Browsing by Subject "Energy Consumption,"
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Item Analysis of Energy Consumption and Energy Saving Opportunity at Solar Powered Water Treatment Plant: A Case Study of Sundarighat Water Treatment Plant(I.O.E. Pulchowk Campus, 2023-10) Mehta, Umesh KumarThis study primarily focused on the fundamentals associated with energy saving opportunity at drinking water supply system. In Nepal water supply system is designed based the immediate requirement without proper study with the limited available resources. Sundarighat water treatment Plant treats the water from Nakhu Khola and pumped to major part of Kathmandu valley for distribution through the pumping system available at WTP. Pumping system at WTP consume most of the energy compared to other electrical load at the treatment plant. The technical status and performance of the equipment, machineries and accessories of pumping system are largely unknown as the evaluation of the system was not performed yet. In this study, an analysis of the energy consumption by performing energy audit at WTP was done to find the status of electrical parameter for energy efficient operation of plant and identifies the opportunity to save energy and cost through pump size selection. A power quality analyzer and three phase clamp meter was used to analyze the patterns of electrical parameters. Hydraulic simulation software EPANET 2.2 was used for determination of pump size by analyzing the water transmission pipelineItem ANALYSIS OF ENERGY OPTIMIZATION OF BTS OF 5G NETWORK OVER SDN ENVIRONMENT(Pulchowk Campus, 2021-08) SINGH, SONUAttracting a huge attention towards energy optimization of mobile communication network as it contains the major part of total energy consumption of Information and Communication Technology (ICT). During low traffic load, the energy is wastage as the design of wireless network is made for maximum traffic load, but the maximum traffic occurs only for few hours. A base station transceiver (BTS) consume more energy than any other network devices used in end access network. Most of the resources of BTS are unused during the low traffic load at night time, which results in unnecessary wastage of energy and decreases energy efficiency. Therefore, to increase the energy efficiency of 5G end access network, the energy optimization of BTS is necessary. In this thesis, the concept of Dynamic Transmitter Sleep (DTS) Technique has introduced over Software Defined Network (SDN) platform to reduce the energy consumption of BTS in 5G network with the addition of smart link sleep approach to save more energy by deactivating network links between network elements of end access network during idle state. During low traffic, DTS approach automatically switch the unnecessary transmitter to sleep mode and wakeup only required transmitter when increasing traffic is noticed, without degrading the Quality of Service (QoS) and to achieve more energy efficiency smart link sleep approach has also implemented which is an SDN based energy optimization technique. This technique is performed over SDN environment where data and control planes are separated providing high flexibility, cost effective and energy efficiency. Hence, DTS technique has applied to the 5G BTS which was simulated in SDN environment where Mininet-WiFi emulator used as network simulation testbed and OpenDayLight as SDN controller. After implementation of DTS mode during low traffic, it saved 34.6% of energy in single BTS and 43.79% of network link energy has been reduced by the use of smart link sleep technique. Therefore, this energy saving approach has potential to reduce noticeable amount of energy consumption under the benefits of SDN.During low traffic load, the energy is wastage as the design of wireless network is made for maximum traffic load, but the maximum traffic occurs only for few hours. A base station transceiver (BTS) consume more energy than any other network devices used in end access network. Most of the resources of BTS are unused during the low traffic load at night time, which results in unnecessary wastage of energy and decreases energy efficiency. Therefore, to increase the energy efficiency of 5G end access network, the energy optimization of BTS is necessary. In this thesis, the concept of Dynamic Transmitter Sleep (DTS) Technique has introduced over Software Defined Network (SDN) platform to reduce the energy consumption of BTS in 5G network with the addition of smart link sleep approach to save more energy by deactivating network links between network elements of end access network during idle state. During low traffic, DTS approach automatically switch the unnecessary transmitter to sleep mode and wakeup only required transmitter when increasing traffic is noticed, without degrading the Quality of Service (QoS) and to achieve more energy efficiency smart link sleep approach has also implemented which is an SDN based energy optimization technique. This technique is performed over SDN environment where data and control planes are separated providing high flexibility, cost effective and energy efficiency. Hence, DTS technique has applied to the 5G BTS which was simulated in SDN environment where Mininet-WiFi emulator used as network simulation testbed and OpenDayLight as SDN controller. After implementation of DTS mode during low traffic, it saved 34.6% of energy in single BTS and 43.79% of network link energy has been reduced by the use of smart link sleep technique. Therefore, this energy saving approach has potential to reduce noticeable amount of energy consumption under the benefits of SDN.Item Task Prioritization and Scheduling of Fog Computing Model in Healthcare Systems(Pulchowk Campus, 2021-09) Pahari, PrakritiHealth-related applications are one of the most sensitive areas which should be delivered on time e ciently. For the storage and processing of enormous health data, Cloud Computing could not be e cient as Cloud Data Centers take a large time to process and send back the results. The new paradigm, called Fog Computing is applicable in cases like these. In this research, we utilize the sample time-critical healthcare system where the IoT sensors' data is divided into critical and normal tasks where critical tasks are prioritized over normal patients' data. To address their management, Fog Computing is used at the edge of the network. In this paper, a new fog-cloud-based algorithm called Prioritized Latency Aware Energy E cient Algorithm (PLAEE) is developed by utilizing the existing algorithms in the fog system and also by process optimization of the core evaluation metrics, latency, and energy usage. This algorithm shows superiority to the existing algorithms in terms of performance metrics. The experimentation is performed using Blood Pressure data collected from the University of Piraeus. In terms of response time, the PLAEE is performing 36.40%, 14.82%, 14.70%, and 6.03% better than Cloud only, Edge-wards, Resource Aware, and SCATTER Algorithm respectively. In terms of Energy Consumption, the PLAEE is performing 23.85%, 14.96%, 10.84%, and 2.83% better than Cloud only, Edge-wards, Resource Aware, and SCATTER Algorithm respectively. Almost 98% of critical data are placed in the FNs according to the Tasks Managed value calculated where 91.70%, 6.28%, and 2.01% of Critical Tasks are placed in FZ1, FZ2, and CDC respectively