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Sensor Intelligence Assisted by Data Analytics and Cognitive Computing

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (15 November 2017) | Viewed by 46879

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Special Issue Editors

Prof. Dr. Min Chen

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Guest Editor

School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China
Interests: cognitive computing; 5G Networks; wearable computing; big data analytics; robotics; machine learning; deep learning; emotion detection; mobile edge computing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Kai Hwang

E-Mail Website
Co-Guest Editor

1. The School of Data Science, Chinese University of Hong Kong, Shenzhen, China
2. Shenzhen Institute for Artificial Intelligence and Robotics for Society (AIRS), Shenzhen, China
Interests: big data analytics; deep learning; cognitive computing; cloud computing; supercomputing

Prof. Dr. Yin Zhang

E-Mail Website
Co-Guest Editor

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
Interests: mobile computing; edge intelligence; cognitive wireless communications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Although sensor intelligence has emerged and has the great potential of changing our lives, especially with ubiquitous sensing and sensory data, data analytics and cognitive computing will make it possible to more deeply understand what is happening in the world. Therefore, data analytics and cognitive computing are significant for sensor intelligence to meet many technical challenges and problems, which need to be addressed to realize this potential, such as big sensory data generation, integration of multiple data sources and types, etc. Furthermore, sensor ecosystems have to be upgraded with new capabilities, such as machine learning, data analytics, and cognitive power for providing human intelligence.

This Special Issue aims to explore recent advances and to disseminate state-of-the-art research related to sensor intelligence in designing, building, and deploying novel technologies, to enable intelligent sensing services and applications. Submissions are encouraged that cover a broad range of topics, which may include, but are not limited to, the following:

Sensor intelligence architecture and infrastructure
Sensor intelligence technology and applications
Contextual sensory data management and mining platforms
Data analytics for sensor systems
Cognitive computing, affective computing, machine learning for sensor systems
Future Internet and network design for sensor systems
Intelligent and interactive interface for sensor systems
Privacy protected discovery and adaptation in cognitive sensor systems

Prof. Dr. Min Chen
Prof. Dr. Kai Hwang
Dr. Yin Zhang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Sensor intelligence
Data analytics
Cognitive computing
Sensor systems
Sensory data

Published Papers (10 papers)

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Research

10 pages, 1971 KiB

Open AccessArticle

Knowledge Reasoning with Semantic Data for Real-Time Data Processing in Smart Factory

by Shiyong Wang, Jiafu Wan, Di Li and Chengliang Liu

Sensors 2018, 18(2), 471; https://doi.org/10.3390/s18020471 - 06 Feb 2018

Cited by 44 | Viewed by 6404

Abstract

The application of high-bandwidth networks and cloud computing in manufacturing systems will be followed by mass data. Industrial data analysis plays important roles in condition monitoring, performance optimization, flexibility, and transparency of the manufacturing system. However, the currently existing architectures are mainly for offline data analysis, not suitable for real-time data processing. In this paper, we first define the smart factory as a cloud-assisted and self-organized manufacturing system in which physical entities such as machines, conveyors, and products organize production through intelligent negotiation and the cloud supervises this self-organized process for fault detection and troubleshooting based on data analysis. Then, we propose a scheme to integrate knowledge reasoning and semantic data where the reasoning engine processes the ontology model with real time semantic data coming from the production process. Based on these ideas, we build a benchmarking system for smart candy packing application that supports direct consumer customization and flexible hybrid production, and the data are collected and processed in real time for fault diagnosis and statistical analysis. Full article

(This article belongs to the Special Issue Sensor Intelligence Assisted by Data Analytics and Cognitive Computing)

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26 pages, 532 KiB

Open AccessArticle

Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster

by Hangyu Fan, Huandong Wang and Yong Li

Sensors 2018, 18(2), 320; https://doi.org/10.3390/s18020320 - 23 Jan 2018

Cited by 1 | Viewed by 3378

Abstract

Decentralized clustering of modern information technology is widely adopted in various fields these years. One of the main reason is the features of high availability and the failure-tolerance which can prevent the entire system form broking down by a failure of a single point. Recently, toolkits such as Akka are used by the public commonly to easily build such kind of cluster. However, clusters of such kind that use Gossip as their membership managing protocol and use link failure detecting mechanism to detect link failures cannot deal with the scenario that a node stochastically drops packets and corrupts the member status of the cluster. In this paper, we formulate the problem to be evaluating the link quality and finding a max clique (NP-Complete) in the connectivity graph. We then proposed an algorithm that consists of two models driven by data from application layer to respectively solving these two problems. Through simulations with statistical data and a real-world product, we demonstrate that our algorithm has a good performance. Full article

