Journal on Internet of Things

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ISSN: 2579-0099 (print)
ISSN: 2579-0080 (online) Publication Frequency: Quarterly

About the Journal

This Journal publishes papers related to all aspects of Internet of Things, from latest advance to review articles. Articles explore IoT system architecture, IoT enabling technologies, IoT communication and networking protocols such as network coding, and IoT services and applications. Investigations related to topical areas of research are especially encouraged, including IoT demands, impacts, and implications on sensors technologies, big data management, and future design for various IoT use cases, such as smart cities, smart environment, smart homes, etc.

Current Issue - Vol.2, No.4, 2020 Most Viewed

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ARTICLE
Non-Linear Localization Algorithm Based on Newton Iterations
Jianfeng Lu^*, Guirong Fei
Journal on Internet of Things, Vol.2, No.4, pp. 129-134, 2020, DOI:10.32604/jiot.2020.07196
Abstract In order to improve the performance of time difference of arrival (TDOA) localization, a nonlinear least squares algorithm is proposed in this paper. Firstly, based on the criterion of the minimized sum of square error of time difference of arrival, the location estimation is expressed as an optimal problem of a non-linear programming. Then, an initial point is obtained using the semi-definite programming. And finally, the location is extracted from the local optimal solution acquired by Newton iterations. Simulation results show that when the number of anchor nodes is large, the performance of the proposed algorithm will be significantly better… More
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ARTICLE
Brain MRI Patient Identification Based on Capsule Network
Shuqiao Liu, Junliang Li, Xiaojie Li^*
Journal on Internet of Things, Vol.2, No.4, pp. 135-144, 2020, DOI:10.32604/jiot.2020.09797
Abstract In the deep learning field, “Capsule” structure aims to overcome the shortcomings of traditional Convolutional Neural Networks (CNN) which are difficult to mine the relationship between sibling features. Capsule Net (CapsNet) is a new type of classification network structure with “Capsule” as network elements. It uses the “Squashing” algorithm as an activation function and Dynamic Routing as a network optimization method to achieve better classification performance. The main problem of the Brain Magnetic Resonance Imaging (Brain MRI) recognition algorithm is that the difference between Alzheimer’s disease (AD) image, the Mild Cognitive Impairment (MCI) image, and the normal image is not… More
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ARTICLE
KNEMAG: Key Node Estimation Mechanism Based on Attack Graph for IoT Security
Bichen Che¹, Long Liu^2,*, Huali Zhang¹
Journal on Internet of Things, Vol.2, No.4, pp. 145-162, 2020, DOI:10.32604/jiot.2020.010035
Abstract With the rapid development and widespread application of the IoT, the at-tacks against IoT vulnerabilities have become more complex and diverse. Most of the previous research focused on node vulnerability and its risk analysis. There is little information available about the importance of the location of the node in the system. Therefore, an estimation mechanism is proposed to assess the key node of the IoT system. The estimation of the key node includes two parts: one is the utilization relationship between nodes, and the other is the impact on the system after the node is conquered. We use the node… More
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ARTICLE
An Optimization Scheme for Task Offloading and Resource Allocation in Vehicle Edge Networks
Yuxin Xu¹, Zilong Jin^1,2,*, Xiaorui Zhang¹, Lejun Zhang³
Journal on Internet of Things, Vol.2, No.4, pp. 163-173, 2020, DOI:10.32604/jiot.2020.011792
Abstract The vehicle edge network (VEN) has become a new research hotspot in the Internet of Things (IOT). However, many new delays are generated during the vehicle offloading the task to the edge server, which will greatly reduce the quality of service (QOS) provided by the vehicle edge network. To solve this problem, this paper proposes an evolutionary algorithm-based (EA) task offloading and resource allocation scheme. First, the delay of offloading task to the edge server is generally defined, then the mathematical model of problem is given. Finally, the objective function is optimized by evolutionary algorithm, and the optimal solution is… More
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ARTICLE
Tibetan Multi-Dialect Speech Recognition Using Latent Regression Bayesian Network and End-To-End Mode
Yue Zhao¹, Jianjian Yue¹, Wei Song^1,*, Xiaona Xu¹, Xiali Li¹, Licheng Wu¹, Qiang Ji²
Journal on Internet of Things, Vol.1, No.1, pp. 17-23, 2019, DOI:10.32604/jiot.2019.05866
Abstract We proposed a method using latent regression Bayesian network (LRBN) to extract the shared speech feature for the input of end-to-end speech recognition model. The structure of LRBN is compact and its parameter learning is fast. Compared with Convolutional Neural Network, it has a simpler and understood structure and less parameters to learn. Experimental results show that the advantage of hybrid LRBN/Bidirectional Long Short-Term Memory-Connectionist Temporal Classification architecture for Tibetan multi-dialect speech recognition, and demonstrate the LRBN is helpful to differentiate among multiple language speech sets. More
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ARTICLE
Radiation Cross Calibration Based on GF-1 Side Swing Angle
Yong Xie¹, Zui Tao^2,*, Wen Shao³, John J. Qu⁴, Hai Huan³, Chuanyang Tian³
Journal on Internet of Things, Vol.1, No.1, pp. 9-16, 2019, DOI:10.32604/jiot.2019.05859
Abstract Radiation cross-calibration is an effective method to check and verify the accuracy and stability of sensor measurements. Satellites with high radiation accuracy are used to calibrate satellites with low radiation accuracy. In order to ensure the reliability of the radiation cross-calibration method, we propose to obtain the gain and offset of the GaoFen-1 satellite by linear regression after the radiation cross-calibration of the satellite with low precision and compare with the official coefficient. Finally, we get the relationship between the error in radiation cross-calibration results and side swing angle. The linear correction coefficients of each band are: 0.618, 0.625, 0.512… More
Views:1054 Downloads:399 Download PDF

