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With the explosive growth of devices and tasks deployed in the industrial Internet of Things (IIoT), the lack of interconnection and collaboration between devices leads to poor timeliness and security in IIoT resource scheduling. This article focuses on the issue of adaptive scheduling of resources in large-scale IIoT. First, a collaborative terminal-edge IIoT architecture is designed, which introduces blockchain and AI technology to support dynamic resource scheduling in untrustworthy environments. Then, a smart contract-based multidimensional resource transaction model is developed to improve the efficiency and security of resource scheduling by establishing a credit-based consensus mechanism. Distributed transaction learning resource scheduling algorithm is further proposed to implement resource-adaptive scheduling between devices in IIoT. Extensive simulation experiments are conducted to evaluate the proposed method with respect to several performance aspects covering the scheduling decision delay, transaction generation ratio, and security. The obtained results demonstrate that the comprehensive scheduling performance of the proposed method outperforms other existing algorithms. [ABSTRACT FROM AUTHOR]

Data collection in underwater acoustic sensor networks (UASNs) and the exposure of node location information pose a threat to the security of the entire network. Therefore, the main challenge for underwater acoustic sensor network security is to protect the security and privacy of the node locations. Compared to active attacks, the characteristics of passive attacks are indistinguishable. Therefore, this research focuses on passive attacks in underwater acoustic sensor networks, and an autonomous underwater vehicle (AUV)-aided data-importance-based scheme for protecting location privacy (DIS-PLP) is proposed. The DIS-PLP comprises three main parts. First, the anchor node calculates the importance of the current data based on the historical dataset and decides whether to send data packets to the base station according to the importance result. Subsequently, an AUV privacy violation is defined, and an AUV path planning method is proposed to prevent attackers from tracking the AUV to obtain the location of the source node. Finally, to prevent nodes from waiting too long for AUVs, a secure multi-hop transmission method is proposed that uses fake source nodes to send fake source data packets to hide the real traffic. The simulation results show that the DIS-PLP offers well performance in terms of safety time and delay, which has strong practical significance for Smart Ocean to improve the level of network security. [ABSTRACT FROM AUTHOR]

The diffusion of toxic gas, biochemical material, and radio-active contamination – known as continuous objects – endangers the safe production of the petrochemical and nuclear industries. To mitigate these well known hazards, the new paradigm of industrial wireless sensor networks (IWSNs) shows great potential in monitoring evolving hazardous phenomena in unfriendly industrial fields. In order to prolong the lifetime of these networks, existing research focuses on energy-efficient boundary nodes selection. However, sensor state cannot be scheduled proactively, due to the difficulty in predicting the spatiotemporal evolution of diffusive hazards. In this article, we propose a motion behavior learning predictive tracking (MBLPT) algorithm for continuous objects in IWSNs. Considering the relatively unpredictable patterns exhibited by continuous objects, the MBLPT uses a data-driven approach for motion state recognition, and then utilizes Bayesian model averaging (BMA) for future boundary prediction. The prediction of the MBLPT provides the knowledge for establishing a wake-up zone, in which standby nodes are activated in advance to participate in tracking the upcoming boundary. Simulation results demonstrate that the MBLPB achieves superior energy efficiency while keeping effective tracking accuracy. [ABSTRACT FROM AUTHOR]

As an effective security mechanism, trust models have been proposed to estimate the reliability of the individual nodes in Underwater Acoustic Sensor Networks (UASNs) during adverse attacks. However, existing trust models neglect the relative importance of the different nodes within the network topology. Further, few trust models study the effects of defective recommendation trust filtering. In this work, we propose an adaptive trust model based on the Long Short-Term Memory (LSTM) network model for UASNs, which we term LTrust. The LTrust is composed of two stages: trust data collection and trust evaluation. In the first stage, the characteristics of the network topology are leveraged towards evaluating direct trust evidence, by aggregating the communication trust and environment trust metrics; a defective recommendation filtering method is designed for broadcasting accurate trust recommendations among the nodes. In the second stage, an adaptive trust model is designed based on the LSTM model, to identify anomalous nodes by evaluating their trust value. The LTrust model has been tested under both hybrid attack and single-mode attack scenarios. Simulation results demonstrate that the LTrust achieves effective performance, as compared to other approaches proposed in the literature, in terms of trust value, accuracy and error rate. [ABSTRACT FROM AUTHOR]

