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Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things [article]

Hamed Rahimi, Iago Felipe Trentin, Fano Ramparany, Olivier Boissier
2021 arXiv   pre-print
This article presents Q-SMASH: a multi-agent reinforcement learning-based approach for self-adaptation of IoT objects in human-centered environments.  ...  SMASH addresses the self-adaptation of IoT applications only according to the human values of users, while the behavior of users is not addressed.  ...  Pierre Maret for providing their support and cooperation throughout the research.  ... 
arXiv:2107.05949v1 fatcat:4feje5qhcvfcbix2smy5q2vgba

Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things

Hamed Rahimi, Iago Felipe Trentin, Fano Ramparany, Olivier Boissier
2021 IEEE/WIC/ACM International Conference on Web Intelligence  
As the number of Human-Centered Internet of Things (HCIoT) applications increases, the self-adaptation of IoT services and devices is becoming a fundamental requirement for addressing the uncertainties  ...  CCS CONCEPTS • Human-centered computing → Ubiquitous and mobile computing design and evaluation methods; Ambient intelligence;  ...  Pierre Maret for providing their support and cooperation throughout the research.  ... 
doi:10.1145/3486622.3493974 fatcat:7xgyd324eve5fk3fa5onivw7qq

A Risk-Based IoT Decision-Making Framework Based on Literature Review with Human Activity Recognition Case Studies

Tazar Hussain, Chris Nugent, Adrian Moore, Jun Liu, Alfie Beard
2021 Sensors  
A structured literature review of the risks and sources of uncertainty in IoT decision-making systems is the basis for the development of the framework and Human Activity Recognition (HAR) case studies  ...  Its output is subsequently integrated with domain knowledge-based action rules to perform decision making in a cost-sensitive and rational manner.  ...  IoT-DM Framework Layers ITU-T Layers Action and Decision Alternative application and interface for human operator.  ... 
doi:10.3390/s21134504 pmid:34209389 pmcid:PMC8271623 fatcat:t6del76y7jhcfdmtkyv6yr45ti

Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

Saeed H. Alsamhi, Faris A. Almalki, Hatem Al-Dois, Soufiene Ben Othman, Jahan Hassan, Ammar Hawbani, Radyah Sahal, Brian Lee, Hager Saleh, Ahmed Mostafa Khalil
2021 Computational Intelligence and Neuroscience  
Finally, we shed light on ML challenges for future IoT research, and we review the current literature based on existing works.  ...  The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities.  ...  actions regularly based on the environmental changes.  ... 
doi:10.1155/2021/6805151 pmid:34589123 pmcid:PMC8476267 fatcat:2rl2s6qkxbcabpwpjwcac4z6oe

Predicting Terroristic Attacks in Urban Environments: An Internet-of-Things Approach

Stavros Petris, Christos Georgoulis, John Soldatos
2014 International Journal of Security and Its Applications  
Unmanned Aerial Vehicles (UAVs)), (C) Human Intelligence (HUMINT)[8], i.e., intelligence based on information collected and provided by human sources including both obvious (overt) and secret (clandestine  ...  Hence, IoT can -on the basisof this broader definition of sensors-support all the different types of intelligence outlined above.  ...  The authors acknowledge help and contributions from all partners of the project.  ... 
doi:10.14257/ijsia.2014.8.4.18 fatcat:vo3gom5txvcktm3qunx5y3tove

Enabling Edge Cloud Intelligence for Activity Learning in Smart Home [article]

Bing Huang, Athman Bouguettaya, Hai Dong
2020 arXiv   pre-print
We propose a novel activity learning framework based on Edge Cloud architecture for the purpose of recognizing and predicting human activities.  ...  In this paper, we utilize temporal features for activity recognition and prediction in a single smart home setting.  ...  ., an activity label) and predicted action can be harnessed to enable the IoT-based prompting system (section IV). III.  ... 
arXiv:2005.06885v1 fatcat:m42f33c7nnbxhks3griv27xuii


M.W.P Maduranga, Ruvan Abeysekera
2020 International Journal of Engineering Applied Sciences and Technology  
The IoT generates big amount data with different characteristics based on location and time.  ...  Among the vast range of IoT applications, IoT based smart agriculture has fascinated many researchers and has used Machine Learning(ML) and IoT technologies to conduct innovative researches.  ...  Moreover most commonly used ML algorithms, K-nearest neighbor, support vector regression (SVR), Naive Bayes, etc. can be use to predict soil dryness based on precipitation and evaporative hydrology data  ... 
doi:10.33564/ijeast.2020.v04i12.004 fatcat:nwwb6qu34vhpjfuyv5g2pmzrgi

The Internet of Things and Architectures of Big Data Analytics: Challenges of Intersection at Different Domains

Dina Fawzy, Sherin M. Moussa, Nagwa L. Badr
2022 IEEE Access  
Despite of the massive studies dedicated for IoT, no explicit processing architecture is proposed based on real investigation of software engineering concepts and big data analytics characteristics in  ...  The study investigates the current techniques and technologies that serve IoT systems from the big data analytics and software engineering perspectives, revealing a matrix for the specific IoT data features  ...  Accordingly, an IoT-based dynamic client-server model was introduced in [52] to predict human location by adapting body sensors relocation.  ... 
doi:10.1109/access.2022.3140409 fatcat:gshllsojgneiregt2kgmt5jlza

