Experimental comparison of the diagnostic capabilities of classification and clustering algorithms for the QoS management in an autonomic IoT platform

The Internet of Things (IoT) platforms must allow the communication between the Applications and Devices according to their non-functional requirements. One of the main non-functional requirements is the Quality of Service (QoS). In a previous work has been defined an autonomic IoT platform for the...

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Published in:	Service oriented computing and applications Vol. 13; no. 3; pp. 199 - 219
Main Authors:	Morales, Luis, Ouedraogo, Clovis Anicet, Aguilar, Jose, Chassot, Christophe, Medjiah, Samir, Drira, Khalil
Format:	Journal Article
Language:	English
Published:	London Springer London 01.09.2019 Springer Nature B.V Springer
Subjects:	Algorithms Artificial Intelligence Classification Clustering Communications systems Computer Appl. in Administrative Data Processing Computer Science Computer simulation Computer Systems Organization and Communication Networks Data analysis Diagnostic systems e-Commerce/e-business Internet of Things IT in Business Machine Learning Management Management of Computing and Information Systems Multivariate analysis Networking and Internet Architecture Original Research Paper Quality of service Software Engineering/Programming and Operating Systems Autonomic cycle of data analysis tasks IoT platforms Clustering Quality of Service Classification Diagnostic capabilities
ISSN:	1863-2386, 1863-2394
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Abstract	The Internet of Things (IoT) platforms must allow the communication between the Applications and Devices according to their non-functional requirements. One of the main non-functional requirements is the Quality of Service (QoS). In a previous work has been defined an autonomic IoT platform for the QoS Management, based on the concept of autonomic cycle of data analysis tasks. In this platform have been defined two autonomic cycles, one based on a classification task that determines the current operational state to define the set of tasks to execute in the communication system to guarantee a given QoS. The other one is based on a clustering task that discovers the current operational state and, based on it, determines the set of tasks to be executed in the communication system. This paper analyzes the diagnostic capabilities of the system based on both approaches, using different metrics. For that, a real scenario has been considered, with simulations that have generated data to test both tasks. Each technique has different aspects to be considered for a correct QoS management in the context of IoT platforms. The classification technique can determine very well the learned operational states, but the clustering approach can carry out a more detailed description of the operational states. Additionally, due to the classification and clustering technique used, called learning algorithm for multivariate data analysis, the paper analyzes the operational state profile determined by them, which is very useful in a diagnostic process.
AbstractList	The IoT platforms must allow the communication between the Applications and Devices according to their non-functional requirements. One of the main non-functional requirements is the Quality of Service (QoS). In a previous work has been defined an Autonomic Internet of Things (IoT) platform for the QoS Management, based on the concept of autonomic cycle of data analysis tasks. In this platform have been defined two autonomic cycles, one based on a classification task that determines the current operational state to define the set of tasks to execute in the communication system to guarantee a given QoS. The other one is based on a clustering task that discovers the current operational state, and based on it, determines the set of tasks to be executed in the communication system. This paper analyzes the diagnostic capabilities of the system based on both approaches, using different metrics. For that, a real scenario has been considered, with simulations that have generated data to test both tasks. Each technique has different aspects to be considered for a correct QoS management in the context of IoT platforms. The classification technique can determine very well the learned operational states, but the clustering approach can carry out a more detailed description of the operational states. Additionally, due to the classification and clustering technique used, called LAMDA (Learning Algorithm for Multivariate Data Analysis), the paper analyzes the operational state profile determined by them, which is very useful in a diagnostic process. The Internet of Things (IoT) platforms must allow the communication between the Applications and Devices according to their non-functional requirements. One of the main non-functional requirements is the Quality of Service (QoS). In a previous work has been defined an autonomic IoT platform for the QoS Management, based on the concept of autonomic cycle of data analysis tasks. In this platform have been defined two autonomic cycles, one based on a classification task that determines the current operational state to define the set of tasks to execute in the communication system to guarantee a given QoS. The other one is based on a clustering task that discovers the current operational state and, based on it, determines the set of tasks to be executed in the communication system. This paper analyzes the diagnostic capabilities of the system based on both approaches, using different metrics. For that, a real scenario has been considered, with simulations that have generated data to test both tasks. Each technique has different aspects to be considered for a correct QoS management in the context of IoT platforms. The classification technique can determine very well the learned operational states, but the clustering approach can carry out a more detailed description of the operational states. Additionally, due to the classification and clustering technique used, called learning algorithm for multivariate data analysis, the paper analyzes the operational state profile determined by them, which is very useful in a diagnostic process.