Classical edge computing algorithms assume that the execution time is always known in resource allocation. However, in practice, the execution time in the edge server is hard to estimate due to the complex environment, especially in Internet of Vehicles (IoV), which makes resource allocation a significant challenge. To address this problem, we propose an optimal resource allocation approach based on Federated Learning (FL). In our proposed approach, we consider both the delay and energy consumption. First, we assume that we have perfect knowledge about the execution time in the edge server, and we obtain the optimal CPU cycles which should be assigned to process 1-bit task. We also verify the Poisson property of the task arrival for each server pool in the edge server. Since we use Delay Energy Product (DEP) as our optimization target for resource allocation, we can derive the optimal balanced delay and energy performance in the scheduling. Next, we propose the Federated Learning based method to estimate the execution time in the edge server. Simulation results obtained demonstrate the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

Rescue assignments strategy are crucial for multiple Autonomous Underwater Vehicle (multi-AUV) systems in three dimensional (3-D) complex underwater environments. Considering the requirements of rescue missions, multi-AUV systems need to be cost-effective, fast-rescuing, and less concerned about the relationship between rescue missions. The real-time rescue plays a vital role in the multi-AUV system with the characteristics mentioned above. In this paper, we propose an efficient Reward acting on Reinforcement Learning and Particle Swarm Optimization (R-RLPSO), to provide a strategy of real-time rescue assignment for the multi-AUV system in the 3-D underwater environment. This strategy consists of the following three parts. Firstly, we present a reward-based real-time rescue assignment algorithm. Secondly, we propose an Attraction Rescue Area containing a Rescue Area. For the waypoints in each Attraction Rescue Area, the reward is calculated by a linear reward function. Thirdly, to speed up the convergence of the R-RLPSO and mark the rescue states of Attraction Rescue Area and rescue area, we develop a Reward Coefficient based on the reward of all Attraction Rescue Areas and Rescue Areas. Finally, simulation results show that the system based on R-RLPSO is more cost-effective and time-saving than that of based on comparison algorithms ISOM and IACO. [ABSTRACT FROM AUTHOR]

This article develops a robust global strategy for identifying the linear parameter varying (LPV) errors-in-variables (EIVs) systems subjected to randomly missing observations and outliers. The parameter interpolated LPV autoregressive exogenous model with an uncertain/noisy input is investigated and a nonlinear state-space model is considered for the input generation model (IGM). The parameters estimation of the LPV EIV systems with nonideal observations is realized using the expectation–maximization algorithm which is particular effective for the incomplete data issue. To ensure the robustness in the identification, the Student’s t-distribution which is characterized by its adjustable degree of freedom, is used to handle the measurement non-normality. Since the posterior distributions of the latent states in the IGM are also involved in the identification process and they are difficult to calculate directly, the particle filter is introduced to recursively approximate them instead. Finally, the verification examples are given to demonstrate the effectiveness of the developed strategy. [ABSTRACT FROM AUTHOR]

