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.
The transport sector has proven to be the largest contributor of CO 2 in the Greenhouse Gas (GHG) emissions. To reduce CO 2 emissions and improve mileage, the existing research has proposed different models for vehicles such as Plug-in Hybrid Electric Vehicle (PHEVs), Electric Vehicle (EVs), solar and hydrogen Vehicles. However, the existing models clearly lack suitable solutions and there is a need to improve the existing vehicle models for lesser CO 2 emissions, longer range, and quicker charging. In this context, we propose A Novel Hybrid Framework for Modern Vehicles, to reduce CO 2 emissions and increase vehicle mileage, by managing energy resources efficiently using Fuzzy Logic. It considers three different energy sources i.e., gasoline, solar and electric power, to charge a vehicle, and a modification in the architecture of EVs is made for the availability of all these energy resources. We use Visual Studio to implement fuzzy logic based algorithm designed to simulate the proposed system and added a small gasoline engine to the existing architecture of EVs to provide energy resources that solve charging issues during long-range travel. We use the Statistical Package for Social Sciences (SPSS) tool to evaluate the performance of the proposed framework for CO 2 emissions and fuel efficiency. The proposed framework achieves the best mileage of 57.6 Kilometer per liter (Km/l) with a 660 Cubic Centimeter (CC) gasoline engine which is 111.11% more efficient than existing frameworks. Moreover, CO 2 emissions through our proposed framework are 41.52 Grams per Kilometer (G/Km) which are 53% lesser than current frameworks. The proposed framework also improves the charging duration of batteries i.e., a 10 Kilowatt-Hour (KwH) battery can be charged in 1 hour and 15 minutes.
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