A review on solar forecasting and power management approaches for energy-harvesting wireless sensor networks

被引:21
|
作者
Sharma, Amandeep [1 ]
Kakkar, Ajay [2 ]
机构
[1] Chandigarh Univ, Elect & Commun Engn, Mohali, India
[2] Thapar Inst Engn & Technol, Elect & Commun Engn, Patiala, Punjab, India
关键词
adaptive duty cycling; energy neutral state; energy prediction; prediction horizons; ARTIFICIAL NEURAL-NETWORK; MACHINE LEARNING-METHODS; GLOBAL RADIATION; IRRADIANCE FORECASTS; HORIZONTAL DIFFUSE; VARIABLE SELECTION; PREDICTION MODEL; EMPIRICAL-MODELS; TERM; LIFETIME;
D O I
10.1002/dac.4366
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
For rechargeable wireless sensor nodes, effective power management is of prime importance because of the stochastic behaviour of the environmental resources. A key issue in integrating solar resources with wireless sensor networks (WSNs) is the need of precise irradiance measurements and power to resource modelling. WSNs are employed in an adhoc manner comprises of numerous sensing nodes and organised as 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. By considering harvested energy as an energy source, certain considerations are different from that of 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, reliable knowledge of solar radiation is essential for informed design, deployment planning, and optimal management of energy in rechargeable WSNs. Further, power management is essential in self-powerssed networks to efficiently utilize the available energy. In this paper, a detailed survey on different solar forecasting techniques has been presented for precise energy estimates. A detailed study on energy efficient power management techniques is also proposed to address the feasibility of energy-harvesting approach in WSNs.
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页数:33
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