We are Increasingly being immersed in a technology-mediated world, where the omni-presence of data introduces increased needs in mechanisms facilitating in-situ cognition, reasoning and sensemaking [2, 3]. In parallel, edge computing, facilitated by future networks, such as 5G, is transforming the way data is being processed and delivered from millions of devices around the world, bringing computing and analytics close to where the data is created [1]. Building on these synergies, this project will investigate the use of edge-based object recognition using distributed neural networks (DNN), as a mechanism for in-situ registration and data processing for mobile, Web-based Immersive Analytics (IA) in Extended Reality (XR). Object-recognition can provide accurate and real-time registration [1], yet its practical application still faces important challenges. Current object-recognition systems are either self-contained, or cloud-based, yet face low latency and poor user experience respectively. Deep Learning, and DNNs, can provide effective solutions for object detection, and ameliorate these challenges [1]. In addition, they have the potential to provide adaptive MR interfaces, and multimodal sensing capabilities useful for advanced IA experiences [2]. [1] P. Ren, X. Qiao, Y. Huang, L. Liu, S. Dustdar and J. Chen, Edge-Assisted Distributed DNN Collaborative Computing Approach for Mobile Web Augmented Reality in 5G Networks, in IEEE Network, vol. 34, no. 2, pp. 254-261, March/April 2020, doi: 10.1109/MNET.011.1900305. [2] P. W. S. Butcher, N. W. John and P.D. Ritsos, VRIA: A Web-based Framework for Creating Immersive Analytics Experiences, in IEEE Transactions on Visualization and Computer Graphics (Early Access), 2020 doi: 10.1109/TVCG.2020.2965109. [3] J. C. Roberts, P.D. Ritsos, S. K. Badam, D. Brodbeck, J. Kennedy and N. Elmqvist, Visualization beyond the Desktop--the Next Big Thing, in IEEE Computer Graphics and Applications, vol. 34, no. 6, pp. 26-34, Nov.-Dec. 2014, doi: 10.1109/MCG.2014.82.

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