(This article belongs to the Special Issue Sensor Intelligence Assisted by Data Analytics and Cognitive Computing)

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16 pages, 4381 KiB

Open AccessArticle

Reliability-Aware Cooperative Node Sleeping and Clustering in Duty-Cycled Sensors Networks

by Jeungeun Song, Yiming Miao, Enmin Song, M. Shamim Hossain and Mohammed F. Alhamid

Sensors 2018, 18(1), 127; https://doi.org/10.3390/s18010127 - 04 Jan 2018

Cited by 6 | Viewed by 3279

Abstract

Duty-cycled sensor networks provide a new perspective for improvement of energy efficiency and reliability assurance of multi-hop cooperative sensor networks. In this paper, we consider the energy-efficient cooperative node sleeping and clustering problems in cooperative sensor networks where clusters of relay nodes jointly transmit sensory data to the next hop. Our key idea for guaranteeing reliability is to exploit the on-demand number of cooperative nodes, facilitating the prediction of personalized end-to-end (ETE) reliability. Namely, a novel reliability-aware cooperative routing (RCR) scheme is proposed to select k-cooperative nodes at every hop (RCR-selection). After selecting k cooperative nodes at every hop, all of the non-cooperative nodes will go into sleep status. In order to solve the cooperative node clustering problem, we propose the RCR-based optimal relay assignment and cooperative data delivery (RCR-delivery) scheme to provide a low-communication-overhead data transmission and an optimal duty cycle for a given number of cooperative nodes when the network is dynamic, which enables part of cooperative nodes to switch into idle status for further energy saving. Through the extensive OPNET-based simulations, we show that the proposed scheme significantly outperforms the existing geographic routing schemes and beaconless geographic routings in wireless sensor networks with a highly dynamic wireless channel and controls energy consumption, while ETE reliability is effectively guaranteed. Full article

(This article belongs to the Special Issue Sensor Intelligence Assisted by Data Analytics and Cognitive Computing)

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1357 KiB

Open AccessArticle

Activity Level Assessment Using a Smart Cushion for People with a Sedentary Lifestyle

by Congcong Ma, Wenfeng Li, Raffaele Gravina, Jingjing Cao, Qimeng Li and Giancarlo Fortino

Sensors 2017, 17(10), 2269; https://doi.org/10.3390/s17102269 - 03 Oct 2017

Cited by 31 | Viewed by 6066

Abstract

As a sedentary lifestyle leads to numerous health problems, it is important to keep constant motivation for a more active lifestyle. A large majority of the worldwide population, such as office workers, long journey vehicle drivers and wheelchair users, spends several hours every day in sedentary activities. The postures that sedentary lifestyle users assume during daily activities hide valuable information that can reveal their wellness and general health condition. Aiming at mining such underlying information, we developed a cushion-based system to assess their activity levels and recognize the activity from the information hidden in sitting postures. By placing the smart cushion on the chair, we can monitor users’ postures and body swings, using the sensors deployed in the cushion. Specifically, we construct a body posture analysis model to recognize sitting behaviors. In addition, we provided a smart cushion that effectively combine pressure and inertial sensors. Finally, we propose a method to assess the activity levels based on the evaluation of the activity assessment index (AAI) in time sliding windows. Activity level assessment can be used to provide statistical results in a defined period and deliver recommendation exercise to the users. For practical implications and actual significance of results, we selected wheelchair users among the participants to our experiments. Features in terms of standard deviation and approximate entropy were compared to recognize the activities and activity levels. The results showed that, using the novel designed smart cushion and the standard deviation features, we are able to achieve an accuracy of (>89%) for activity recognition and (>98%) for activity level recognition. Full article

(This article belongs to the Special Issue Sensor Intelligence Assisted by Data Analytics and Cognitive Computing)

