F. Rismanian Yazdi; M. Hosseinzadeh; S. Jabbehdari
Abstract
Wireless body area networks (WBAN) are innovative technologies that have been the anticipation greatly promote healthcare monitoring systems. All WBAN included biomedical sensors that can be worn on or implanted in the body. Sensors are monitoring vital signs and then processing the data and transmitting ...
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Wireless body area networks (WBAN) are innovative technologies that have been the anticipation greatly promote healthcare monitoring systems. All WBAN included biomedical sensors that can be worn on or implanted in the body. Sensors are monitoring vital signs and then processing the data and transmitting to the central server. Biomedical sensors are limited in energy resources and need an improved design for managing energy consumption. Therefore, DTEC-MAC (Diverse Traffic with Energy Consumption-MAC) is proposed based on the priority of data classification in the cluster nodes and provides medical data based on energy management. The proposed method uses fuzzy logic based on the distance to sink and the remaining energy and length of data to select the cluster head. MATLAB software was used to simulate the method. This method compared with similar methods called iM-SIMPLE and M-ATTEMPT, ERP. Results of the simulations indicate that it works better to extend the lifetime and guarantee minimum energy and packet delivery rates, maximizing the throughput.
I.3.7. Engineering
B. Hosseinzadeh Samani; H. HouriJafari; H. Zareiforoush
Abstract
In this study, the energy consumption in the food and beverage industries of Iran was investigated. The energy consumption in this sector was modeled using artificial neural network (ANN), response surface methodology (RSM) and genetic algorithm (GA). First, the input data to the model were calculated ...
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In this study, the energy consumption in the food and beverage industries of Iran was investigated. The energy consumption in this sector was modeled using artificial neural network (ANN), response surface methodology (RSM) and genetic algorithm (GA). First, the input data to the model were calculated according to the statistical source, balance-sheets and the method proposed in this paper. It can be seen that diesel and liquefied petroleum gas have respectively the highest and lowest shares of energy consumption compared with the other types of carriers. For each of the evaluated energy carriers (diesel, kerosene, fuel oil, natural gas, electricity, liquefied petroleum gas and gasoline), the best fitting model was selected after taking the average of runs of the developed models. At last, the developed models, representing the energy consumption of food and beverage industries by each energy carrier, were put into a finalized model using Simulink toolbox of Matlab software. Results of data analysis indicated that consumption of natural gas is being increased in Iran food and beverage industries, while in the case of fuel oil and liquefied petroleum gas a decreasing trend was estimated.
