Distributed Internet of Things (IoT) nodes powered by wind energy harvesters (WEHs) have enabled long-term, maintenance-free environmental monitoring in remote and extreme scenarios. However, the large fluctuations in wind speed may cause structural fatigue and dynamical instability of conventional WEHs. To achieve stable and durable operation under high wind speeds (up to 25 m/s), this study reports a Feynman-sprinkler-like WEH (FSL-WEH). In contrast to conventional WEHs that rely on turbine blades directly driven by the wind, the FSL-WEH harnesses the reaction force generated by airflow expelled through Feynman nozzles to produce rotational motion. Simulation and experiments indicate that the FSL-WEH achieves a maximum power output of 4.34 W and a peak power density of 66.77 kW/m3at a wind speed of 25 m/s. Moreover, the FSL-WEH successfully powers an environmental monitoring system, which wirelessly transmits temperature and humidity data to connected terminals, validating the operational reliability and durability of the device in extremely high wind speeds and high-humidity conditions. This study provides a promising solution for scavenging wind energy to power distributed environmental sensor networks under high-wind, high-humidity conditions.

This article is concerned with the problem of functional observer-controller (FOC) design when a complex unmeasurable premise variable (UPV) exists in the Takagi-Sugeno (T-S) fuzzy systems. Considering the nonlinearities in the UPV, a new transformation method is designed to linearize the premise variable (PV). Then, the PV could be estimated through the proposed FOC. The observer and controller gains are derived by deducing convex robust and stability conditions. Furthermore, a robust separation principle is utilized to make the estimate and control error system stable. Finally, simulation examples are provided to illustrate the effectiveness of the proposed method.

Untethered capsules are capable of entering the gastrointestinal (GI) tract and collecting fluid samples containing microbial communities from specific locations, facilitating the study of chronic diseases. However, existing sampling capsules are designed for single-site sampling, making it challenging to gather samples from multiple targets. This paper reports a magnetic-driven capsule for multiple sampling within the GI tract and an on-demand magnetic-triggered fluid sampling strategy. The capsule consists of a body, a magnetic-triggered negative pressure unit, and a reservoir unit. Composed of an elastic membrane and Magnet I, the negative pressure unit controls pressure change inside the capsule cavity on demand to pump the sample by switching the magnetic field, while the embedded Magnet I also enables real-time magnetic localization for regional targeting and position tracking. The reservoir unit integrates three sampling papers for fluid absorption, two waterproof layers that maintain contamination levels below 25% to ensure reliable multi-site sampling, and a rotating arm embedded with Magnet II for posture adjustment of the sampling paper. The pumping and storage performance of the capsule was systematically evaluated and optimized. Meanwhile, the capsule, actuated by an external magnetic field, was evaluated for its active locomotion performance. Finally, the feasibility of using the capsule to perform active navigation and multi-target sampling in a porcine intestine was validated viaex vivoexperiments.

The Base-New Trade-off (BNT) problem universally exists during the optimization of CLIP-based prompt tuning, where continuous fine-tuning on base (target) classes leads to a simultaneous decrease of generalization ability on new (unseen) classes. Existing approaches attempt to regulate the prompt tuning process to balance BNT by appending constraints. However, imposed on the same target prompt, these constraints fail to fully avert the mutual exclusivity between the optimization directions for base and new. As a novel solution to this challenge, we propose the plug-andplay Dual-Prompt Collaboration (DPC) framework, the first that decoupling the optimization processes of base and new tasks at the prompt level. Specifically, we clone a learnable parallel prompt based on the backbone prompt, and introduce a variable Weighting-Decoupling framework to independently control the optimization directions of dual prompts specific to base or new tasks, thus avoiding the conflict in generalization. Meanwhile, we propose a Dynamic Hard Negative Optimizer, utilizing dual prompts to construct a more challenging optimization task on base classes for enhancement. For interpretability, we prove the feature channel invariance of the prompt vector during the optimization process, providing theoretical support for the Weighting-Decoupling of DPC. Extensive experiments on multiple backbones demonstrate that DPC can significantly improve base performance without introducing any external knowledge beyond the base classes, while maintaining generalization to new classes. Code is available at: https://github.com/JREion/DPC.

This paper proposes a whole process method of multi-quadrotors from detecting and locating to encircle and track targets. The reconnaissance quadrotor realizes accurate target detection based on the one-stage target detector of convolutional neural network. Then, based on a pinhole camera projection model, the target is located from the 2D pixel coordinates to 3D North East Down(NED) coordinates world. Finally, the hunter quadrotors realize the target encircling and the time-varying formation tracking based on the consensus theory. At the same time, we prove the stability of the time-varying formation tracking control. We built a multiple quadrotors platform composed of one reconnaissance quadrotor and four hunter quadrotors, and deployed the method on the platform to conduct a series of experiments with a minibus as the target for validation. The results indicate that reconnaissance quadrotor can accurately detect target and have small localization errors in the north and east directions. Hunter quadrotors can encircle and track targets in time-varying formation based on target information provided by reconnaissance quadrotor. Experiments have demonstrated that the method achieves high-speed and accurate target encirclement.

