A predictive target tracking framework for IoT using CNN–LSTM-Reference-Cited by-同舟云学术

A predictive target tracking framework for IoT using CNN–LSTM

Published:2023-07 Issue: Volume:22 Page:100744
ISSN:2542-6605
Container-title:Internet of Things
language:en
Short-container-title:Internet of Things

Author:

Hussain Lana Alhaj,Singh Shakti,Mizouni Rabeb,Otrok Hadi,Damiani Ernesto^ORCID

Publisher

Elsevier BV

Subject

Management of Technology and Innovation,Artificial Intelligence,Computer Science Applications,Hardware and Architecture,Engineering (miscellaneous),Information Systems,Computer Science (miscellaneous),Software

Reference57 articles.

1. An energy-efficient predictive model for object tracking sensor networks;Paris,2019

2. Pmt2: A predictive mobile target tracking algorithm in wireless multimedia sensor networks;Boulanouar,2014

3. A data-driven approach to increasing the lifetime of iot sensor nodes;Suryavansh;Sci. Rep.,2021

4. An overview of wireless sensor networks towards internet of things;Kocakulak,2017

5. Multispectral information fusion with reinforcement learning for object tracking in iot edge devices;Saha;IEEE Sens. J.,2020

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Overcoming cold start and sensor bias: A deep learning-based framework for IoT-enabled monitoring applications;Journal of Network and Computer Applications;2024-02

2. TRACE: Transformer-based continuous tracking framework using IoT and MCS;Journal of Network and Computer Applications;2024-02

3. FirePred: A hybrid multi-temporal convolutional neural network model for wildfire spread prediction;Ecological Informatics;2023-12

4. Novel Soil Impact Prediction Model on Bandwidth;2023 10th International Conference on Wireless Networks and Mobile Communications (WINCOM);2023-10-26

5. Reinforcement learning framework for UAV-based target localization applications;Internet of Things;2023-10