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ARTICLE
A Perceptron Algorithm for Forest Fire Prediction Based on Wireless Sensor Networks
Haoran Zhu¹, Demin Gao^1,2,*, Shuo Zhang¹
Journal on Internet of Things, Vol.1, No.1, pp. 25-31, 2019, DOI:10.32604/jiot.2019.05897
Abstract Forest fire prediction constitutes a significant component of forest management. Timely and accurate forest fire prediction will greatly reduce property and natural losses. A quick method to estimate forest fire hazard levels through known climatic conditions could make an effective improvement in forest fire prediction. This paper presents a description and analysis of a forest fire prediction methods based on machine learning, which adopts WSN (Wireless Sensor Networks) technology and perceptron algorithms to provide a reliable and rapid detection of potential forest fire. Weather data are gathered by sensors, and then forwarded to the server, where a fire hazard index… More
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ARTICLE
Electrical Data Matrix Decomposition in Smart Grid
Qian Dang¹, Huafeng Zhang¹, Bo Zhao², Yanwen He², Shiming He^3,*, Hye-Jin Kim⁴
Journal on Internet of Things, Vol.1, No.1, pp. 1-7, 2019, DOI:10.32604/jiot.2019.05804
Abstract As the development of smart grid and energy internet, this leads to a significant increase in the amount of data transmitted in real time. Due to the mismatch with communication networks that were not designed to carry high-speed and real time data, data losses and data quality degradation may happen constantly. For this problem, according to the strong spatial and temporal correlation of electricity data which is generated by human’s actions and feelings, we build a low-rank electricity data matrix where the row is time and the column is user. Inspired by matrix decomposition, we divide the low-rank electricity data… More
Views:891 Downloads:278 Download PDF

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ARTICLE
An Influence Maximization Algorithm Based on the Influence Propagation Range of Nodes
Yong Hua¹,Bolun Chen^1,2,∗,Yan Yuan¹, Guochang Zhu¹, Fenfen Li¹
Journal on Internet of Things, Vol.1, No.2, pp. 77-88, 2019, DOI:10.32604/jiot.2019.05941
Abstract The problem of influence maximization in the social network G is to find k seed nodes with the maximum influence. The seed set S has a wider range of influence in the social network G than other same-size node sets. The influence of a node is usually established by using the IC model (Independent Cascade model) with a considerable amount of Monte Carlo simulations used to approximate the influence of the node. In addition, an approximate effect (1-1/e) is obtained, when the number of Monte Carlo simulations is 10000 and the probability of propagation is very small. In this paper,… More
Views:792 Downloads:412 Download PDF

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ARTICLE
A New Method Based on Evolutionary Algorithm for Symbolic Network Weak Unbalance
Yirong Jiang¹, Weijin Jiang^2,3,4,*, Jiahui Chen^2,*, Yang Wang², Yuhui Xu², Lina Tan², Liang Guo⁵
Journal on Internet of Things, Vol.1, No.2, pp. 41-53, 2019, DOI:10.32604/jiot.2019.07231
Abstract The symbolic network adds the emotional information of the relationship, that is, the “+” and “-” information of the edge, which greatly enhances the modeling ability and has wide application in many fields. Weak unbalance is an important indicator to measure the network tension. This paper starts from the weak structural equilibrium theorem, and integrates the work of predecessors, and proposes the weak unbalanced algorithm EAWSB based on evolutionary algorithm. Experiments on the large symbolic networks Epinions, Slashdot and WikiElections show the effectiveness and efficiency of the proposed method. In EAWSB, this paper proposes a compression-based indirect representation method, which… More
Views:699 Downloads:472 Download PDF