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Energy efficient computation offloading in mobile edge computing / / Ying Chen [and three others]
| Energy efficient computation offloading in mobile edge computing / / Ying Chen [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (167 pages) |
| Disciplina | 004 |
| Collana | Wireless Networks |
| Soggetto topico |
Mobile computing
Edge computing |
| ISBN | 3-031-16822-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Contents -- Acronyms -- 1 Introduction -- 1.1 Background -- 1.1.1 Mobile Cloud Computing -- 1.1.1.1 Architecture of Mobile Cloud Computing -- 1.1.1.2 Characteristics of Mobile Cloud Computing -- 1.1.1.3 Cloudlet -- 1.1.1.4 Fog Computing -- 1.1.1.5 Data Security and Privacy Protection -- 1.1.1.6 Challenges of Mobile Cloud Computing -- 1.1.2 Mobile Edge Computing -- 1.1.2.1 Definition of Mobile Edge Computing -- 1.1.2.2 Architecture of Mobile Edge Computing -- 1.1.2.3 Advantages of Mobile Edge Computing -- 1.1.2.4 Applications of Mobile Edge Computing -- 1.1.2.5 Challenges of Mobile Edge Computing -- 1.1.3 Computation Offloading -- 1.1.3.1 Minimize Latency -- 1.1.3.2 Minimize Energy Consumption -- 1.1.3.3 Weighted Sum of Latency and Energy Consumption -- 1.2 Challenges -- 1.3 Contributions -- 1.4 Book Outline -- References -- 2 Dynamic Computation Offloading for Energy Efficiency in Mobile Edge Computing -- 2.1 System Model and Problem Statement -- 2.1.1 Network Model -- 2.1.2 Task Offloading Model -- 2.1.3 Task Queuing Model -- 2.1.4 Energy Consumption Model -- 2.1.5 Problem Statement -- 2.2 EEDCO: Energy Efficient Dynamic Computing Offloading for Mobile Edge Computing -- 2.2.1 Joint Optimization of Energy and Queue -- 2.2.2 Dynamic Computation Offloading for Mobile Edge Computing -- 2.2.3 Trade-Off Between Queue Backlog and Energy Efficiency -- 2.2.4 Convergence and Complexity Analysis -- 2.3 Performance Evaluation -- 2.3.1 Impacts of Parameters -- 2.3.1.1 Effect of Tradeoff Parameter -- 2.3.1.2 Effect of Arrival Rate -- 2.3.1.3 Effect of Transmit Power -- 2.3.1.4 Effect of Channel Power Gain -- 2.3.1.5 Effect of Number of IoT Devices -- 2.3.2 Performance Comparison with EA and QW Schemes -- 2.4 Literature Review -- 2.5 Summary -- References.
3 Energy Efficient Offloading and Frequency Scaling for Internet of Things Devices -- 3.1 System Model and Problem Formulation -- 3.1.1 Network Model -- 3.1.2 Task Model -- 3.1.3 Queuing Model -- 3.1.4 Energy Consumption Model -- 3.1.5 Problem Formulation -- 3.2 COFSEE: Computation Offloading and Frequency Scaling for Energy Efficiency of Internet of Things Devices -- 3.2.1 Problem Transformation -- 3.2.2 Optimal Frequency Scaling -- 3.2.3 Local Computation Allocation -- 3.2.4 MEC Computation Allocation -- 3.2.5 Theoretical Analysis -- 3.3 Performance Evaluation -- 3.3.1 Impacts of System Parameters -- 3.3.1.1 Effect of Tradeoff Parameter V -- 3.3.1.2 Effect of Arrival Rate -- 3.3.1.3 Effect of Slot Length -- 3.3.2 Performance Comparison with RLE, RME and TSSchemes -- 3.4 Literature Review -- 3.5 Summary -- References -- 4 Deep Reinforcement Learning for Delay-Aware and Energy-Efficient Computation Offloading -- 4.1 System Model and Problem Formulation -- 4.1.1 System Model -- 4.1.2 Problem Formulation -- 4.2 Proposed DRL Method -- 4.2.1 Data Prepossessing -- 4.2.2 DRL Model -- 4.2.2.1 Reinforcement