Author	Medjiah, Samir Morales, Luis Aguilar, Jose Chassot, Christophe Ouedraogo, Clovis Anicet Drira, Khalil
Author_xml	– sequence: 1 givenname: Luis surname: Morales fullname: Morales, Luis organization: Departamento de Automatización y Control Industrial, Escuela Politécnica Nacional – sequence: 2 givenname: Clovis Anicet surname: Ouedraogo fullname: Ouedraogo, Clovis Anicet organization: CNRS-LAAS – sequence: 3 givenname: Jose orcidid: 0000-0003-4194-6882 surname: Aguilar fullname: Aguilar, Jose email: aguilar@ula.ve organization: CEMISID, Universidad de Los Andes – sequence: 4 givenname: Christophe surname: Chassot fullname: Chassot, Christophe organization: CNRS-LAAS, INSA – sequence: 5 givenname: Samir surname: Medjiah fullname: Medjiah, Samir organization: Université de Toulouse, UPS, CNRS-LAAS – sequence: 6 givenname: Khalil surname: Drira fullname: Drira, Khalil organization: CNRS-LAAS
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References	S. Medjiah and C. Chassot (2017) On the enhancement of non-functional requirements for cloud-assisted middleware-based IoT and other applications (invited paper). In: To be publish in the second workshop on adaptive service-oriented and cloud application, Malaga, Spain Vizcarrondo J, Aguilar J, Exposito E, Subias A (2012) ARMISCOM: autonomic reflective middleware for management service composition. In: Proceedings of the 4th global information infrastructure and networking symposium, IEEE communication society, Choroni, Venezuela KumarJZaveriMhierarchical clustering for dynamic and heterogeneous internet of thingsProc Comput Sci20169327628210.1016/j.procs.2016.07.211 AguilarJCorderoJBarbaLSanchezMValdiviezoPChambaLLearning analytics tasks as services in smart classroomUnivers Access Inf Soc J201817469370910.1007/s10209-017-0525-0 Goel D, Chaudhury S, Ghosh H (2017) An IoT approach for context-aware smart traffic management using ontology. In: International conference on web intelligence, pp 42–49. Leipzig, Germany Siby S, Ranjan R, Tippenhauer N (2017) IoTScanner: detecting privacy threats in IoT neighborhoods. In: 3rd ACM international workshop on IoT privacy, trust, and security, pp 23–30, Abu Dhabi Medved J et al (2014) Opendaylight: Towards a model-driven sdn controller architecture. In: World of wireless, IEEE 15th international symposium on a mobile and multimedia networks (WoWMoM), Sydney, Australia ETSI TS 102 690 (2010) Machine-to-machine communications (M2M); Functional architecture, v2.1.1 Ouedraogo C, Medjiah S, Chassot C (2018) Autonomic middleware based on data analysis tasks for the QoS management in the IoT. Technical Report, LAAS ZhaoSYuLChengBChenJIoT service clustering for dynamic service matchmakingSensors2017178172710.3390/s17081727 KumarJZaveriMClustering approaches for pragmatic two-layer IoT architectureWirel Commun Mobile Comput2018201810.1155/2018/8739203 LAMDA–RD (2018) An extension to the LAMDA classifier in the clustering context. Expert Syst Appl (in revision) HongKMoonJWonCJuJSamiaBIdentifying service contexts for QoS support in IoT service oriented software defined networksMobile, secure, and programmable networking2017BerlinSpringer99108 oneM2M TS v1.6.1 (2015) oneM2M functional architecture WhiteGNallurVClarkeSQuality of service approaches in IoT: a systematic mappingJ Syst Softw201713218620310.1016/j.jss.2017.05.125 Otebolaku A, Lee G (2017) Towards context classification and reasoning in IoT. 