In maritime Internet of things (IoT), unmanned surface vehicles (USVs) can replace humans to perform some dangerous and challenging tasks. However, due to complex environment in a remote maritime scenario (e.g. fierce waves and electromagnetic interference) and limited communication resources, determining an optimal transmission schedule for USVs to accomplish tracking tasks efficiently presents a significant challenge. Therefore, in this paper, we investigate a strategic approach to solving the transmission scheduling problem for USV tracking in a remote maritime scenario. A novel maritime IoT framework integrating sensing, transmission, and control is proposed. Firstly, a learning-based adaptive Kalman filtering algorithm (LAKF) capable of operating in time-varying noise environments is developed. Secondly, a novel dueling double deep Q-network tracking (D3QNT) algorithm incorporating the discrete-time kinematic model characteristics of USVs is proposed. The reward mechanism of this algorithm, which is designed based on the estimation of the future states, provides effective solutions for transmission scheduling problems in USV tracking tasks. Building upon this foundation, the scenario has further been extended to that with an infinite temporal dimension. The simulation results show that, compared with other learning-based algorithms in existing literature, our proposed LAKF-D3QNT algorithm has better control performance and significantly reduces the tracking error in infinite-time scenarios.

The existing human-computer interaction often presents problems such as bulky components, simple interaction methods, and poor universality. Here, we proposed a flexible electronic tattoo keyboard (FETK) system based on an electronic tattoo triboelectric nanogenerator (ET-TENG) which consist of an improved thermoplastic polyurethanes (i-TPU) and conductive silver paste. The i-TPU is fabricated by adding tributyl citrate (TBC) and calcium carbonate (CaCO3) powder to thermoplastic polyurethanes (TPU) to improve its flexibility and wear resistance. Considering the portability in practical use, we have designed a signal acquisition circuit and several post-processing programs to obtain and identify signals from human finger touch accurately and stably. Furthermore, we proposed a dual-mode/multi-channel strategy to increase the number of channels and achieve one-to-one triggering of commonly used keyboard keys. Finally, we demonstrated the interactive process of using it as a keyboard to control computer output. It is worth mentioning that the FETK system we prepared can be used as a universal keyboard for plug-and-play portable use.

Accurate prediction of Sea Surface Temperature (SST) is critical for understanding marine phenomena, studying climate dynamics, and forecasting environmental changes. However, SST time series exhibit varying trends across different regions due to differences in latitude, ocean currents, and depth, which complicates precise forecasting. To address this, our proposed framework, the Dynamic Scale Interaction Network (DSIN), dynamically adapts to these regional variations by selecting appropriate temporal scales in the frequency domain. This process is facilitated by a dynamic routing module that does not require the predefinition of scale numbers, enabling our model to generalize across diverse geographic areas with varying SST trends. Additionally, SST is significantly influenced by local climate phenomena, such as storms, making the modeling process more complex. DSIN addresses this challenge by incorporating multivariate interactions and leveraging a cross-attention mechanism to capture the correlations between SST and exogenous variables, such as storm data. This enhances the model’s ability to accurately forecast SST even under the influence of localized climate events. Moreover, to enhance the model’s adaptability to complex spatiotemporal dynamics and improve its robustness across different SST prediction tasks, the framework employs a Graph Neural Network (GNN) to capture intricate correlations. By incorporating a probabilistic loss function, we estimate the uncertainty of SST predictions, thereby providing valuable information for more informed decision-making in climate modeling, marine navigation, and fisheries management. Extensive experiments conducted on the OISST SST dataset demonstrate that DSIN consistently outperforms state-of-the-art methods, proving its superiority in SST prediction.

This article investigates the prescribed-time formation control in the task space of multirobot systems (MRSs), which is subject to the uncertain nonlinear dynamics and the position requirements. The strategy constructs a cascade system consisting of control and reference layers by connections of coupled prescribed-time control units, which separately guarantee the convergence of coordination errors, accuracy of states, and synchronization between layers. Meanwhile, the transformation from task space to joint space is built based on the pseudo-inverse of the Jacobi matrix, which avoids the singular value problem brought by computing the inverse kinematics. The adaptive control method utilizes parameter estimation to eliminate the inaccuracy problem brought by the pseudo-inverse of Jacobi matrix transformation. Then, this article provides solutions to the formation control and the formation along the trajectory control. Correspondingly, the Lyapunov analysis process proves the system’s stability and the parameter estimation’s boundedness, which confirms the sufficient conditions for realizing the prescribed-time formation control of the MRSs. Finally, this article presents examples of time-varying formation and along-trajectories formation, thereby demonstrating the effect of the controller.

