Shi, Peng

Wang, Hongqiao

Wu, Jinsong

Zhao, Wenbing

English

EngagedScholarship @ Cleveland State University

Cleveland State University
EngagedScholarship@CSU
Electrical Engineering & Computer Science Faculty
Publications
Electrical Engineering & Computer Science
Department
11-20-2018
Intelligent Sensing and Decision Making in Smart
Technologies
Wenbing Zhao
Cleveland State University, w.zhao1@csuohio.edu
Jinsong Wu
Universidad de Chile
Peng Shi
University of Science and Technology of China
Hongqiao Wang
Xi’an Research Institute of Hi-Tech
Follow this and additional works at: https://engagedscholarship.csuohio.edu/enece_facpub
Part of the Electrical and Computer Engineering Commons
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This Editorial is brought to you for free and open access by the Electrical Engineering & Computer Science Department at EngagedScholarship@CSU.
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EngagedScholarship@CSU. For more information, please contact library.es@csuohio.edu.
Repository Citation
Zhao, Wenbing; Wu, Jinsong; Shi, Peng; and Wang, Hongqiao, "Intelligent Sensing and Decision Making in Smart Technologies"
(2018). Electrical Engineering & Computer Science Faculty Publications. 435.
https://engagedscholarship.csuohio.edu/enece_facpub/435
International Journal of
Distributed
Editorial Sensor Networks
Intelligent sensing and decision making 
in smart technologies
International Journal of Distributed 
Sensor Networks 
2018, Vol. 14(11)
© The Author(s) 2018
DOI: 10.1177/1550147718813754 
journals.sagepub.com/home/dsn
Over the past several years, we have seen dramatic 
advancement in many application domains enabled by 
the use of mobile, wearable sensors, and other Internet 
of Things.1 These hardware systems are made smart by 
various computational intelligence algorithms and sen­
sing technologies.2 This development has resulted in 
many emerging highly multidisciplinary research areas 
typically termed as smart-* technologies, including 
smart-healthcare,3 6 smart-home,7,8 smart-grid,9 as well 
as smart vehicles and intelligent transportation sys- 
tems.10 These new technologies are transforming our 
society and have enormous economic impact. This 
Special Collection aims to bring together researchers 
and engineers from all these areas to disseminate their 
findings and to foster a community for researchers and 
engineers to exchange ideas and to form collaborations 
to further the advancement of the state of the art of 
smart and intelligent technologies.
This Special Collection accepted 14 articles sub­
mitted by authors from China, Taiwan, South Korea, 
Japan, United Kingdom, and Slovenia after rigorous 
peer reviews. These articles can be roughly divided into 
two categories: (1) research on fundamental issues 
related to wireless sensor networks, such as routing, 
energy management, and so on and (2) applications of 
wireless sensor networks in various fields, such as 
indoor localization, human activity and gesture recog­
nition, and structural health monitoring.
Four articles belong to the first category. Liu et al. 
proposed a new way to evaluate the energy efficiency 
of data compression algorithms in wireless sensor net­
works in their article titled ‘‘A new scheme for evaluat­
ing energy efficiency of data compression in wireless 
sensor networks.'' They identified two key factors 
related to energy efficiency: (1) compression ratio and 
(2) the ratio of hardware energy consumption factor to 
the software energy consumption factor. The energy 
efficiency is higher if the compression ratio is higher
and the hardware to software ratio is lower. By consid­
ering these two factors, they proposed a mechanism to 
improve the energy efficiency by the most effective 
compression algorithm according to the radio fre­
quency power.
Lu et al. presented a path selection algorithm for 
anonymous communication in wireless sensor networks 
in their article titled ‘‘Path selection algorithm with min­