Performing effective medium access control (MAC) encounters great challenges for mobile underwater acoustic networks (UANs), because it suffers from low signal-to-noise ratio, Doppler shift, large communication latency, and so on. Multi-carrier code-division multiple access (MC-COMA) is a promising modulation technique appropriate for solving the above restrictions. In this article, we design a novel MC-COMA-based cross-layer MAC scheme for mobile UANs, called MCI-MAC, to achieve efficient high-concurrency communication. Specifically, this method allocates spread spectrum sequences dynamically on the basis of velocity, propagation distance, data size, and data grade to improve robustness and flexibility of communication. Moreover, we propose an ant-colony-based optimal channel selection algorithm to decrease inter-carrier interference and inter-symbol interference, which concurrently maximizes energy efficiency and network throughput. In addition, we further propose a solution to solve multihop networks' collisions. Simulation results show that MCI-MAC is more effective than the state-of-the-art methods in mobile UANs. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensor networks (UASNs) have been widely promoted for developing various categories of marine applications, where the sensor nodes cooperate to complete specific tasks. Given the fact that the sensor nodes are unattended while continuously exposed to harsh environments, an associated trust model plays a significant role in node trustworthiness evaluation and defective node detection, such as the case of adverse attacks on the network. However, the existing trust models only evaluate the communication behavior and the energy of the sensor nodes, ignoring the effects of underwater environmental noise on trust reliability. Further, most trust models are designed with arbitraty weighted trust metrics, causing inevitable evaluation errors. To achieve the accurate calculation of node trust, we propose a new anomaly and attack resilient trust model, based on the isolation forest. We refer to this model as ITrust. The proposed ITrust model consists of two phases: trust metrics specifics and defective node detection. In the first phase, the trust dataset is integrated from four types of trust metrics: communication trust, data trust, energy trust, and environment trust. In the second stage, trust is evaluated with the obtained trust dataset using the isolation forest algorithm. Simulation results demonstrate that the proposed ITrust can detect defective nodes effectively, and achieves higher detection accuracy than that of the existing trust models in a noisy environment. [ABSTRACT FROM AUTHOR]

The development of the industrial Internet of Things paradigm brings forth the possibility of a significant transformation within the manufacturing industry. This paradigm is based on sensing large amounts of data, so that it can be employed by intelligent control systems (i.e., artificial intelligence algorithms) eliciting optimal decisions in real time. Ensuring the accuracy and reliability of the intelligent wireless sensing and control system pipeline is crucial toward achieving this goal. Nevertheless, the presence of noise in actual wireless transmission processes considerably affects the quality of the sensed data. Typically, noise and anomalies present in the data are very difficult to distinguish from each other. Conventional anomaly-detection techniques generate many error reports, which cause the control systems to issue incorrect responses that hinder the industrial production. In this article, a novel solution is proposed to denoise data while simultaneously preserving the actual anomalies. The proposed approach operates by measuring both the neighbor and background contrasts in computing a noise score. The trust level of each data point is then calculated through a correlation measure to purge spurious data. Extensive experiments on real datasets demonstrate that the proposed approach yields effective performance, as compared to existing methods, and it meets the requirements of low latency—facilitating the normal operation of the monitored control systems. [ABSTRACT FROM AUTHOR]

In recent years, developments in data collection schemes based on multiple autonomous underwater vehicles (AUVs) are facilitating the realization of the so-called underwater 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 a state 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. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensor networks (UASNs) have been widely applied in marine scenarios, such as offshore exploration, auxiliary navigation and marine military. Due to the limitations in communication, computation, and storage of underwater sensor nodes, traditional security mechanisms are not applicable to UASNs. Recently, various trust models have been investigated as effective tools towards improving the security of UASNs. However, the existing trust models lack flexible trust update rules, particularly when facing the inevitable dynamic fluctuations in the underwater environment and a wide spectrum of potential attack modes. In this study, a novel trust update mechanism for UASNs based on reinforcement learning (TUMRL) is proposed. The scheme is developed in three phases. First, an environment model is designed to quantify the impact of underwater fluctuations in the sensor data, which assists in updating the trust scores. Then, the definition of key degree is given; in the process of trust update, nodes with higher key degree react more sensitively to malicious attacks, thereby better protecting important nodes in the network. Finally, a novel trust update mechanism based on reinforcement learning is presented, to withstand changing attack modes while achieving efficient trust update. The experimental results prove that our proposed scheme has satisfactory performance in improving trust update efficiency and network security. [ABSTRACT FROM AUTHOR]

