing optimization with the truck carrying multiple drones.

Deep Learning (DL) is being applied in various domains, especially in safety-critical applications such as autonomous driving. Consequently, it is of great significance to ensure the robustness of these methods and thus counteract uncertain behaviors caused by adversarial attacks. In this paper, we use gradient heatmaps to analyze the response characteristics of the VGG-16 model when the input images are mixed with adversarial noise and statistically similar Gaussian random noise. In particular, we compare the network response layer by layer to determine where errors occurred. Several interesting findings are derived. First, compared to Gaussian random noise, intentionally generated adversarial noise causes severe behavior deviation by distracting the area of concentration in the networks. Second, in many cases, adversarial examples only need to compromise a few intermediate blocks to mislead the final decision. Third, our experiments revealed that specific blocks are more vulnerable and easier to exploit by adversarial examples. Finally, we demonstrate that the layers Block4_conv1 and Block5_ cov1 of the VGG-16 model are more susceptible to adversarial attacks. Our work could potentially provide useful insights into developing more reliable Deep Neural Network (DNN) models.

The Internet of Things (IoT) is becoming an indispensable part of everyday life, enabling a variety of emerging services and applications. However, the presence of rogue IoT devices has exposed the IoT to untold risks with severe consequences. The first step in securing the IoT is detecting rogue IoT devices and identifying legitimate ones. Conventional approaches use cryptographic mechanisms to authenticate and verify legitimate devices’ identities. However, cryptographic protocols are not available in many systems. Meanwhile, these methods are less effective when legitimate devices can be exploited or encryption keys are disclosed. Therefore, noncryptographic IoT-device identification and rogue device detection become efficient solutions to secure existing systems and will provide additional protection to systems with cryptographic protocols. Noncryptographic approaches require more effort and are not yet adequately investigated. In this article, we provide a comprehensive survey on machine learning technologies for the identification of IoT devices along with the detection of compromised or falsified ones from the viewpoint of passive surveillance agents or network operators. We classify the IoT-device identification and detection into four categories: 1) device-specific pattern recognition; 2) deep learning-enabled device identification; 3) unsupervised device identification; and 4) abnormal device detection. Meanwhile, we discuss various ML-related enabling technologies for this purpose. These enabling technologies include learning algorithms, feature engineering on network traffic traces and wireless signals, incremental learning, and abnormality detection.

Abnormal event detection with the lowest latency is an indispensable function for safety-critical systems, such as cyber defense systems. However, as systems become increasingly complicated, conventional sequential event detection methods become less effective, especially when we need to define indicator metrics from complicated data manually. Although deep neural networks (DNNs) have been used to handle heterogeneous data, the theoretic assurability and explainability are still insufficient. This article provides a holistic framework for the quickest and sequential detection of abnormalities and time-dependent abnormal events. We explore the latent space characteristics of zero-bias neural networks considering the classification boundaries and abnormalities. We then provide a novel method to convert zero-bias DNN classifiers into performance-assured binary abnormality detectors. Finally, we provide a sequential quickest detection (QD) scheme that provides the theoretically assured lowest abnormal event detection delay under false alarm constraints using the converted abnormality detector. We verify the effectiveness of the framework using real massive signal records in aviation communication systems and simulation. Codes and data are available athttps://github.com/pcwhy/AbnormalityDetectionInZbDNN.

Compared with traditional images, multispectral images (MSIs) contain more spectral bands and higher data dimensions. The existing MSI classification model has high computational complexity and consumes a lot of computing resources. In this letter, we propose a lightweight multispectral classification method named CABNN based on binary neural networks (BNNs) to effectively have a trade-off between model performance and computational cost. First, we modify and binarize the MobileNetV1 network and add almost computation-free shortcuts to enhance the expressive capability. Secondly, since the BNN is sensitive to the distribution of activation functions, we introduce RPReLU with learnable coefficients to automatically adjust activation distribution at almost no extra cost. Lastly, considering that MSIs have multiple channels, we utilize an efficient channel attention (ECA) module to assign different weights to each channel to concentrate on crucial features and suppress insignificant features. We conduct experiments on four public MSI datasets, including NaSC-TG2, EuroSAT, GID Fine land-cover classification, and UC Merced Land Use. Extensive experiments demonstrate that the proposed CABNN has higher efficiency and better comprehensive performance than the state-of-the-art methods across the board.