imal delay in wireless sensor networks.'' The path selec­
tion considers the round-trip time as a way to improve 
the performance of communication.
Wang et al. reported their work on service recom­
mending in an article titled ‘‘Hybrid recommendation- 
based quality of service prediction for sensor services.'' 
They combined the latent Dirichlet allocation model to 
compute the similarity of the latent topics of the ser­
vices, and the user's latent semantic themes to extract 
the potential services that are of most interest to the 
user. They showed that this hybrid recommendation 
method is more effective than traditional methods.
Xie et al. proposed a distributed beamforming algo­
rithm for device-to-device communication in their arti­
cle titled ‘‘Distributed beamforming algorithm based 
on game theory in device-to-device communications.'' 
Wireless device-to-device communication is becoming 
more popular among smartphone users because they 
can easily share multimedia and other types of files. 
The authors framed the problem as a noncooperative 
game and fully analyzed the complexity and overhead 
of proposed algorithm. They also assessed their algo­
rithm with detailed simulations.
The remaining 10 articles belong to the second cate­
gory. The article ‘‘Electromyogram-based hand gesture 
recognition robust to various arm postures'' by Rhee 
and Shin proposed a new way of recognizing hand ges­
ture that is robust to diverse arm postures based on 
fusion of accelerometer sensor data and electromyo­
gram. They showed experimentally that their method
Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License 
(http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without 
further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/ 
open-access-at-sage).
2 International Journal of Distributed Sensor Networks
could achieve an average detection accuracy of 85.7%, 
which is a significant improvement over competing 
methods.
The article ‘‘Life prediction of the tensile damage 
progress for high-speed train gearbox shell based on 
acoustic emission sensor and an automatic optimization 
method'' by Ai, Sun and Zhang reported their work on 
using data collected from acoustic emission sensors to 
predict the tensile damage progress for high-speed train 
gearbox shell. This method can make prediction in real­
time and it is non-destructive. Their experiments show 
that their method can achieve a life prediction within 
50 s. Their work can be very useful for passenger eva­
luation and train failure prevention.
The article ‘‘A hierarchical vision-based localization 
of rotor unmanned aerial vehicles for autonomous 
landing'' by Yuan et al. presented a hierarchical frame­
work to increase the working range of autonomous 
landing for unmanned aerial vehicles using computer 
vision-based localization. They conducted both simula­
tion and field tests to verify their framework.
The article ‘‘A wireless sensor data-based coal mine 
gas monitoring algorithm with least squares support 
vector machines optimized by swarm intelligence tech­
niques'' by Chen at al. proposed a new algorithm to 
monitor coal mine gas level using wireless sensors. 
They developed a least squares support vector machine 
and optimized the algorithm with swarm intelligence 
techniques.
The article ‘‘Indoor positioning system for wireless 
sensor networks based on two-stage fuzzy inference'' 
by Cheng and Yan proposed a new algorithm for more 
accurate indoor positioning system using wireless sen­
sor networks. The algorithm is based on two-stage 
fuzzy inference to minimize the uncertainty in the 
received signal strength indicator from reference nodes. 
Via a simulation study, they confirmed that their algo­
rithm performs better than non-fuzzy-based algo­
rithms, including k-nearest neighbor-based localization 
and traditional triangular splitting schemes.
The article ‘‘Safety monitoring and evacuation guide 
system for pipeline testing laboratory by indoor posi­
