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Cluster Computing

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24.06.2017

Energy efficient node selection algorithm based on node performance index and random waypoint mobility model in internet of vehicles

verfasst von: M. K. Priyan, G. Usha Devi

Erschienen in: Cluster Computing | Ausgabe 1/2018

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Abstract

Internet of vehicles (IoV) is an improved version of internet of things to resolve a number of issues in urban traffic environment. In this paper IoV technology is used to select the best ambulance based on a novel node selection algorithm. The proposed IoT healthcare monitoring system consists of number of mobile doctors, patient and mobile ambulance. Performance rank (PR) index is calculated for each mobile ambulance based on the medical capacity (b) of the mobile ambulance, the number of patients currently using the mobile ambulance (n), and the Euclidean distance from a neighboring mobile ambulance. The minimum PR index is considered as best ambulance to provide a service to the patient. Random waypoint mobility model is used to simulate the proposed IoT based healthcare monitoring system. The proposed energy efficient node selection algorithm is compared with various node selection algorithms such as cluster based routing protocol, workload-aware channel assignment algorithm and scenario-based clustering algorithm for performance evaluation. The packet delivery fraction, normalized routing load and average end-to-end delay are calculated to evaluate the performance of the proposed energy efficient node selection algorithm. We have used NS-2 simulator for the node simulation to show the performance of the energy efficient node selection framework. Experimental results prove that the efficiency of the proposed energy efficient node selection algorithm in IoT healthcare environment.

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Titel: Energy efficient node selection algorithm based on node performance index and random waypoint mobility model in internet of vehicles
verfasst von: M. K. Priyan
G. Usha Devi
Publikationsdatum: 24.06.2017
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 1/2018
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-017-0998-x

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