The ubiquitous deployment of 5G New Radio (5G NR) stimulates Unmanned Aircraft Systems (UAS) swarm networking to evolve to achieve more imminent progress. The heterogeneous collaboration between UAS swarm enhances the complexity and the efficiency of mission complement that requires robustness, flexibility, and sustainability of throughput in UAS swarm networking. The conventional approaches mainly are based on the hierarchical architectures that are limited to satisfy the challenges of UAS swarm with high dynamics on a large scale. In this paper, we propose an optimal cell wall paradigm to enhance the throughput in heterogeneous UAS swarm networking. With the weight adjustment of each link, we map the optimization into a polyhedron scheduling problem and formula the problem into Max-min Throughput Fair Scheduling (MTFS). Further, we propose a max-min throughput algorithm to optimize the minimum throughput of cell wall paradigm. With the optimal max-min throughput, we optimize the schedule with edge-coloring to achieve global MTFS solving. The normalized MTFS shows our algorithm can achieve over 40% improvement of MTFS globally. In terms of MTFS solving, our algorithms have promising potential to improve the throughput and mitigate the incidents for multiple beams enabling of UAS in cell wall communication. With the throughput enhancement, the advanced aerial mobility of UAS swarm networking can be escalated on a large scale.

The ubiquitous of 5G New Radio (5G NR) accelerates the massive implementations in many fields including swarm Unmanned Aircraft System (UAS) networking. The ultra capacities of 5G NR can provide more sufficient networking services for the swarm UAS networking which can enable swarm UAS to deploy in more complex and challenging scenarios to achieve missions. However, the conventional swarm UAS networking are mainly centralized or hierarchical which is vulnerable to the dynamics and the deployment of swarm UAS networking on a large scale. In this paper, we formulate a cell wall communications for the heterogeneous swarm UAS networking with the inspiration of biological cell wall communication. Fueled by reinforcement learning, we resolve the edge-coloring problem of cell wall communication scheduling to achieve the maximum throughput between the heterogeneous swarm UAS networking globally. The evaluation shows our proposed reinforcement learning enabled algorithm can surpass the conventional scheduling algorithms over 90% when the time piece is less than 0.01s and achieve the optimal throughput for the heterogeneous swarm UAS networking.

Agriculture production plays a crucial role in the growth of the world’s economy. However, technologies and innovations in the Precision Agriculture (PA) domain are still in their infancy. Typically, the problem of incorrect identification of crop disease still remains unsolved, thus causes agricultural production losses worldwide. To solve this problem, we apply transfer learning based Convolutional Neural Networks (CNNs) to identify different diseases of the crops. Specifically, this study focuses on evaluating the identification performance of the four state-of-the-art deep learning architectures: Residual Network (ResNet), MobileNet GoogLeNet and AlexNet. The PlantVillage dataset used in this study is made publicly available for academic study at Kaggle repository, which consists of 54,323 images of 38 kinds of crop diseases, and 14 kinds of crop diseases from them are derived as dataset for this study. By applying transfer learning, we make a comprehensive performance comparison among the implemented architectures, and evaluation results show that the MobileNet performs better in identifying crop disease, achieving an accuracy of 99.27%, followed by the GoogLeNet achieving an accuracy of 99.14%, AlexNet achieving an accuracy of 97.92%, and ResNet achieving an accuracy of 97.03%. Experiment results in this study prove the effectiveness of transfer learning based CNN models in crop disease identification, as well as provide a guidance on choosing an appropriate network to identify different crop diseases.

The Internet of Things (IoT) is reshaping modern society by allowing a decent number of RF devices to connect and share information through RF channels. However, such an open nature also brings obstacles to surveillance. For alleviation, a surveillance oracle, or a cognitive communication entity needs to identify and confirm the appearance of known or unknown signal sources in real-time. In this paper, we provide a deep learning framework for RF signal surveillance. Specifically, we jointly integrate the Deep Neural Networks (DNNs) and Quickest Detection (QD) to form a sequential signal surveillance scheme. We first analyze the latent space characteristic of neural network classification models, and then we leverage the response characteristics of DNN classifiers and propose a novel method to transform existing DNN classifiers into performance-assured binary abnormality detectors. In this way, we seamless integrate the DNNs with parametric quickest detection. Finally, we propose an enhanced Elastic Weight Consolidation (EWC) algorithm with better numerical stability for DNNs in signal surveillance system to evolve incrementally, we demonstrate that the zero-bias DNN is superior than regular DNN models considering incremental learning and decision fairness. We evaluated the proposed framework using real signal datasets and we believe this framework is helpful in developing a trustworthy IoT ecosystem.

