| Title |
Best fit power weighted difference method for fog node selection in smart cities |
| ID_Doc |
42661 |
| Authors |
Malhotra, A; Dhurandher, SK; Gupta, M; Kumar, B |
| Title |
Best fit power weighted difference method for fog node selection in smart cities |
| Year |
2020 |
| Published |
Iet Communications, 14, 19 |
| DOI |
10.1049/iet-com.2019.1234 |
| Abstract |
Growing urbanisation has lead to the concept of smart cities. The idea of smart city is realised with the help of internet of things (IoT) devices. The existing cloud services lack the potential to meet the peculiar requirements of IoT devices. IoT devices generate a huge amount of data and demand quick response time. Fog computing can be used to process the IoT tasks since fog nodes are comparatively closer to the IoT devices. The fog node selection for prospective IoT tasks is a multiple-criteria decision-making problem due to the diversity in the IoT requirements and the presence of a number of heterogeneous fog nodes in the smart city. However, there is no study that focused on the fog node selection mechanism keeping in view the user preferences, user requirements (URs), and optimised resource usage. This study presents the best fit-power weighted difference (BF-PWD) method for fog node selection that is used to rank and subsequently select the fog nodes for task offloading from IoT devices. The simulation results show that the proposed technique BF-PWD not only improves the success rate but also reduces the communication latency and the UR violations compared to the existing protocols. |
| Author Keywords |
Internet of Things; cloud computing; decision making; smart cities; town and country planning; internet of things devices; IoT devices; fog computing; prospective IoT tasks; IoT requirements; heterogeneous fog nodes; smart city; multiple criteria decision-making methods; fog node selection mechanism; fit power weighted difference method |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000595799400017 |
| WoS Category |
Engineering, Electrical & Electronic |
| Research Area |
Engineering |
| PDF |
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-com.2019.1234
|