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Opportunistic Computation Offloading with Learning-based Prediction for UAV Clustering Networks
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Young-Bae Ko | - |
| dc.contributor.author | VALENTINO RICO | - |
| dc.date.accessioned | 2019-04-01T16:41:07Z | - |
| dc.date.available | 2019-04-01T16:41:07Z | - |
| dc.date.issued | 2019-02 | - |
| dc.identifier.other | 28539 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/15009 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :컴퓨터공학과,2019. 2 | - |
| dc.description.tableofcontents | CHAPTER 1 INTRODUCTION １ CHAPTER 2 BACKGROUND AND RELATED WORK ９ 2.1 COMPUTATION OFFLOADING ９ 2.2 MOBILITY MODEL FOR UAV AD HOC NETWORKS １３ CHAPTER 3 OPPORTUNISTIC COMPUTATION OFFLOADING SCHEME １５ 3.1 OPPORTUNISTIC COMPUTATIONAL OFFLOADING SCHEME １７ 3.2 COMPUTATION OFFLOADING DECISION MODULE ２０ 3. ANN-BASED RESPONSE TIME PREDICTION MODULE ２５ 3.4 TASK OFFLOADING SERVICE ２８ CHAPTER 4 PERFORMANCE EVALUATION ２９ 4.1 SIMULATION ENVIRONMENT ２９ 4.2 PERFORMANCE EVALUATION ３１ 4.2.1 OFFLOADING PERFORMANCE ３２ 4.2.2 PREDICTION MODULE ３７ 4.2.3 ENERGY CONSUMPTION ４１ CHAPTER 5 CONCLUSION ４３ REFERENCES 45 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | Opportunistic Computation Offloading with Learning-based Prediction for UAV Clustering Networks | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.department | 일반대학원 컴퓨터공학과 | - |
| dc.date.awarded | 2019. 2 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 905284 | - |
| dc.identifier.uci | I804:41038-000000028539 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/common/orgView/000000028539 | - |
| dc.description.alternativeAbstract | Drones or Unmanned Aerial Vehicles (UAV) have recently become popular, especially in civilian and military applications. Examples of drone utilization are reconnaissance mission, packet delivery system, and surveillance system. As the technology grows to be more advanced, drone’s tasks become more complex and they need more computation power. Hence, clusters or swarms of drones are preferred since the swarms can offer more flexibility, reliability, and coverage. One weak point of drones is their limited computing and energy resources; therefore, it is hard for drones to process all the application on time. One possible solution to mitigate the problem is by doing offloading scheme between drone clusters. In this study, we proposed an opportunistic computation offloading system, which permits swarm drones with high computation applications to opportunistically borrow neighbor drone cluster’s available computation resources. Furthermore, we designed a response time estimation module by adopting artificial neural network technique in order to decide whether it is faster to complete the jobs by offloading the tasks to the nearby drone clusters or not. The offloading process will be only done if the estimated offloading scheme’s response time is smaller than the response time of executing the tasks locally. Our simulation results show that the proposed scheme can decrease the application response time by conducting opportunistic offloading process. | - |
- Appears in Collections:
- Graduate School of Ajou University > Department of Computer Engineering > 3. Theses(Master)
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