0% Complete
Home
/
14th International Conference on Computer and Knowledge Engineering
Smart Home Connectivity: Identifying the Best IoT Application Layer Protocols
Authors :
Hossein Shahinzadeh
1
Zohreh Azani
2
Sundus F. Al-Hameedawi
3
S. Mohammadali Zanjani
4
Saiedeh Mehrabani-Najafabadi
5
Mohammadreza Hemmati
6
1- Amirkabir University of Technology (Tehran Polytechnic)
2- Amirkabir University of Technology (Tehran Polytechnic)
3- University of Shahrekord
4- Najafabad Branch, Islamic Azad University, Najafabad, Iran
5- Najafabad Branch, Islamic Azad University, Najafabad, Iran
6- Najafabad Branch, Islamic Azad University, Najafabad, Iran
Keywords :
Smart Home،Internet of Things،Application Layer،Protocol،HTTP،MQTT،CoAP،WebSocket،DDS،XMPP
Abstract :
The Internet of Things (IoT) bridges the physical and digital worlds by utilizing sensors, actuators, communication technologies, computing power, and data analytics to enable precise monitoring and control of the surrounding environment. Leveraging the data derived from IoT can lead to optimal decision-making for system management. In smart homes, IoT has ushered in a new generation known as connected homes. Given the diverse range of protocols available for the IoT application layer, selecting the appropriate protocol to connect smart home devices (based on their specific requirements) to the internet gateway is a critical issue. This paper first identifies the key factors influencing the choice of application layer protocols in smart homes. Then, it examines and analyzes some of the most commonly used IoT application layer protocols, including HTTP, MQTT, CoAP, WebSocket, DDS, XMPP, AMQP, STOMP, LwM2M, Zigbee, Z-Wave, BLE, and 6LoWPAN, based on these factors. Finally, recommendations for protocol selection in various sections of a smart home are provided based on the analysis conducted.
Papers List
List of archived papers
Innovative Customer Segmentation based on Multi-Step Sequential Deep Clustering in the Telecommunication Industry
Fatemeh Jalali Farahani - Shima Tabibian
Uncertainty-Aware Deep Ensembles for Confident Customer Churn Prediction with Rejection Option
Fatemeh Moradi - Mehran Tarif - Mohammadhossein Homaei
Speech Emotion Recognition Using a Hierarchical Adaptive Weighted Multi-Layer Sparse Auto-Encoder Extreme Learning Machine with New Weighting and Spectral/SpectroTemporal Gabor Filter Bank Features
Fatemeh Daneshfar - Seyed Jahanshah Kabudian
Efficient Prediction of Cardiovascular Disease via Extra Tree Feature Selection
Mina Abroodi - Mohammad Reza Keyvanpour - Ghazaleh Kakavand Teimoory
Compressing Deep Neural Networks Using Explainable AI
Kimia Soroush - Mohsen Raji - Behnam Ghavami
Automatic Infrared-Based Volume and Mass Estimation System for Agricultural Products
Seyed Muhammad Hossein Mousavi - S. Muhammad Hassan Mosavi
Sensitivity Reliability Analysis of Power Distribution Networks Using Fuzzy Logic
Mohammed Wadi - Wisam Elmasry - Ismail Kucuk - Hossein Shahinzadeh
Density Estimation Helps Adversarial Robustness
Afsaneh Hasanebrahimi - Bahareh Kaviani Baghbaderani - Reshad Hosseini - Ahmad Kalhor
SUBoost: A Novel Boosting-Based Selective Undersampling for handling Imbalanced Data
Nima Rasi Baghmishe - Jafar Tanha - Ehsan Roshan
Deep Learning-based Processing of Autonomous Vehicle Radar Data to Achieve High Resolution
Nima Abdolrahimi Shahamat - Vahideh Moghtadaiee - Esfandiar Mehrshahi
more
Samin Hamayesh - Version 44.5.0