Accurate multivariate time series forecasting (MTSF) is critical for intelligent web services in Web of Things. When confronted with unseen multivariate time series (MTS), the industry typically invests significant time and resources in training multiple models to identify the optimal model for deployment. This paper proposes a novel, efficient, and scalable MTSF model selection method that directly selects suitable MTSF methods based on data characteristics without extensive model training. Model selection is a core component of AutoML, which has made significant progress in recent years. However, existing methods incur high operational costs and cannot be directly applied to MTSF tasks. Moreover, there is a lack of a comprehensive and cohesive public time series library for MTSF model selection. To address these challenges, we compile the first large heterogeneous labeled MTSF model selection dataset, called the ModelPile, which covers 41 mainstream datasets across 11 domains. We then propose AutoMTSF, a large model-enabled model selection method that transforms the MTSF model selection problem into a time series classification problem and utilizes the ModelPile to unlock large-scale multi-dataset training. AutoMTSF first uses the pre-trained large model to encode raw MTS. Given the coarse-grained limitations of large model encoding, Recursive Temporal Pattern Feature (RTPF) is proposed to capture both fine-grained and global temporal feature evolution, thereby effectively mapping data characteristics to the MTSF method space. Experiments comparing AutoMTSF with 2 baselines, 17 MTSF methods, and 4 large time series models show that AutoMTSF outperforms state-of-the-art methods while maintaining comparable execution time. This work represents a critical step in validating the accuracy and efficiency of large model-enabled classification for MTSF.

Accurate long-term traffic forecasting improves traffic efficiency and safety in Intelligent Transportation Systems (ITS). However, previous methods typically overlook the complex mixed characteristics. So they struggle to capture the intricate features of newly added data effectively under distribution shifts, which exacerbates the complexity of spatial-temporal variations. Moreover, these methods fail to model global and local dynamic spatial correlations effectively, efficiently, and comprehensively. To mitigate these issues, this paper proposes an innovative Spatial-Temporal Wavelet Attention Network (STWAN). STWAN first decomposes traffic data into stable trends and fluctuating events, effectively dealing with the adverse effects of distribution shifts. Then, spatial-temporal encoder captures component-specific temporal variations and extracts dynamic global-local spatial correlations comprehensively with linear computational complexity. Additionally, transformer attention and forecasting decoder model the latent patterns, while trend-event fusion module integrates essential information for accurate forecasts. Comprehensive experiments across two real-world traffic forecasting tasks indicate that STWAN significantly outperforms state-of-the-art methods in terms of accuracy and robustness, attaining a maximum reduction of 5.54% in MAE and showcasing its robustness and broad applicability in long-term forecasting.

Wireless Sensor Network (WSN) reliability evaluation is essential for ensuring the stable operation of network. Traditional methods usually focus on the network topology structure, and calculate the normal operation probability of WSNs. However, these methods usually ignore the energy consumption and network lifetime. In this paper, a novel reliability evaluation algorithm TLR is proposed, which calculates the network lifetime under dynamic network environment according to the pre-set network topology structure reliability threshold, and realizes the comprehensive reliability analysis of network topology and lifetime. In addition, as the basis for reliability evaluation, this paper proposes a new deep reinforcement learning network framework GNN-AC combining graph neural network and actor-critic network, which solves the challenge of constructing Virtual Backbone Network (VBN) in dynamically operating networks. Based on the self-defined fitness matrix and fitness value, the objective function is set to optimize the VBN construction scheme to accurately calculate the network lifetime, and the relationship between the reliability of network topology and network lifetime is discussed. Simulations are carried out for various sizes of WSNs to show the advantages and effectiveness of the proposed approach in estimating network lifetime and reliability evaluation.

