Simultaneous Multi-Source Integrated Energy Harvesting System for IoE Applications

Badr, Mohamed; Aboudina, Mohamed M.; Hussien, Faisal; Mohieldin, Ahmed N.

doi:10.1109/mwscas.2019.8884893

Cited by 10 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given such a frame of reference, the scientific literature reports interesting investigations on the integration of diverse EH converters, from the points of view of design and architecture of interface electronics, necessary to optimize energy extraction [190]- [192]. To this end, two relevant aspects must be kept in mind.…”

Section: ) Multi-source Platformmentioning

confidence: 99%

Review and Perspectives of Micro/Nano Technologies as Key-Enablers of 6G

Iannacci

Poor

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

Section: ) Multi-source Platformmentioning

confidence: 99%

Review and Perspectives of Micro/Nano Technologies as Key-Enablers of 6G

Iannacci

Poor

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In [46], multiscale metamaterials with super-normal functions on energy manipulation are utilized in multi-field renewable energy harvesting and absorbing, which can enhance the local energy density by confining and focusing the energy before it is harvested. To waive the energy discontinuity problem, the literature investigates the development of multi-source EH platforms, where miniaturized energy harvesters addressing different environmental sources are integrated together [47]- [49]. Relying on orthogonal sources, those platforms can effectively overcome discontinuity due to the shortage of a specific source at a particular moment.…”

Section: A Energy Harvesting Iotmentioning

confidence: 99%

Deep Learning on Energy Harvesting IoT Devices: Survey and Future Challenges

2022

IEEE Access

View full text Add to dashboard Cite

Internet-of-Things (IoT) devices are becoming both intelligent and green. On the one hand, Deep Neural Network (DNN) compression techniques make it possible to run deep learning applications on devices equipped with low-end microcontrollers (MCUs). By performing deep learning on IoT devices, in-situ decision-making can be made, which can improve the responsiveness of such devices to the environment and reduce data uploading to edge servers or clouds to save valuable network bandwidth. On the other hand, many IoT devices in the future will be powered by energy harvesters instead of batteries to reduce environmental pollution and achieve permanent service free of battery maintenance. As the energy output of energy harvesters is tiny and unstable, energy harvesting IoT (EH-IoT) devices will experience frequent power failures during their execution, making the software task hard to progress. The deep learning tasks running on such devices must face this challenge and, at the same time, ensure satisfactory execution efficiency. We believe deploying deep learning on EH-IoT devices that execute intermittently will be a challenging yet promising research direction. To motivate research in this direction, this paper summarizes existing solutions and provides an in-depth discussion of future challenges that deserve further investigation. With IoT devices becoming more intelligent and green, DNN inference on EH-IoT devices will generate a much more significant impact in the future in academia and industry.

show abstract

“…Aiello et al [ 51 ] have developed a self-contextualizing service for IoE that separates the logical part from the physical contexts. Others such as Badr et al [ 52 ] focused on energy harvesting and Ryoo et al [ 53 ] covered security and privacy concerns in IoE.…”

Section: Background and Related Workmentioning

confidence: 99%

Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments

Janbi

Katib

Albeshri

et al. 2020

Sensors

View full text Add to dashboard Cite

Artificial intelligence (AI) has taken us by storm, helping us to make decisions in everything we do, even in finding our “true love” and the “significant other”. While 5G promises us high-speed mobile internet, 6G pledges to support ubiquitous AI services through next-generation softwarization, heterogeneity, and configurability of networks. The work on 6G is in its infancy and requires the community to conceptualize and develop its design, implementation, deployment, and use cases. Towards this end, this paper proposes a framework for Distributed AI as a Service (DAIaaS) provisioning for Internet of Everything (IoE) and 6G environments. The AI service is “distributed” because the actual training and inference computations are divided into smaller, concurrent, computations suited to the level and capacity of resources available with cloud, fog, and edge layers. Multiple DAIaaS provisioning configurations for distributed training and inference are proposed to investigate the design choices and performance bottlenecks of DAIaaS. Specifically, we have developed three case studies (e.g., smart airport) with eight scenarios (e.g., federated learning) comprising nine applications and AI delivery models (smart surveillance, etc.) and 50 distinct sensor and software modules (e.g., object tracker). The evaluation of the case studies and the DAIaaS framework is reported in terms of end-to-end delay, network usage, energy consumption, and financial savings with recommendations to achieve higher performance. DAIaaS will facilitate standardization of distributed AI provisioning, allow developers to focus on the domain-specific details without worrying about distributed training and inference, and help systemize the mass-production of technologies for smarter environments.

show abstract

Simultaneous Multi-Source Integrated Energy Harvesting System for IoE Applications

Cited by 10 publications

References 11 publications

Review and Perspectives of Micro/Nano Technologies as Key-Enablers of 6G

Review and Perspectives of Micro/Nano Technologies as Key-Enablers of 6G

Deep Learning on Energy Harvesting IoT Devices: Survey and Future Challenges

Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments

Contact Info

Product

Resources

About