Stochastic solar harvesting characterisation for sustainable sensor node operation

TLDR: The authors propose simple-yet-efficient analytical circuit model for solar-assisted supercapacitor charging and statistical model for characterising the solar intensity distribution, and derive a novel solar charging rate distribution for super capacitor.

Abstract: 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.

Figures

TABLE I: System parameters for 3 W panel at STC.

Fig. 3: Output characteristics of solar panel.

Fig. 6: Validation of solar charging rate CDF for different regions.

Fig. 7: Sampling rate versus energy outage probability.

Fig. 4: Validation of polynomial-fit based mixture distribution of G.

TABLE II: Voltage and Current consumption of Waspmote [27] components.

Frequently asked questions

Q1. What contributions have the authors mentioned in the paper "Stochastic solar harvesting characterization for sustainable sensor node operation" ?

To address this requirement the authors investigate the practical feasibility of sustainable green sensor network with solar-powered nodes. The authors propose simple yet efficient ( i ) analytical circuit model for solar panel assisted supercapacitor charging and ( ii ) statistical model for characterizing the solar intensity distribution. To gain analytical insights, the authors also propose an ideal diode based tight approximation for the practical supercapacitor charging circuit model. Further, the authors use the proposed models to estimate the practical supercapacitor and solar panel sizes required to ensure sustainability of sensor node operation at different geographical locations with varying sensing rate. 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 6 on-board toxic gas sensors with a sampling rate of 65 samples per day. 

Q2. What future works have the authors mentioned in the paper "Stochastic solar harvesting characterization for sustainable sensor node operation" ?

The authors plan to move in this direction in the future. 

Q3. What is the shunt resistance of the diode D2?

When forward bias voltage of the diode D2 is below V intx , D2 acts as an open circuit, during which most of the generated solar current Iirr flows to the supercapacitor due to a large value of shunt resistance RP . 

Q4. Why is the output current of a solar panel less than the original value?

Due to loss of the generated energy by the series and the shunt resistances (respectively denoted by RS and RP ), the output current (IM ) and voltage (VM ) of the solar panel are lesser than their original values at the source. 

Q5. How many sensors can be mounted on a waspmote?

1. Maximum of 6 gas sensors (carbon monoxide (CO), ammonia (NH3), nitrogen dioxide (NO2), carbon dioxide (CO2), volatile organic compounds (V OC), and methane (CH4)) can be mounted on-board waspmote. 

Q6. How is the solar panel model used for fast charging?

Fast charging is achieved by ensuring that the charging current is linear on average by restricting the working range of the solar panel. 

Q7. What is the upper bound of Cu1 for a solar panel model?

The upper bound Cu1 is obtained when the available solar energy is stored such that there is no consumption by the sensor node, i.e., P avgcons = 0. 

Q8. How do the cluster heads aggregate data from their nodes?

In order to increase the lifetime of WSN, the nodes are grouped into clusters and the the cluster heads aggregate data from its cluster nodes and send it to remote base station. 

Q9. What is the need to develop an analytical model for a solar harvesting sensor node?

there is a need to develop an analytical charging model for a solar harvesting sensor node to evaluate its performance directly from the parameters available in the solar panel datasheet, solar intensity distribution at the location where sensor node would be deployed, and the sensor node’s energy consumption. 

Q10. What is the effect of the reverse bias of the diode D2?

it ceases to charge once its voltage VC close to V intx , during which all the solar current flows through the diode D2. 

Q11. What type of data collection application is used for solar energy harvesting?

Though for analysis the authors consider a periodic data collection application [14] where the sensor nodes are equipped with wake-up receiver for green data communication, the findings are applicable to sustainability studies of any solar harvesting WSNs irrespective of the type of data collection. 

Q12. What is the voltage VC across the supercapacitor?

Refering to Fig. 1, when this D1 is forward biased, the voltage VC across supercapacitor is equal to VM , which is a function of solar intensity G and charging time t. Solar charging rate Γ can be defined as the rate of change of voltage VC across the supercapacitor, i.e., Γ = dVCdt . 

Q13. What is the order of the polynomial and its coefficients?

C. Validation of Proposed Distribution ModelThe order of the polynomial and its coefficients depend on the required goodness-of-fit of the distribution. 

Q14. What is the probability of solar intensity being 0?

As this corresponds to nearly half the 24-hour duration of a day, the probability of solar intensity being 0 is maximum (≈ 0.5) out of all other intensity values. 

Q15. What is the key contribution of this work?

The key contributions of this work are as follows: 1) A novel distribution model is proposed to characterizethe spatio-temporal randomness of solar intensity which can be used at any geographical location on earth. 

Q16. Why do of the works on solar harvesting wireless sensor nodes use maximum power point tracking?

In order to have a low complexity node, majority of the works on solar harvesting wireless sensor nodes, including commercially available Libelium Waspmote [27], do not use maximum power point tracking based charge controller.