tioning technique and distributed sensor network'' by 
Ding et al. reported the design of a safety monitoring 
and evacuation guide system for pipeline laboratory. 
The system employs an indoor positioning technique 
and a distributed sensor network. The system has two 
key components. One is to detect gas levels with gas 
sensors. The other is to quickly plan on an evaluation 
route based on the gas detection and indoor position­
ing information.
The article ‘‘Development of unmanned remote 
smart rescue platform applying Internet of Things tech­
nology'' by Kim, Sul, and Choi described the design
and implementation of an unmanned remote smart res­
cue platform that can quickly respond on the occur­
rence of a distress call. This system consists of an 
unmanned remote smart rescue box, a cloud service, 
and an Android mobile application. The unmanned 
remote smart rescue box was tested under a sequence 
of scenarios with success.
Wang, Wu, and Zhang proposed a new energy man­
agement technique that can be used toward smart cities 
in their article titled ‘‘Consumer preference-enabled 
intelligent energy management for smart cities using 
game theoretic social tie.'' They framed the problem as 
a consumer preference-induced game and applied the 
cooperative game theory toward more intelligent energy 
management.
Wei et al. presented their work on sensor selection 
for activity recognition using wearable wireless sensors 
for human activity recognition in their article titled 
‘‘Sensor Selection Scheme in Activity Recognition 
based on Hierarchical Feature Reduction.'' They pro­
posed a new scheme to determine the number of sensors 
needed and their placement on the body by applying 
hierarchical feature reduction.
Xue et al. proposed a way of identifying structural 
parameters for structural health monitoring in their 
article titled ‘‘Parameter identification for structural 
health monitoring based on Monte Carlo method and 
likelihood estimate.'' Their method is based on Monte 
Carlo method and likelihood estimate, where para­
meters such as stiffness and damping are examined 
under three different conditions with no noise, with 
Gaussian noise, and with non-Gaussian noise.
Wenbing Zhao1, Jinsong Wu2, Peng Shi3 and 
Hongqiao Wang4
1Cleveland State University, Cleveland, OH, USA 
2Universidad de Chile, Santiago, Chile
3University of Science and Technology of China, Hefei, 
China
4Xi'an Research Institute of Hi-Tech, Xi'an, China 
References
1. Qiu T, Chen N, Li K, et al. How can heterogeneous 
internet of things build our future: a survey. IEEE Com-
mun Surv Tut 2018; 20(3): 2011-2027.
2. Zhao W, Luo X, Liu H, et al. Scientific programming 
towards a smart world. Sci Program 2017; 2017: 
3706232.
3. Zhao W, Luo X and Qiu T. Smart healthcare. Appl Sci
2017; 7(11): 1176.
4. Chen M, Li Y, Luo X, et al. A novel human activity rec­
ognition scheme for smart health using multilayer 
extreme learning machine. IEEE Internet Things. Epub 
ahead of print 16 July 2018. DOI: 10.1109/ 
JIOT.2018.2856241.
Editorial 3
5. Zhao W, Lun R, Gordon C, et al. A human-centered 
activity tracking system: toward a healthier workplace.
IEEE T Hum-Mach Syst 2017; 47(3): 343-355.
6. Zhao W, Wu Q, Reinthal A, et al. Design, implementa­
tion, and field testing of a privacy-aware compliance 
tracking system for bedside care in nursing homes. Appl
Syst Innov 2017; 1(1): 3.
7. Demiris G and Hensel BK. Technologies for an aging
society: a systematic review of ‘‘smart home'' applica­
tions. Yearb Med Inform 2008; 17(1): 33-40.
8. Zhao W and Lun R. A kinect-based system for promot­
ing healthier living at home. In: Proceedigns of the inter­
national conference on systems, man, and cybernetics, 
Budapest, 9-12 October 2016, pp.258-263. New York: 
IEEE.
9. Farhangi H. The path of the smart grid. IEEE Power 
Energy Mag 2010; 8(1): 18-28.
10. Dimitrakopoulos G and Demestichas P. Intelligent trans­
portation systems. IEEE Veh Technol Mag 2010; 5(1): 
77-84.