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13741 KiB

Open AccessArticle

An Acoustic Signal Enhancement Method Based on Independent Vector Analysis for Moving Target Classification in the Wild

by Qin Zhao, Feng Guo, Xingshui Zu, Yuchao Chang, Baoqing Li and Xiaobing Yuan

Sensors 2017, 17(10), 2224; https://doi.org/10.3390/s17102224 - 28 Sep 2017

Cited by 4 | Viewed by 3983

Abstract

In this paper, we study how to improve the performance of moving target classification by using an acoustic signal enhancement method based on independent vector analysis (IVA) in the unattended ground sensor (UGS) system. Inspired by the IVA algorithm, we propose an improved IVA method based on a microphone array for acoustic signal enhancement in the wild, which adopts a particular multivariate generalized Gaussian distribution as the source prior, an adaptive variable step strategy for the learning algorithm and discrete cosine transform (DCT) to convert the time domain observed signals to the frequency domain. We term the proposed method as DCT-G-IVA. Moreover, we design a target classification system using the improved IVA method for signal enhancement in the UGS system. Different experiments are conducted to evaluate the proposed method for acoustic signal enhancement by comparing with the baseline methods in our classification system under different wild environments. The experimental results validate the superiority of the DCT-G-IVA enhancement method in the classification system for moving targets in the presence of dynamic wind noise. Full article

(This article belongs to the Special Issue Sensor Intelligence Assisted by Data Analytics and Cognitive Computing)

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414 KiB

Open AccessArticle

Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks

by Jing Zhang, Qingjie Zhou, Derrick Wing Kwan Ng and Minho Jo

Sensors 2017, 17(9), 2125; https://doi.org/10.3390/s17092125 - 15 Sep 2017

Cited by 8 | Viewed by 3788

Abstract

In wireless powered communication networks (WPCNs), it is essential to research energy efficiency fairness in order to evaluate the balance of nodes for receiving information and harvesting energy. In this paper, we propose an efficient iterative algorithm for optimal energy efficiency proportional fairness in WPCN. The main idea is to use stochastic geometry to derive the mean proportionally fairness utility function with respect to user association probability and receive threshold. Subsequently, we prove that the relaxed proportionally fairness utility function is a concave function for user association probability and receive threshold, respectively. At the same time, a sub-optimal algorithm by exploiting alternating optimization approach is proposed. Through numerical simulations, we demonstrate that our sub-optimal algorithm can obtain a result close to optimal energy efficiency proportional fairness with significant reduction of computational complexity. Full article

(This article belongs to the Special Issue Sensor Intelligence Assisted by Data Analytics and Cognitive Computing)

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3880 KiB

Open AccessArticle

An Ultra-Low Power Turning Angle Based Biomedical Signal Compression Engine with Adaptive Threshold Tuning

by Jun Zhou and Chao Wang

Sensors 2017, 17(8), 1809; https://doi.org/10.3390/s17081809 - 06 Aug 2017

Cited by 5 | Viewed by 4060

Abstract

Intelligent sensing is drastically changing our everyday life including healthcare by biomedical signal monitoring, collection, and analytics. However, long-term healthcare monitoring generates tremendous data volume and demands significant wireless transmission power, which imposes a big challenge for wearable healthcare sensors usually powered by batteries. Efficient compression engine design to reduce wireless transmission data rate with ultra-low power consumption is essential for wearable miniaturized healthcare sensor systems. This paper presents an ultra-low power biomedical signal compression engine for healthcare data sensing and analytics in the era of big data and sensor intelligence. It extracts the feature points of the biomedical signal by window-based turning angle detection. The proposed approach has low complexity and thus low power consumption while achieving a large compression ratio (CR) and good quality of reconstructed signal. Near-threshold design technique is adopted to further reduce the power consumption on the circuit level. Besides, the angle threshold for compression can be adaptively tuned according to the error between the original signal and reconstructed signal to address the variation of signal characteristics from person to person or from channel to channel to meet the required signal quality with optimal CR. For demonstration, the proposed biomedical compression engine has been used and evaluated for ECG compression. It achieves an average (CR) of 71.08% and percentage root-mean-square difference (PRD) of 5.87% while consuming only 39 nW. Compared to several state-of-the-art ECG compression engines, the proposed design has significantly lower power consumption while achieving similar CRD and PRD, making it suitable for long-term wearable miniaturized sensor systems to sense and collect healthcare data for remote data analytics. Full article

(This article belongs to the Special Issue Sensor Intelligence Assisted by Data Analytics and Cognitive Computing)