Artificial Intelligence-empowered Industrial Control Networks coordinate massive heterogeneous devices and contain multi-node spatial-temporal information. Multivariate Time Series Anomaly Detection (MTS-AD) can discover data-fault behaviors for ensuring the security of distributed networks. However, existing studies tend to rely heavily on single temporal features or neglect the rich spatial-temporal correlations, which leads to the serious underutilization of interactive embeddings between the time and space domains. In this article, a novel Multiview Spatial-Temporal Interaction Attention Network (MSTIA-Net) scheme is proposed for the unsupervised MTS-AD task to better tackle these challenges. MSTIA-Net focuses on jointly modeling the comprehensive spatial-temporal dependencies by means of incorporating complex interactive contents and dynamic relations from multiview patterns. To fully leverage the content-oriented interactions, a spatial-temporal interactions aggregation module is presented to explicitly learn content-aware representations with a parallel-attention mechanism and a low-rank bilinear fusion manner. Simultaneously, considering the potential correlations among different variables as contextual cues, a spatial-temporal correlations learning module is developed to adaptively capture the relevant context for relation-aware representations. On this basis, both types of aware clues are further integrated by the dual attention-enhanced contrastive reconstruction, which can enrich the cross-aware fusion representations and generate the local and global outputs through a cross-view contrastive learning strategy. Experiments conducted on six benchmark datasets demonstrate the superiority of our MSTIA-Net over state-of-the-art baselines.

Coverage maximization under full connection constraints involves many factors and poses huge challenge in mobile wireless sensor networks. Most of research works on this issue are based on the disk model, which have high complexity and long iterations, and therefore cannot be applied to dynamic complex networks. In this paper, the problem of confident information coverage maximization under full connectivity constraints (CIC-CC) is defined based on the confident information coverage model (CIC). To address this problem, a 3-stage connectivity constrained coverage maximization algorithm (3-CCC) is proposed with the time complexity of O(T*n2). 3-CCC contains three stages: maximizing coverage (MC), full connectivity (FC), and maximizing connectivity constrained coverage (MCCC). These three stages can be used in whole or in part to achieve coverage maximization with connectivity constraints depending on the network state. The stable matching mechanism, greedy algorithm, and Q-learning are utilized to improve the algorithm’s efficiency. Experiments show that the proposed algorithm has good performance in terms of iteration number, running time, and coverage rate.

Wireless sensor network plays a crucial role in sensing everything in Internet of Everything (IoE) applications. Network reliability, which measures the ability of the network to satisfy specific requirements, is one of the core factors influencing the quality of service of the network and a vital support for ensuring the normal operation of IoE applications. Existing reliability evaluation methods are mainly based on minimum cutsets or paths, which are inefficient and not suitable for large-scale networks. Furthermore, most work either focuses on coverage functionality or connectivity functionality, lacking energy awareness. To address these limitations, this article proposes a multidimensional target full-coverage reliability (TFCR). TFCR comprehensively considers various factors affecting network reliability. To evaluate TFCR, a graph-empowered confident information coverage (CIC) and signal-to-interference and noise ratio (SINR)-based energy-aware reliability algorithm (CSERA) is proposed. This algorithm evaluates network coverage based on the CIC model. Additionally, graph neural networks and the SINR-based fade tail connectivity (FTC) model are used to evaluate network connectivity functionality. CSERA balances computational accuracy and efficiency, providing reliability evaluation values within an acceptable margin of error. Extensive simulations and comparative experiments from multiple perspectives demonstrate the superiority of the proposed method CSERA over existing approaches.

