{"title":"Scoping Sustainable Collaborative Mixed Reality","authors":"Yasra Chandio, Noman Bashir, Tian Guo, Elsa Olivetti, Fatima Anwar","doi":"arxiv-2409.07640","DOIUrl":null,"url":null,"abstract":"Mixed Reality (MR) is becoming ubiquitous as it finds its applications in\neducation, healthcare, and other sectors beyond leisure. While MR end devices,\nsuch as headsets, have low energy intensity, the total number of devices and\nresource requirements of the entire MR ecosystem, which includes cloud and edge\nendpoints, can be significant. The resulting operational and embodied carbon\nfootprint of MR has led to concerns about its environmental implications.\nRecent research has explored reducing the carbon footprint of MR devices by\nexploring hardware design space or network optimizations. However, many\nadditional avenues for enhancing MR's sustainability remain open, including\nenergy savings in non-processor components and carbon-aware optimizations in\ncollaborative MR ecosystems. In this paper, we aim to identify key challenges,\nexisting solutions, and promising research directions for improving MR\nsustainability. We explore adjacent fields of embedded and mobile computing\nsystems for insights and outline MR-specific problems requiring new solutions.\nWe identify the challenges that must be tackled to enable researchers,\ndevelopers, and users to avail themselves of these opportunities in\ncollaborative MR systems.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"23 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - EE - Systems and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.07640","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Mixed Reality (MR) is becoming ubiquitous as it finds its applications in
education, healthcare, and other sectors beyond leisure. While MR end devices,
such as headsets, have low energy intensity, the total number of devices and
resource requirements of the entire MR ecosystem, which includes cloud and edge
endpoints, can be significant. The resulting operational and embodied carbon
footprint of MR has led to concerns about its environmental implications.
Recent research has explored reducing the carbon footprint of MR devices by
exploring hardware design space or network optimizations. However, many
additional avenues for enhancing MR's sustainability remain open, including
energy savings in non-processor components and carbon-aware optimizations in
collaborative MR ecosystems. In this paper, we aim to identify key challenges,
existing solutions, and promising research directions for improving MR
sustainability. We explore adjacent fields of embedded and mobile computing
systems for insights and outline MR-specific problems requiring new solutions.
We identify the challenges that must be tackled to enable researchers,
developers, and users to avail themselves of these opportunities in
collaborative MR systems.