Crowd tracking and monitoring middleware via Map-Reduce
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866916710778404864 |
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| author | Gazis, Alexandros Katsiri, Eleftheria |
| author_facet | Gazis, Alexandros Katsiri, Eleftheria |
| contents | This paper presents the design, implementation, and operation of a novel distributed fault-tolerant middleware. It uses interconnected WSNs that implement the Map-Reduce paradigm, consisting of several low-cost and low-power mini-computers (Raspberry Pi). Specifically, we explain the steps for the development of a novice, fault-tolerant Map-Reduce algorithm which achieves high system availability, focusing on network connectivity. Finally, we showcase the use of the proposed system based on simulated data for crowd monitoring in a real case scenario, i.e., a historical building in Greece (M. Hatzidakis' residence).The technical novelty of this article lies in presenting a viable low-cost and low-power solution for crowd sensing without using complex and resource-intensive AI structures or image and video recognition techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2201_09550 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Crowd tracking and monitoring middleware via Map-Reduce Gazis, Alexandros Katsiri, Eleftheria Distributed, Parallel, and Cluster Computing K.6.3; C.5.2; C.5.3; C.5.5; C.5.m; C.5.0 This paper presents the design, implementation, and operation of a novel distributed fault-tolerant middleware. It uses interconnected WSNs that implement the Map-Reduce paradigm, consisting of several low-cost and low-power mini-computers (Raspberry Pi). Specifically, we explain the steps for the development of a novice, fault-tolerant Map-Reduce algorithm which achieves high system availability, focusing on network connectivity. Finally, we showcase the use of the proposed system based on simulated data for crowd monitoring in a real case scenario, i.e., a historical building in Greece (M. Hatzidakis' residence).The technical novelty of this article lies in presenting a viable low-cost and low-power solution for crowd sensing without using complex and resource-intensive AI structures or image and video recognition techniques. |
| title | Crowd tracking and monitoring middleware via Map-Reduce |
| topic | Distributed, Parallel, and Cluster Computing K.6.3; C.5.2; C.5.3; C.5.5; C.5.m; C.5.0 |
| url | https://arxiv.org/abs/2201.09550 |