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|Title:||Optimization of Environmentally Powered Wireless Sensor Networks for Efficient Energy Harvesting|
|Keywords:||Wireless Sensor Networks;Energy Harvesting;Solar forecasting;Photovolatic (PV) systems;Average node duty cycle;EWMA;WCMA;Pro-Energy;FoBa;LeapFor-ward;Spikeslab;Cubist;bagEarthGCV|
|Abstract:||Focusing on environmentally powered Wireless Sensor Networks (WSNs), this thesis studies optimized operation of individual sensor node in terms of average duty cycle. In particular, the focus lies in gaining high average duty cycle with high stability. To achieve this objective, an energy neu- tral approach based efficient power management system is introduced and investigated in different working conditions. WSNs deployed in ad hoc manner comprise of numerous sensing nodes organised in a network for the sake of checking and balancing the environmental factors. Each node has sensing, computation, communication and locomotion capabilities but operates with limited battery life. Energy harvesting is a way of powering these WSNs by harvesting energy from the environment. Using harvesting energy as source, certain considerations are different from that battery operated networks. Nondeterministic energy availability with respect to time is the reason behind these differences which put a limit on the maximum rate at which energy can be used. Thus, power management is of prime importance in self-powered networks. The thesis begins with development of efficient solar forecasting algorithm for accurate estimation of energy availability. Reliable knowledge of solar radiation is essential for in- formed design, deployment planning and optimal management of energy in rechargeable sensor networks. In the proposed work, an optimized Pro-Energy algorithm is developed using level and trend factors in time series analysis for future solar irradiance estimation. The performance of proposed algorithm has been compared with EWMA, WCMA, and Pro-Energy on the basis of prediction error. The problem of solar irradiance forecasting has been further addressed by machine learning methodologies over historical data set. In proposed work, forecasts have been done using FoBa, leapForward, spikeslab, Cubist and bagEarthGCV models. To achieve more precise and consistent forecast, four Sta- tistical Ensemble (SE) approaches have been presented. To validate the effectiveness of these methodologies, a series of experimental evaluations have been presented in terms of forecast accuracy, correlation coefficient and Root Mean Square Error (RMSE). The R interface has been used as simulation platform for these evaluations. Based on forecasted solar energy profiles, an integrated approach of energy assignment principles with adaptive duty cycling has been introduced to efficiently utilize the avail- able energy. For this purpose, four factors of the system including energy generation rate, energy consumption rate, storage and controlled energy allotment to the sensor node havebeen formulated in to a theoretical model. The dynamic programming has also been used for theoretical analysis of the system. The analytical models of solar powered wireless sensor node have been used to validate the effectiveness of proposed work. The extensive simulations have been conducted on real time solar energy profiles in terms of magnitude and stability of sensors average duty cycle. The experimental results shows that the proposed approach offers perpetual node operation with high energy efficiency.|
|Appears in Collections:||Doctoral Theses@ECED|
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