Edge computing has become increasingly important to fulfill the diversified Quality-of-Service (QoS) or Quality-of-Experience (QoE) demands for Industrial Internet of Things (IIoT) applications, such as machine condition monitoring, fault diagnosis, intelligent production scheduling, and production quality control. Due to the heterogeneity of IIoT systems, it is of urgent necessity to concentrate on the cloud–edge–end cooperative partial task offloading and resource allocation (CPTORA) problem for realizing workload balancing, efficient resource utilization, and better QoS/QoE of IIoT applications. However, the challenge lies in how to make real-time, accurate, decentralized task offloading (TO) and resource allocation (RA) decisions for dynamic and device-intensive IIoT. Therefore, this work examines the CPTORA problem for IIoT, aiming at minimizing its long-run overall delay and energy costs. To lower the problem complexity, this problem is decomposed into the TO subproblem and the RA subproblem. Then, an improved soft actor–critic-based decentralized multiagent deep reinforcement learning (MADRL) algorithm is proposed to address the TO subproblem, where each IIoT device can learn its globally optimal policy and make its decisions independently. This algorithm innovatively combines the divergence regularization, the distributional reinforcement learning, and the value function decomposition methods to improve convergence speed and accuracy of the existing MADRL methods. After receiving the TO decisions of every IIoT device, every edge server employs the Lagrange multiplier method and Karush–Kuhn–Tucker condition to solve its RA subproblem. The experimental results show that the proposed algorithm decreases the overall delay and energy costs more effectively, compared to the other state-of-the-art MADRL approaches.

One-stage 3D object detection, known for its simplicity and high-speed inference, is attracting increasing attention in autonomous driving scenarios. However, current one-stage detectors tend to perform sub-optimally compared to two-stage competitors. Our experimental findings suggest that one-stage detectors underperform due to the underutilization of spatial information in feature embedding and classification. Concretely, the spatial context is severely lost during feature propagation, inducing distorted spatial awareness. On the other hand, category recognition relies on the full utilization of spatial information, which is neglected by current detectors. This inadequate spatial awareness of the classification branch can exacerbate misclassification. To address these issues, we propose Spatial-aware Learning in Feature Embedding and Classification for One-stage 3D Object Detection (SLDet). Specifically, to restore the distorted spatial awareness, Category-wise Spatial Augmentation (CSA) is proposed to adaptively bring the network with pre-encoding multi-scale spatial contexts. As for misclassification, Spatial Guiding Classification (SGC) is introduced to guide the classification using explicit scale information. It employs the natural scale divergences among categories to rectify misclassification. Comprehensive experiments demonstrate that SLDet efficiently utilizes spatial information and achieves newly state-of-the-art performance on both the Waymo Open Dataset and the ONCE Dataset. Furthermore, additional experiments demonstrate the excellent generalization capacity of SLDet.

Both goal-agnostic and goal-oriented tasks have practical value for robotic grasping: goal-agnostic tasks target all objects in the workspace, while goal-oriented tasks aim at grasping pre-assigned goal objects. However, most current grasping methods are only better at coping with one task. In this work, we propose a bifunctional push-grasping synergistic strategy for goal-agnostic and goal-oriented grasping tasks. Our method integrates pushing along with grasping to pick up all objects or pre-assigned goal objects with high action efficiency depending on the task requirement. We introduce a bifunctional network, which takes in visual observations and outputs dense pixel-wise maps of$Q$values for pushing and grasping primitive actions, to increase the available samples in the action space. Then we propose a hierarchical reinforcement learning framework to coordinate the two tasks by considering the goal-agnostic task as a combination of multiple goal-oriented tasks. To reduce the training difficulty of the hierarchical framework, we design a two-stage training method to train the two types of tasks separately. We perform pre-training of the model in simulation, and then transfer the learned model to the real world without any additional real-world fine-tuning. Experimental results show that the proposed approach outperforms existing methods in task completion rate and grasp success rate with less motion number. Supplementary material is available at https://github.com/DafaRen/Learning_Bifunctional_Push-grasping_Synergistic_Strategy_for_Goal-agnostic_and_Goal-oriented_Tasks.

Acoustic ranging is required to obtain the location of underwater mobile vehicles in the Internet of Underwater Things (IoUT). As seawater is an inhomogeneous medium, the sound velocity in the ocean is not constant, thereby causing sound waves to deviate from a straight line of propagation and bend. Thus, the travel time of the sound wave from the transmitter to receiver cannot be directly converted to a range value using a linear relationship as is done in the case of wireless radio ranging in the air. Therefore, the concept of effective sound velocity was introduced to account for the differences between the sound velocities at a transmitter and receiver. This enables conversion of the travel time to slant distance via a linear relationship. However, the existing methodologies for computing effective sound velocities are computationally intensive, which hinders the use of acoustic ranging in small underwater mobile vehicles operating in the deep sea. This study aimed to resolve this problem by developing an effective, computationally less demanding algorithm that could improve the precision of acoustic ranging on such platforms. The proposed algorithm converts global integrals into local integrals that correspond to depth variation ranges, thereby reducing the amount of integral calculation. The performance of the proposed algorithm is evaluated using extensive simulations and real deep-sea experimental data sets obtained at a depth of 3000 m. The results verify the efficacy of the algorithm in realizing real-time underwater acoustic ranging in deep sea. The proposed algorithm can improve the accuracy of real-time effective sound velocity measurement by small underwater mobile vehicles, and subsequently realize low-complexity underwater acoustic ranging in the deep-sea IoUT networks.