With the development of unmanned aerial systems (UAS), the ubiquitous deployment of UAS is becoming a trend. With the digital transceiver, the remote pilot can control the UAS remotely and effectively. With the deployment of 5G technology on a large scale, the convenience of remote control is becoming obvious and stable. However, the convenience of remote control technologies also brings more vulnerabilities to UAS. Software defined radio (SDR) has been explored to play system exploitation and penetration test for the radio system widely. With block programming, an SDR can become a hands-on system exploitation tool for research. With the adjustment of antennas and programmings, we can exploit the vulnerabilities of the UAS system and fixed the problem in advance. In this paper, we introduce an approach to leverage SDR to realize signal spoofing for GPS location and injection for digital communication. Based on SDR, we analyze the security of UAS on the GPS and digital connections. With the adjustment of antennas, we can define the SDR into a GPS spoofing tool and inject the packets in the communication of the digital transceiver. The evaluation shows the approach can delay the GPS searching for tens of minutes and disorder the connections between ground control station to UAS.

With massive data being generated daily and the ever-increasing interconnectivity of the world’s Internet infrastructures, a machine learning based intrusion detection system (IDS) has become a vital component to protect our economic and national security. In this paper, we perform a comprehensive study on NSL-KDD, a network traffic dataset, by visualizing patterns and employing different learning-based models to detect cyber attacks. Unlike previous shallow learning and deep learning models that use the single learning model approach for intrusion detection, we adopt a hierarchy strategy, in which the intrusion and normal behavior are classified firstly, and then the specific types of attacks are classified. We demonstrate the advantage of the unsupervised representation learning model in binary intrusion detection tasks. Besides, we alleviate the data imbalance problem with SVM-SMOTE oversampling technique in 4-class classification and further demonstrate the effectiveness and the drawback of the oversampling mechanism with a deep neural network as a base model.

With the ubiquitous deployment of 5G cellular networking in many fields, unmanned aerial vehicle (UAV) networking, as one of the main parts of the Internet of Things (IoT), is playing a pivot role in the extension of smart cities. Different from the conventional approaches, the 5G enabled UAV networking can be more capable of multiple and complex mission executions with high requirements of collaborations and incorporation. In this paper, we leverage reinforcement learning based scheduling to optimize the throughput of heterogeneous UAV networking. To improve the throughput of the heterogeneous UAV networking, we focus on the balance for the inter-and intra-networking with the reduction of collisions occurring in the time slots. With reinforcement learning enabled scheduling, we can achieve the optimum selections on link activation and time allocation. Compared with the edge coloring of Karloff, our approach can achieve a higher enhancement on the throughput. The experimental results show that our approach reaches the global optimization when tsand tgare less than 0.01. Generally, DQN achieves 57.58% improvement on average which exceeds Karloff. The proposed approach can improve the throughput of heterogeneous UAV networking significantly.

Deep learning (DL) has been utilized pervasively in the Internet of Things (IoT). One typical application of DL in IoT is device identification from wireless signals, namely, noncryptographic device identification (NDI). However, learning components in NDI systems have to evolve to adapt to operational variations, such a paradigm is termed as incremental learning (IL). Various IL algorithms have been proposed and many of them require dedicated space to store the increasing amount of historical data, and therefore, they are not suitable for IoT or mobile applications. Besides, conventional IL schemes can not provide satisfying performance when historical data are not available. In this article, we address the IL problem in NDI from a new perspective, first, we provide a new metric to measure the degree of topological maturity of DNN models from the degree of conflict of class-specific fingerprints. We discover that an important cause for performance degradation in IL-enabled NDI is owing to the conflict of devices’ fingerprints. Second, we also show that the conventional IL schemes can lead to low topological maturity of DNN models in NDI systems. Thirdly, we propose a new channel separation-enabled IL (CSIL) scheme without using historical data, in which our strategy can automatically separate devices’ fingerprints in different learning stages and avoid potential conflict. Finally, We evaluated the effectiveness of the proposed framework using real data from automatic-dependent surveillance-broadcast (ADS-B), an application of IoT in aviation. The proposed framework has the potential to be applied to accurate identification of IoT devices in a variety of IoT applications and services. Data and code available at IEEE Dataport (DOI: 10.21227/1bxc-ke87) andhttps://github.com/pcwhy/CSIL.

