Open AccessStochastic solar harvesting characterisation for sustainable sensor node operation
Author(s) -
Kaushik K,
Mishra Deepak,
De Swades
Publication year - 2019
Publication title -
iet wireless sensor systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.433
H-Index - 27
eISSN - 2043-6394
pISSN - 2043-6386
DOI - 10.1049/iet-wss.2018.5009
Subject(s) - wireless sensor network , node (physics) , photovoltaic system , supercapacitor , computer science , solar energy , energy harvesting , electronic engineering , energy (signal processing) , electrical engineering , engineering , mathematics , capacitance , computer network , statistics , physics , structural engineering , electrode , quantum mechanics
Self‐sustainability of wireless sensor nodes is the need of the hour to realise ubiquitous wireless networks. To this end, the authors investigate practical feasibility of a sustainable green sensor network with solar‐powered nodes. They propose simple‐yet‐efficient (i) analytical circuit model for solar‐assisted supercapacitor charging and (ii) statistical model for characterising the solar intensity distribution. Combining these circuit and statistical models, they derive a novel solar charging rate distribution for supercapacitor. For analytical insights, they also propose an ideal diode‐based tight approximation for the practical supercapacitor charging circuit model. Accuracy of these proposed analytical models are validated by extensive numerical simulations based on real‐world solar intensity profile and panel characteristics. The derived solar charging rate distribution is used to investigate the energy outage probability of a sensor node for a given sensing rate. The results suggest that, for an energy outage probability of 0.1, at New Delhi, a 40 F supercapacitor and a 3 W solar panel can support the operation of Waspmote with six on‐board gas sensors at a rate of 65 samples per day. Furthermore, they use the proposed models to estimate the practical supercapacitor and panel sizes for sustainable operation at different geographical locations with varying sensing rate.