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In: Fifth international conference on advances in recent technologies in communication and computing, pp 267–274, Bangalore, India Floodlight. Available: http://floodlight.openflowhub.org ZhangQFitzekFMission Critical IoT Communication in5GSoc Inform Telecommun Eng20151593541 BotíaJFBotíaDOn LAMDA clustering method based on typicality degree and intuitionistic fuzzy setsExpert Syst Appl201810719622110.1016/j.eswa.2018.04.022 Open Networking Foundation (2015) OpenFlow Switch Specification Version 1.5.1 Kumar J, Zaveri M (2016) Clustering for collaborative processing in IoT network. In: Second international conference on IoT in urban space, pp 95–97, Tokyo Japan JianliPJamesMFuture edge cloud and edge computing for internet of things applicationsIEEE Internet Things J20185143944910.1109/JIOT.2017.2767608 SandeshMRailkarPPMahallePMathematical representation of quality of service (QoS) parameters for internet of things (IoT)Int J Rough Sets Data Anal2017439610710.4018/IJRSDA.2017070107 AguilarJBuendiaOCorderoJSpecification of the autonomic cycles of learning analytic tasks for a smart classroomJ Edu Comput Res201856686689110.1177/0735633117727698 Ferrando R, Stacey P (2017) Classification of device behaviour in internet of things infrastructures: towards distinguishing the abnormal from security threats. In: International conference on internet of things and machine learning, Liverpool, United Kingdom IBM (2005) An architectural blueprint for autonomic computing. 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References_xml	– reference: AguilarJBuendiaOCorderoJSpecification of the autonomic cycles of learning analytic tasks for a smart classroomJ Edu Comput Res201856686689110.1177/0735633117727698 – reference: Azevedo R, Ribeiro R (2017) Distributed data clustering in the context of the internet of things: a data traffic reduction approach. In: 23rd Brazilian symposium on multimedia and the web, pp 313–316, Gramado, Brazil – reference: AntunesMGomesDAguiaRTowards IoT data classification through semantic featuresFuture Gener Comput Syst20188679279810.1016/j.future.2017.11.045 – reference: S. Medjiah and C. Chassot (2017) On the enhancement of non-functional requirements for cloud-assisted middleware-based IoT and other applications (invited paper). In: To be publish in the second workshop on adaptive service-oriented and cloud application, Malaga, Spain – reference: Kumar J, Zaveri M (2016) Clustering for collaborative processing in IoT network. In: Second international conference on IoT in urban space, pp 95–97, Tokyo Japan – reference: Open Networking Foundation (2015) OpenFlow Switch Specification Version 1.5.1 – reference: Ferrando R, Stacey P (2017) Classification of device behaviour in internet of things infrastructures: towards distinguishing the abnormal from security threats. In: International conference on internet of things and machine learning, Liverpool, United Kingdom – reference: MichellVOlwenJOchsTRiemannUThe Human-IoT ecosystem: an approach to functional situation context classificationThe internet of things in the modern business environment2017PennsylvaniaIGI Global22324810.4018/978-1-5225-2104-4.ch012 – reference: IBM (2005) An architectural blueprint for autonomic computing. In: IBM white paper, 3th ed – reference: IsazaCAguilar-MartinJLe LannMVAguilarJRios-BolivarAAn optimization method for the data space partition obtained by classification techniques for the monitoring of dynamic processesFront Artif Intell Appl20061468087 – reference: AguilarJCorderoJBarbaLSanchezMValdiviezoPChambaLLearning analytics tasks as services in smart classroomUnivers Access Inf Soc J201817469370910.1007/s10209-017-0525-0 – reference: WhiteGNallurVClarkeSQuality of service approaches in IoT: a systematic mappingJ Syst Softw201713218620310.1016/j.jss.2017.05.125 – reference: JianliPJamesMFuture edge cloud and edge computing for internet of things applicationsIEEE Internet Things J20185143944910.1109/JIOT.2017.2767608 – reference: ETSI TS 102 690 (2010) Machine-to-machine communications (M2M); Functional