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Big Data Analytics, the Internet of Things (IoTs), and Robotics

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 393

Special Issue Editors


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Guest Editor
Robotics and Big Data Lab, Computer Science Department, University of Haifa, Haifa 3498838, Israel
Interests: core-sets; robotics; big-data

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Guest Editor
Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA
Interests: deep learning; machine learning; vision; robotics; big data

Special Issue Information

Dear Colleagues,

This Special Issue invites colleagues to submit research papers that cover  innovative ideas, approaches, developments, and research surveys in the areas of Big Data, the Internet of Things (IoT), and robotics. Multi-disciplinary papers that combine one or more of these subjects are especially encouraged.

Big Data includes both theory and experimental results, offline algorithms for learning data in sub-linear time (such as property testing), or very fast (e.g., using convex optimization) on-line algorithms (on-the-fly learning), streaming algorithms using small memory, fast update times, or both core-sets and sketches for lossy or non-lossy compression, and related systems of all kinds.

The Internet of Things includes results using Arduino, RaspberryPi, etc., as well as wearable devices, smartphone applications, smart watches, and small medical or EEG devices.

Robotics includes autonomous cars, drones, and quadcopters; SLAM (Simultaneous Localization and Mapping); and results in deep learning or embedding sensorial data, space exploration, LIDAR, or TOF (Time-of-Light) sensors.

Combinations may include small autonomous drones that collect and process data in real-time using micro-computers on-board, micro-computers that runs robotics tasks, and algorithms that use GPU to manage large amounts of data.

Prof. Dr. Dan Feldman
Dr. Alaa Maalouf
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.

Keywords

  • core-sets
  • streaming
  • robotics
  • SLAM
  • big data

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Published Papers (1 paper)

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Research

24 pages, 4494 KiB  
Article
Resilient Multi-Robot Coverage Path Redistribution Using Boustrophedon Decomposition for Environmental Monitoring
by Junghwan Gong, Hyunbin Kim and Seunghwan Lee
Sensors 2024, 24(23), 7482; https://doi.org/10.3390/s24237482 - 23 Nov 2024
Viewed by 250
Abstract
This study introduces a resilient and adaptive multi-robot coverage path planning approach based on the Boustrophedon Cell Decomposition algorithm, designed to dynamically redistribute coverage tasks in the event of robot failures. The proposed method ensures minimal disruption and maintains a balanced workload across [...] Read more.
This study introduces a resilient and adaptive multi-robot coverage path planning approach based on the Boustrophedon Cell Decomposition algorithm, designed to dynamically redistribute coverage tasks in the event of robot failures. The proposed method ensures minimal disruption and maintains a balanced workload across operational robots through a propagation-based redistribution strategy. By iteratively reallocating the failed robot’s coverage path to neighboring robots, the method prevents any single robot from becoming overburdened, ensuring efficient task distribution and continuous environmental monitoring. Simulations conducted in five distinct environments, ranging from simple open areas to complex, obstacle-rich terrains, demonstrate the method’s robustness and adaptability. A key strength of the proposed approach is its fast and efficient task reallocation process, achieved with minimal propagation cycles, making it suitable for real-time applications even in complex scenarios. The approach reduces task variance and maintains balanced coverage throughout the mission. Full article
(This article belongs to the Special Issue Big Data Analytics, the Internet of Things (IoTs), and Robotics)
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