A Microservices Architecture for Reactive and Proactive Fault Tolerance in IoT Systems

Alexander Power, Gerald Kotonya
2018 2018 IEEE 19th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)  
Providing fault-tolerance (FT) support to Internet of Things (IoT) systems is an open challenge, with many implementations providing static, tightly coupled FT support that does not adapt and evolve like  ...  IoT systems do.  ...  Contribution We propose an FT framework based on a microservices architecture to provide a scalable means of applying real-time and predictive FT support to IoT systems.  ... 
doi:10.1109/wowmom.2018.8449789 dblp:conf/wowmom/PowerK18 fatcat:e6fss7dmsfhmbntlfnycjuhlri

A Holistic Overview of Anticipatory Learning for the Internet of Moving Things: Research Challenges and Opportunities

Hung Cao, Monica Wachowicz
2020 ISPRS International Journal of Geo-Information  
This paper examines the state of the art of IoMT systems and discusses their crucial role in supporting anticipatory learning.  ...  However, it is expected that, in the near future, optimization and prediction tasks will have a larger impact on the way citizens interact with smart cities.  ...  The authors also appreciate the insightful comments and suggestions provided by three anonymous reviewers and the guest editors on the previous version of this manuscript.  ... 
doi:10.3390/ijgi9040272 fatcat:l4guim65rjg3hpnzntqwmih3wq

A Context-Aware IoT and Deep-Learning-Based Smart Classroom for Controlling Demand and Supply of Power Load

Prabesh Paudel, Sangkyoon Kim, Soonyoung Park, Kyoung-Ho Choi
2020 Electronics  
With added deep learning for action recognition, IoT sensors implemented in real-time appliances monitor and control the extra usage of energy in buildings.  ...  Here, we present a novel proposal through context-aware architecture for energy saving in classrooms, combining Internet of Things (IoT) sensors and video action recognition.  ...  [37] proposed a 3D Convolutional Neural Network (CNN)-based human detector and head tracker to segment human subjects in videos. Sun et al.  ... 
doi:10.3390/electronics9061039 fatcat:ciiw4ilslvaopirsmbcjpsnjdq

Industrial Internet of Things (IIoT) – An IoT Integrated Services for Industry 4.0 : A Review

Joyce Jacob J
2020 International Journal of Applied Science and Engineering  
In this paper, we reviewed the transition of various IoT based systems to IIOT with the insights of concepts, devices and technologies of IoT and IIoT integrated applications which outlines the various  ...  Such systems play a vital role in automating data sensing, machine and manufacturing process monitoring, product quality checking and lactation based smart shipping in real world industrial environment  ...  Some of the popular IoT based cloud environments like Thingworx [13] , Xively [14] , CISCO IoT cloud, AWS IoT Platform, etc.. supports for IIoT application development.  ... 
doi:10.30954/2322-0465.1.2020.5 fatcat:qznxabp76beuzhlf4oeqkmtk54

A Solution Framework for Managing Internet of Things (IOT)

Sukant K. Mohapatra, Jay N. Bhuyan
2016 International Journal of Computer Networks & Communications  
analytic support, etc., in managing IoT.  ...  Internet of Things (IoT) refers to heterogeneous systems and devices (often referred to as smart objects) that connect to the internet, and is an emerging and active area of research with tremendous technological  ...  ACKNOWLEDGEMENTS This research was supported in part by NSF Grant # 1614845.  ... 
doi:10.5121/ijcnc.2016.8606 fatcat:hah27wcajnau3k23bmadlqspma

Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy

Viviane Cunha Farias da Costa, Luiz Oliveira, Jano de Souza
2021 Sensors  
The paradigm of the Internet of everything (IoE) is advancing toward enriching people's lives by adding value to the Internet of things (IoT), with connections among people, processes, data, and things  ...  in IoE applications, an in-depth classification of IoE enablers (sensors and actuators); (3) validation of the defined taxonomy with 50 IoE applications; and (4) identification of issues and challenges  ...  In healthcare application domains, robots and humans will work together in a smart medical environment, and diagnostic processes based on evidence-supported results and treatments will be automated and  ... 
doi:10.3390/s21020568 pmid:33466895 fatcat:tsmf2psgunbqrhajqwm6siakju

Machine Learning for Internet of Things

Manisha Bagri, Neha Aggarwal
2019 International Journal Of Engineering And Computer Science  
The data can be feed to machines for learning patterns, based on training the machines can identify to predict for the future. This paper gives a brief explanation of IoT.  ...  An algorithm is also proposed for weather prediction using SVM for IoT.  ...  The devices on the network can communicate without human-to-human or human-to-computer interaction.  ... 
doi:10.18535/ijecs/v8i07.4346 fatcat:n5avkbzzazbvxnhoeof6cz3iiu
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