Accurately detecting multi-class objects in a single pass is critical but challenging for real-world autonomous driving scenarios. Several single-class anchor-based methods have recently achieved the state-of-the-art performance in the car category, but when extending to multi-class detection tasks, their performance on small objects (i.e., pedestrians and cyclists) is limited. We find that the core problem that causes this phenomenon lies in the unbalanced sample quality and the classification objective. To address this problem, we proposed a single-model multi-class 3D object detector with balanced sample assignment and objective, named BSAODet. Specifically, the quality-balanced sample assignment (QBSA) is introduced to dynamically collect stable high-quality samples for each class according to the predicted sample performance and geometric constraints. In conjunction with the QBSA, the class-balanced classification objective (CBCO) performs instance-wise label normalization and weighting on positive samples, preventing the model from biasing toward objects with more samples. Extensive experiments on the popular KITTI dataset, the latest large-scale ONCE dataset, and the challenging Waymo Open Dataset show that our method steadily improves the performance of current state-of-the-art detectors by 2–7 mAP in pedestrians and cyclists while maintaining competitiveness in cars. Moreover, our best model achieves 66.31 mAP on three classes, outperforming all published LiDAR-only detectors on the KITTI benchmark.

Quaternion singular value decomposition (QSVD) is a robust technique of digital watermarking that extracts high quality watermarks from watermarked images with low distortion. However, the existing QSVD-based watermarking schemes face the obstacle of "explosion of complexity" and have much room for improvement in terms of real-time, invisibility, and robustness. In this paper, we overcome such obstacle by introducing a new real structure-preserving QSVD algorithm and propose a novel QSVD-based watermarking scheme with high efficiency. Secret information is transmitted blindly by incorporating two new strategies: coefficient pair selection and adaptive embedding. The highly correlated coefficient pairs determined by the normalized cross-correlation method reduce the impact of embedding by reducing the maximum modification of the coefficient values, resulting in high fidelity of the watermarked image. Large-size 8-color binary watermark and QR code effectively verify that the proposed watermarking scheme can resist various image attacks in numerical experiments. Two keys designed by Logistic chaotic map ensure the security of the watermarking system. Under the premise of considering the correlation of color channels, the proposed watermarking scheme not only performs well in real-time and invisibility, but also has satisfactory advantages in robustness compared with the state-of-the-art methods.

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.

As a prototype of the underwater Internet of Things-enabled maritime transportation systems, multi-Autonomous Underwater Vehicle (AUV)-based Underwater Wireless Networks (UWNs) have become an important research topic due to their distribution and robustness. In this paper, the concept of multi-AUV-based UWNs is first defined, where AUV is regarded as a network node, and communication among the AUVs is the potential network links. Then, to improve network scalability and controllability, a paradigm of Software Defined multi-AUV-based UWNs (SD-UWNs) is proposed, where the Software Defined Network (SDN) technique is used to upgrade the UWN architecture by directing intelligent network functions. Topology and artificial potential field theories are applied to construct a network control model for the SD-UWNs. Based on the efficient data sharing ability of the SD-UWNs, an early warning obstacle avoidance-enabled path planning scheme is proposed to guarantee safe sailing of the SD-UWNs, where comprehensive obstacle avoidance scenarios are taken into account. Simulation results demonstrate that the proposed method is effective in planning the cooperative operation for the SD-UWNs and is capable of performing accurate and reliable obstacle avoidance tasks.

Few-shot semantic segmentation(FSS) is intended to segment a foreground object from a query image with a novel object using only a few annotated support images. Although attracting the attention of many researchers, this challenging problem remains to be not well solved due to two critical issues: (1)The information mismatching between support and query features leads to model distraction. (2)The key feature of query images is not activated well. In this paper, we introduce the Prior Semantic Harmonization Network(PSHNet) to tackle these limitations. PSHNet is composed of three effective modules. The Semantic Harmonization Module(SHM) corrects the information matching between support and query images, while the Feature Activation Module(FAM) activates the key feature of query images. Furthermore, we introduce a Hierarchical Aggregation Module(HAM) to refine each output of the multi-scale module. Experiments show that our model achieves an excellent performance on both PASCAL-5iand COCO-20idatasets.

Open-world object detection (OWOD) is a challenging problem that combines object detection with incremental learning and open-set learning. Compared to standard object detection, the OWOD setting is task to: 1) detect objects seen during training while identifying unseen classes, and 2) incrementally learn the knowledge of the identified unknown objects when the corresponding annotations is available. We propose a novel and efficient OWOD solution from a prototype perspective, which we call OCPL: Open-world object detection via discriminative Class Prototype Learning, which consists of a Proposal Embedding Aggregator (PEA), an Embedding Space Compressor (ESC) and a Cosine Similarity-based Classifier (CSC). All our proposed modules aim to learn the discriminative embeddings of known classes in the feature space to minimize the overlapping distributions of known and unknown classes, which is beneficial to differentiate known and unknown classes. Extensive experiments performed on PASCAL VOC and MS-COCO benchmark demonstrate the effectiveness of our proposed method.

Pairwise ranging between nodes plays a crucial role in Internet-of-Underwater-Things (IoUT) networks, and it typically impacts the overall performance of such networks. As the sound speed depends on several parameters, time-of-flight-based techniques cannot work well under varying sound speeds in actual underwater conditions. Pairwise ranging algorithms that consider stratification should be studied to improve ranging accuracy. However, there is a tradeoff between underwater acoustic ranging accuracy and number of calculations. This makes it challenging to implement underwater acoustic ranging algorithms in IoUT networks. In this article, we propose an underwater acoustic horizontal ranging algorithm that rapidly and accurately estimates the horizontal range from a sender to a receiver under an arbitrary sound-speed profile in the deep sea. Simulation results show that the proposed algorithm accurately calculates the horizontal range with a low computational complexity. We validate the performance of the proposed algorithm using the data collected during an ultrashort baseline precision test in the South China Sea.