Due to the flammability, explosiveness and toxicity of continuous objects (e.g., chemical gas, oil spill, radioactive waste) in the petrochemical and nuclear industries, boundary tracking of continuous objects is a critical issue for industrial wireless sensor networks (IWSNs). In this article, we propose a continuous object boundary tracking algorithm for IWSNs – which fully exploits the collective intelligence and machine learning capability within the sensor nodes. The proposed algorithm first determines an upper bound of the event region covered by the continuous objects. A binary tree-based partition is performed within the event region, obtaining a coarse-grained boundary area mapping. To study the irregularity of continuous objects in detail, the boundary tracking problem is then transformed into a binary classification problem; a hierarchical soft margin support vector machine training strategy is designed to address the binary classification problem in a distributed fashion. Simulation results demonstrate that the proposed algorithm shows a reduction in the number of nodes required for boundary tracking by at least 50 percent. Without additional fault-tolerant mechanisms, the proposed algorithm is inherently robust to false sensor readings, even for high ratios of faulty nodes ($\approx 9\%$ ≈ 9 % ). [ABSTRACT FROM AUTHOR]

The Internet of Underwater Things (IoUT) gradually becomes the future direction of ocean. Many devices, which are called the sources, are deployed in the sea to fulfill scientific, civilian, and military needs. If the locations of these sources are leaked, it will cause a serious economic and military crisis. Thus, in this context, the source location privacy protection becomes critical. In this paper, we focus on the source location privacy protection of underwater acoustic sensor networks (UASNs), where a multi-channel interference based source location privacy protection scheme (MCISLP) is proposed. The MCISLP mainly contains four steps. First, to enhance the sense of autonomous underwater vehicles (AUV) for nodes, a noise reduction method is proposed. Then, based on the previous step, an AUV movement prediction method for nodes is presented to ensure the success of packet transmission. After that, for the information exchange among nodes, given the interference of multiple underwater channels, this interference is then utilized to protect the source location privacy. Further, to reduce the channel interference on the source packet, the time slot control is added to weaken the influence. Last, the ant colony algorithm is adopted for the data collection of AUV. The simulation results show that the MCISLP can increase the network safety time by up to 57 $\%$ (compared with data collection algorithms), and balance the energy consumption of nodes (for about 20 $\%$ energy saving), with a minor compromise of energy consumption of AUV and delay. [ABSTRACT FROM AUTHOR]

The coming 6G communication technology introduces the possibility of practice underwater Internet of Things (UIoT) applications with high-speed and reliable underwater communications. Among them, the cooperative coverage path planning (CPP) with autonomous underwater vehicles (AUVs) is a promising approach for enabling deep ocean exploration. The cooperative CPP in underwater is challenged by several marine factors, typically the harsh underwater communication environment, which brings difficulties in sharing the coverage progresses of AUVs and the environmental information such as the seafloor bathymetry, obstacles, etc. Accordingly, this paper proposes a novel CPP method based on 6G enabled cooperative AUVs, named the Pac-AUV. As the name suggests, the method is based on the mechanism of the Ms. Pac-Man game, where AUV-assisted subsea exploration is considered as cooperative Pac-Men sharing the Pac-Dots distributed on the seafloor. The Pac-AUV involves two steps in cooperative CPP. One is a dot-spreading-based mission assignment (DMA), which is discretely performed by each AUV and requires support from reliable underwater communication in sharing the Pac-Dots. The other step is virtual attraction-based coverage path planning (V-CPP), which adopts the virtual attraction force from the Pac-Dots, results in low computational complexities in generating the coverage paths and avoids obstacles. Simulations are performed to demonstrate the performance of the Pac-AUV, and the results prove the advantages of cooperative and balanced CPP executions. [ABSTRACT FROM AUTHOR]

