Kuo Zhao

An efficient multidimensional fusion algorithm for IoT data based on partitioning

The Internet of Things (IoT) implies a worldwide network of interconnected objects uniquely addressable, via standard communication protocols. The prevalence of IoT is bound to generate large amounts of multisource, heterogeneous, dynamic, and sparse data. However, IoT offers inconsequential practical benefits without the ability to integrate, fuse, and glean useful information from such massive amounts of data. Accordingly, preparing us for the imminent invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve process efficiency and provide advanced intelligence. In order to determine an acceptable quality of intelligence, diverse and voluminous data have to be combined and fused. Therefore, it is imperative to improve the computational efficiency for fusing and mining multidimensional data. In this paper, we propose an efficient multidimensional fusion algorithm for IoT data based on partitioning. The basic concept involves the partitioning of dimensions (attributes), i.e., a big data set with higher dimensions can be transformed into certain number of relatively smaller data subsets that can be easily processed. Then, based on the partitioning of dimensions, the discernible matrixes of all data subsets in rough set theory are computed to obtain their core attribute sets. Furthermore, a global core attribute set can be determined. Finally, the attribute reduction and rule extraction methods are used to obtain the fusion results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is illustrated.

A survey from the perspective of evolutionary process in the internet of things

The title of this paper may suggest such topics as routing, networking, and data mining, but we focus on new research angles regarding the Internet of Things (IoT) as the theme of this paper. These research angles come from other disciplines and are in the process of being adopted by the IoT. Our paper serves a key purpose: from the perspective of correlative technologies based on time, to review the evolutionary process of the IoT and depict the relations between the correlation techniques which are largely missing in current literature in which the focus has been more on the introduction and comparison of existing technologies and less on issues describing evolutionary process of the IoT. We consider that the latter is crucial to understanding the evolution of the IoT. Through generalizations of particular focus in different stages of each technology, we can better understand the current phase of the IoT and therefore predict future challenges. This paper aims to bridge this gap by providing guidance in terms of the evolutionary process of the IoT and gives readers a panoramic view of the IoT field without repeating what is already available in existing literature so as to complement the existing IoT survey papers which have not covered the evolutionary process of the IoT.

Recent Advances in the Internet of Things: Multiple Perspectives

ABSTRACT In this paper, we categorize and summarize recent advances in the Internet of Things (IoT) to complement existing IoT survey papers that have not covered its very latest developments. We do this from the three perspectives: information fusion in the IoT, service-oriented IoT, and cloud-centric IoT. These three aspects are largely missing in current literature. This survey contributes to better understanding of the challenges posed by the existing IoT and its likely future developments. The IoT is in the early stages of development. Challenges for future development include network scalability, a continuously produced data stream, and methods for handling heterogeneous data. This review provides insight regarding the three aforementioned aspects. In addition, we briefly summarize the existing literature with explicit references to the corresponding survey papers, thus, providing a panoramic view of the IoT without repeating what is already available in the literature.

A Data Processing Middleware Based on SOA for the Internet of Things

The Internet of Things (IoT) emphasizes on connecting every object around us by leveraging a variety of wireless communication technologies. Heterogeneous data fusion is widely considered to be a promising and urgent challenge in the data processing of the IoT. In this study, we first discuss the development of the concept of the IoT and give a detailed description of the architecture of the IoT. And then we design a middleware platform based on service-oriented architecture (SOA) for integration of multisource heterogeneous information. New research angle regarding flexible heterogeneous information fusion architecture for the IoT is the theme of this paper. Experiments using environmental monitoring sensor data derived from indoor environment are performed for system validation. Through the theoretical analysis and experimental verification, the data processing middleware architecture represents better adaptation to multisensor and multistream application scenarios in the IoT, which improves heterogeneous data utilization value. The data processing middleware based on SOA for the IoT establishes a solid foundation of integration and interaction for diverse networks data among heterogeneous systems in the future, which simplifies the complexity of integration process and improves reusability of components in the system.

Feature selection considering two types of feature relevancy and feature interdependency

A novel feature selection method is proposed based on information theory.Our method divides feature relevancy into two categories.We performed experiments over 12 public data sets.Our method outperforms five competing methods in terms of accuracy.Our method selects few number of features when it achieves the highest accuracy. Feature selection based on information theory, which is used to select a group of the most informative features, has extensive application fields such as machine learning, data mining and natural language processing. However, numerous previous methods suffer from two common defects. (1) Feature relevancy is defined without distinguishing candidate feature relevancy and selected feature relevancy. (2) Some interdependent features may be misinterpreted as redundant features. In this study, we propose a feature selection method named Dynamic Relevance and Joint Mutual Information Maximization (DRJMIM) to address these two defects. DRJMIM includes four stages. First, the relevancy is divided into two categories: candidate feature relevancy and selected feature relevancy. Second, according to candidate feature relevancy that is joint mutual information, some redundant features are selected. Third, the redundant features are combined with a dynamic weight to reduce the selection possibility of true redundant features while increasing the false ones. Finally, the most informative and interdependent features are selected and true redundant features are eliminated simultaneously. Furthermore, our method is compared with five competitive feature selection methods on 12 publicly available data sets. The classification results show that DRJMIM performs better than other five methods. Its statistical significance is verified by a paired two-tailed t-test. Meanwhile, DRJMIM obtains few number of selected features when it achieves the highest classification accuracy.

