Machine Learning and Internet of Things for Smart Living: A Comprehensive Review and Analysis

The role of machine learning is to provide intelligent processing, analysis, and prediction for the data being used in the smart Internet of Things (IoT) applications to enhance quality and efficiency. This study aims to analyze the taxonomy of machine learning algorithms used in the specific type of IoT smart living applications. This chapter demonstrates an analysis of the data extracted from the 52 peer-reviewed scientific publications describing IoT observations on machine learning algorithms (clustering, classification, regression), optimization metrics, data size, data type (text, audio, video, image), data collection method, data processing location (cloud, fog, edge), and routing protocol (hierarchical clustering, point-to-point, multi-hop routing). The results show that most of the studies utilized cloud computing as their data processing location in IoT (Smart City: 55.88% and Smart Health: 60.61%) over Fog computing environments (Smart City: 11.76% and Smart Health: 15.15%). The chapter has shown that the classification is more used (Smart City: 45.46%, and Smart Health: 70.37%) than clustering and regression techniques in the IoT smart living applications. Furthermore, this work shows that among the four types of data formats (text, audio, video, and image) have been used in IoT applications, text data in Smart City (66.67%) and Smart Health (61.11%) was the primary format that has been used. Future work should focus on the adaptation of fog computing while applying machine learning for different smart applications, which will lead to a decrease in network traffic and optimized bandwidth.

[1]  Xuan Zeng,et al.  HeartID: A Multiresolution Convolutional Neural Network for ECG-Based Biometric Human Identification in Smart Health Applications , 2017, IEEE Access.

[2]  Khaled Eskaf,et al.  Aggregated Activity Recognition Using Smart Devices , 2016, 2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI).

[3]  Victor Callaghan,et al.  Understanding and personalising smart city services using machine learning, The Internet-of-Things and Big Data , 2017, 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE).

[4]  Aakanksha Gadagkar,et al.  Optimization of water consumption using dynamic quota based smart water management system , 2017, 2017 IEEE Region 10 Symposium (TENSYMP).

[5]  Athanasios V. Vasilakos,et al.  When things matter: A survey on data-centric internet of things , 2016, J. Netw. Comput. Appl..

[6]  Xin Tao,et al.  Clustering massive small data for IOT , 2014, The 2014 2nd International Conference on Systems and Informatics (ICSAI 2014).

[7]  Gonzalo Mateos,et al.  Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges , 2015, 2015 IEEE International Conference on Services Computing.

[8]  Ahlame Douzal Chouakria,et al.  Multiple metric learning for large margin kNN classification of time series , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

[9]  Sushma Nagaraj,et al.  Edge-based street object detection , 2017, 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).

[10]  Klaus Moessner,et al.  Predictive Analytics for Complex IoT Data Streams , 2017, IEEE Internet of Things Journal.

[11]  Hossam S. Hassanein,et al.  Towards a Practical Crowdsensing System for Road Surface Conditions Monitoring , 2018, IEEE Internet of Things Journal.

[12]  Anshuman Chhabra,et al.  A predictive approach to task scheduling for Big Data in cloud environments using classification algorithms , 2017, 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence.

[13]  Luis Orozco-Barbosa,et al.  Citizen security using machine learning algorithms through open data , 2016, 2016 8th IEEE Latin-American Conference on Communications (LATINCOM).

[14]  Georges Kaddoum,et al.  A waste city management system for smart cities applications , 2017, 2017 Advances in Wireless and Optical Communications (RTUWO).

[15]  Malka Halgamuge.  Efficient Battery Management for Sensor Lifetime , 2007, 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07).

[16]  Arindam Ghosh,et al.  On automatizing recognition of multiple human activities using ultrasonic sensor grid , 2017, 2017 9th International Conference on Communication Systems and Networks (COMSNETS).

[17]  Albert Y. Zomaya,et al.  A Dimension Reduction Model and Classifier for Anomaly-Based Intrusion Detection in Internet of Things , 2017, 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech).

[18]  M. Zukerman,et al.  High Powered Cluster Heads for Extending Sensor Network Lifetime , 2006, 2006 IEEE International Symposium on Signal Processing and Information Technology.

[19]  Robert J. Piechocki,et al.  Extending the battery lifetime of wearable sensors with embedded machine learning , 2018, 2018 IEEE 4th World Forum on Internet of Things (WF-IoT).

[20]  Diogo Gomes,et al.  Vehicular dataset for road assessment conditions , 2017, 2017 International Smart Cities Conference (ISC2).

[21]  Dimitrios Zissis,et al.  Intelligent security on the edge of the cloud , 2017, 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC).

[22]  Michele Zorzi,et al.  Automatic Rate-Distortion Classification for the IoT: Towards Signal-Adaptive Network Protocols , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[23]  Rashid Mehmood,et al.  UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities , 2018, IEEE Access.

[24]  Yang Ji,et al.  A hybrid method for short-term sensor data forecasting in Internet of Things , 2014, 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[25]  Dorothy Ndedi Monekosso,et al.  Data reconciliation in a smart home sensor network , 2013, Expert Syst. Appl..

