Emerging Technologies: IoT, Big Data, and CPS with Sensory … · 2019. 7. 30. · Editorial...

EditorialEmerging Technologies: IoT, Big Data, and CPS withSensory Systems

Qing Tan ,1 Nashwa El-Bendary ,2 Magdy A. Bayoumi,3 Xiaokun Zhang,1

Javier Sedano ,4 and José R. Villar 5

1School of Computing and Information Systems, Athabasca University, Athabasca, AB, Canada2Arab Academy for Science, Technology, and Maritime Transport, Smart Village, Giza, Egypt3University of Louisiana at Lafayette, Lafayette, LA, USA4Instituto Tecnológico de Castilla y León, Polg. Ind. Villalonquejar, López Bravo 70, 09001 Burgos, Spain5Computer Science Department, University of Oviedo, EIMEM, c/ Independencia 13, 33004 Oviedo, Spain

Correspondence should be addressed to Qing Tan; [email protected]

Received 5 December 2017; Accepted 5 December 2017; Published 18 February 2018

Copyright © 2018 Qing Tan et al. This is an open access article distributed under the Creative Commons Attribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This collection of studies is focused on a three-legged standin which now is focused on the research community: theinternet of things (IoT), the cyber-physical systems (CPS),and the data-driven knowledge extraction based on big data.The availability of uniquely addressable heterogeneous elec-tronic (UAHE)—including sensors, actuators, smart devices,RFID tags, embedded computers, and mobile devices—iscontinuously growing day by day. From a networking per-spective, the IoT relies on interconnected UAHE for creatinga mesh of devices, producing information, and building aworldwide network of real physical objects. In this context,the IoT presents a technology that enables loosely coupleddecentralized systems of cooperating smart objects of auton-omous physical-digital devices, augmented with sensing/actuating, processing, and networking capabilities.

Over the past 17 years, since the term IoT was first coinedby Kevin Ashton in 1999, radio frequency identification(RFID) system is shaping up to be an important buildingblock and a prerequisite for the IoT. The RFID is a keytechnology that automatically utilizes devices to identify,track and monitor, or control objects through radio waves.However, the performance of RFID equipment can be easilyaffected by factors in the surrounding environment. Con-sequently, that can impose a negative impact on the RFsignal and the source data. H. Xu et al. proposed a sliding

window cleaning algorithm called VSMURF, which is basedon the traditional SMURF algorithm. In this research, thealgorithm they proposed combines the dynamic changeof RFID tags and the value analysis of confidence (δ) inorder to solve the false negative problems affecting thequality of the collected data. Also, they used only oneRFID reader and 25 tags for their experimental setting.The obtained experimental results showed that their pro-posed VSMURF algorithm outperformed the typical SMURFalgorithm in most conditions, and when the tag’s speed islow or high.

However, the frontiers between IoT and CPS are notalways well defined. Some real world problems, like thoserelated with eHealth and health monitoring, are in between:on the one hand, they gather information from the user envi-ronment with the aim of delivering the advice to either theuser, the relatives, or the medical staff. P. M. Vergara et al.analyzed this problem in a real context of the epilepsy seizuredetection. This research studies the problem of a system that,using wearable devices and Smartphones, copes with thedetection of tonic-clonic seizures. The main factors in thedesign issues for such a platform are detailed. Finally, adescription of a platform that is being developed is detailed,providing an experimentation on the robustness of the datacommunications. This experimentation would eventually

HindawiJournal of SensorsVolume 2018, Article ID 3407542, 3 pageshttps://doi.org/10.1155/2018/3407542

http://orcid.org/0000-0002-6447-2133http://orcid.org/0000-0001-6553-4159http://orcid.org/0000-0002-4191-8438http://orcid.org/0000-0001-6024-9527https://doi.org/10.1155/2018/3407542

help in the online task distribution among the differentcomputational resources.

Another example of the difficulty in establishing thelimits of IoT and CPS is the problem of mobile crowdsensing: that is, using the sensory systems carried by eachindividual in a population in order to extract some rele-vant conclusion on a specific topic, like environment mon-itoring and positioning. No need to argue, this big datachallenge as well as the problem of choosing the features,and how to perform the data fusion, and so on are topicscovered with big data techniques. Nevertheless, L.-Y. Jianget al. studied the problem of the trust ability of the dataas a problem of avoiding individuals to introduce falsedata—either intentionally or unintentionally. Furthermore,the authors propose an incentive system to tackle this issue.The proposed incentive system is based on the conditionsof recruiters and candidates, becoming in a quality assess-ment optimization problem solved by a polynomial-timegreedy approximation algorithm the authors proposed. Thesimulation-based experimentation is complete and fullof interest.

