The Internet of Nano Things (IoNT) refers to an interconnected network of nanoscale components engineered to perform tasks such as data processing, storage, and actuation. IoNT has broad applications, including environmental monitoring and pollution source localization. In order to achieve monitoring and localization for multiple releasing sources (RSs), the deployment of nanosensor networks is indispensable. However, constrained by spatial limitations and high costs, sensors can only be sparsely deployed, resulting in severe degradation in localization performance. In this paper, we consider a turbulent diffusion molecular communication scenario and the objective is to enable multi-source localization and obstacle perception with sparse nanosensors. For sparse signal recovery, we first propose a real-symmetric based on Truncated Nuclear Norm Regularization with Alternating Direction Method of Multipliers (RS-TNNR) matrix completion algorithm, which utilizes the spatial symmetry of molecular diffusion to achieve precise data recovery under high missing ratios. Furthermore, for multi-source localization and obstacle perception, we also propose an Adaptive Iterative Grid based on Sparse Bayesian Learning (AIG-SBL) algorithm, which enhances the localization accuracy with SBL, mitigates off-grid errors via the proposed adaptive iterative grid, and simultaneously estimates obstacle position and radii. Simulation results demonstrate the effectiveness of the proposed algorithms for RS-TNNR and AIG-SBL.

In Internet of things (IoT) systems enabled by 6G, unmanned aerial vehicles (UAVs), acting as communication nodes, have the advantages of flexible deployment and wide-area coverage. The channel prediction capability of UAVs for ground communication is of great significance for improving the reliability of IoT communication systems. We propose an innovative and interpretable paradigm for channel prediction based on “physical feature extraction + machine learning”. Specificallywe proposes a real-time UAV-to-ground channel prediction method that leverages propagation environment sensing data, aiming to enhance prediction accuracy and generalization by deeply integrating environmental and communication information. Firstly, we construct the first UAV sensing-communication integrated dataset featuring multi-band, multi-dimensional channel parameters, including UAV-to-ground RGB images, depth maps, and channel data. We then extract multimodal features with clear physical significance relevant to wireless propagation, such as relative position, relative altitude, relative volume, and transmitter-receiver distance. Finally, this paper designs a fusion architecture based on convolutional neural network (CNN) and multilayer perceptron (MLP). This architecture takes multimodal feature data as input, utilizes CNN to extract local features of multi-modal features, and models the fusion of multi-modal features through MLP. Experimental results demonstrate that our model consistently outperforms comparative model. Importantly, our feature analysis quantitatively reveals—for the first time—that building volume is the most influential factor in channel behavior, and that prediction accuracy degrades with increasing flight altitude. Furthermore, system-level simulations confirm that channel prediction leads to substantial improvements in network performance. This work presents a robust and interpretable framework for environment-aware channel characterization, laying a foundation for future 6G intelligent communication systems.

To address the suboptimal fusion of depth data in RGB-D instance segmentation, we propose a novel frame work with two synergistic modules. The Depth Gradient Guidance Module (DGGM) provides fine-grained boundary cues by processing an explicit depth gradient map. Concurrently, the Enhanced Depth-Sensitive Attention Module (E-DSAM) adaptively captures scene context using a lightweight predic tor to make its attention mechanism dynamic. Extensive experiments on the NYUv2-48 dataset validate our approach, which achieves 26.8 mAP (a 4.1-point improvement over a strong baseline) and generates qualitatively superior masks. The code is available at https://github.com/TheoBald200814/RGB-D Instance-Segmentation.

Clustering-based methods are emerging as a ubiquitous technology in unsupervised object Re-Identification (ReID), which alternate between pseudo-label generation and representation learning. Recent advances in this field mainly fall into two groups: pseudo-label correction and robust representation learning. Differently, in this work, we improve unsupervised object ReID from feature calibration, a completely different but complementary insight from the current approaches. Specifically, we propose to insert a conceptually simple yet empirically powerful Feature Calibration Module (FCM) before pseudo-label generation. In practice, FCM calibrates the features using a nonparametric graph attention network, enforcing similar instances to move together in the feature space while allowing dissimilar instances to separate. As a result, we can generate more reliable pseudo-labels using the calibrated features and further improve subsequent representation learning. FCM is simple, effective, parameter-free, training-free, plug-and-play, and can be considered as a catalyst, increasing the ’chemical reaction’ between pseudo-label generation and representation learning. Moreover, it maintains the efficiency of testing time with negligible impact on training time. In this paper, we insert FCM into a simple baseline. Experiments across different scenarios and benchmarks show that FCM consistently improves the baseline (e.g., 8.2% mAP gain on MSMT17), and achieves the new state-of-the-art results. Code is available at: https://github.com/lhf12278/FCM-ReID.