[26]  Igor Kotenko,et al.  Neural network approach to forecast the state of the Internet of Things elements , 2015, 2015 XVIII International Conference on Soft Computing and Measurements (SCM).

[27]  Harishchandra Dubey,et al.  Smart fog: Fog computing framework for unsupervised clustering analytics in wearable Internet of Things , 2017, 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[28]  Wilhelm Stork,et al.  Personalization of monitoring system parameters to support ambulatory care for dementia patients , 2018, 2018 IEEE Sensors Applications Symposium (SAS).

[29]  Abdulhamit Subasi,et al.  Traffic accident detection using random forest classifier , 2018, 2018 15th Learning and Technology Conference (L&T).

[30]  Malka N. Halgamuge,et al.  Energy efficient cluster formation in wireless sensor networks , 2003, 10th International Conference on Telecommunications, 2003. ICT 2003..

[31]  Carlos E. Palau,et al.  Fall detection system for elderly people using IoT and Big Data , 2018, ANT/SEIT.

[32]  Sandeep K. Sood,et al.  Fog Assisted-IoT Enabled Patient Health Monitoring in Smart Homes , 2018, IEEE Internet of Things Journal.

[33]  D. Toshniwal,et al.  Clustering techniques for streaming data-a survey , 2013, 2013 3rd IEEE International Advance Computing Conference (IACC).

[34]  Djamel Djenouri,et al.  A Study of Wireless Sensor Networks for Urban Traffic Monitoring: Applications and Architectures , 2013, ANT/SEIT.

[35]  Udit Satija,et al.  Real-Time Signal Quality-Aware ECG Telemetry System for IoT-Based Health Care Monitoring , 2017, IEEE Internet of Things Journal.

[36]  Jay Shah,et al.  Building Modern Clouds: Using Docker, Kubernetes & Google Cloud Platform , 2019, 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC).

[37]  Stefan Decker,et al.  Real time analysis of sensor data for the Internet of Things by means of clustering and event processing , 2015, 2015 IEEE International Conference on Communications (ICC).

[38]  Bo Tang,et al.  Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities , 2017, IEEE Transactions on Industrial Informatics.

[39]  Nazim Agoulmine,et al.  Enhancing eHealth smart applications: A Fog-enabled approach , 2015, 2015 17th International Conference on E-health Networking, Application & Services (HealthCom).

[40]  Ritu Garg,et al.  Energy Management in Wireless Sensor Networks: A State of Art , 2018, ICICI-2018.

[41]  Jwan K. Alwan,et al.  The utilisation of machine learning approaches for medical data classification and personal care system mangementfor sickle cell disease , 2017, 2017 Annual Conference on New Trends in Information & Communications Technology Applications (NTICT).

[42]  Amit P. Sheth,et al.  Machine learning for Internet of Things data analysis: A survey , 2017, Digit. Commun. Networks.

[43]  Dimitrios Soudris,et al.  ECG signal analysis and arrhythmia detection on IoT wearable medical devices , 2016, 2016 5th International Conference on Modern Circuits and Systems Technologies (MOCAST).

[44]  Jaganathan Venkatesh,et al.  Modular and Personalized Smart Health Application Design in a Smart City Environment , 2018, IEEE Internet of Things Journal.

[45]  Zhenghua Chen,et al.  Smartphone-based Human Activity Recognition in buildings using Locality-constrained Linear Coding , 2015, 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA).

[46]  Christopher Leckie,et al.  Parking availability prediction for sensor-enabled car parks in smart cities , 2015, 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP).

[47]  Jerry Zeyu Gao,et al.  Autonomous UAV forced graffiti detection and removal system based on machine learning , 2017, 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).

[48]  Sandeep K. Sood,et al.  Cloud-centric IoT based disease diagnosis healthcare framework , 2017, J. Parallel Distributed Comput..

[49]  Hitesh Shah,et al.  An anomaly detection in smart cities modeled as wireless sensor network , 2016, 2016 International Conference on Signal and Information Processing (IConSIP).

[50]  Tapio Salakoski,et al.  Medical warning system based on Internet of Things using fog computing , 2016, 2016 International Workshop on Big Data and Information Security (IWBIS).

[51]  Giovanni Maria Farinella,et al.  Park Smart , 2017, 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[52]  Himadri Nath Saha,et al.  Optimizing routine collection efficiency in IoT based garbage collection monitoring systems , 2018, 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC).

[53]  Jeonghan Hong,et al.  Multivariate time-series classification of sleep patterns using a hybrid deep learning architecture , 2017, 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom).

[54]  Naveen K. Chilamkurti,et al.  Deep Learning: The Frontier for Distributed Attack Detection in Fog-to-Things Computing , 2018, IEEE Communications Magazine.

[55]  Chandrasekar Vuppalapati,et al.  Adaptive edge analytics for creating memorable customer experience and venue brand engagement, a scented case for Smart Cities , 2017, 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).