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DC Field | Value | Language |
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dc.contributor.author | Kaur, A. | - |
dc.contributor.author | Auluck, N. | - |
dc.date.accessioned | 2021-07-04T09:25:48Z | - |
dc.date.available | 2021-07-04T09:25:48Z | - |
dc.date.issued | 2021-07-04 | - |
dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/1998 | - |
dc.description.abstract | Fog Computing extends traditional cloud based services to the edge of the network, close to where the data is generated. This technology fills a performance void in the cloud-to-thing architecture. By leveraging Fog Computing, the computation, storage, communication and decision making can be carried out by fog nodes. Due to significant latency, the cloud is not the best option for emergency response services, such as fire-fighting. For efficient fire-fighting, decisions should be made accurately and rapidly. In this paper, we propose an algorithm called FAFCA (Fog Assisted Fire Control Algorithm). In the wake of a fire in a building , this algorithm efficiently routes the evacuees to the shortest and least congested exits in a short span of time. The crux of the algorithm is that due to the lower communication delay between the fog nodes and the evacuees, data processing is done faster, which results in making evacuation decisions rapidly. The best path for the evacuees present in the building is calculated by the algorithm after taking various parameters into account, such as exit capacity, distance to exits and distribution of evacuees. Simulation results show that our proposed algorithm FAFCA decreases the latency as well ascost significantly, when compared to a cloud based algorithm and a random path selection algorithm. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | Internet of Things | en_US |
dc.subject | Fog computing | en_US |
dc.subject | Cloud computing | en_US |
dc.subject | Smart building | en_US |
dc.subject | Micro data center | en_US |
dc.subject | Cloud data center | en_US |
dc.title | A fog based building fire evacuation framework | en_US |
dc.type | Article | en_US |
Appears in Collections: | Year-2020 |
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