A Novel Fog-Based Multi-Level Energy-Efficient Framework for IoT-Enabled Smart Environments
A Novel Fog-Based Multi-Level Energy-Efficient Framework for IoT-Enabled Smart Environments
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DOI：
10.1109/access.2020.3010157
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发表时间：
2020-07
期刊：
影响因子：
3.9
通讯作者：
Muhammad Ammad;M. A. Shah;Saif ul Islam;C. Maple;Abdullah A. Alaulamie;J. Rodrigues;Shafaq Mussadiq-Shafaq-Mussa
中科院分区：
文献类型：
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作者：
Muhammad Ammad;M. A. Shah;Saif ul Islam;C. Maple;Abdullah A. Alaulamie;J. Rodrigues;Shafaq Mussadiq-Shafaq-Mussa
物联网（IoT）已经成为通过采用各种智能设备（包括智能手机、智能手表、传感器、车载单元和其他网络设备）来提高人类生活水平的有前景的范例。然而，这些设备消耗大量的能量来执行它们的操作，这对环境、产品成本和设备的寿命具有显著影响。鉴于这一事实，智能环境的节能解决方案已经得到了研究人员和工业界的极大关注。在此背景下，提出了一种新的基于雾的多层次节能框架，用于支持物联网的智能环境。为了实现这一目标，所提出的框架在现有的物联网雾云架构中增加了额外的两层-基于传感器的节能硬件层和策略层，以监控能耗并实现能源感知决策。首先，确定物联网智能环境中的主要能源消耗来源。此外，估计设备执行特定任务的能量需求。此外，替代设备执行相同的任务，使用更少的能量被搜索出来。最后，选择一个设备或一组设备，以处理消耗较低能量的作业，同时确保作业要求。为了验证所提出的框架，考虑了四个案例研究-智能停车场，智能医院特别是ICU，智能农业和智能机场。使用iFogsim工具包进行仿真，结果表明，通过采用所提出的框架可以节省大量的能量。
The Internet of Things (IoT) has emerged as a promising paradigm to enhance the living standard of human life by employing varied smart devices including smart phones, smart watches, sensors, on-board units and other networking equipment. However, these devices consume a considerable amount of energy to perform their operations that has a significant impact on the environment, product cost and life of the device. Given this fact, energy-efficient solutions for smart environments have gained great attention from researchers and the industrial community. In this context, a novel fog-based multi-level energy-efficient framework for IoT-enabled smart environments has been proposed. To achieve this, the proposed framework adds additional two layers in the existing IoT-fog-cloud architecture – sensors-based energy-efficient hardware layer and policy layer, to monitor the energy consumption and to enable the energy-aware decision making. Initially, the main sources of energy consumption in an IoT-enabled smart environment are identified. Further, the energy requirements of a device to perform a specific task are estimated. Moreover, the alternative devices to perform the same task using less energy are searched out. Finally, a device or a set of devices, to process the job consuming lower energy while ensuring the job requirements, is selected. To validate the proposed framework, four case studies are considered – smart parking, smart hospital specifically ICU, smart agriculture and smart airport. Simulations are conducted using iFogsim toolkit and results show that a significant amount of energy can be conserved by employing the proposed framework.