Abstract The working face is the most dangerous place of coal mines, and it is difficult (even impossible) to replenish energy by replacing batteries of sensor nodes in the working faces. This paper presents a wireless charging method for this scenario that is composed of two sub methods called mining charging and maintaining charging, respectively. Mining charging provides opportunistic charging services during the process of coal cutting through two airborne Mobile Chargers (MCs) installed on the two ends of the shearer and two portable MCs carried by shearer drivers. Maintaining charging provides opportunistic charging services for nodes in the charging radius when scraper conveyor repairmen and hydraulic support repairmen check or repair equipment, with each repairman carrying one portable MC. Simulation results show that both mining charging and maintaining charging can give energy replenishment for nodes in coal faces. When charging power of MCs is greater than or equal to 5.2 W, the first row of nodes can work sustainably. If the energy requirements of the second row of nodes are also met, the charging power of MCs cannot be less than 12 W.

Abstract It is of great importance to power the nodes of the rechargeable wireless sensor network to detect events continuously in the area of interest. This paper proposes a joint data transmission and on‐demand charging algorithm based on the space correlation. The new algorithm optimises the event detection, data forwarding and node charging jointly to improve the charging efficiency. First, the active nodes participating in the event detection are selected using an improved iterative node selection method to reduce the number of nodes working concurrently. Then, the greedy data transmission scheme based on grid partition is proposed to transmit the observed data to the sink node. Finally, the nodes in the networks are charged using the on‐demand charging method based on grid partition, which greatly decreases the charging frequency and energy loss of the mobile charger. The simulation results demonstrate that the proposed method has superior performance in the distance travelled by the mobile charger, the energy utilisation, the average energy consumption of the mobile charger and the node charging latency.

Most nodes of Mine Internet of Things (Mine IoT) are powered by batteries, and wireless charging using mobile chargers (MCs) is an effective way to make nodes work sustainably. A novel hybrid charging method combining the controlled and opportunistic MCs (C&O charging) is proposed in this study. Workers (such as the repairmen and gas inspectors) carrying portable chargers are proposed to be opportunistic MCs to provide an incidental charging service for the surrounding rechargeable Mine IoT nodes while doing its own work to reduce the payload of controlled MCs. The hybrid charging model based on the incidental charging ability of the opportunistic MC is constructed and the scheduling strategy of the controlled MC and the queueing management scheme of the charging request are also proposed. The simulation results indicate that the power demands of the majority of the nodes in the maintenance areas can be met or partially met by opportunistic MCs and the charging time of C&O charging is greatly decreased compared to that of only using controlled MCs.

The nodes of the mine Internet of things (MIoT) are powered by batteries, for which the wireless charging is essential for their continuous and stable operation. This study proposed a method of directional mobile charging for the MIoT based on smart antenna, in which the mobile chargers charge the nodes need power not only when stationary, but also when moving. The theoretical calculation method of the charged energy is studied and established its approximate calculation algorithm, which can greatly reduce the computational complexity, based on the discretised model of effective charging distance of smart antenna. Then, the method for estimating the residual energy of the nodes was designed, and the upper bound of the transmitting power of the mobile charger was determined. The results of the simulation experiments indicated that this method has high charging efficiency and can meet the power demand of MIoT nodes.

It is popular that there exist multiple objectives in practical control system. To solve this problem, a dynamic multi-objective control algorithm based on NSGA-II is presented. Based on the multi-objective evolutionary algorithm and the tight relation between the system states of the neighboring sampling instants, a multi-objective iterative compatible control algorithm is proposed which can cope with both the convex/non-convex control problem as well as improve the computing speed. Considering the two objectives speed and energy cost in the control of IPMCs propelled robotic fish, the algorithm is successfully applied to it to illuminate its validity. The result also shows the potential for the multi-objective evolutionary algorithm to the real-time control field.

In this paper we present a novel framework for computing the steady turning motion of a robotic fish undergoing periodic body and/or tail deformation. Taking the turning radius and the angular velocity as unknowns, we obtain the absolute motion trajectories of points on the ldquospinal columnrdquo of robotic fish by superimposing relative body/tail motions on the rigid body circular motion. The hydrodynamic reactive force and the resulting moment are then computed from the motion trajectories, using Lighthill's large-amplitude elongated-body theory, in terms of the two turning parameters. By integrating the dynamics of rigid body motion and averaging out oscillations, implicit equations involving the turning parameters can be established and solved. We also discuss the plan of applying the proposed framework to the modeling of steady turning maneuvers of biomimetic robotic propelled by an ionic polymer-metal composite (IPMC) caudal fin.

A new two-layer multi-objective compatible control algorithm is proposed for a class of control problems with two conflicting control objectives, control error and energy consumption. The first layer is devoted to obtaining a user’s desired controlled objectives region, assured to be not only achievable but also Pareto-optimal. The second layer is devoted to designing an effective controller by optimizing the most important controlled objective (such as the energy consumption), subject to system constraints from the controlled objectives region in the first layer. This control algorithm provides an effective robust controller design method for multiobjective control problems with precise models and uncertain initial conditions. Simulations illustrate that the two-layer multi-objective compatible control (MOCC) algorithm has some advantages over traditional multi-objective control methods.