Submarine pipelines are among the most efficient methods of marine oil transportation, and they are widely used in offshore oil exploitation. However, the complex environment of the ocean and malicious attackers may damage the pipeline. They can be efficiently monitored by deploying a multiplicity of sensor nodes, organized in underwater acoustic sensor networks (UASNs). UASNs is located near oil wells, so that UASNs can sense, broadcast, and receive data surveying the status of the submarine infrastructure timely. Inevitably, UASNs may experience security problems, owing to the open nature of the underwater environment. To address these security issues, this paper proposes a new source location privacy protection scheme based on an offshore oil acquisition platform. In the proposed scheme, the nodes are defined as pipeline and environment locations, and are allocated to different clusters. The concept of forming anonymous clusters by exchanging the identities of a source and a cluster head is introduced to determine fake confounding sources. In addition, a data fragmentation method is devised to reduce the energy consumption resulting from fake packet transmission. Particularly, we propose the deployment of disguised AUVs that collect data from fake sources, as to conceal and protect the location of the actual sources. Simulation results show that the safety time of the network is increased by about 6%. Due to the adoption of the data fragmentation method, the energy consumption of nodes is also decreased. [ABSTRACT FROM AUTHOR]

Multi-UAV is a complete system represented by multiple unmanned aerial vehicles (UAV) through collaborative technology. Besides, the UAV group has the advantages in the number and functions that a single UAV does not have. In a complex environment, a single UAV cannot complete a large number of material transportations and escort functions. This paper, from the perspective of cooperative control and material escort of the UAV group, solves the escort problem in the UAV group cruise. Then, an autonomous cooperative flocking algorithm for heterogeneous UAV swarm (ACHF) is proposed. Firstly, the movement control factor of the escort UAV is proposed, and the escort UAV is deployed in the periphery of the transport UAV in the flocking process. Secondly, the system loss function including position and angle is proposed to make the escort UAV deployed as evenly as possible in the periphery of the transport UAV. Finally, in the case of different numbers of UAVs and different environments, the effectiveness of ACHF is supported by theoretical analysis and demonstrated by experiments. The results show that the escort UAVs can be evenly distributed in the periphery of the transport UAVs, forming a group of UAVs with autonomous cooperative capability. [ABSTRACT FROM AUTHOR]

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

In this paper, we take an in-depth study on the data collection scheduling issue in underwater acoustic sensor networks (UASNs). To address this issue, we propose the use of software-defined networking (SDN) technique, based on which the paradigm of software-defined underwater acoustic sensor networks (SD-UASNs) is proposed. With SD-UASNs, we propose an efficient data collection scheme on account of node clustering. We take the data scale into account, propose a fast node clustering approach based on an improved Birch algorithm including node pre-clustering and node re-clustering. In particular, for the node pre-clustering procedure, we improve the cluster feature tree of the Birch algorithm and limit the nodes in each cluster to be within an optimal distance. Based on the proposed pre-clustering approach, we propose a greedy data collection scheme that can heuristically seek optimal solutions when the compressed sampling is taken into account. Evaluation results demonstrate that our scheme performs better in network efficiency than some popular schemes. [ABSTRACT FROM AUTHOR]

This paper proposes a two-way multi-ringed forest (TMR-Forest) to estimating the malignancy of the pulmonary nodules for false positive reduction (FPR). Based on our previous work of deep decision framework, named MR-Forest, we generate a growing path mode on predefined pseudo-timeline of $L$ time slots to build pseudo-spatiotemporal features. It synchronously works with FPR based on MR-Forest to help predict the labels from a dynamic perspective. Concretely, Mask R-CNN is first used to recommend the bounding boxes of ROIs and classify their pathological features. Afterward, hierarchical attribute matching is introduced to obtain the input ROIs’ attribute layouts and select the candidates for their growing path generation. The selected ROIs can replace the fixed-sized ROIs’ fitting results at different time slots for data augmentation. A two-stage counterfactual path elimination is used to screen out the input paths of the cascade forest. Finally, a simple label selection strategy is executed to output the predicted label to point out the input nodule's malignancy. On 1034 scans of the merged dataset, the framework can report more accurate malignancy labels to achieve a better CPM score of 0.912, which exceeds those of MR-Forest and 3DDCNNs about 2.8% and 4.7%, respectively. [ABSTRACT FROM AUTHOR]