The input/output (I/O) pins of an industry-level fluorescent optical fiber temperature sensor readout circuit need on-chip integrated high-performance electro-static discharge (ESD) protection devices. It is difficult for the failure level of basic N-type buried layer gate-controlled silicon controlled rectifier (NBL-GCSCR) manufactured by the 0.18 µm standard bipolar-CMOS-DMOS (BCD) process to meet this need. Therefore, we propose an on-chip integrated novel deep N-well gate-controlled SCR (DNW-GCSCR) with a high failure level to effectively solve the problems based on the same semiconductor process. Technology computer-aided design (TCAD) simulation is used to analyze the device characteristics. SCRs are tested by transmission line pulses (TLP) to obtain accurate ESD parameters. The holding voltage (24.03 V) of NBL-GCSCR with the longitudinal bipolar junction transistor (BJT) path is significantly higher than the holding voltage (5.15 V) of DNW-GCSCR with the lateral SCR path of the same size. However, the failure current of the NBL-GCSCR device is 1.71 A, and the failure current of the DNW-GCSCR device is 20.99 A. When the gate size of DNW-GCSCR is increased from 2 µm to 6 µm, the holding voltage is increased from 3.50 V to 8.38 V. The optimized DNW-GCSCR (6 µm) can be stably applied on target readout circuits for on-chip electrostatic discharge protection.

This article addresses the trajectory consensus problem of coordinating the trajectories of vehicles at multiple future time points. The objective is the consensus of the geometry of the vehicles' planned trajectories. The geometric feature of trajectories is parameterized by a set of trajectory states defined as required lengths along the trajectory to reduce the distance to its ending point to specific values. To solve this special consensus problem involving coupled state variables, the conventional consensus model is extended by attaching it to a mapping from the state variables to the trajectory's geometry. This mapping is established using a homotopic structure that creates a compact and efficient form for the mapping. The geometry of the homotopic structure is based on the shapes of its envelopes, and the elements in the structure are derived from their deformation. Through a homotopic search in the structure, an asymptotic consensus of trajectory states is achieved. Simulation results show the proposed coupled state consensus method can achieve better performance on the consensus of multiple vehicles than the conventional isolated state consensus method.

The offline generative Siamese trackers are equipped with the pre-defined anchors and the fixed target template. They overlook the target-background discriminative information, and lack the flexible target-specific update strategy. To overcome above drawbacks, we propose an adaptive and discriminative Siamese complementary tracking network with flexible update scheme. It consists of three collaborate subnetworks: anchor-free Siamese attention classification and regression subnetwork, online discriminative learning with multi-attention and multi-peak suppression, classifier guided template update subnetwork. All of them are interdependent and complementary to enhance each other for accurate target location. Specifically, an anchor-free multi-attention Siamese tracking subnetwork directly classifies the corresponding image patches with reliability assessment, and cascaded regresses the bounding boxes to progressively refine the predicting accuracy. Its evaluation is flexible and general with both proposal and anchor free in per-pixel prediction manner. Then, we integrate an online discriminative classifier optimizing module as a complementary subnetwork. It introduces spatial-temporal attention mechanism to fully explore multi-view multi-scale target-specific features, and evaluates multi-peak suppression to obtain a single centered peak response map. Its classified results can be fused with Siamese classification branch for accurate target location. Finally, the template update subnetwork is guided by the online discriminative classification scores. Extensive experiments on recent tracking datasets verify its top-ranked tracking accuracy and robustness against some state-of-the-art trackers.

In this article, the distributed fusion estimation issue with the dimensionality reduction strategy under DoS attacks and deception attacks is investigated for a class of stochastic uncertain systems with fading measurements. The stochastic uncertainties existed in the system and measurement equations are represented by state-dependent noises. The fading measurements are depicted by stochastic variables with known statistics. Then, a novel attack and compensation model is proposed to display the randomly occurring behaviors of the DoS attacks and the deception attacks within a unified framework. Furthermore, a distributed multisensor fusion estimation (DMSFE) algorithm is presented. An explicit form of dimensionality reduction is designed against attacks. Stability conditions are derived such that the mean square errors (MSEs) of the proposed DMSFE are bounded. A sequential covariance intersection fusion estimator (SCIFE) is designed to prevent the cross fusion covariance matrices calculating, which owns lower accuracy by smaller computation cost than DMSFE. An illustrative example is provided to show the effectiveness and merits of the proposed algorithm.