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801 KiB

Open AccessArticle

Energy Harvesting Based Body Area Networks for Smart Health

by Yixue Hao, Limei Peng, Huimin Lu, Mohammad Mehedi Hassan and Atif Alamri

Sensors 2017, 17(7), 1602; https://doi.org/10.3390/s17071602 - 10 Jul 2017

Cited by 31 | Viewed by 5930

Abstract

Body area networks (BANs) are configured with a great number of ultra-low power consumption wearable devices, which constantly monitor physiological signals of the human body and thus realize intelligent monitoring. However, the collection and transfer of human body signals consume energy, and considering the comfort demand of wearable devices, both the size and the capacity of a wearable device’s battery are limited. Thus, minimizing the energy consumption of wearable devices and optimizing the BAN energy efficiency is still a challenging problem. Therefore, in this paper, we propose an energy harvesting-based BAN for smart health and discuss an optimal resource allocation scheme to improve BAN energy efficiency. Specifically, firstly, considering energy harvesting in a BAN and the time limits of human body signal transfer, we formulate the energy efficiency optimization problem of time division for wireless energy transfer and wireless information transfer. Secondly, we convert the optimization problem into a convex optimization problem under a linear constraint and propose a closed-form solution to the problem. Finally, simulation results proved that when the size of data acquired by the wearable devices is small, the proportion of energy consumed by the circuit and signal acquisition of the wearable devices is big, and when the size of data acquired by the wearable devices is big, the energy consumed by the signal transfer of the wearable device is decisive. Full article

(This article belongs to the Special Issue Sensor Intelligence Assisted by Data Analytics and Cognitive Computing)

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3528 KiB

Open AccessArticle

Block-Diagonal Constrained Low-Rank and Sparse Graph for Discriminant Analysis of Image Data

by Tan Guo, Xiaoheng Tan, Lei Zhang, Chaochen Xie and Lu Deng

Sensors 2017, 17(7), 1475; https://doi.org/10.3390/s17071475 - 22 Jun 2017

Cited by 2 | Viewed by 4699

Abstract

Recently, low-rank and sparse model-based dimensionality reduction (DR) methods have aroused lots of interest. In this paper, we propose an effective supervised DR technique named block-diagonal constrained low-rank and sparse-based embedding (BLSE). BLSE has two steps, i.e., block-diagonal constrained low-rank and sparse representation (BLSR) and block-diagonal constrained low-rank and sparse graph embedding (BLSGE). Firstly, the BLSR model is developed to reveal the intrinsic intra-class and inter-class adjacent relationships as well as the local neighborhood relations and global structure of data. Particularly, there are mainly three items considered in BLSR. First, a sparse constraint is required to discover the local data structure. Second, a low-rank criterion is incorporated to capture the global structure in data. Third, a block-diagonal regularization is imposed on the representation to promote discrimination between different classes. Based on BLSR, informative and discriminative intra-class and inter-class graphs are constructed. With the graphs, BLSGE seeks a low-dimensional embedding subspace by simultaneously minimizing the intra-class scatter and maximizing the inter-class scatter. Experiments on public benchmark face and object image datasets demonstrate the effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue Sensor Intelligence Assisted by Data Analytics and Cognitive Computing)

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1598 KiB

Open AccessArticle

Autonomous Multi-Robot Search for a Hazardous Source in a Turbulent Environment

by Branko Ristic, Daniel Angley, Bill Moran and Jennifer L. Palmer

Sensors 2017, 17(4), 918; https://doi.org/10.3390/s17040918 - 21 Apr 2017

Cited by 27 | Viewed by 4412

Abstract

Finding the source of an accidental or deliberate release of a toxic substance into the atmosphere is of great importance for national security. The paper presents a search algorithm for turbulent environments which falls into the class of cognitive (infotaxi) algorithms. Bayesian estimation of the source parameter vector is carried out using the Rao–Blackwell dimension-reduction method, while the robots are controlled autonomously to move in a scalable formation. Estimation and control are carried out in a centralised replicated fusion architecture assuming all-to-all communication. The paper presents a comprehensive numerical analysis of the proposed algorithm, including the search-time and displacement statistics. Full article

(This article belongs to the Special Issue Sensor Intelligence Assisted by Data Analytics and Cognitive Computing)

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