One of the problems that the IoT research communityneeds to tackle is to ensure the security: not only theproblems concerning the unauthorized and/or destructiveactions on the devices and/or infrastructure but also thoserelated with cyberattacks and any kind of malicious strikes.Surprisingly, this issue has not been studied in depth due tothe variability of the IoT scenarios: different communicationhardware and software, the computation restrictions inthe nodes, and so on. This issue has been analyzed inT. Andrysiak et al., proposing several interesting conceptsthat need to be considered when dealing with securityissues. Furthermore, the authors proposed a very interestingmethod for detecting anomalies in the network traffic thatmight arise from cyberattacks. To do so, the authorsproposed to estimate the normal network behaviour by(i) filtering outliers; (ii) computing the exponential smooth-ing models, either with Brown’s, Holt’s, or Winter’s models;(iii) analysing the fluctuations of the estimations usingBollinger’s bands; and (iv) comparing the obtained estima-tions of normal behaviour with what is really happening inthe network. A very complete experimentation is performedusing smart metering networks, a real IoT problem indeed.

Besides, challenges in the deployment of IoT solutionsare also focused on energy efficiency. In passive backscattercommunication systems, sensor nodes need to harvest RFenergy from transceivers or readers and use it to rechargetheir finite energy storage capacity. However, RF energy har-vesting also faces unpredictable environmental challenges,which make the sensors’ sensing, processing, and communi-cation activities difficult. Therefore, an efficient energymanagement is necessary in order to guarantee the sensors’activities and QoS (quality of service) of backscatter com-munication. In this special issue, the article contributed byS. Hu et al. present an RF energy harvesting and a colocatedpassive quadrature amplitude modulation (QAM) backscat-ter communication signal models. The proposed modelsaim at achieving good quality of service (QoS) level throughminimizing interference resulted from multisensor and

increasing spectral efficiency. Experimental results, basedon numerical analysis and simulations, showed that themaximum throughput inversely relates to the consumedpower and the number of sensors. Also, for a given consumedpower of sensors, it was observed that the throughputdecreases with duty cycle, and the number of sensors haslittle effect on the throughput.

On the other hand, the coupling between the IoT and thebig data communities is strong as big data analytics hasbecome an essential component for extracting value fromdata. Big data as one of the most important and recentresearch challenges with a paradigm relies on the collectionof tremendous amount of data to support innovation in theupcoming decades. A dataset is considered as big data whenit meets the “four Vs” requirements, namely, volume, variety,velocity, and value. The keystone of big data exploitation is toleverage the existing datasets to create new information andpredict future happenings, enriching the decision valuechain. Accordingly, as the IoT continuously collects dataabout the surrounding living environments, it is consideredas a prototypical example of big data and a great applicationarea of big data analytics.

Dealing with large amount of dynamic data is one ofimportant characteristics of big data analytics. Entity resolu-tion (ER) is to disambiguate manifestations of real-worldentities in the dataset by linking and grouping and to reducethe complexity of data processing, which is a crucial step toachieve effective, efficient, and accurate data processing inbig data analysis. H.-J. Zhu et al. proposed a type-basedmultiblock technique to improve data quality. In the article,they present a new ER solution using a hybrid approach. Inparticular, through their new ER workflow, it is capable toreduce the searching space for entity pairs. The performanceof their proposed method has been validated through usinga real-life dataset created from an IoT real project. Theseresults have been obtained using up to five standard met-rics. The experimental results conclude that their proposedapproach can be a promising alternative for ER overlarge-scale data.

Besides, CPS are emerging from the integration ofembedded computing devices, smart objects, people, andphysical environments, which are typically tied by a commu-nication infrastructure. So, the design of CPS and theimplementation of their applications need to rely on IoT-enabled architectures and protocols that, both locally andglobally, enable collecting, managing, and processing largedata sets and support complex processes to manage and con-trol such systems. Thus, as a matter of fact, the large-scalenature of IoT-based CPS can be effectively and efficientlyfacilitated and supported via utilizing the cloud computinginfrastructures and platforms for providing flexible computa-tional power, resource virtualization, and high-capacity stor-age for data streams in addition to ensuring safety, security,and privacy.

