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Mobile Systems for Environmental Sensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 2218

Special Issue Editors


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Guest Editor
Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy
Interests: edge computing; Cloud-to-Thing-Continuum; industrial IoT
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Dipartimento di Matematica, Università degli Studi di Padova, 35131 Padova, Italy
Interests: wireless networks; web squared; online entertainment; mobile applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Environmental sensing technology has reduced in size and cost and can now be deployed at many points across a city where it would have been impractical only a few years ago. Mobile technology has advanced to provide robust connectivity solutions, enabling these sensors to be deployed and exploited efficiently and effectively by city authorities and other bodies. Environmental monitoring can exploit IoT sensors, smartphones, UAVs and vehicles to measure many parameters in cities, the countryside, rivers, lakes and the sky, including air and water quality, weather, noise, traffic, smoke and crowding. As these systems grow in complexity, greater capability and flexibility are gained by extending the autonomy of system processes. With data processing close to the edge, quick responses to events are better assured, and with processing deployed across layers from the edge to the cloud, resilience and security are increased. Edge data analytics is gaining major importance, advocated to cope with the surge in IoT data, enabling fast and privacy-preserving computation. This Special Issue aims to investigate the design, implementation, deployment, operation and evaluation of mobile systems and solutions for environmental sensing with an emphasis on solutions that can synergistically leverage techniques and insights from the domains of sensing, networking, scalable computing and artificial intelligence (AI) technologies. Clearly, we are not only considering the so-called first world as the scenario for this evolution; we also refer to those areas where ICT is currently less widespread, hoping that it may represent a societal development opportunity rather than a source of further divides.

The topics of interest include but are not limited to:

  • Wearable computing, sensing and context awareness;
  • The management, configuration and deployment of systems supporting mobility;
  • Data management and analytics at the edge;
  • Mobile and pervasive systems with elements of networked sensing;
  • Decentralized learning algorithms and models for environmental intelligence;
  • Geospatial data analytics and algorithms;
  • Environmental modelling and mapping;
  • Data mining, machine learning and AI techniques for spatial–temporal analytics;
  • Public mapping for better citizen engagement;
  • Collaborative sensing, networking and computing.

Dr. Armir Bujari
Prof. Dr. Claudio Palazzi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

21 pages, 26353 KiB  
Article
Efficient Ray Tracing of Large 3D Scenes for Mobile Distributed Computing Environments
by Woong Seo, Sanghun Park and Insung Ihm
Sensors 2022, 22(2), 491; https://doi.org/10.3390/s22020491 - 10 Jan 2022
Cited by 1 | Viewed by 1707
Abstract
Cluster computing has attracted much attention as an effective way of solving large-scale problems. However, only a few attempts have been made to explore mobile computing clusters that can be easily built using commodity smartphones and tablets. To investigate the possibility of mobile [...] Read more.
Cluster computing has attracted much attention as an effective way of solving large-scale problems. However, only a few attempts have been made to explore mobile computing clusters that can be easily built using commodity smartphones and tablets. To investigate the possibility of mobile cluster-based rendering of large datasets, we developed a mobile GPU ray tracer that renders nontrivial 3D scenes with many millions of triangles at an interactive frame rate on a small-scale mobile cluster. To cope with the limited processing power and memory space, we first present an effective 3D scene representation scheme suitable for mobile GPU rendering. Then, to avoid performance impairment caused by the high latency and low bandwidth of mobile networks, we propose using a static load balancing strategy, which we found to be more appropriate for the vulnerable mobile clustering environment than a dynamic strategy. Our mobile distributed rendering system achieved a few frames per second when ray tracing 1024 × 1024 images, using only 16 low-end smartphones, for large 3D scenes, some with more than 10 million triangles. Through a conceptual demonstration, we also show that the presented rendering scheme can be effectively explored for augmenting real scene images, captured or perceived by augmented and mixed reality devices, with high quality ray-traced images. Full article
(This article belongs to the Special Issue Mobile Systems for Environmental Sensing)
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