Sami Ben Cheikh
Stephan Sigg
ABSTRACT
This study presents our ongoing activities, along with a demonstration that showcases the integration of these endeavours into a real-world application. We demonstrate the integration of IoT devices with energy harvesting systems, as well as the incorporation of deep learning techniques into IoT devices. Finally, we consider the utilization of radio frequency (RF) technology for gesture detection and classification, based on deep learning algorithms.
- Zahra Aghapour, Saeed Sharifian, and Hassan Taheri. 2023. Task offloading and resource allocation algorithm based on deep reinforcement learning for distributed AI execution tasks in IoT edge computing environments. Computer Networks 223 (2023), 109577.Google Scholar
Digital Library
- Sarina Aminizadeh, Arash Heidari, Shiva Toumaj, Mehdi Darbandi, Nima Jafari Navimipour, Mahsa Rezaei, Samira Talebi, Poupak Azad, and Mehmet Unal. 2023. The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things. Computer Methods and Programs in Biomedicine (2023), 107745.Google Scholar
- Juan M Arteaga, Samer Aldhaher, George Kkelis, Christopher Kwan, David C Yates, and Paul D Mitcheson. 2018. Dynamic capabilities of multi-MHz inductive power transfer systems demonstrated with batteryless drones. IEEE Transactions on Power Electronics 34, 6 (2018), 5093–5104.Google ScholarCross Ref
- T Becker, V Borjesson, O Cetinkaya, C Baoxing, J Colomer-Farrarons, D Maeve, A Elefsiniotis, L Govoni, Z Hadas, M Hayes, 2021. Energy harvesting for a green internet of things: PSMA white paper. In PSMA White Paper Series. PSMA, 1–66.Google Scholar
- Andreas Christ, Mark G Douglas, John M Roman, Emily B Cooper, Alanson P Sample, Benjamin H Waters, Joshua R Smith, and Niels Kuster. 2012. Evaluation of wireless resonant power transfer systems with human electromagnetic exposure limits. IEEE Transactions on Electromagnetic compatibility 55, 2 (2012), 265–274.Google Scholar
- Abhishek Hazra, Pradeep Rana, Mainak Adhikari, and Tarachand Amgoth. 2023. Fog computing for next-generation internet of things: fundamental, state-of-the-art and research challenges. Computer Science Review 48 (2023), 100549.Google ScholarDigital Library
- Haochen Hua, Yutong Li, Tonghe Wang, Nanqing Dong, Wei Li, and Junwei Cao. 2023. Edge computing with artificial intelligence: A machine learning perspective. Comput. Surveys 55, 9 (2023), 1–35.Google ScholarDigital Library
- Chip Huyen. 2022. Designing machine learning systems. " O’Reilly Media, Inc.".Google Scholar
- Manila Kodali, Stephan Sigg, 2021. Towards battery-less RF sensing. In 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 352–355.Google ScholarCross Ref
- Shailja Kumari and Divya Gupta. 2023. Review on Energy Management for Green IoT in Smart World. In 2023 8th International Conference on Communication and Electronics Systems (ICCES). IEEE, 322–327.Google Scholar
- Andre Kurs, Aristeidis Karalis, Robert Moffatt, John D Joannopoulos, Peter Fisher, and Marin Soljacic. 2007. Wireless power transfer via strongly coupled magnetic resonances. science 317, 5834 (2007), 83–86.Google Scholar
- Pedro Victor Borges Caldas da Silva, Chantal Taconet, Sophie Chabridon, Denis Conan, Everton Cavalcante, and Thais Batista. 2023. Energy awareness and energy efficiency in internet of things middleware: a systematic literature review. Annals of Telecommunications 78, 1-2 (2023), 115–131.Google Scholar
- Mohsen Soori, Behrooz Arezoo, and Roza Dastres. 2023. Internet of things for smart factories in industry 4.0, a review. Internet of Things and Cyber-Physical Systems (2023).Google Scholar
- Xiaojie Wang, Jiameng Li, Zhaolong Ning, Qingyang Song, Lei Guo, Song Guo, and Mohammad S Obaidat. 2023. Wireless powered mobile edge computing networks: A survey. Comput. Surveys (2023).Google ScholarDigital Library
- Yin Zhang, Chi Jiang, Binglei Yue, Jiafu Wan, and Mohsen Guizani. 2022. Information fusion for edge intelligence: A survey. Information Fusion 81 (2022), 171–186.Google ScholarDigital Library
- Barret Zoph, Ekin D Cubuk, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, and Quoc V Le. 2020. Learning data augmentation strategies for object detection. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXVII 16. Springer, 566–583.Google Scholar
Index Terms
- Towards Green Edge Intelligence
Recommendations
Performance evaluation of FIWARE: A cloud-based IoT platform for smart cities
AbstractAs the Internet of Things (IoT) becomes a reality, millions of devices will be connected to IoT platforms in smart cities. These devices will cater to several areas within a smart city such as healthcare, logistics, and transportation. ...
Highlights- Comprehensive performance evaluation of a cloud-based IoT platform, FIWARE.
- ...
Edge Intelligence based Social Internet of Things for Smart Cities
ICIT '22: Proceedings of the 2022 10th International Conference on Information Technology: IoT and Smart CitySocial Internet of Things (SIoT), inspired by human social networks, is the next phase in the evolution of the Internet of Things (IoT) that enables a plethora of devices with diverse sensors to build and maintain social relationships among them. These ...
Best fit power weighted difference method for fog node selection in smart cities
Growing urbanisation has lead to the concept of smart cities. The idea of smart city is realised with the help of internet of things (IoT) devices. The existing cloud services lack the potential to meet the peculiar requirements of IoT devices. IoT ...
Comments