Intelligent Sensing and Decision Making in Smart Technologies

Cleveland-Marshall College of Law

Cleveland State UniversityEngagedScholarship@CSUElectrical Engineering & Computer Science FacultyPublicationsElectrical Engineering & Computer ScienceDepartment11-20-2018Intelligent Sensing and Decision Making in SmartTechnologiesWenbing ZhaoCleveland State University, w.zhao1@csuohio.eduJinsong WuUniversidad de ChilePeng ShiUniversity of Science and Technology of ChinaHongqiao WangXi’an Research Institute of Hi-TechFollow this and additional works at: https://engagedscholarship.csuohio.edu/enece_facpubPart of the Electrical and Computer Engineering CommonsHow does access to this work benefit you? Let us know!This Editorial is brought to you for free and open access by the Electrical Engineering & Computer Science Department at EngagedScholarship@CSU.It has been accepted for inclusion in Electrical Engineering & Computer Science Faculty Publications by an authorized administrator ofEngagedScholarship@CSU. For more information, please contact library.es@csuohio.edu.Repository CitationZhao, Wenbing; Wu, Jinsong; Shi, Peng; and Wang, Hongqiao, "Intelligent Sensing and Decision Making in Smart Technologies"(2018). Electrical Engineering & Computer Science Faculty Publications. 435.https://engagedscholarship.csuohio.edu/enece_facpub/435International Journal ofDistributedEditorial Sensor NetworksIntelligent sensing and decision making in smart technologiesInternational Journal of Distributed Sensor Networks 2018, Vol. 14(11)© The Author(s) 2018DOI: 10.1177/1550147718813754 journals.sagepub.com/home/dsnOver the past several years, we have seen dramatic advancement in many application domains enabled by the use of mobile, wearable sensors, and other Internet of Things.1 These hardware systems are made smart by various computational intelligence algorithms and sen­sing technologies.2 This development has resulted in many emerging highly multidisciplinary research areas typically termed as smart-* technologies, including smart-healthcare,3 6 smart-home,7,8 smart-grid,9 as well as smart vehicles and intelligent transportation sys- tems.10 These new technologies are transforming our society and have enormous economic impact. This Special Collection aims to bring together researchers and engineers from all these areas to disseminate their findings and to foster a community for researchers and engineers to exchange ideas and to form collaborations to further the advancement of the state of the art of smart and intelligent technologies.This Special Collection accepted 14 articles sub­mitted by authors from China, Taiwan, South Korea, Japan, United Kingdom, and Slovenia after rigorous peer reviews. These articles can be roughly divided into two categories: (1) research on fundamental issues related to wireless sensor networks, such as routing, energy management, and so on and (2) applications of wireless sensor networks in various fields, such as indoor localization, human activity and gesture recog­nition, and structural health monitoring.Four articles belong to the first category. Liu et al. proposed a new way to evaluate the energy efficiency of data compression algorithms in wireless sensor net­works in their article titled ‘‘A new scheme for evaluat­ing energy efficiency of data compression in wireless sensor networks.'' They identified two key factors related to energy efficiency: (1) compression ratio and (2) the ratio of hardware energy consumption factor to the software energy consumption factor. The energy efficiency is higher if the compression ratio is higherand the hardware to software ratio is lower. By consid­ering these two factors, they proposed a mechanism to improve the energy efficiency by the most effective compression algorithm according to the radio fre­quency power.Lu et al. presented a path selection algorithm for anonymous communication in wireless sensor networks in their article titled ‘‘Path selection algorithm with min­imal delay in wireless sensor networks.'' The path selec­tion considers the round-trip time as a way to improve the performance of communication.Wang et al. reported their work on service recom­mending in an article titled ‘‘Hybrid recommendation- based quality of service prediction for sensor services.'' They combined the latent Dirichlet allocation model to compute the similarity of the latent topics of the ser­vices, and the user's latent semantic themes to extract the potential services that are of most interest to the user. They showed that this hybrid recommendation method is more effective than traditional methods.Xie et al. proposed a distributed beamforming algo­rithm for device-to-device communication in their arti­cle titled ‘‘Distributed beamforming algorithm based on game theory in device-to-device communications.'' Wireless device-to-device communication is becoming more popular among smartphone users because they can easily share multimedia and other types of files. The authors framed the problem as a noncooperative game and fully analyzed the complexity and overhead of proposed algorithm. They also assessed their algo­rithm with detailed simulations.The remaining 10 articles belong to the second cate­gory. The article ‘‘Electromyogram-based hand gesture recognition robust to various arm postures'' by Rhee and Shin proposed a new way of recognizing hand ges­ture that is robust to diverse arm postures based on fusion of accelerometer sensor data and electromyo­gram. They showed experimentally that their methodCreative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/ open-access-at-sage).2 International Journal of Distributed Sensor Networkscould achieve an average detection accuracy of 85.7%, which is a significant improvement over competing methods.The article ‘‘Life prediction of the tensile damage progress for high-speed train