Machine learning methods for IoT and their Future Applications

With the advent of rapid developments, large number of heterogeneous devices is able to connect with the help of IOT technology. Although IOT possess very complex architecture because of connectivity of variety of devices and services in the system. In this paper, a brief concept of urban IOT system is presented which are designed to support smart city and advanced communication technologies. Hence a comprehensive survey of architecture, technologies, and computational frameworks is provided for a smart IOT. It also discusses the major vulnerabilities and challenges faced by IOT and also present how machine learning is applied to IOT. Hence smart city is considered as the use case and it explains how various techniques are applied to data in order to extract great results with good efficiency.

[1]  M. Dave,et al.  An Empirical Comparison Of Supervised Learning Processes , 2007 .

[2]  Lei Chen,et al.  Enhancing Privacy and Availability for Data Clustering in Intelligent Electrical Service of IoT , 2019, IEEE Internet of Things Journal.

[3]  Wee Siong Ng,et al.  Study of Design Method for Tangible User Interface in IoT Paradigm , 2018, TENCON 2018 - 2018 IEEE Region 10 Conference.

[4]  Lennart Ljung,et al.  Kernel methods in system identification, machine learning and function estimation: A survey , 2014, Autom..

[5]  Usha Devi Gandhi,et al.  A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases , 2017, Comput. Electr. Eng..

[6]  Junaid Qadir,et al.  Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey , 2019, IEEE Access.

[7]  N. Sairam,et al.  Enhanced Classification Performance Using Computational Intelligence , 2011, CSE 2011.

[8]  Mohsen Guizani,et al.  Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services , 2018, IEEE Internet of Things Journal.

[9]  Chee Yen Leow,et al.  An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges , 2018, IEEE Internet of Things Journal.

[10]  Laurence T. Yang,et al.  Data Mining for Internet of Things: A Survey , 2014, IEEE Communications Surveys & Tutorials.

[11]  Lalu Banoth,et al.  A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection , 2017 .

[12]  Chris Aldrich,et al.  Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods , 2013, Advances in Computer Vision and Pattern Recognition.

[13]  Hae-Chang Rim,et al.  Some Effective Techniques for Naive Bayes Text Classification , 2006, IEEE Transactions on Knowledge and Data Engineering.

[14]  Amanda Saint Where next for the internet of things? [Information Technology Internet of Things] , 2015 .

[15]  Masahiro Inoue,et al.  IoT Monitoring System for Early Detection of Agricultural Pests and Diseases , 2018, 2018 12th South East Asian Technical University Consortium (SEATUC).

[16]  Ayodeji Olalekan Salau,et al.  Recent Trends in IoT and Its Requisition with IoT Built Engineering: A Review , 2018, Lecture Notes in Electrical Engineering.

[17]  Nasir Ghani,et al.  Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-Scale IoT Exploitations , 2019, IEEE Communications Surveys & Tutorials.

[18]  Shahzad A. Malik,et al.  Fog/Edge Computing-Based IoT (FECIoT): Architecture, Applications, and Research Issues , 2019, IEEE Internet of Things Journal.

[19]  Yaping Fang,et al.  Prediction of core cancer genes using multi-task classification framework. , 2013, Journal of theoretical biology.

[20]  Ferat Sahin,et al.  A survey on feature selection methods , 2014, Comput. Electr. Eng..

[21]  Vijay Sivaraman,et al.  Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics , 2019, IEEE Transactions on Mobile Computing.

[22]  Ayodeji Olalekan Salau,et al.  Numerical calculation of fuel burn-up rate in a cylindrical nuclear reactor , 2018, Journal of Radioanalytical and Nuclear Chemistry.

[23]  Satyavrat Wagle,et al.  Regression based prediction algorithm for remote controlling of IoT based applications , 2015, 2015 International Conference on Computing and Network Communications (CoCoNet).

[24]  Indrajit Mandal,et al.  Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System , 2012, Journal of Medical Systems.

[26]  Shubham Saloni,et al.  WiFi-aware as a connectivity solution for IoT pairing IoT with WiFi aware technology: Enabling new proximity based services , 2016, 2016 International Conference on Internet of Things and Applications (IOTA).

[27]  Eduardo R. Hruschka,et al.  An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks , 2013, Data Knowl. Eng..

[28]  Raed M. Shubair,et al.  Classification of Indoor Environments for IoT Applications: A Machine Learning Approach , 2018, IEEE Antennas and Wireless Propagation Letters.

[29]  Ni Bin,et al.  Research on Methods and Techniques for IoT Big Data Cluster Analysis , 2018, 2018 International Conference on Information Systems and Computer Aided Education (ICISCAE).

[30]  M. Shamim Hossain,et al.  Cognitive Smart Healthcare for Pathology Detection and Monitoring , 2019, IEEE Access.

[31]  Eiji Oki,et al.  Prioritization of Mobile IoT Data Transmission Based on Data Importance Extracted From Machine Learning Model , 2019, IEEE Access.

[32]  J. Manyika Big data: The next frontier for innovation, competition, and productivity , 2011 .

[33]  Amanda Saint Where next for the Internet of internet of things , 2015 .

[34]  Giancarlo Fortino,et al.  Evaluating Critical Security Issues of the IoT World: Present and Future Challenges , 2018, IEEE Internet of Things Journal.

[35]  Grenville J. Armitage,et al.  A survey of techniques for internet traffic classification using machine learning , 2008, IEEE Communications Surveys & Tutorials.

[36]  Leif R. Wilhelmsson,et al.  NB-WiFi: IEEE 802.11 and Bluetooth Low Energy Combined for Efficient Support of IoT , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).

[37]  Michael W. Condry,et al.  Using Smart Edge IoT Devices for Safer, Rapid Response With Industry IoT Control Operations , 2016, Proceedings of the IEEE.

[38]  Xiaofeng Zhu,et al.  Efficient kNN Classification With Different Numbers of Nearest Neighbors , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[39]  Ricard Vilalta,et al.  Integration of IoT, Transport SDN, and Edge/Cloud Computing for Dynamic Distribution of IoT Analytics and Efficient Use of Network Resources , 2018, Journal of Lightwave Technology.

[40]  Mohsen Guizani,et al.  Deep Learning for IoT Big Data and Streaming Analytics: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[41]  Kenneth Li-Minn Ang,et al.  Application Specific Internet of Things (ASIoTs): Taxonomy, Applications, Use Case and Future Directions , 2019, IEEE Access.

[42]  Sanjay Jha,et al.  The Design, Implementation, and Deployment of a Smart Lighting System for Smart Buildings , 2019, IEEE Internet of Things Journal.

[43]  Lars Bauer,et al.  From Cloud Down to Things: An Overview of Machine Learning in Internet of Things , 2019, IEEE Internet of Things Journal.

[44]  Biplab Sikdar,et al.  A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures , 2019, IEEE Access.

[45]  Kuo-Hui Yeh,et al.  A Secure IoT-Based Healthcare System With Body Sensor Networks , 2016, IEEE Access.

[46]  Zhenxing Qian,et al.  Evolutionary selection extreme learning machine optimization for regression , 2012, Soft Comput..

[47]  Erik Elmroth,et al.  Connecting Fog and Cloud Computing , 2017, IEEE Cloud Comput..