Learning Framework -- 4.2.2.2 Deep Reinforcement Learning Model -- 4.2.3 Training -- 4.2.3.1 Initialization -- 4.2.3.2 Exploration and Data Acquisition -- 4.2.3.3 Replay Experience Buffer -- 4.2.3.4 Learning -- 4.2.3.5 Reward Clipping -- 4.3 Performance Evaluation -- 4.4 Literature Review -- 4.5 Summary -- References -- 5 Energy-Efficient Multi-Task Multi-Access Computation Offloading via NOMA -- 5.1 System Model and Problem Formulation -- 5.1.1 Motivation -- 5.1.2 System Model -- 5.1.3 Problem Formulation -- 5.2 LEEMMO: Layered Energy-Efficient Multi-Task Multi-Access Algorithm -- 5.2.1 Layered Decomposition of Joint Optimization Problem -- 5.2.2 Proposed Subroutine for Solving Problem (TEM-E-Sub). 5.2.3 A Layered Algorithm for Solving Problem (TEM-E-Top) -- 5.2.4 DRL-Based Online Algorithm -- 5.3 Performance Evaluation -- 5.3.1 Impacts of Parameters -- 5.3.2 Performance Comparison with FDMA Based Offloading Schemes -- 5.4 Literature Review -- 5.5 Summary -- References -- 6 Conclusion -- 6.1 Concluding Remarks -- 6.2 Future Directions -- References. |
| Record Nr. | UNINA-9910624378303321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Energy efficient computation offloading in mobile edge computing / / Ying Chen [and three others]
| Energy efficient computation offloading in mobile edge computing / / Ying Chen [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (167 pages) |
| Disciplina | 004 |
| Collana | Wireless Networks |
| Soggetto topico |
Mobile computing
Edge computing |
| ISBN | 3-031-16822-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Contents -- Acronyms -- 1 Introduction -- 1.1 Background -- 1.1.1 Mobile Cloud Computing -- 1.1.1.1 Architecture of Mobile Cloud Computing -- 1.1.1.2 Characteristics of Mobile Cloud Computing -- 1.1.1.3 Cloudlet -- 1.1.1.4 Fog Computing -- 1.1.1.5 Data Security and Privacy Protection -- 1.1.1.6 Challenges of Mobile Cloud Computing -- 1.1.2 Mobile Edge Computing -- 1.1.2.1 Definition of Mobile Edge Computing -- 1.1.2.2 Architecture of Mobile Edge Computing -- 1.1.2.3 Advantages of Mobile Edge Computing -- 1.1.2.4 Applications of Mobile Edge Computing -- 1.1.2.5 Challenges of Mobile Edge Computing -- 1.1.3 Computation Offloading -- 1.1.3.1 Minimize Latency -- 1.1.3.2 Minimize Energy Consumption -- 1.1.3.3 Weighted Sum of Latency and Energy Consumption -- 1.2 Challenges -- 1.3 Contributions -- 1.4 Book Outline -- References -- 2 Dynamic Computation Offloading for Energy Efficiency in Mobile Edge Computing -- 2.1 System Model and Problem Statement -- 2.1.1 Network Model -- 2.1.2 Task Offloading Model -- 2.1.3 Task Queuing Model -- 2.1.4 Energy Consumption Model -- 2.1.5 Problem Statement -- 2.2 EEDCO: Energy Efficient Dynamic Computing Offloading for Mobile Edge Computing -- 2.2.1 Joint Optimization of Energy and Queue -- 2.2.2 Dynamic Computation Offloading for Mobile Edge Computing -- 2.2.3 Trade-Off Between Queue Backlog and Energy Efficiency -- 2.2.4 Convergence and Complexity Analysis -- 2.3 Performance Evaluation -- 2.3.1 Impacts of Parameters -- 2.3.1.1 Effect of Tradeoff Parameter -- 2.3.1.2 Effect of Arrival Rate -- 2.3.1.3 Effect of Transmit Power -- 2.3.1.4 Effect of Channel Power Gain -- 2.3.1.5 Effect of Number of IoT Devices -- 2.3.2 Performance Comparison with EA and QW Schemes -- 2.4 Literature Review -- 2.5 Summary -- References.