The Cloud-Fog Automation is a newly proposed digital industrial automation architecture aimed at accelerating the integration and collaboration of communication, computing, and control towards next-generation cyber-physical systems (CPSs). Data reliability is one of the key considerations for achieving Cloud-Fog Automation. Sensor nodes serve as infrastructures for data collection within industrial CPSs and are essential for maintaining ultra-high data reliability. However, the underlying sensor nodes communicate frequently, are damage-prone and difficult to identify, which dramatically shortens the network lifetime and poses great challenges to data reliability. Motivated by this fact, this paper co-designs communication architecture, algorithms, and computing models for next-generation industrial CPSs with Cloud-Fog Automation to ensure data reliability and functional security. First, a four-layer energy-efficient communication architecture is proposed and a cluster head computing algorithm based on double deep Q-learning (CH-DDQ) is designed inside the architecture. Besides, a 2-stage hyBrid fault detection scheme (2-Brain) is proposed for underlying sensor nodes. 2-Brain first incorporates the Obstacle Triple Jump Protocol (OTP) and OTP packets to improve hard fault detection performance. Then, an unsupervised sensor reading soft fault detection model (SR-SFD) based on contrastive learning, momentum, and tensor is adopted to learn discriminative representations of sensor readings and identify soft faults. Simulations and a case study in the nuclear power industry manifest CH-DDQ improves the network lifetime by 5.4%~484.3% compared to three peer methods, and OTP performs better than baselines by 33.1% on average. Additionally, SR-SFD exhibits high efficiency in sensor soft fault detection and other application scenarios.

The large-scale applications of wireless sensor networks (WSNs) place higher demands on their reliability. WSNs deployed in the intelligent tunnel are often affected by environmental interference or malicious intrusions, which lead to untrusted paths and affect the reliability of transmission. A trusted path ensures that the collected information can be successfully and reliably transmitted to the sink node. To solve the transmission reliability problem of wireless sensor networks, a trusted multi-source shortest path-based transmission reliability (TMSPR) algorithm is proposed in this paper. A lightweight trust management model with a node relation matrix (RM) is applied to identify and exclude untrusted nodes, thereby establishing secure transmission links. Meanwhile, the shortest transmission path is selected based on the minimum path to save the energy of the nodes. The information transmitted through trusted multi-source shortest paths (TMSPs) can successfully reach the sink node, which significantly improves the transmission reliability of the network. Furthermore, a transmission reliability indexTRelis defined as a probabilistic measure to assess reliability. Simulation results demonstrate that the proposed algorithm exponentially reduces both computation time and memory usage, while enhancing transmission reliability by approximately 5%.

Voice assistants, as crucial components of the Internet of Things (IoT), are vulnerable to voice spoofing attacks and pose great threats to the security of IoT. Passive liveness detection distinguishes between genuine and spoofing voices by analyzing the collected voice, eliminating the need for deploying additional sensors. This method plays a crucial role in detecting spoofing speeches and ensuring the security of the IoT. However, current passive liveness detection methods typically require users to adopt specific gestures. Meanwhile, these methods are often designed for specific attacks and cannot accommodate multivariate attacks. To address these challenges, this paper proposes an efficient and robust liveness feature called VoiceID, which utilizes the inherent vocal cord vibrations and voiced language to authenticate the collected voice. The VoiceID is defined as the set of maximum magnitude-peak frequency bins in the magnitude spectrum of each frame for voice. VoiceID can be combined with existing acoustic features to compensate for the granularity gap in extracting fine-grained features and distinguishing between genuine and spoofing voices. Furthermore, to leverage VoiceID, this paper proposes a solid fake voice liveness detection system named SFSys and elaborates on a series of acoustic features that can work with VoiceID. Extensive experiments on authoritative ASVspoof 2019 and ASVspoof 2021 datasets reveal that VoiceID reduces the equal error rate and the minimum tandem decision cost function of the existing acoustic features by at most 6.19% and 0.2479. Moreover, SFSys outperforms existing voice liveness detection schemes and exhibits robustness in various advanced spoofing attack environments.