CPS are the extension of traditional closed-loop systemswith the computer-based control and the Internet-basedcommunication, in which physical components, computeralgorithms, and sensing elements are tightly integrated viathe Internet communication link. Sensors are the essential

2 Journal of Sensors

elements in any closed-loop system. In this special issue, thearticle contributed by L. Sanchez et al. present a model-basedvirtual sensor to be used for Li-Ion batteries’ conditionmonitoring in cyber-physical vehicle systems. A principle-based model encoded with the expert knowledge aboutbattery behaviour has been turned into a soft sensor. Feedingwith the information extracted from data collected fromon-vehicle measurements, the soft sensor is able to approxi-mate the state of health of a battery. The experimental resultscoming from the implementation of the model-based softsensor for fault detection and diagnosis of the batteries havedemonstrated the soft sensor’s high efficiency.

As an example of CPS, W. Yu et al. proposed a newapproach to generate flexible gait for bionic leg’s controlbased on muscle synergies extracted from sEMG (surfaceelectromyogram) signal. The approach was inspired by thefact that muscle synergies leading to dimensionality reduc-tion may simplify motor control and learning. Usually,typical multichannel sEMG signal-based controls need massdata and vary greatly with time which cause worse latencyand other performance-related issues and make it difficultto generate compliant gait. The paper addresses two ques-tions to highlight the essential features in the proposedapproach with an interesting experimental result. The firstquestion addresses whether the same set of muscle synergiescan explain the different phases of jumping movement withvarious velocities. The second question is about building amodel for generating velocity-adapted jumping gait withmuscle synergies, in which a wavelet neural network is pro-posed to predict the reference gait pattern, while fuzzyinference system is adopted to merge these reference gaitsin order to create more generalized gaits with different jump-ing rhythms. The proposed method can be adopted as thedecoder in sEMG-based controls for a bionic leg. Moreover,linear combinations of synergies may describe complex forceand motion patterns in reduced dimensions, and the robustrepresentations of synergies within the control scheme cangenerate flexible gaits for other complex motions.

Qing TanNashwa El-BendaryMagdy A. Bayoumi

Xiaokun ZhangJavier SedanoJosé R. Villar

3Journal of Sensors

International Journal of

AerospaceEngineeringHindawiwww.hindawi.com Volume 2018

RoboticsJournal of

Hindawiwww.hindawi.com Volume 2018


Active and Passive Electronic Components

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawiwww.hindawi.com

Volume 2018

Hindawi Publishing Corporation http://www.hindawi.com Volume 2013Hindawiwww.hindawi.com

The Scientific World Journal

Volume 2018

Control Scienceand Engineering

Journal of


Hindawiwww.hindawi.com

Journal ofEngineeringVolume 2018

SensorsJournal of



RotatingMachinery


Modelling &Simulationin EngineeringHindawiwww.hindawi.com Volume 2018


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation


Hindawi

www.hindawi.com Volume 2018

Advances in

Multimedia

Submit your manuscripts atwww.hindawi.com
https://www.hindawi.com/journals/ijae/https://www.hindawi.com/journals/jr/https://www.hindawi.com/journals/apec/https://www.hindawi.com/journals/vlsi/https://www.hindawi.com/journals/sv/https://www.hindawi.com/journals/ace/https://www.hindawi.com/journals/aav/https://www.hindawi.com/journals/jece/https://www.hindawi.com/journals/aoe/https://www.hindawi.com/journals/tswj/https://www.hindawi.com/journals/jcse/https://www.hindawi.com/journals/je/https://www.hindawi.com/journals/js/https://www.hindawi.com/journals/ijrm/https://www.hindawi.com/journals/mse/https://www.hindawi.com/journals/ijce/https://www.hindawi.com/journals/ijap/https://www.hindawi.com/journals/ijno/https://www.hindawi.com/journals/am/https://www.hindawi.com/https://www.hindawi.com/

Emerging Technologies: IoT, Big Data, and CPS with Sensory … · 2019. 7. 30. · Editorial...

Documents

Transcript of Emerging Technologies: IoT, Big Data, and CPS with Sensory … · 2019. 7. 30. · Editorial...