3 Energy Efficient Offloading and Frequency Scaling for Internet of Things Devices -- 3.1 System Model and Problem Formulation -- 3.1.1 Network Model -- 3.1.2 Task Model -- 3.1.3 Queuing Model -- 3.1.4 Energy Consumption Model -- 3.1.5 Problem Formulation -- 3.2 COFSEE: Computation Offloading and Frequency Scaling for Energy Efficiency of Internet of Things Devices -- 3.2.1 Problem Transformation -- 3.2.2 Optimal Frequency Scaling -- 3.2.3 Local Computation Allocation -- 3.2.4 MEC Computation Allocation -- 3.2.5 Theoretical Analysis -- 3.3 Performance Evaluation -- 3.3.1 Impacts of System Parameters -- 3.3.1.1 Effect of Tradeoff Parameter V -- 3.3.1.2 Effect of Arrival Rate -- 3.3.1.3 Effect of Slot Length -- 3.3.2 Performance Comparison with RLE, RME and TSSchemes -- 3.4 Literature Review -- 3.5 Summary -- References -- 4 Deep Reinforcement Learning for Delay-Aware and Energy-Efficient Computation Offloading -- 4.1 System Model and Problem Formulation -- 4.1.1 System Model -- 4.1.2 Problem Formulation -- 4.2 Proposed DRL Method -- 4.2.1 Data Prepossessing -- 4.2.2 DRL Model -- 4.2.2.1 Reinforcement Learning Framework -- 4.2.2.2 Deep Reinforcement Learning Model -- 4.2.3 Training -- 4.2.3.1 Initialization -- 4.2.3.2 Exploration and Data Acquisition -- 4.2.3.3 Replay Experience Buffer -- 4.2.3.4 Learning -- 4.2.3.5 Reward Clipping -- 4.3 Performance Evaluation -- 4.4 Literature Review -- 4.5 Summary -- References -- 5 Energy-Efficient Multi-Task Multi-Access Computation Offloading via NOMA -- 5.1 System Model and Problem Formulation -- 5.1.1 Motivation -- 5.1.2 System Model -- 5.1.3 Problem Formulation -- 5.2 LEEMMO: Layered Energy-Efficient Multi-Task Multi-Access Algorithm -- 5.2.1 Layered Decomposition of Joint Optimization Problem -- 5.2.2 Proposed Subroutine for Solving Problem (TEM-E-Sub). 5.2.3 A Layered Algorithm for Solving Problem (TEM-E-Top) -- 5.2.4 DRL-Based Online Algorithm -- 5.3 Performance Evaluation -- 5.3.1 Impacts of Parameters -- 5.3.2 Performance Comparison with FDMA Based Offloading Schemes -- 5.4 Literature Review -- 5.5 Summary -- References -- 6 Conclusion -- 6.1 Concluding Remarks -- 6.2 Future Directions -- References. |
| Record Nr. | UNISA-996495560003316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Proceedings of the second International Workshop on Cloud Data Management
| Proceedings of the second International Workshop on Cloud Data Management |
| Autore | Meng Xiaofeng <1964-> |
| Pubbl/distr/stampa | [Place of publication not identified], : ACM, 2010 |
| Descrizione fisica | 1 online resource (64 pages) |
| Collana | ACM Conferences |
| Soggetto topico |
Engineering & Applied Sciences
Computer Science |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | CIKM '10 International Conference on Information and Knowledge Management, Toronto, ON, Canada - October 26 - 30, 2010 |
| Record Nr. | UNINA-9910376009903321 |
Meng Xiaofeng <1964->
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| [Place of publication not identified], : ACM, 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Using Educational Robots to Enhance Learning : An Analysis of 100 Academic Articles
| Using Educational Robots to Enhance Learning : An Analysis of 100 Academic Articles |
| Autore | Liu Dejian |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , 2024 |
| Descrizione fisica | 1 online resource (266 pages) |
| Altri autori (Persone) |
HuangRonghuai
ChenYing AdarkwahMichael Agyemang ZhangXiangling LiXin ZhangJunjie DaTing |
| Collana | Smart Computing and Intelligence Series |
| ISBN | 981-9758-26-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910890189903321 |
|
Liu Dejian
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| Singapore : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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