Green wireless networks Wake-up radio Energy harvesting Routing Markov decision process Reinforcement learning 1. Introduction With 14.2 billions of connected things in 2019, over 41.6 billions expected by 2025, and a total spending on endpoints and services that will reach well over $1.1 trillion by the end of 2026, the Internet of Things (IoT) is poised to have a transformative impact on the way we live and on the way we work [1–3]. The vision of this ‘‘connected continuum’’ of objects and people, however, comes with a wide variety of challenges, especially for those IoT networks whose devices rely on some forms of depletable energy support. This has prompted research on hardware and software solutions aimed at decreasing the depen- dence of devices from ‘‘pre-packaged’’ energy provision (e.g., batteries), leading to devices capable of harvesting energy from the environment, and to networks – often called green wireless networks – whose lifetime is virtually infinite. Despite the promising advances of energy harvesting technologies, IoT devices are still doomed to run out of energy due to their inherent constraints on resources such as storage, processing and communica- tion, whose energy requirements often exceed what harvesting can provide. The communication circuitry of prevailingmore »
Visualization (nD, VR, AR)
A growing number of community energy initiatives have enlarged energy-related social networks to the community level. Information provision is deemed as an important role in such programs while energy data disclosure offers a great opportunity to promote energy savings by engaging energy-related actors. However, it is crucial to communicate this data in an effective way. In this research, we develop a virtual reality (VR) integrated eco-feedback system that enables both occupants and facility managers to interact with real-time energy consumption data represented in a community scale 3D immersive environment. This paper presents the detailed front-end and back-end design and development of this novel VR-integrated eco-feedback system using Georgia Tech’s campus as a test case for implementation. The VR-integrated community scale eco-feedback system is capable of visually characterizing differences in energy consumption across a large number of buildings of different types, and will be tested by users in future research. This research, when deployed broadly in cities, may help promote energy-aware behaviors of occupants and timely intervention strategies to achieve energy savings in urban areas.
- Award ID(s):
- 1837021
- Publication Date:
- NSF-PAR ID:
- 10119012
- Journal Name:
- Development of a Virtual Reality Integrated Community-Scale Eco-Feedback System
- Page Range or eLocation-ID:
- 87 to 94
- Sponsoring Org:
- National Science Foundation
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