In order to resist network attacks on IoT devices, identifying IoT devices is the first step for ensuring device security. The traditional passive method identifies IoT devices by mining the potential relationship between traffic characteristics and devices. However, the form of selected traffic features are too singular without considering device behavioral characteristics and the classifier used is too specific with simple structure in these methods. This paper proposes a stacking ensemble learning approach for IoT device identification, ENSIOT, which fully considering the behavioral characteristics of devices and integrating the advantages of various machine learning methods to achieve efficient identification of IoT devices. Firstly, in the process of traffic processing, our method selects features from activity cycles, port numbers, signalling patterns, and cipher suites. Then, in model integration, many machine learning methods are used as base models to learn features selected, and output preliminary recognition results. Finally, the meta model learns the relationship between label and the recognition results of each base model and outputs the final device identification result. This stacking structure stacks the base models and the meta model to make a classifier with strong identification and generalization ability. Incremental learning is used to improve identification accuracy when traffic pattern changing. Comparative experiments are conducted on two datasets of UNSW and TMA-2021. The experimental results verify the effectiveness of ENSIOT, which achieve the accuracy of over 98% on two dataset and bring a noticeable improvement in terms of both accuracy and macro F1 score.

\beginabstract Social network has obtained extensive attention in recommender system. Existing social recommendation models mostly leverage social relations to capture potential interactions between users and items, thereby enhancing recommendation performance. However, these methods ignore the fine-grained bidirectional trust weight and the constraint on the relative positions of entities in social network and user-item interaction network. To this end, in this paper, we propose a social recommendation framework with Trust mining and multi-semantic reGularization (TrustGo). Specifically, we firstly construct a trust network based on the observed social network and establish a high-quality item implicit network. Then, we integrate the trust network, item implicit network, and user-item interaction network into a heterogeneous network. We introduce a meta-path based aggregation in this heterogeneous network to map the users and items into a latent space. And then, by using an ensemble method, we can obtain the final prediction ratings. Considering the users' different behaviors in social network and user-item interaction network, we define two semantic spaces, i.e., the social semantic space and user-item interactional semantic space. And a multi-semantic regularization module is designed to adjust the relative positions of entities in the two kinds of semantic spaces, respectively. Extensive experiments on three real-world datasets demonstrate that our TrustGo model is superior to other state-of-the-art recommendation models. \endabstract

The continuous combination of digital network technology and traditional financial services has given birth to digital financial networks, which explore massive economic data under the AI-driven models to achieve intelligent connections among financial institutions, markets, transactions, and instruments. Empirical asset pricing is a challenging task in financial analysis, which has attracted research attention. However, existing studies only focus on tackling the challenges of equity risk premium in the single stock market. Considering multiple economic linkages between the two countries, the transaction history of the US stock market as empirical knowledge is a powerful supplement to improve the prediction of equity risk premium in the China market. In this paper, we aim to fully leverage the prior information in two stock markets for empirical asset pricing models. Due to the rich financial domain knowledge, there may be various characteristic signals that partially overlap in different periods. To address these issues, we propose a framework based on long-short dual-mode knowledge distillation, termed as LSDM-KD, which incorporates US and China stock market models, and a shared characteristic signals model. The method effectively understands the relationships between assets and market behaviour, reducing reliance on expensive correlation databases and professional knowledge. Extensive experiments conducted on US and China stock market datasets demonstrate that our LSDM-KD can significantly improve the performance of empirical asset pricing.

The Internet of Things (IoT) has recently experienced a significant increase in the frequency of cyberattacks, leading to an urgent need for high security and reliability in intelligent IoT applications. Ensuring that interconnected devices within the system operate as expected and provide accurate data has become an essential concern. Reliable coverage can provide a trusted data source for the system. Comprehensively considering various factors such as node multi-state, potential intrusions, and interferences, a trust-based intrusion-tolerant coverage reliability evaluation algorithm (T-ITCR) is proposed to evaluate the coverage reliability based on the trust-based reliable confident information coverage model (T-RCIC). In T-ITCR, trust management is deeply integrated throughout the evaluation process, facilitating dynamic adjustments in node states, network connectivity, and node coverage weights. Malicious nodes are identified and excluded to guarantee the security of data sensing and transmission. Furthermore, to predict node states more accurately, a precise energy assessment mechanism is conducted based on node interaction processes. A significant number of experiments have demonstrated the performance of the proposed algorithm. Consequently, the T-ITCR algorithm demonstrates its ability to efficiently detect malicious intrusions and adjust network states, which significantly strengthens the security and reliability of the networks.