As an enhanced system for marine monitoring and autonomous navigation, the space-based automatic identification system (AIS) has attracted extensive attention and become a hot topic for research. However, it encounters the problem of Doppler shift stemmed from the relative motion between satellites and ships, which leads to performance degradation. To circumvent this issue, in this correspondence, we propose an improved Doppler shift estimation algorithm for down-link signals of space-based AIS, utilizing the calculation of the autocorrelation and ratio on the Rice factor. Specifically, the addressed method is robust to the non-Gaussian noise. Further, the suggested approach has low complexity compared with the existing algorithm. Finally, numerical simulations, such as the standard deviation of the estimated Doppler shift versus signal-to-noise ratio, Rice factor and non-Gaussian noise, and the complexity comparisons, are carried out to validate the theoretical analysis, and demonstrate the superior performance of the proposed estimation approach. [ABSTRACT FROM AUTHOR]

In recent years, underwater acoustic sensor networks (UASNs) have been widely investigated for ocean environmental monitoring, offshore exploration, and marine military. The core function of UASNs is to collect data for related operations. A number of factors make the monitoring challenging; ocean thermoclines may affect the communication of the underwater nodes and gliders, reducing their communication range at varying depth; moreover, the node movement caused by Ekman drifting effect can significantly interfere with the data transmissions. Thus, these factors are regarded essential towards characterizing the ocean environment. To address these challenges, a sleep-scheduling-based hierarchical data collection algorithm (SSHDCA) for underwater gliders is designed. The UASN is split into multiple virtual cubes, where the nodes in different virtual cubes sleep and work alternately to save energy. Then, the SSHDCA divides the network into a dynamic layer and a static layer. In the dynamic layer, a virtual-cube-based multi-hop method is leveraged to transmit data packets to the central area. In the static layer, an improved density-based clustering technique is applied to assign each node to an appropriate cluster, while the underwater gliders collect data from the cluster heads. Further, to reduce energy consumption, the SSHDCA compresses key and non-key data, reducing the size of the packets. Simulation results have shown that the proposed algorithm is effective in reducing the path length of the gliders and the average energy consumption of the nodes, while increasing the remaining operational life of the whole network. [ABSTRACT FROM AUTHOR]

Intelligent data collection is a key component in underwater acoustic sensor networks, and it plays an important role in seabed environment monitoring, marine resource detection and marine disaster early warning. Owing to the particularities of the underwater environment, such as reduced infrastructure and noisy communication channels, the data collected by underwater nodes are more efficiently transmitted to a control center on the surface by way of an autonomous underwater vehicle (AUV). However, with the increasing complexity of the underwater tasks, using a single AUV for data collection cannot meet the requirements of low latency and low power consumption. To solve this problem, a multi-AUV collaborative data collection algorithm that reduces the load of data collection task on a single AUV is proposed. The algorithm is divided into two stages: multi-AUV task allocation and Q-learning-based AUV path planning. The data transmission of the clusters is regarded as a set of different tasks, which are assigned to the AUVs for completion. Subsequently, path planning is performed to guide the AUVs, so that the tasks are completed promptly and at a reduced cost. Simulation results show that the proposed algorithm can leverage the energy consumption of a network and extend its lifetime. The performance of the proposed algorithm in energy consumption is increased by about 10%, and the delay of data collection is also significantly reduced. [ABSTRACT FROM AUTHOR]

Illegally forged signals in automatic identification system (AIS) pose a threat to maritime traffic safety management. In this paper, a multi-level sparse representation based identification (MSRI) algorithm is proposed for specific emitter identification (SEI) in the AIS. The MSRI innovatively combines neural networks with sparse representation based classification (SRC). Channel attention mechanism is introduced to a multi-scale convolutional neural network (CNN) for extracting hidden features in the signal. These extracted features are divided into shallow and deep features according to the depth of the network layer they are extracted from. The original AIS signals and the two-level features are spliced together to form a multi-level dictionary. Subsequently, a sparse representation based identification is performed on the decorrelated multi-level dictionary using the principal components analysis (PCA) method. The proposed MSRI is evaluated on a dataset composed of real-world AIS signals, and compared with the state-of-the-art identification algorithms. The evaluation is based on several factors including computational complexity, number of training samples, and number of emitters. Numerical results indicate that the proposed algorithm can identify emitters with higher accuracy and requires lower training time compared to other methods. Given more than 15 training samples at each emitter, the MSRI can identify nine emitters with an accuracy higher than 90%. [ABSTRACT FROM AUTHOR]

