Joint Crowdsensing and Offloading Algorithms for Edge-Assisted Internet of Intelligent Vehicles
In this paper, we aim to propose a new joint crowdsensing and offloading scheme that considers the benefits of social welfare. To induce the sensing participation, we adopt the ideas of <italic>cooperative multi-agent reinforcement learning</italic> (<italic>CMARL</italic>) t...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10154470/ |
| _version_ | 1797787385117802496 |
|---|---|
| author | Sungwook Kim |
| author_facet | Sungwook Kim |
| author_sort | Sungwook Kim |
| collection | DOAJ |
| description | In this paper, we aim to propose a new joint crowdsensing and offloading scheme that considers the benefits of social welfare. To induce the sensing participation, we adopt the ideas of <italic>cooperative multi-agent reinforcement learning</italic> (<italic>CMARL</italic>) to develop a novel crowdsensing algorithm. Due to the limitation of computation and communication resources in the IoIV system, the <italic>Lozano, Hinojosa, and Mármol solution</italic> (<italic>LHMS</italic>) is applied to solve the IoIV resource allocation problem. Our proposed crowdsensing and offloading algorithms are tightly coupled and work together to reach a consensus with reciprocal advantages. The main merits possessed by our hybrid approach are its flexibility and adaptability to current IoIV system situations. Performance evaluations on the proposed scheme show the superiority of our joint approach by comparing it with three existing baseline protocols. |
| first_indexed | 2024-03-13T01:21:12Z |
| format | Article |
| id | doaj.art-390baa5b8bea44b6b0ab35358c14a9b5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T01:21:12Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-390baa5b8bea44b6b0ab35358c14a9b52023-07-04T23:00:25ZengIEEEIEEE Access2169-35362023-01-0111648976490610.1109/ACCESS.2023.328685110154470Joint Crowdsensing and Offloading Algorithms for Edge-Assisted Internet of Intelligent VehiclesSungwook Kim0https://orcid.org/0000-0003-1967-151XDepartment of Computer Science, Sogang University, Seoul, Mapo-gu, South KoreaIn this paper, we aim to propose a new joint crowdsensing and offloading scheme that considers the benefits of social welfare. To induce the sensing participation, we adopt the ideas of <italic>cooperative multi-agent reinforcement learning</italic> (<italic>CMARL</italic>) to develop a novel crowdsensing algorithm. Due to the limitation of computation and communication resources in the IoIV system, the <italic>Lozano, Hinojosa, and Mármol solution</italic> (<italic>LHMS</italic>) is applied to solve the IoIV resource allocation problem. Our proposed crowdsensing and offloading algorithms are tightly coupled and work together to reach a consensus with reciprocal advantages. The main merits possessed by our hybrid approach are its flexibility and adaptability to current IoIV system situations. Performance evaluations on the proposed scheme show the superiority of our joint approach by comparing it with three existing baseline protocols.https://ieeexplore.ieee.org/document/10154470/Internet of Intelligent Vehiclesvehicular crowdsensingvehicular offloadingcooperative multi-agent reinforcement learningLozanoHinojosa |
| spellingShingle | Sungwook Kim Joint Crowdsensing and Offloading Algorithms for Edge-Assisted Internet of Intelligent Vehicles IEEE Access Internet of Intelligent Vehicles vehicular crowdsensing vehicular offloading cooperative multi-agent reinforcement learning Lozano Hinojosa |
| title | Joint Crowdsensing and Offloading Algorithms for Edge-Assisted Internet of Intelligent Vehicles |
| title_full | Joint Crowdsensing and Offloading Algorithms for Edge-Assisted Internet of Intelligent Vehicles |
| title_fullStr | Joint Crowdsensing and Offloading Algorithms for Edge-Assisted Internet of Intelligent Vehicles |
| title_full_unstemmed | Joint Crowdsensing and Offloading Algorithms for Edge-Assisted Internet of Intelligent Vehicles |
| title_short | Joint Crowdsensing and Offloading Algorithms for Edge-Assisted Internet of Intelligent Vehicles |
| title_sort | joint crowdsensing and offloading algorithms for edge assisted internet of intelligent vehicles |
| topic | Internet of Intelligent Vehicles vehicular crowdsensing vehicular offloading cooperative multi-agent reinforcement learning Lozano Hinojosa |
| url | https://ieeexplore.ieee.org/document/10154470/ |
| work_keys_str_mv | AT sungwookkim jointcrowdsensingandoffloadingalgorithmsforedgeassistedinternetofintelligentvehicles |