With the development of recommendation algorithms, researchers are paying increasing attention to fairness issues such as user discrimination in recommendations. To address these issues, existing works often filter users’ sensitive information that may cause discrimination during the process of learning user representations. However, these approaches overlook the latent relationship between items’ content attributes and users’ sensitive information. In this article, we propose DALFRec, a fairness-aware recommendation algorithm based on user-side and item-side adversarial learning to mitigate the effects of sensitive information on both sides of the recommendation process. First, we conduct a statistical analysis to demonstrate the latent relationship between items’ information and users’ sensitive attributes. Then, we design a dual-side adversarial learning network that simultaneously filters out users’ sensitive information on the user and item side. Additionally, we propose a new evaluation strategy that leverages the latent relationship between items’ content attributes and users’ sensitive attributes to better assess the algorithm’s ability to reduce discrimination. Our experiments on three real datasets demonstrate the superiority of our proposed algorithm over state-of-the-art methods.

The widespread applications of the Hybrid Internet of Things (HIoT) have put forward higher requirements for network reliability. Coverage reliability is one of the important metrics of reliability, and reliable coverage ensures network data perception and transmission to improve the Quality of Service (QoS). In this article, we define Confident Information Coverage Reliability (CICR) based on the Confident Information Coverage Model (CIC), which comprehensively considers sensor multistate, sensor energy, coverage rate, and connectivity robustness to evaluate coverage reliability. Furthermore, a Tensor-based Confident Information Coverage Reliability Algorithm (T-CICR) is proposed based on tensor modeling to evaluateCICR. The algorithm uses a tensor-based Markov model to predict sensor multistate. Three tensors of coverage rate, sensor multistate, and sensor energy are constructed to provide unified representations. Simulation results show that our proposed algorithm can significantly improve coverage reliability in terms of duty cycle, coverage rate requirement, sensing range, Root Mean Square Error (RMSE) threshold, connectivity robustness requirement, and link reliability.

To solve the problem of optimizing the operation process of freight trains with complex line conditions, this paper adopts the freight train multi-mass model and establishes a freight train multi-objective optimization model with the objectives of energy saving and time saving. A method combining hybrid operation strategy and optimization algorithm is applied to calculate the optimal sequence of maneuvering conditions for freight trains, and a multi-objective slime mould algorithm incorporating preference strategy is proposed for the train operation process. The simulation results show that the proposed multi-objective slime mould algorithm has good convergence and distribution, and the optimization results can meet the preferences of decision makers, which has practical engineering application value.

The emergence of new applications of Internet of Things (IoT) makes its security and reliability become one of the most concerning issues and requires more breakthroughs. To ensure reliable operation of IoT, network reliability measures are essential for quantifying the performance of such networks. In this paper, we focus on the problem of coverage reliability of IoT intrusion detection systems based on Attack-Defense Game Design. A comprehensive coverage reliability algorithm is proposed based on Monte Carlo simulations. The algorithm employs Byzantine attack and defense ideas to determine network node attributes and uses confident information model to calculate the network coverage area. Furthermore, we propose a system reliability metric based on the analytic hierarchy process method, which takes advantage of node attributes, network coverage and connectivity. The metric is used to compare algorithms in simulated experiments, and a series of simulation comparisons illustrate the superiority and usability of the proposed approach.

The development of 5G has brought new opportunities for the application of Internet of Things (IoT). The integration of 5G and IoT technologies promote high availability, resilience, and reliability of the network infrastructures. IoT deployment optimization is the core issue of 5G IoT. Traditionally, IoT node deployment methods mostly used disk coverage model or probabilistic detection coverage model, which only utilizes the sensing capability of a single IoT node, which results in higher deployment costs. In this article, we study the network resilience of coverage estimation error and solve the coverage problem of resilient deployment of smart nodes in 5G IoT. The coverage formulation in the deployment optimization method is defined based on the confident information coverage (CIC). In order to obtain the optimal deployment with a given coverage quality and with a given budget, the mixed-integer linear programming models (CICILP-COST) and (CICILP-ERROR) are proposed based on the CIC model. After analyzing the model complexity, the proposed models are solved by the variable relaxation algorithm (CICVR-COST) and dichotomous search algorithm (CICDS-ERROR), respectively. Simulations on air pollution datasets in Lyon, France, show that the proposed model yields a lower cost optimal deployment than existing peer schemes.