In recent years, developments in data collection schemes based on multipleautonomous underwater vehicles(AUVs) are facilitating the realization of the so-calledunderwater acoustic sensor networks(UASNs). As yet, the lack of suitable collaboration mechanisms among multiple AUVs, which are based on functional or resource distributions, prevents effective information sharing and yields increased data collection delays, thus reducing the capacity of the networks. In this article, to address these shortcomings, we propose astate prediction-based data collection(SPDC) algorithm for UASNs. The principle of operation is as follows. First, some cluster pairs named observation clusters obtain and exchange the state information about AUVs between the adjacent subregions. Based on the shared information, the AUVs predict each other’s status and adjust their data collection areas. Then, the AUVs use a heuristic strategy to complete the path planning based on the updated access area. Finally, a scheduling data forwarding mechanism reduces the diving number of the AUVs, by reasonably allocating the overlapped data unloading intervals between the AUVs and a mobile sink. Experimental results prove that the proposed algorithm shows satisfactory performance in reducing data collection delays and in improving the total network lifetime.

Industrial Internet of Things inevitably leads to the implementation of highly data-intensive devices, where the associated sensing nodes accelerate the energy consumption rate, which ultimately produces an energy bottleneck. To address this issue, this article proposes adynamic collaborative charging algorithmthat acts on both the mobile nodes and the static nodes in a sensing node network. The proposed scheme is to design a collaborative group of charging robots that can rendezvous with the sensing nodes. The group includes aerial charging vehicles (ACVs)—able to charge the underpowered mobile nodes, and terrestrial charging vehicles (TCVs), which charge their targeted static nodes. The aim of this study is to optimize the charging effect and the energy cost in the rendezvous process. This approach consists of two subalgorithms: 1) a charging algorithm for mobile nodes (CAMNs) and 2) a charging algorithm for static nodes (CASNs). The CAMNs is designed so that each underpowered mobile node can be charged by a dedicated ACV. For this purpose, a deep learning model is trained to divide the underpowered mobile nodes into appropriate clusters, each of which is equipped with a mobile base station. The rendezvous process is then constructed as a mixed continuous/discrete optimization problem, which is solved by using the firefly algorithm. In addition, the CASNs ensures that the TCVs traverse their routes, charging static nodes as they proceed. This traversing process was formulated as a multiobjective optimization problem, solved by using genetic algorithm. Through various experiments and case studies, the results have demonstrated both the feasibility and the efficiency of the proposed algorithms.

As a result of the increasing deployment of Industrial-Internet-of-Things (IIoT) architectures, large volumes of multidimensional data are continuously generated. An important issue with these data is that higher dimensionality increases the degree of fragmentation. Furthermore, data sets collected by IIoT nodes often display outliers, which are usually caused by anomalous events or errors. These outliers contain considerable valuable information, which prevent the normal operation of the system. Thus, methodologies are able to quantify the obtained information to protect the high priority IIoT nodes, are crucial. This study aims at developing such a method driven by sixth-generation (6G) networks. The proposed algorithm uses a multidimensional data relationship diagram to characterize the spatiotemporal correlations among heterogeneous data. Then, an autoregressive exogenous model is used to eliminate the effects of noise on sensor data, and to help in detecting anomalies. Finally, the algorithm produces a Cumulative Coefficient of Value (CCoV), to identify high-value sensing devices and enable massive Internet of Things (IoT) with 6G-using the characteristic patterns hidden within the data. The experimental results demonstrate that the proposed method can effectively handle the effects of the ubiquitous interference noise in complex industrial environments. Moreover, the method yields effective anomaly detection and compensates for some of the shortcomings in traditional methods.

The fifth-generation of wireless communication (5G) is a promising paradigm toward massive interconnectivity within Internet-of-Things (IoT) networks. However, because the data traffic throughput sharply increases with the number of IoT devices, a tremendous burden on the backhaul links and core networks results. With this in mind, mobile edge caching is an effective method that can relieve stress of the backhaul links, while decreasing the service latency. The purpose of this study is to analyze the problem of jointly optimizing cooperative edge caching and radio resource allocation in 5G-enabled massive IoT networks. For that, a joint optimization long-term nonlinear integer programming problem is posed. This class of problems is known to be NP-hard; thus, to reduce the problem complexity, a divide and conquer scheme will be applied—the task at hand will be divided into two subproblems: 1) cooperative edge caching and 2) radio resource allocation. The cooperative edge caching subproblem is formulated as a constrained Markov decision process. Herein, a deep reinforcement learning method to optimize the caching decisions for all the edge nodes. Then, based on the resulting optimal caching decisions, the radio resource allocation subproblem for each edge node is posed as an NLIP problem, and an improved branch-and-bound method is proposed to yield the optimal radio resource allocation decisions for each edge node. Extensive simulations were performed to confirm that the proposed methods have the capability of enhancing the content caching hit ratio, while lessening the content retrieving delays for 5G-enabled massive IoT networks—improving over various baseline algorithms.

Due to the complicated model characteristics, only a few results focusing on stability analysis have appeared on singularly perturbed descriptor systems (SPDSs). This article instead proposes an integral sliding-mode control strategy for a kind of Takagi-Sugeno fuzzy approximation-based nonlinear SPDSs under time-varying nonlinear perturbation. An appropriate fuzzy integral switching manifold that fully accommodates the system features is designed to completely reject the matched perturbation without amplifying the unmatched one. To facilitate the synthesis of the high-level controller (HLC), the sliding-mode dynamics (SMD) is transformed into an augmented form. Thanks to the adoptions of a novel singular perturbation Lyapunov function, Finsler's lemma, as well as the fixed-point principle, the existence and uniqueness of the solution and the exponential admissibility for the augmented SMD are analyzed. A solution for the designed HLC is further provided. To guarantee the sliding motion, a fuzzy integral sliding-mode controller (FISMC) is synthesized by analyzing the sliding motion reachability. An adaptive FISMC is also given to deal with the unknown upper bounds of the matched perturbation. Finally, the applicability of the developed FISMC strategy is testified by a practical example.