gearbox shell based on acoustic emission sensor and an automatic optimization method'' by Ai, Sun and Zhang reported their work on using data collected from acoustic emission sensors to predict the tensile damage progress for high-speed train gearbox shell. This method can make prediction in real­time and it is non-destructive. Their experiments show that their method can achieve a life prediction within 50 s. Their work can be very useful for passenger eva­luation and train failure prevention.The article ‘‘A hierarchical vision-based localization of rotor unmanned aerial vehicles for autonomous landing'' by Yuan et al. presented a hierarchical frame­work to increase the working range of autonomous landing for unmanned aerial vehicles using computer vision-based localization. They conducted both simula­tion and field tests to verify their framework.The article ‘‘A wireless sensor data-based coal mine gas monitoring algorithm with least squares support vector machines optimized by swarm intelligence tech­niques'' by Chen at al. proposed a new algorithm to monitor coal mine gas level using wireless sensors. They developed a least squares support vector machine and optimized the algorithm with swarm intelligence techniques.The article ‘‘Indoor positioning system for wireless sensor networks based on two-stage fuzzy inference'' by Cheng and Yan proposed a new algorithm for more accurate indoor positioning system using wireless sen­sor networks. The algorithm is based on two-stage fuzzy inference to minimize the uncertainty in the received signal strength indicator from reference nodes. Via a simulation study, they confirmed that their algo­rithm performs better than non-fuzzy-based algo­rithms, including k-nearest neighbor-based localization and traditional triangular splitting schemes.The article ‘‘Safety monitoring and evacuation guide system for pipeline testing laboratory by indoor posi­tioning technique and distributed sensor network'' by Ding et al. reported the design of a safety monitoring and evacuation guide system for pipeline laboratory. The system employs an indoor positioning technique and a distributed sensor network. The system has two key components. One is to detect gas levels with gas sensors. The other is to quickly plan on an evaluation route based on the gas detection and indoor position­ing information.The article ‘‘Development of unmanned remote smart rescue platform applying Internet of Things tech­nology'' by Kim, Sul, and Choi described the designand implementation of an unmanned remote smart res­cue platform that can quickly respond on the occur­rence of a distress call. This system consists of an unmanned remote smart rescue box, a cloud service, and an Android mobile application. The unmanned remote smart rescue box was tested under a sequence of scenarios with success.Wang, Wu, and Zhang proposed a new energy man­agement technique that can be used toward smart cities in their article titled ‘‘Consumer preference-enabled intelligent energy management for smart cities using game theoretic social tie.'' They framed the problem as a consumer preference-induced game and applied the cooperative game theory toward more intelligent energy management.Wei et al. presented their work on sensor selection for activity recognition using wearable wireless sensors for human activity recognition in their article titled ‘‘Sensor Selection Scheme in Activity Recognition based on Hierarchical Feature Reduction.'' They pro­posed a new scheme to determine the number of sensors needed and their placement on the body by applying hierarchical feature reduction.Xue et al. proposed a way of identifying structural parameters for structural health monitoring in their article titled ‘‘Parameter identification for structural health monitoring based on Monte Carlo method and likelihood estimate.'' Their method is based on Monte Carlo method and likelihood estimate, where para­meters such as stiffness and damping are examined under three different conditions with no noise, with Gaussian noise, and with non-Gaussian noise.Wenbing Zhao1, Jinsong Wu2, Peng Shi3 and Hongqiao Wang41Cleveland State University, Cleveland, OH, USA 2Universidad de Chile, Santiago, Chile3University of Science and Technology of China, Hefei, China4Xi'an Research Institute of Hi-Tech, Xi'an, China References1. Qiu T, Chen N, Li K, et al. How can heterogeneous internet of things build our future: a survey. IEEE Com-mun Surv Tut 2018; 20(3): 2011-2027.2. Zhao W, Luo X, Liu H, et al. Scientific programming towards a smart world. Sci Program 2017; 2017: 3706232.3. Zhao W, Luo X and Qiu T. Smart healthcare. Appl Sci2017; 7(11): 1176.4. Chen M, Li Y, Luo X, et al. A novel human activity rec­ognition scheme for smart health using multilayer extreme learning machine. IEEE Internet Things. Epub ahead of print 16 July 2018. DOI: 10.1109/ JIOT.2018.2856241.Editorial 35. Zhao W, Lun R, Gordon C, et al. A human-centered activity tracking system: toward a healthier workplace.IEEE T Hum-Mach Syst 2017; 47(3): 343-355.6. Zhao W, Wu Q, Reinthal A, et al. Design, implementa­tion, and field testing of a privacy-aware compliance tracking system for bedside care in nursing homes. ApplSyst Innov 2017; 1(1): 3.7. Demiris G and Hensel BK. Technologies for an agingsociety: a systematic review of ‘‘smart home'' applica­tions. Yearb Med Inform 2008; 17(1): 33-40.8. Zhao W and Lun R. A kinect-based system for promot­ing healthier living at home. In: Proceedigns of the inter­national conference on systems, man, and cybernetics, Budapest, 9-12 October 2016, pp.258-263. New York: IEEE.9. Farhangi H. The path of the smart grid. IEEE Power Energy Mag 2010; 8(1): 18-28.10. Dimitrakopoulos G and Demestichas P. Intelligent trans­portation systems. IEEE Veh Technol Mag 2010; 5(1): 77-84.

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