Internet-of-Things (IoT)-based bridge structural health monitoring (BSHM) has recently attracted considerable attention from both academic and industrial communities of civil engineering and computer science. In conjunction with researchers from civil engineering and computer science, this article studied a fundamental problem motivated from practical IoT-based BSHM: how to effectively prolong network lifetime while guaranteeing desired coverage. Integrating a promising reinforcement learning model named learning automata (LA) with confident information coverage (CIC) model, this article presented an energy-efficient sensor scheduling strategy for partial CIC coverage in IoT-based BSHM system to guarantee network coverage and prolong network lifetime. The proposed scheme fully exploits cooperation among deployed nodes and alternatively schedules the wake/sleep status of nodes while satisfying network connectivity and partial coverage ratio. Especially, the proposed scheme takes full advantage of the LA model to adaptively learn the optimal sensor scheduling strategy and significantly extend network lifetime. A series of comparison simulations using real data sets collected by a practical BSHM system strongly verify the effectiveness and energy efficiency of the proposed algorithm. To the best of our knowledge, this is the first study on how to combine the reinforcement learning mechanism with partial coverage for maximizing the network lifetime of the IoT-based BSHM.

Social Internet of Things (SIoT), integrating the social networks and Internet of Things (IoT), leads to heterogeneous interactions of thing to thing, human to human, and human to thing, which in turn generates exploded information. Hence, as the soul of SIoT, information with its interaction and diffusion, records the track of humans and things and contains the hidden value for social administration and people's lives. Therefore, how to characterize the interplay between behavior spreading and information diffusion in SIoT is essential to predict and manage the information. Motivated by this, a more comprehensive understanding of the coupled modeling of social interaction and information diffusion processes in SIoT is conceived first. With the widespread adoption of cloud-edge computing, different nodes have different consciousness on information. Hence, a cloud-edge-aided information diffusion model is proposed for efficient interactions, which incorporates the role of edge in timely processing and feedback. On this basis, a blockchain-based cloud-edge SIoT architecture is proposed for traceability and security of information diffusion. Furthermore, the dynamical analysis of the coupled model in SIoT is provided, which illustrates the outbreak threshold, stability, and scale of information propagation. An interesting finding is that interactive behavior spreading only influences the final size of information propagation, not the spreading threshold. Extensive simulation results and detailed performance analysis verify the theoretical results, which are beneficial to provide traceable dissemination so as to find the most influential node and control the scale of information diffusion.

As an emerging network paradigm, the Internet of Things (IoT) which consists of a significant number of multifunctional and heterogeneous IoT nodes has attracted dramatic attentions from both academia and industry. With the merits of intelligent capacity, desirable scalability, and high reliability, the IoT recently has been applied for smart ocean applications to provide protection for ocean environment monitoring and surveillance. Aiming to provide coverage service for ocean border environmental surveillance, this article studies the barrier coverage problem which investigates how to select a collection of IoT nodes to obtain an IoT node chain and build barrier paths to detect intruders and trespassers crossing the border region of interest. To overcome the disadvantages in the existing works on barrier coverage, we adopt a novel and widely adopted confident information coverage (CIC) model as the fundamental coverage model and formulate the CIC barrier path construction (CICBC) problem with the goals of maximizing the number of barrier paths and minimizing the amount of IoT nodes in each barrier path. We propose a distributed CIC barrier path (CICBP) construction approach based on learning automata (CBLA). The CBLA includes four crucial phases which are initialization phase, learning phase, monitoring phase, and repairing phase. Each IoT node equips a learning automaton. CBLA selects an optimal IoT node to construct the barrier path by learning. The simulation results show that the performance of the CBLA algorithm outperforms two peer algorithms in terms of the number of barrier paths and the average number of nodes in each barrier path.