A semantics-based approach to multi-source heterogeneous information fusion in the internet of things

Multi-source heterogeneous information fusion in the Internet of Things (IoT) is a type of information processing that attempts to provide a comprehensive, timely and accurate perception and feedback relative to physical things. Traditional multi-sensor data fusion can deal with the same type of data effectively. However, as new characteristics emerge in the IoT, interoperable service-oriented technologies are required to share real-world data among heterogeneous devices to integrate and fuse the multi-source heterogeneous IoT data. To address these issues, an architecture that can provide guidance for the development of IoT information fusion is required. We compare features of IoT data and information with an existing wireless sensor network and the Internet, which, to the best of our knowledge, is the first comparison of this kind. Then, we design a framework for multi-source heterogeneous information fusion in the IoT and use an experimental simulation platform to build an environmental monitoring system to assess the framework.

Estimating online vacancies in real-time road traffic monitoring with traffic sensor data stream

Real-time road traffic monitoring is widely considered to be a promising traffic management approach in urban environments. In the smart cities scenario, traffic trajectory sensor data streams are constantly produced in real time from probe vehicles, which include taxis and buses. By exploiting the mass sensor data streams, we can effectively predict and prevent traffic jams in a timely manner. However, there are two urgent challenges to processing the massive amounts of continuously generated trajectory sensor data: (1) the inhomogeneous sparseness in both spatial and temporal dimensions that is introduced by probe vehicles moving at their own will, and (2) processing stream data in real time manner with low latency. In this study, we aim to ameliorate the aforementioned two issues. We propose an online approach to addresses the major defect of inhomogeneous sparseness, which focuses on employing only real-time data rather than mining historical data. Furthermore, we set up a real-time system to process trajectory data with low latency. Our tests are performed using field test data sets derived from taxis in an urban environment; the results show that our proposed method lends validity and efficiency advantages for tackling the sparseness, and our real-time system is viable for low latency applications such as trafficmonitoring.

Missing Value Imputation Based on Gaussian Mixture Model for the Internet of Things

This paper addresses missing value imputation for the Internet of Things (IoT). Nowadays, the IoT has been used widely and commonly by a variety of domains, such as transportation and logistics domain and healthcare domain. However, missing values are very common in the IoT for a variety of reasons, which results in the fact that the experimental data are incomplete. As a result of this, some work, which is related to the data of the IoT, can’t be carried out normally. And it leads to the reduction in the accuracy and reliability of the data analysis results. This paper, for the characteristics of the data itself and the features of missing data in IoT, divides the missing data into three types and defines three corresponding missing value imputation problems. Then, we propose three new models to solve the corresponding problems, and they are model of missing value imputation based on context and linear mean (MCL), model of missing value imputation based on binary search (MBS), and model of missing value imputation based on Gaussian mixture model (MGI). Experimental results showed that the three models can improve the accuracy, reliability, and stability of missing value imputation greatly and effectively.

Optimization of the deployment of temperature nodes based on linear programing in the internet of things

The Internet of Things emphasizes the concept of objects connected with each other, which includes all kinds of wireless sensor networks. An important issue is to reduce the energy consumption in the sensor networks since sensor nodes always have energy constraints. Deployment of thousands of wireless sensors in an appropriate pattern will simultaneously satisfy the application requirements and reduce the sensor network energy consumption. This article deployed a number of sensor nodes to record temperature data. The data was then used to predict the temperatures of some of the sensor node using linear programming. The predictions were able to reduce the node sampling rate and to optimize the node deployment to reduce the sensor energy consumption. This method can compensate for the temporarily disabled nodes. The main objective is to design the objective function and determine the constraint condition for the linear programming. The result based on real experiments shows that this method successfully predicts the values of unknown sensor nodes and optimizes the node deployment. The sensor network energy consumption is also reduced by the optimized node deployment.

Environment Observation System based on Semantics in the Internet of Things

The Internet of Things is rapidly developing in recent years. A number of devices connect with the Internet. Hence, the interoperability, which is the access and interpretation of unambiguous data, is strongly needed by distributed and heterogeneous devices. The semantics promotes the interoperability in the Internet of Things using ontology to provide precise definition of concepts and relations. In this paper, we demonstrate the importance of the semantics in three aspects: firstly, the semantics means that the machines could understand and respond to the human command. Secondly, the semantics is mainly reflected in the ontology of the physical world. Thirdly, the semantics is important for interoperability, data integration and reasoning. Then, we introduce a semantic approach to construct an environment observation system in the Internet of Things. The environment observation system contains three major components, the first is the ontology of the environment observation system, the second is the semantic map of the environment observation system, and the last is exposing the observation data. Finally, the system publishes the environment observation data on the Web successfully. The environment observation system with semantics provides a better service in the Internet of Things