The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
AVE: Autonomous Vehicular Edge Computing Framework with ACO-Based Scheduling
Abstract
With the emergence of in-vehicle applications, providing the required computational capabilities is becoming a crucial problem. This paper proposes a framework named autonomous vehicular edge (AVE) for edge computing on the road, with the aim of increasing the computational capabilities of vehicles in a decentralized manner. By managing the idle computational resources on vehicles and using them efficiently, the proposed AVE framework can provide computation services in dynamic vehicular environments without requiring particular infrastructures to be deployed. Specifically, this paper introduces a workflow to support the autonomous organization of vehicular edges. Efficient job caching is proposed to better schedule jobs based on the information collected on neighboring vehicles, including GPS information. A scheduling algorithm based on ant colony optimization is designed to solve this job assignment problem. Extensive simulations are conducted, and the simulation results demonstrate the superiority of this approach over competing schemes in typical urban and highway scenarios.
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Cites
Edge computing: Vision and challenges
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2579198 · Cited by 8,733
Ant colony system: a cooperative learning approach to the traveling salesman problem
Dorigo, M., & Gambardella, L. M. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585892 · Cited by 8,081
MAUI: making smartphones last longer with code offload
Cuervo, E., Balasubramanian, A., Cho, D.-K., Wolman, A., Saroiu, S., Chandra, R., & Bahl, P. (2010) · Proceedings of the 8th international conference on Mobile systems, applications, and services · doi:10.1145/1814433.1814441 · Cited by 2,281
(2011) · doi:10.1145/1966445.1966473 · Cited by 1,870
Towards wearable cognitive assistance
Ha, K., Chen, Z., Hu, W., Richter, W., Pillai, P., & Satyanarayanan, M. (2014) · Proceedings of the 12th annual international conference on Mobile systems, applications, and services · doi:10.1145/2594368.2594383 · Cited by 476
Cited by
Resource Scheduling in Edge Computing: A Survey
Luo, Q., Hu, S., Li, C., Li, G., & Shi, W. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3106401 · Cited by 588
Learning-Based Computation Offloading for IoT Devices With Energy Harvesting
Min, M., Xiao, L., Chen, Y., Cheng, P., Wu, D., & Zhuang, W. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2890685 · Cited by 560
El-Sayed, H., Sankar, S., Prasad, M., Puthal, D., Gupta, A., Mohanty, M., & Lin, C.-T. (2018) · IEEE Access · doi:10.1109/access.2017.2780087 · Cited by 435
Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues
Du, Z., Wu, C., Yoshinaga, T., Yau, K.-L. A., Ji, Y., & Li, J. (2020) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2020.2992630 · Cited by 393
Self-citationAdaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems
Sun, Y., Guo, X., Song, J., Zhou, S., Jiang, Z., Liu, X., & Niu, Z. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2895593 · Cited by 369
Metadata
- Authors
- Feng, J., Liu, Z., Wu, C., & Ji, Y.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2017-12
- Volume
- 66
- Issue
- 12
- Pages
- 10660-10675
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W2626588362
- Cited by
- 330
- References
- 5
- FWCI
- 25.13
- Type
- journal article
- Language
- en
- Open access
- closed
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- Feng, J., Liu, Z., Wu, C., & Ji, Y. (2017). AVE: Autonomous Vehicular Edge Computing Framework with ACO-Based Scheduling. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/tvt.2017.2714704
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