The improvement of coverage quality in the construction of multiple-barrier coverage is a critical problem in a wireless sensor network. In this article, we investigate the K -barrier coverage construction problem in passive bistatic radar sensor networks. In contrast to traditional bistatic radar networks, the transmitters in a passive bistatic radar network are predeployed and noncooperative. To construct K barriers, we need to deploy receivers that couple with predeployed transmitters to build continuous barriers. In this work, we focus on the minimum number of receivers problem of constructing K -barrier coverage, where the minimum number of receivers is based on the predeployed transmitters. To handle this problem, we first investigate the optimal placement of receivers between adjacent transmitters for a sub-barrier formation and then determine the optimal placement of receivers for the one-barrier construction. For multiple-barrier coverage construction, we introduce a weighted transmitter graph (WTG) to describe the relation among different transmitters, where the weight in the graph is the minimum number of receivers needed for these two transmitters for a sub-barrier formation. Based on WTG, the minimum receivers problem changes to a problem of how to find K -disjoint paths with the minimum total weight in the graph. For large-scale networks, we also propose two efficient heuristic algorithms to solve the corresponding problem. Finally, we conduct extensive experiments to validate the correctness and the efficiency of the proposed algorithms.

The output power of wind turbine has great relation with its health state, and the health status assessment for wind turbines influences operational maintenance and economic benefit of wind farm. Aiming at the current problem that the health status for the whole machine in wind farm is hard to get accurately, in this paper, we propose a health status assessment method in order to assess and predict the health status of the whole wind turbine, which is based on the power prediction and Mahalanobis distance (MD). Firstly, on the basis of Bates theory, the scientific analysis for historical data from SCADA system in wind farm explains the relation between wind power and running states of wind turbines. Secondly, the active power prediction model is utilized to obtain the power forecasting value under the health status of wind turbines. And the difference between the forecasting value and actual value constructs the standard residual set which is seen as the benchmark of health status assessment for wind turbines. In the process of assessment, the test set residual is gained by network model. The MD is calculated by the test residual set and normal residual set and then normalized as the health status assessment value of wind turbines. This method innovatively constructs evaluation index which can reflect the electricity generating performance of wind turbines rapidly and precisely. So it effectively avoids the defect that the existing methods are generally and easily influenced by subjective consciousness. Finally, SCADA system data in one wind farm of Fujian province has been used to verify this method. The results indicate that this new method can make effective assessment for the health status variation trend of wind turbines and provide new means for fault warning of wind turbines.

The ever-growing Industrial Internet of Things (IoT) provides a powerful method to sense a series of critical industrial environments. This paper studies how to deploy the fixed number of IoT nodes so that the network lifetime is maximized in a sensing field with obstacles while guaranteeing the requirements of confident information coverage, network connectivity, energy efficiency, fault tolerance, and reliability. An IoT node deployment scheme based on an improved nature-inspired genetic algorithm is proposed to solve the defined constrained optimization problem. In the proposed IoT node deployment scheme, we utilize a population initialization based on the Delaunay triangulation to generate the better initial population, a chromosome modification operation to achieve both connectivity and coverage for each chromosome and a chromosome mirror-crossover operation to produce the better offsprings. Experimental results show that our deployment schema equips better performance in terms of longer network lifetime and comparable coverage ratio compared with the other four peer algorithms.