With the rapid development of urbanization, enormous amounts of vehicular services have been emerging and challenge both the architectures and protocols of the Internet of Vehicles. The high-speed mobility features of nodes in the vehicular networks changes the network topology frequently, resulting in low routing efficiency, and higher packet loss. In this article, we utilize software-defined networking (SDN) technology to decouple the network control plane from the data forwarding plane, and divide the vehicular networks into three functional layers: data, control, application layers. Based on the proposed network architecture, we propose an adaptive traffic engineering (TE) mechanism to guarantee the V2V continuous traffic in vehicular networks with high-speed mobile vehicles or dynamic network topology. In particular, the proposed TE is based on a proposed active network measurement mechanism under the assistance of the centralized management ability of the SDN technique. The proposed active network measurement approach is a greedy approach where the next hop determination for the measurement packet takes multiple link reliability factors (e.g., the delay, the length, the packet error rate, the neighbors, etc.) into account. Then, we utilize the artificial bee colony (ABC) algorithm to optimize the TE mechanism that can be deployed and executed in the SDN controller. By the proposed TE mechanism, multiple candidate end-to-end paths can be concurrently measured, and the optimal data forwarding path can be adaptively switched. Simulation results demonstrate that our approach performs better than some recent research outcomes, especially in the aspect of performing reliable data forwarding (almost 5% better than the compared objects).

This article presents a fault-tolerant tracking control strategy for Takagi–Sugeno fuzzy model-based nonlinear systems which combines integral sliding mode control with adaptive control technique. Two common actuator faults: 1) loss of effectiveness and 2) increased bias input, are considered simultaneously. The fuzzy tracking control system is first established by incorporating the integral term of the output tracking error. Then, an appropriate fuzzy integral switching surface is designed such that the corresponding sliding motion only suffers from the unamplified unmatched disturbance. The solution of the nominal tracking controller can be transformed into a to convex optimization problem. In particular, an adaptive fuzzy sliding mode tracking controller is synthesized to ensure the accessibility of the sliding motion despite the effect of actuator faults and unknown disturbances. Finally, the proposed tracking strategy is verified by applying it to the dynamic positioning control of unmanned marine vehicles.

The Internet-of-Things (IoT) system is a novel networking technology that connects smart communication devices through Internet-enabled infrastructure to enhance wireless communications. The explosive growth of IoT devices connectivity has increased energy consumption drastically that raises economic and physical environment concerns. To meet the challenges posed by high energy consumption, energy efficiency has become an urgent need for IoT networks recently. This article examines resource allocation and formulates the joint optimization problem for power allocation and user selection subject to the maximum transmit power and different Quality-of-Service (QoS) requirements, to achieve an improved energy efficiency performance in IoT networks under channel uncertainty. Furthermore, the formulated optimization problem is mixed-integer nonlinear programming (MINLP) with no practical solutions. Due to the nonconvexity and NP-hardness of the MINLP problem, the primal optimization problem is transformed into a convex problem and solved optimally for power allocation and user selection, by applying the Lagrangian dual decomposition method and the Kuhn-Munkres algorithm, respectively. An efficient joint iterative algorithm is proposed to maximize energy efficiency performance with guaranteed convergence within few numbers of iterations. The numerical results validate the robustness of the proposed algorithm and significantly show its superior performance as compared with the baseline algorithms.

Internet of Things (IoT) is an emerging networking paradigm that enhances smart device communications through Internet-enabled systems. Due to massive IoT devices connectivity with economic and greenhouse emission effects, the energy-efficiency poses critical concerns. Under imperfect channel state information (CSI), this article investigates joint optimization of user selection, power allocation, and the number of activated base station (BS) antennas of multiple IoT devices considering the transmit power and different Quality-of-Service (QoS) requirements in combinatorial mode to maximize energy-efficiency. The optimization problem formulated is a nonconvex mixed-integer nonlinear programming, which is NP-hard with no practical solution. The primal optimization problem is transformed into a tractable convex optimization problem and separated into inner and outer loop subproblems. This article proposes a joint energy-efficient iterative algorithm, which utilizes a successive convex approximation technique and the Lagrangian dual decomposition method to achieve near-optimal solutions with guaranteed convergence. The simulation results are provided to evaluate the proposed algorithm and its significant performance gain over the baseline algorithms in terms of energy-efficiency maximization.

This article reports an automated optically-induced dielectrophoresis (ODEP) system for characterizing the specific membrane capacitance (SMC) of individual cells. The simulation of cell motion is conducted to analyze the electrokinetic forces acting on the cell. A self-developed visual tracking algorithm for multicells is used to realize an automated process for determining the frequency-sweeping range, crossover frequencies, and cell radii. The SMC values of malignant bladder cancer cells (T24 and RT4) and normal urothelial cells (SV-HUC-1) were quantified using the automated system, demonstrating that the system has a measurement speed of ~1 cell/s, an accuracy of 1 kHz for the crossover frequency determination, and an accuracy of 0.2 μm for the cell radius measurement.

