Smart City Gnosys
Smart city article details
| Title | Demand-Based Target Selection And Resource Trade For Parked-Vehicle Assisted Computing |
|---|---|
| ID_Doc | 18248 |
| Authors | Liu Z.; Feng W.; Liu Y.; Wang X.; Lin S. |
| Year | 2023 |
| Published | IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC |
| DOI | http://dx.doi.org/10.1109/ITSC57777.2023.10422177 |
| Abstract | To meet the ever-increasing computing demands of smart cities, intelligent vehicles with rapid growth can be an effective supplement to the computing power network by sharing their underutilized computing resources. Considering that most of the vehicles are in the parked state, we call the computing paradigm as Parked-Vehicle Assisted Computing (PAC). However, current PAC systems ignore the parking demands of vehicles, which should consider not only the income brought by the computing resource trade but also the time cost and energy consumption of vehicles during traveling to the parking lot. In this paper, we design a trade framework and two trade models in a PAC system to encourage vehicles to rent out idle computing resources to enhance the computing power network. The costs of time and energy of vehicles during traveling and the computing energy consumption of both vehicles and Edge Servers (ESs) are considered. Based on the models, we design two optimization problems: 1) the individual trade value maximization problem to obtain personalized trade information of a vehicle, including the target parking lot, and 2) the trade allocation problem for determining trade results by maximizing the combined trade value, i.e. the maximum sum of the individual trade value of the requesting vehicles. We propose a low-complexity greedy-based Trade Allocation Rule (TAR) to obtain the trade allocation strategy. Simulation results show that the proposed algorithm outperforms other benchmark schemes in allocation time and combined trade value. © 2023 IEEE. |
| Author Keywords | Computing resource trade; parked-vehicle assisted computing; value maximization |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
6158  | 0.868 | Rizvi S.R.; Zehra S.; Olariu S.; El-Tawab S. | Adam: An Auction-Based Datacenter Management In Vehicular Cloud | 2023 IEEE International Conference on Smart Mobility, SM 2023 (2023) |
| 8425 | 0.864 | Dande B.; Chen P.-Y.; Wei H.-Y. | An Integrated Charging And Computation Scheduling Of Electric Vehicles In Edge Computing System | IEEE Transactions on Intelligent Transportation Systems, 26, 1 (2025) |
| 5183 | 0.862 | Aljohani M.; Olariu S.; Alali A.; Jain S. | A Survey Of Parking Solutions For Smart Cities | IEEE Transactions on Intelligent Transportation Systems, 23, 8 (2022) |
| 18636 | 0.855 | Gavalas D.; Konstantopoulos C.; Pantziou G. | Design And Management Of Vehicle-Sharing Systems: A Survey Of Algorithmic Approaches | Smart Cities and Homes: Key Enabling Technologies (2016) |
| 26797 | 0.854 | Saharan S.; Bawa S.; Kumar N.; Buyya R. | Foggy-Park: A Dynamic Pricing And Nsga Based Allocation Scheme For On-Street Parking System | ZTA-NextGen 2024 - Proceedings of the SIGCOMM Workshop on Zero Trust Architecture for Next Generation Communications, Part of: SIGCOMM 2024 (2024) |
| 58122 | 0.853 | Oualhaj O.A.; Azougaghe A.; Naja A.; Mabrouk A. | Towards Efficient Urban Network Mobility: A Cloud-Based Ride-Sharing System | 20th International Wireless Communications and Mobile Computing Conference, IWCMC 2024 (2024) |
| 8864 | 0.853 | Carrese S.; D'Andreagiovanni F.; Giacchetti T.; Nardin A.; Zamberlan L. | An Optimization Model For Renting Public Parking Slots To Carsharing Services | Transportation Research Procedia, 45 (2020) |
| 21262 | 0.852 | Nakrani D.; Khuman J.; Yadav R.N. | Dynamic Edge Server Placement For Computation Offloading In Vehicular Edge Computing | International Conference on Information Networking, 2023-January (2023) |
| 32466 | 0.85 | Wu Y.; Fang X.; Min G.; Chen H.; Luo C. | Intelligent Offloading Balance For Vehicular Edge Computing And Networks | IEEE Transactions on Intelligent Transportation Systems, 26, 5 (2025) |
| 19899 | 0.85 | Strauss T.; Oechsle M.; Bauknecht U. | Differentiable Optimization For Orchestration: Resource Offloading For Vehicles In Smart Cities | IEEE Access, 12 (2024) |