Publication
A Cloud Based Disaster Management System
| dc.contributor.author | Cheikhrouhou, Omar | |
| dc.contributor.author | Koubaa, Anis | |
| dc.contributor.author | Zarrad, Anis | |
| dc.date.accessioned | 2020-02-19T14:36:49Z | |
| dc.date.available | 2020-02-19T14:36:49Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | The combination of wireless sensor networks (WSNs) and 3D virtual environments opens a new paradigm for their use in natural disaster management applications. It is important to have a realistic virtual environment based on datasets received from WSNs to prepare a backup rescue scenario with an acceptable response time. This paper describes a complete cloud-based system that collects data from wireless sensor nodes deployed in real environments and then builds a 3D environment in near real-time to reflect the incident detected by sensors (fire, gas leaking, etc.). The system’s purpose is to be used as a training environment for a rescue team to develop various rescue plans before they are applied in real emergency situations. The proposed cloud architecture combines 3D data streaming and sensor data collection to build an efficient network infrastructure that meets the strict network latency requirements for 3D mobile disaster applications. As compared to other existing systems, the proposed system is truly complete. First, it collects data from sensor nodes and then transfers it using an enhanced Routing Protocol for Low-Power and Lossy Networks (RLP). A 3D modular visualizer with a dynamic game engine was also developed in the cloud for near-real time 3D rendering. This is an advantage for highly-complex rendering algorithms and less powerful devices. An Extensible Markup Language (XML) atomic action concept was used to inject 3D scene modifications into the game engine without stopping or restarting the engine. Finally, a multi-objective multiple traveling salesman problem (AHP-MTSP) algorithm is proposed to generate an efficient rescue plan by assigning robots and multiple unmanned aerial vehicles to disaster target locations, while minimizing a set of predefined objectives that depend on the situation. The results demonstrate that immediate feedback obtained from the reconstructed 3D environment can help to investigate what–if scenarios, allowing for the preparation of effective rescue plans with an appropriate management effort. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.3390/jsan9010006 | pt_PT |
| dc.identifier.issn | 2224-2708 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/15491 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | MDPI | pt_PT |
| dc.relation.publisherversion | https://www.mdpi.com/2224-2708/9/1/6 | pt_PT |
| dc.subject | RLP | pt_PT |
| dc.subject | Wireless sensor networks | pt_PT |
| dc.subject | Cloud computing | pt_PT |
| dc.subject | Disaster management | pt_PT |
| dc.subject | Smart 3D virtual environment | pt_PT |
| dc.subject | Traveling salesman problem | pt_PT |
| dc.title | A Cloud Based Disaster Management System | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 23 | pt_PT |
| oaire.citation.issue | 1 | pt_PT |
| oaire.citation.startPage | 1 | pt_PT |
| oaire.citation.title | Journal of Sensor and Actuator Networks | pt_PT |
| oaire.citation.volume | 9 | pt_PT |
| person.familyName | cheikhrouhou | |
| person.familyName | Koubaa | |
| person.givenName | omar | |
| person.givenName | Anis | |
| person.identifier | 989131 | |
| person.identifier.ciencia-id | CA19-2399-D94A | |
| person.identifier.orcid | 0000-0002-9898-3898 | |
| person.identifier.orcid | 0000-0003-3787-7423 | |
| person.identifier.scopus-author-id | 15923354900 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | 09fb7bcf-ad9c-4fdf-9dee-dbfc2baf91ab | |
| relation.isAuthorOfPublication | 0337d7df-5f77-46a4-8269-83d14bd5ea6b | |
| relation.isAuthorOfPublication.latestForDiscovery | 09fb7bcf-ad9c-4fdf-9dee-dbfc2baf91ab |