Among several new technologies, such as social and cognitive mobile computing, wireless sensor networks (WSNs) constitute the founding pillar of the industrial Internet of Things. These networks are expected to play an increasingly important role in our daily lives. Social and cognitive mobile computing requires the sharing of data recorded by sensor nodes. However, the data can be vulnerable to attacks. It is of utmost importance to protect the users privacy while ensuring the security of the WSNs. This investigation is focused on the source-location privacy (SLP) of WSNs. This article proposes a dynamic multipath privacy-preserving routing (DMPPR) scheme based on multiple sinks for protecting the privacy. Different from single sink schemes, the technique of using multiple sink nodes to protect SLP is discussed in this article. Furthermore, a packet-slicing transmission scheme that generates a large number of dynamic routings based on multiple sink nodes is adopted for transmitting the packets. Local adversaries are considered, and to cope with these adversaries, a transmission loop, constructed using real and fake packets, is proposed to confuse the adversaries during the source detection process. The aim is to break the sociality between the sensor nodes. Simulations performed in MATLAB show that the proposed method outperforms similar existing schemes in terms of the secure time, adversary's capture probability, and node utilization ratio. Moreover, the DMPPR scheme also reduces energy consumption by allowing more nodes in the nonhotspot areas to participate in the packet transmission process.

Instability and slowness are two main problems in deep reinforcement learning. Even if proximal policy optimization (PPO) is the state of the art, it still suffers from these two problems. We introduce an improved algorithm based on proximal policy optimization, mixed distributed proximal policy optimization (MDPPO), and show that it can accelerate and stabilize the training process. In our algorithm, multiple different policies train simultaneously and each of them controls several identical agents that interact with environments. Actions are sampled by each policy separately as usual, but the trajectories for the training process are collected from all agents, instead of only one policy. We find that if we choose some auxiliary trajectories elaborately to train policies, the algorithm will be more stable and quicker to converge especially in the environments with sparse rewards.

The unmanned surface vehicles (USVs) have become a major trend in the construction of naval equipment and its flexibility and intelligence making it widely used in real-scenes. For cooperative defense with multiple USVs to intercept intruders, it is proposed that planning the path with obstacle avoidance and protecting the target by task allocation actions. The particle swarm optimization based on probe mechanism (PM-PSO) is proposed for pathing planning with obstacle avoidance. With the consideration of the constraints of different defense schemes such as the path cost, the interception loss, the defense income and so on, it is proposed that the dispersed particle swarm optimization based on genetic algorithm (GA-PSO) for the interception task allocation. Furthermore, the fitness function is proposed to evaluate the feasibility of the interception path and the quality of the allocation scheme. Extensive simulation experiments are conducted and demonstrated the effectiveness, rationality and superiority of the proposed methods.

Caching content with fog computing at the edge nodes has been a promising alternative to mitigate burdens of backbone networks and improve mobile users' quality of experience in mobile social networks (MSNs). However, as edge node may be vulnerable due to the attacks from malicious users, the design of secure caching schemes for the fog/edge enabled MSNs becomes a new challenge. In this paper, to tackle the above problem, we propose a secure caching scheme for disaster backup in MSNs with fog computing. Specifically, to protect the privacy, a partitioning and scrambling method is first designed to encrypt the contents. Then, the encrypted contents are replicated to multiple replicates, where these replicates are delivered and stored in different servers. Based on the recovery time objective and content delivery latency, an auction game model is developed to determine the optimal servers, where both edge nodes and cloud servers can obtain the maximum utilities. Extensive simulations are conducted to show the effectiveness and reliability of the proposed scheme.

Based on the multi-agents system control theory and technology, this paper presents the cooperative defense process of multiple unmanned surface vehicles (USVs) operating in complicated sea environment, and explains the quantification of the battle effectiveness, cooperative strategy, task allocation and finally describes in detail the cooperative strategies on random graph. Then we point out the problems in the current cooperative defense process and the future development direction. The cooperative defense research of USVs in the sea environment has a great significance to the effective promotion of social and military efficiency.

Unmanned combat system is one of the development trend of modern weapons and equipment and has applied to military affairs. The major goal of the Unmanned Surface Vehicles (USVs in short) is to destroy protected targets in the shortest time. This paper originates from biology, putting forward a new attack strategy—The USV combat Based on Wolves Attack with Weight. Namely, using the characteristics of the wolves attack to study the process of attacking. It also discusses the attack measures from weights, velocity and firepower of USVs through weight distribution and summarizes the advantages of wolves attack in USV combat.

Based on the multi-agents system control theory and technology, this paper presents the construction of cooperative defense system of multiple unmanned surface vehicles (USVs) operating in complicated sea environment, develops the mathematical formula describing the trajectory equation when the USV intercepts the intruder, and explains the proposed coordination control method used in the corresponding defense system. Then we point out the future development requirement of the cooperative defense system. The cooperative defense system research of USVs in the sea environment has a great significance to the effective promotion of social and military efficiency, and it is the basis of the implement about cooperative strategies.