Cloud of Things (CoT): Cloud-Fog-IoT Task Offloading for Sustainable Internet of Things
| Autor: | Saif Ul Islam, Salman Tariq Lone, Assad Abbas, Mohammad Aazam |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Control and Optimization
Renewable Energy Sustainability and the Environment Computer science business.industry Distributed computing Mobile computing Unstructured data Cloud computing Energy consumption computer.software_genre Computational Theory and Mathematics Hardware and Architecture Middleware (distributed applications) Scalability CloudSim business computer Mobile device Software |
| Zdroj: | IEEE Transactions on Sustainable Computing. 7:87-98 |
| ISSN: | 2377-3790 |
| Popis: | With the rise in popularity of Internet of Things (IoT), mobile computing, and wearable devices, a huge amount of data is being generated. Running complex tasks such as that are machine learning-based with minimum energy consumption is a challenge. It requires complex algorithms to run locally such as on middleware fog within the proximity of the devices generating data, or globally in a cloud to analyze the acquired data and create robust and smart applications. Deep learning is a technique used to analyze the raw, unstructured data coming from the sensors and mobile devices. However, it depends on the type of task execution policy applied at each level; local or global, to decide on energy and performance efficiency, since deep learning algorithms are high in complexity. Hence, task execution will be hierarchically distributed among the IoT nodes, fog, and cloud. We present in this paper a three-tier IoT-fog-cloud model by augmenting CloudSim toolkit. We argue that with distributed task execution, we can achieve high scalability of IoT services, and manage the global energy consumption as well. As a proof-of-concept, we evaluate our three-tier architecture by considering computational tasks for various applications in IoT related to medical, multimedia, location-based, and text. |
| Databáze: | OpenAIRE |
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