With the development of the industrial Internet of Things and the advancements in wireless sensor networking technologies, the smart grid based on edge computing now is regarded as being essential for real-time monitoring and automatic control of the electricity generation and distribution. In this article, we propose a highly efficient energy control framework supported by edge computing to reduce energy waste and increase the benefit for industrial users. To this end, battery energy storage systems (BESSs) are currently being employed to store energy for stability of supply and quality of power. The optimal load patterns and corresponding energy storage capacities of the BESSs can be obtained through the framework, according to the energy market and the historical load data of industrial users. However, computing these requires considering the tradeoff between equipment cost, time-of-use electricity price, running expenses, and other related factors, which would be an NP-hard problem. To address this challenge, we also propose an adaptive mixed differential evolution algorithm with a novel mutation strategy. Experiments on real-world data demonstrate the effectiveness of the proposed algorithm and framework. [ABSTRACT FROM AUTHOR]

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

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). [ABSTRACT FROM AUTHOR]

The existing intelligent fault diagnosis methods of rotor-bearing system mainly focus on vibration analysis under steady operation, which has low adaptability to new scenes. In this article, a new framework for rotor-bearing system fault diagnosis under varying working conditions is proposed by using modified convolutional neural network (CNN) with transfer learning. First, infrared thermal images are collected and used to characterize the health condition of rotor-bearing system. Second, modified CNN is developed by introducing stochastic pooling and Leaky rectified linear unit to overcome the training problems in classical CNN. Finally, parameter transfer is used to enable the source modified CNN to adapt to the target domain, which solves the problem of limited available training data in the target domain. The proposed method is applied to analyze thermal images of rotor-bearing system collected under different working conditions. The results show that the proposed method outperforms other cutting edge methods in fault diagnosis of rotor-bearing system. [ABSTRACT FROM AUTHOR]

The data in industrial informatics may be high-dimensional and mislabeled. Irrelevant or noisy features pose a significant challenge to the detection of high-dimensional mislabeling. The traditional method usually adopts a two-step solution, first finding the relevant subspace and then using it for mislabeling detection. This two-step method struggles to provide the optimal mislabeling detection performance, since it separates the procedures of feature selection and label error detection. To solve this problem, in this article, we integrate the two steps and propose a sequential ensemble noise filter (SENF). In the SENF, relevant features are selected and used to generate a noise score for each instance. Continuously, these noise scores guide feature selection in the regression learning. Thus, the SENF falls in the scope of sequential ensemble learning. We evaluate our approach on several benchmark datasets with high dimensionality and much label noise. It is shown that the SENF is significantly better than other existing label noise detection methods. [ABSTRACT FROM AUTHOR]

Data are widely collected via the IoT for machine learning tasks in in-home health monitoring applications and mislabeled training data lead to unreliable machine learning models in in-home health monitoring. Researchers have proposed a wide arrangement of algorithms to deal with mislabeled training data, in which one straightforward and effective solution is to directly filter noise from training data so that the negative effects of mislabeled data can be minimized. In essence, noise filtering might be a suboptimal solution because the mislabeled data are not completely useless. The features and distributions of mislabeled data are still useful for learning, especially when training data are insufficient. In this work, we propose a novel framework to learn from mislabeled training data through ambiguous learning (LeMAL). LeMAL mainly consists of two parts. First, it converts the original training data to ambiguous data. Second, an ambiguous learning algorithm is applied to the ambiguous data. In this work, we propose a novel distance-based ambiguous learning algorithm so that the ambiguous data can be used in a better way. Finally, we demonstrate that LeMAL can effectively improve learning performance over existing noise filtering methods. [ABSTRACT FROM AUTHOR]