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With the drastic development of Internet of things, the number of connected mobile users (MUs) is increasing at an unprecedented speed. The increasing popularity of MUs has triggered more and more new mobile applications. However, these applications are sensitive to latency, which inevitably increases pressure on MUs. Fortunately, computation offloading of mobile edge computing is becoming a promising technology that can improve quality of service for MUs. However, it becomes much difficult when there are multiple edge servers (ESs) near to the MU, even to the multiple MUs. On the other hand, as the resources of ESs are heterogeneous and finite, and thus it is challenge to design effective offloading strategies for multiple MUs. To tackle the above challenges, we firstly establish a multi-objective optimization model concerning time consumption and energy consumption of MUs, and resource utilization of ESs. Moreover, we devise an end-edge-cloud collaborative computing offloading method based on improved Strength Pareto Evolutionary Algorithm 2 for addressing this mode. Finally, compared to benchmark methods, numerous experiments have proved that our proposed method is effective and efficient and can be widely used for the scenario of multiple MUs and multiple heterogeneous ESs. [ABSTRACT FROM AUTHOR]

In order to increase the automatic quality control level in the textile industry, depending on the big data collected by the Internet of things of the textile factories, this paper proposes a novel visual saliency–based defect detection algorithm, which has the capability of automatically detecting defect in both nonpatterned and patterned fabrics. The algorithm employs the histogram features extracted from the saliency maps to detect the fabric defects. The algorithm involves three main steps: (1) saliency map generation to highlight the defective regions and suppress the defect‐free regions, (2) saliency histogram features extraction and selection to obtain the feature vectors that can effectively discriminate between the defective and defect‐free fabric images, and (3) fabric defect detection using a two‐class support vector machine classifier that has been trained using sets of feature vectors extracted from defective and defect‐free fabric samples. Experimental results show that our method yields accurate detections, outperforming other state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

The implementation of smart networks has made great progress due to the development of the Internet of Things (IoT). LoRa is one of the most prominent technologies in the Internet of Things industry, primarily due to its ability to achieve long-distance transmission while consuming less power. In this work, we modeled different environments and assessed the performances of networks by observing the effects of various factors and network parameters. The path loss model, the deployment area size, the transmission power, the spreading factor, the number of nodes and gateways, and the antenna gain have a significant effect on the main performance metrics such as the energy consumption and the data extraction rate of a LoRa network. In order to examine these parameters, we performed simulations in OMNeT++ using the open source framework FLoRa. The scenarios which were investigated in this work include the simulation of rural and urban environments and a parking area model. The results indicate that the optimization of the key parameters could have a huge impact on the deployment of smart networks. [ABSTRACT FROM AUTHOR]

In the Industrial Internet of Things (IIoT) era, we have billions of Internet-connected devices (also referred to as "things"), which range from unmanned/autonomous vehicles (e.g., unmanned aerial vehicle - UAV) to domestic robots to traffic sensors. The round-based blockchain time-stamping scheme is also designed to be scalable, in the sense that the constant number of bytes embedded into a blockchain is independent from the number of input documents. In conclusion, the papers included in this special section have contributed to different aspects of the blockchain literature, and hopefully benefit the readers interested in the potential application of blockchain. [Extracted from the article]

Floods result in substantial damage throughout the world every year. Accurate predictions of floods can significantly alleviate casualties and property losses. However, due to the complexity of hydrology process especially in a city with complicated pipe network, the accuracy of traditional flood forecasting models suffer from the performance degradation with the increasing of required prediction period. In the work, based on the collected historical data of Xixian City, Henan Province, China, using the Internet of Things system (IoT) in 2011-2018, a Bidirectional Gated Recurrent Unit (BiGRU) multi-step flood prediction model with attention mechanism is proposed. In our model, the attention mechanism is used to automatically adjust the matching degree between the input features and output. Besides, we use a bidirectional GRU model, which can process the input sequence from two directions of time series (chronologically and antichronologically), then merge their representations together. Compared with the prediction model using Long Short Term Memory (LSTM), our method can generate better prediction result, as can be seen from the arrival time error and peak error of floods during multi-step predictions. • A BiGRU multi-step flood prediction model based on attention mechanism was proposed. • Model tests were designed and compared with relevant models based on LSTM neural network. • The results show that the model has smaller deterministic coefficient and RMSE. • Within the allowable error range, the prediction accuracy of our model can reach 100%. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification is essential for satellite Internet of Things (IoT) to build a large scale land-cover surveillance system. After acquiring real-time land-cover information, the edge of the network transmits all the hyperspectral images by satellites with low-latency and high-efficiency to the cloud computing center, which are provided by satellite IoT. A gigantic amount of remote sensing data bring challenges to the storage and processing capacity of traditional satellite systems. When hyperspectral images are used in annotation of land-cover application, data dimension reduction for classifier efficiency often leads to the decrease of classifier accuracy, especially the region to be annotated consists of natural landform and artificial structure. This paper proposes encoding spectral-spatial features for hyperspectral image classification in the satellite Internet of Things system to extract features effectively, namely attribute profile stacked autoencoder (AP-SAE). Firstly, extended morphology attribute profiles EMAP is used to obtain spatial features of different attribute scales. Secondly, AP-SAE is used to extract spectral features with similar spatial attributes. In this stage the program can learn feature mappings, on which the pixels from the same land-cover class are mapped as closely as possible and the pixels from different land-cover categories are separated by a large margin. Finally, the program trains an effective classifier by using the network of the AP-SAE. Experimental results on three widely-used hyperspectral image (HSI) datasets and comprehensive comparisons with existing methods demonstrate that our proposed method can be used effectively in hyperspectral image classification. [ABSTRACT FROM AUTHOR]