architecture, v2.1.1 – reference: Morales L, Aguilar J, Chavez D, Izasa C (2018) LAMDA–HAD, an extension to the LAMDA classifier in the context of supervised learning. Int J Inf Technol Decis Making (in revision) – reference: El MouaatamidOLahmerMBelkasmiMInternet of things security: layered classification of attacks and possible countermeasuresElectron J Inf Technol201692537 – reference: oneM2M TS v1.6.1 (2015) oneM2M functional architecture – reference: Siby S, Ranjan R, Tippenhauer N (2017) IoTScanner: detecting privacy threats in IoT neighborhoods. In: 3rd ACM international workshop on IoT privacy, trust, and security, pp 23–30, Abu Dhabi – reference: KumarJZaveriMhierarchical clustering for dynamic and heterogeneous internet of thingsProc Comput Sci20169327628210.1016/j.procs.2016.07.211 – reference: Goel D, Chaudhury S, Ghosh H (2017) An IoT approach for context-aware smart traffic management using ontology. In: International conference on web intelligence, pp 42–49. Leipzig, Germany – reference: SandeshMRailkarPPMahallePMathematical representation of quality of service (QoS) parameters for internet of things (IoT)Int J Rough Sets Data Anal2017439610710.4018/IJRSDA.2017070107 – reference: Otebolaku A, Lee G (2017) Towards context classification and reasoning in IoT. In: 14th international conference on telecommunications (ConTEL), Zagreb, Croatia, pp 147–154 – reference: Meidan Y, Bohadana M, Shabtai A, Guarnizo J, Ochoa M, Tippenhauer N, Elovici Y (2017) ProfilIoT: a machine learning approach for IoT device identification based on network traffic analysis. In: Symposium on applied computing, pp 506–509, Marrakesh, Morocco – reference: Medved J et al (2014) Opendaylight: Towards a model-driven sdn controller architecture. In: World of wireless, IEEE 15th international symposium on a mobile and multimedia networks (WoWMoM), Sydney, Australia – reference: VizcarrondoJAguilarJExpositoESubiasAMAPE-K as a service-oriented architectureIEEE Latin Am Trans20171561163117510.1109/TLA.2017.7932705 – reference: ZhaoSYuLChengBChenJIoT service clustering for dynamic service matchmakingSensors2017178172710.3390/s17081727 – reference: HongKMoonJWonCJuJSamiaBIdentifying service contexts for QoS support in IoT service oriented software defined networksMobile, secure, and programmable networking2017BerlinSpringer99108 – reference: ZhangQFitzekFMission Critical IoT Communication in5GSoc Inform Telecommun Eng20151593541 – reference: Ouedraogo C, Medjiah S, Chassot C (2018) Autonomic middleware based on data analysis tasks for the QoS management in the IoT. 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Snippet	The Internet of Things (IoT) platforms must allow the communication between the Applications and Devices according to their non-functional requirements. One of... The IoT platforms must allow the communication between the Applications and Devices according to their non-functional requirements. One of the main...
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SubjectTerms	Algorithms Artificial Intelligence Classification Clustering Communications systems Computer Appl. in Administrative Data Processing Computer Science Computer simulation Computer Systems Organization and Communication Networks Data analysis Diagnostic systems e-Commerce/e-business Internet of Things IT in Business Machine Learning Management Management of Computing and Information Systems Multivariate analysis Networking and Internet Architecture Original Research Paper Quality of service Software Engineering/Programming and Operating Systems
Title	Experimental comparison of the diagnostic capabilities of classification and clustering algorithms for the QoS management in an autonomic IoT platform
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