The promising Internet of Things (IoT) provides a powerful platform for practical smart applications. The limited resources of the IoT nodes as well as the emerged coverage holes pose a great challenge on the quality of service of the IoT. Mobile edge computing (MEC), which can improve the IoT nodes energy consumption efficiency and optimize the utilization effectiveness of the limited resources, provides a novel view for coping with the challenge. Based on the MEC, this paper focuses on how to solve the problem of dispatch of mobile edge nodes for confident information coverage holes repairing (DMEN-CICHR) with the goal of maximizing the network lifetime and guaranteeing the network connectivity. To deal with the DMEN-CICHR problem, we develop an energy-balanced and obstacle-adaptive mobile edge node dispatch algorithm called EBOADMEN-CICHR, which restricts the mobile edge nodes from moving too long distance by setting a bound for each CIC hole and repeatedly updating the bound by a competition mechanism. To guarantee the network connectivity, the EBOADMEN-CICHR recursively performs breadth first search on a constructed undirected graph to find all disconnected subgraphs and then dispatches some mobile edge nodes to connect those disconnected subgraphs until the whole network is connected. A number of experiments emulating the realistic scenarios in radiological pollution monitoring in uranium tailings are executed to verify the effectiveness of the proposed EBOADMEN-CICHR solution. Experimental results show the EBOADMEN-CICHR can perform better than other peer methods in term of higher energy efficiency and longer network lifetime.

The promising Industrial Internet of Things (IIoT) consisting of heterogeneous resource-restricted IoT nodes recently has attracted great attention from both academia and industry communities. However, the battery-powered, computing and communication resource-constrained, and randomly uneven distributed features of the IoT nodes pose several great tough hurdles, including the quality of services of real-time processing, energy efficiency, network lifetime, and coverage holes to the IIoT-based industrial applications. To deal with these challenges, based on the emerging edge computing paradigm and the novel confident information coverage (CIC) model, this paper investigates how to relocate redundant IoT edge nodes to provide timely CIC service in an offloading-assisted energy-balanced manner while extending the network lifetime, which is called as the CIC-based IoT edge node relocation (CICENR) problem. To effectively handle the CICENR problem, we propose an offloading-assisted energy-balanced IoT edge node relocation approach CIC-based offloading-assisted energy-balanced approach (CIC-OAEBA) and the other CIC-based direct replacement approach. Specially, the CIC-OAEBA adopts the Grid-Quorum strategy to quickly detect the redundant IoT edge nodes by offloading the communication-intensive and computing-intensive tasks from grid header nodes to peer IoT edge nodes, and make full use of the cascaded movement strategy to move the nearest redundant IoT edge nodes to the requesting CIC hole locations. Experimental results indicate the proposed approaches remarkably outperform other peer methods in terms of response time, energy efficiency, and especially the network lifetime and coverage performance.

The Internet of Things (IoT) evolving from the conventional wireless sensor networks (WSNs) with more smart sensors has attracted significant attention. As one of the most crucial metrics for evaluating the quality of service (QoS) of both IoT and WSNs, sensing coverage characterizes the monitoring status of a sensing field of interest. However, the existence of coverage holes will remarkably degrade the QoS of the IoT. Based on the novel confident information coverage (CIC) model, this paper provides an in-depth study on how to energy-efficiently heal the multimodal CIC holes (MCICH) in a narrowband IoT (NB-IoT)-enabled hybrid IoT deployed for radiological pollution monitoring, where both mobile and stationary sensors equip multimodal sensing units for sensing dissimilar multimodal physical attributes and the NB-IoT provides satisfied network connectivity. We pinpoint the MCICH healing (MCICHH) problem with the objective of energy-efficiently dispatching a series of multimodal mobile IoT sensors to the CIC holes such that the MCIC holes can be headed and the CIC performance can be satisfied. After proving the NP-completeness of MCICHH by reducing it to the set partition problem, we develop a family of effective heuristic schemes including the centralized-MCICHH, the distributed-MCICHH and random CIC hole healing, all of which target for efficiently healing the MCIC holes while minimizing the total moving energy consumption of the dispatched multimodal mobile sensors or maximizing the average remaining energy of the multimodal mobile sensors. Extensive experiments verify the effectiveness and practicality of the proposed schemes.