Abstract
Information Technology (IT) is crucial for many innovations in products, services, and processes around the globe. However, IT is growing in importance in the share of energy consumption in the world. As a reaction to this negative effect, the so-called Green IT movement emerged. This field of study is aimed to reduce IT-related energy consumption and overall IT environmental impact including a variety of aspects like power consumption, lower carbon emissions and their environmental impact. One of the leading technologies in the IT arena is Reality technologies including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). These technologies have impacted sectors like Real Estate, Education, Healthcare, Marketing, Travel and Manufacturing, citing just some of the most relevant application areas. Taking into account the importance of these technologies and the expected impact in the future, authors conducted a systematic literature review devoted to investigate their “greenability”. Authors are aware of the importance of the topic and aim to identify, evaluate, and synthesize research published concerning aspects like energy consumption and eco-effectiveness of main reality technologies. By searching five major bibliographic databases, 5596 articles related to the topic were identified and 49 of these papers were selected as primary studies.
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Appendix
Appendix
Key | Author | Title |
|---|---|---|
S1 | Patti et al. (2017) | Information modeling for virtual and augmented reality |
S2 | Natephra et al. (2017) | Integrating building information modeling and virtual reality development engines for building indoor lighting design |
S3 | Kim et al. (2015b) | Health smart home services incorporating a MAR-based energy consumption awareness system |
S4 | Cho et al. (2019) | Energy management system based on augmented reality for human–computer interaction in a smart city |
S5 | Au et al. (2018) | Emerging simulation and VR for green innovations: a case study on promoting a zero-carbon emission platform in Hong Kong |
S6 | Pelliccia et al. (2016) | Energy visualization techniques for machine tools in virtual reality |
S7 | Nugraha Bahar et al. (2014) | CAD data workflow toward the thermal simulation and visualization in virtual reality |
S8 | Purmaissur et al. (2018) | Augmented reality computer-vision assisted disaggregated energy monitoring and IoT control platform |
S9 | Ho and Chui (2019) | Monitoring energy consumption of individual equipment in a workcell using augmented reality technology |
S10 | Bekaroo et al. (2018) | Enhancing awareness on green consumption of electronic devices: The application of augmented reality |
S11 | Amici et al. (2018) | Augmented reality applied to a wireless power measurement system of an industrial 4.0 advanced manufacturing line |
S12 | Mylonas et al. (2019) | An augmented reality prototype for supporting IoT-based educational activities for energy-efficient school buildings |
S13 | Chou et al. (2017) | Spatiotemporal analysis and visualization of power consumption data integrated with building information models for energy savings |
S14 | Ćwil and Bartnik (2019) | Physically extended virtual reality (PEVR) as a new concept in railway driver training |
S15 | Nacu et al. (2018) | Towards autonomous EV by using virtual reality and Prescan-Simulink simulation environments |
S16 | Shea et al. (2017) | Towards fully offloaded cloud-based AR: design, implementation, and experience |
S17 | Chen et al. (2018) | MARVEL: enabling mobile augmented reality with low energy and low latency |
S18 | Shi et al. (2015) | Offloading guidelines for augmented reality applications on wearable devices |
S19 | Noreikis et al. (2017) | SeeNav: seamless and energy-efficient indoor navigation using augmented reality |
S20 | Lee et al. (2016) | Exploiting remote GPGPU in mobile devices |
S21 | Dolezal et al. (2016) | Performance evaluation of computation offloading from mobile device to the edge of mobile network |
S22 | Qvarfordt et al. (2018) | High-quality mobile XR: requirements and feasibility |
S23 | Chakareski (2019) | UAV-IoT for next-generation virtual reality |
S24 | Chatzieleftheriou, Iosifidis, Koutsopoulos, and Leith (2018) | Towards resource-efficient wireless edge analytics for mobile augmented reality applications |
S25 | Meng et al. (2018) | Radio resource allocation scheme for drone-assisted AR applications |
S26 | Dang and Peng (2019) | Joint radio communication, caching, and computing design for mobile virtual reality delivery in fog radio access networks |
S27 | Liu and Zhang (2019) | Code-partitioning offloading schemes in mobile edge computing for augmented reality |
S28 | Al-Shuwaili and Simeone (2017) | Energy-efficient resource allocation for mobile edge computing-based augmented reality applications |
S29 | Diguet et al. (2015) | Dedicated object processor for mobile augmented reality—sailor assistance case study |
S30 | Hong et al. (2015) | A 27 mW reconfigurable marker-less logarithmic camera pose estimation engine for mobile augmented reality processor |
S31 | Kim et al. (2015a) | K-glass: real-time markerless augmented reality smart glasses platform |
S32 | Kim et al. (2015b) | A 1.22 TOPS and 1.52 mW/MHz augmented reality multicore processor with neural network NoC for HMD applications |
S33 | Lee et al. (2017) | Fast stereoscopic rendering on mobile ray-tracing GPU for virtual reality applications |
S34 | Shao et al. (2018) | MARBLE: Mobile Augmented Reality Using a Distributed BLE Beacon Infrastructure |
S35 | Song et al. (2019) | Energy consumption minimization control for augmented reality applications based on multi-core smart devices |
S36 | Yan et al. (2018) | Exploring eye adaptation in head mounted display for energy-efficient smartphone virtual reality |
S37 | Wee et al. (2018) | FocusVR: effective 8 usable VR display power management |
S38 | Tiwari et al. (2019) | A comparative study on display sources for augmented reality-based technology in defense applications |
S39 | Eo et al. (2017) | High performance and low power timing controller design for LCoS microdisplay system |
S40 | Zemblys and Komogortsev (2018) | Developing Photo-sensor Oculography (PS-OG) system for virtual reality headsets |
S41 | Li et al. (2017) | Ultra-low power gaze tracking for virtual reality |
S42 | Yen et al. (2018) | Differentiated handling of physical scenes and virtual objects for mobile augmented reality |
S43 | Choi et al. (2017) | Analyzing head mounted AR device energy consumption on a frame rate perspective |
S44 | Jiang et al. (2017) | Power evaluation of 360 VR video streaming on head mounted display devices |
S45 | Karaman et al., 2016) | Resource usage analysis of a sensor-based mobile augmented reality application |
S46 | Saeedi et al. (2018) | Navigating the landscape for real-time localization and mapping for robotics and virtual and augmented reality |
S47 | Ge et al. (2017) | Multipath cooperative communications networks for augmented and virtual reality transmission |
S48 | Shea et al. (2017) | Location-based augmented reality with pervasive smartphone sensors: inside and beyond Pokemon Go! |
S49 | Chen et al. (2018) | Understanding the characteristics of mobile augmented reality applications |
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Khakpour, A., Sánchez-Gordón, M., Colomo-Palacios, R. (2020). What We Know About the Greenability of Reality Technologies: A Systematic Literature Review. In: Ratten, V. (eds) Entrepreneurship and Organizational Change. Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-030-35415-2_5
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