The Internet of Things (IoT) provides applications and services that would otherwise not be possible. However, the open nature of IoT makes it vulnerable to cybersecurity threats. Especially, identity spoofing attacks, where an adversary passively listens to the existing radio communications and then mimic the identity of legitimate devices to conduct malicious activities. Existing solutions employ cryptographic signatures to verify the trustworthiness of received information. In prevalent IoT, secret keys for cryptography can potentially be disclosed and disable the verification mechanism. Noncryptographic device verification is needed to ensure trustworthy IoT. In this article, we propose an enhanced deep learning framework for IoT device identification using physical-layer signals. Specifically, we enable our framework to report unseen IoT devices and introduce the zero-bias layer to deep neural networks to increase robustness and interpretability. We have evaluated the effectiveness of the proposed framework using real data from automatic dependent surveillance-broadcast (ADS-B), an application of IoT in aviation. The proposed framework has the potential to be applied to the accurate identification of IoT devices in a variety of IoT applications and services.

Medical image processing is one of the most important topics in the Internet of Medical Things (IoMT). Recently, deep learning methods have carried out state-of-the-art performances on medical imaging tasks. In this paper, we propose a novel transfer learning framework for medical image classification. Moreover, we apply our method COVID-19 diagnosis with lung Computed Tomography (CT) images. However, well-labeled training data sets cannot be easily accessed due to the disease's novelty and privacy policies. The proposed method has two components: reduced-size Unet Segmentation model and Distant Feature Fusion (DFF) classification model. This study is related to a not well-investigated but important transfer learning problem, termed Distant Domain Transfer Learning (DDTL). In this study, we develop a DDTL model for COVID-19 diagnosis using unlabeled Office-31, Caltech-256, and chest X-ray image data sets as the source data, and a small set of labeled COVID-19 lung CT as the target data. The main contributions of this study are: 1) the proposed method benefits from unlabeled data in distant domains which can be easily accessed, 2) it can effectively handle the distribution shift between the training data and the testing data, 3) it has achieved 96% classification accuracy, which is 13% higher classification accuracy than "non-transfer" algorithms, and 8% higher than existing transfer and distant transfer algorithms.

In this paper, we study a not well-investigated but important transfer learning problem termed Distant Domain Transfer Learning (DDTL). This topic is closely related to negative transfer. Unlike conventional transfer learning problems which assume that the source domain and the target domain are more or less similar to each other, DDTL aims to make efficient transfers even when the domains or the tasks are completely different. As an extreme example in image classification, there are only a sufficient amount of unlabeled images of watches, airplanes, and horses in the source domain, and the target domain only has a small set of labeled human face images. Previously, a few instance-based distant domain transfer algorithms were proposed to deal with this type of binary distant domain image classification problems. Yet most existing algorithms are very task-specific and they are only good at binary classification tasks. In this study, we propose a novel feature-based distant domain transfer learning algorithm, which requires only a tiny set of labeled target data and unlabeled source data from completely different domains. Instead of selecting intermediate instances, we introduced Distant Feature Fusion (DFF), a novel feature selection method, to discover general features cross distant domains and tasks by using convolutional autoencoder with a domain distance measurement as a feature extractor. As the novelty of this study, it can effectively handle both distant domain mutil-class image classification and binary image classification problems. More importantly, it has achieved up to 19% higher classification accuracy than "non-transfer" algorithms, and up to 9% higher than existing distant transfer algorithms.

Automatic Dependence Surveillance-Broadcast (ADS-B) is transforming all aspects of aviation, including commercial aircraft and unmanned aerial systems (UAS). ADS-B service broadcasts traffic information and flight information to improve aviation safety and efficiency in the air and on runways, reduce costs, and lessen harmful effects on the environment. However, due to its broadcast nature, on one hand, ADS-B is vulnerable to malicious cybersecurity attacks relevant to broadcasting; on the other hand, broadcasting could be leveraged to further improve the safety of aviation. The objective of this paper is to investigate how to enhance aviation security and safety by leveraging the inherent broadcast property of ADS-B. To be specific, we propose two novel schemes for enhancing aviation cybersecurity and safety, respectively. The first scheme, motivated by the fact that fountain codes have been successfully applied in various broadcasting scenarios for ensuring the reliability, we propose to leverage Fountain Code to enhance the security of ADS-B. The second scheme, which is based on information integration, to detect collaboratively the comprehensive weather information to enhance the safety of aviation. The performance evaluation results demonstrate the feasibility of integrating fountain codes with ADS-B for encryption and the feasibility of collaborative detection to bypass turbulent weather conditions. To our knowledge, our work is the first attempt to apply fountain code to enhance aviation security and safety.