Smart City Gnosys
Smart city article details
| Title | Energy- And Time-Efficient Tasks Offloading And Dynamic Resource Allocation In Smart City |
|---|---|
| ID_Doc | 23410 |
| Authors | Zhao B.; Peng K.; Zhang H.; Xu X. |
| Year | 2020 |
| Published | Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020 |
| DOI | http://dx.doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00122 |
| Abstract | With the rapid development of information society, people urgently need to develop the traditional city model into a highly information and intelligent city model to meet the needs of users and the development of sociality. Therefore, people put forward a new concept called smart city. Correspondingly, as a concept of multiple intelligence, smart city has also got an excellent opportunity for development in terms of service performance with the emergence of mobile cloud computing, mobile edge computing and some new technologies. Specifically, based on the concept of mobile edge computing, there are many edge servers are installed close to users in smart city, and users can offload tasks to these edge servers to obtain better services by computation offloading. However, in the further development process of this new concept, smart city still need to face some problems in terms of energy consumption, latency, resource utilization and even in privacy security. These issues will affect the service quality of smart city seriously. In view of this, the optimization problem of smart city is studied in this paper. Considering the diversity and large number of users, the tasks generated are defined as a series of complex tasks composed of time-constrained workflows and concurrent constrained workflows, and an energy- and time-efficient method with privacy preservation is proposed, which can optimize the energy consumption, time consumption and the resource of the system jointly. Experimental results have shown that the proposed method can reduce the energy consumption and improve resource utilization for the system effectively. © 2020 IEEE. |
| Author Keywords | Dynamic Resource Allocation; Energy and Time-Efficient; Mobile Edge Computing; Smart City; Task Offloading |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
23409  | 0.895 | Zhao, BH; Peng, K; Zhu, FY; Xue, SJ | Energy- And Reliability-Aware Computation Offloading With Security Constraints In Mec-Enabled Smart Cities | CLOUD COMPUTING, CLOUDCOMP 2021, 430 (2022) |
| 21768 | 0.893 | Rajagopal S.; Tripathi P.K.; Deshmukh M.; Choudari S.; Kumar A.; Long C.S. | Edge Computing- Smart Cities: Optimizing Data Processing & Resource Management In Urban Environments | Journal of Information Systems Engineering and Management, 10 (2025) |
| 49549 | 0.893 | Chen W.; Zhang Z.; Liu B. | Smart Cities Enabled By Edge Computing | Edge Computing (2020) |
| 21732 | 0.882 | Trigka M.; Dritsas E. | Edge And Cloud Computing In Smart Cities | Future Internet, 17, 3 (2025) |
| 14443 | 0.881 | di Martino B.; Di Sivo D.; Amato A. | Cloud, Edge, And Mobile Computing: Synergies For The Future Of Smart Cities | Lecture Notes on Data Engineering and Communications Technologies, 250 (2025) |
| 53542 | 0.877 | Padidem P.; Lee A. | Studying Offloading Optimization For Energy-Latency Tradeoff With Collaborative Edge Computing | Proceedings of the 2022 16th International Conference on Ubiquitous Information Management and Communication, IMCOM 2022 (2022) |
| 1144 | 0.877 | Chaudhary N.K.; Rath A.; Babbar G.; Verma A.; Sinha S.D.; Mohapatra H. | A Critical Analysis On Edge Computing In Smart City Applications | Risk-Based Approach to Secure Cloud Migration (2025) |
| 53956 | 0.873 | Wang L.; Pang S.; Gui H.; He X.; Wang N.; Qiao S.; Zhao Z. | Sustainable Energy-Efficient Multi-Objective Task Processing Based On Edge Computing | IEEE Transactions on Network and Service Management (2025) |
| 2802 | 0.873 | Liu Z.R. | A Multi-Joint Optimisation Method For Distributed Edge Computing Resources In Iot-Based Smart Cities | Journal of Grid Computing, 21, 4 (2023) |
| 37279 | 0.871 | Chanu A.D.; Shelar S.; Nath S.B. | Mobile Edge Computing For Efficient Vehicle Management In Smart City | 2025 IEEE 14th International Conference on Communication Systems and Network Technologies, CSNT 2025 (2025) |