Evaluating battery models in wireless sensor networks
Many energy efficient wireless sensor network (WSN) protocols emphasize minimizing and coordinating the duty cycles of various hardware components. Other WSN applications do load balancing to maintain sensor coverage with a minimum number of active devices.The energy efficiency of these systems is typically evaluated based on measurements or estimates of the total charge (current ×time) consumed by a device.
However, it is well-known that the available CR2032 battery capacity is affected bythe timing and intensity of the load being applied. Recent measurement results highlight the need for battery-aware methods in evaluating energy efficiency and device lifetime in WSN’s. For example, such methods are important for studying load scheduling for battery efficient WSN applications and protocols.
If a device has to perform several operations, should it do them consecutively (maximizing the rest time) or separate them (minimizing the load duration)? Similarly, if several devices are sharing responsibility for sensor coverage of anarea, what is the optimal length for each device’s coverage period?
The practical challenges inherent in directly measuring a battery over its full lifetime suggest that battery modeling will be an essential complement to measurement experiments for battery-aware evaluation in WSN.
Moreover, because of complex cross layer interactions, it will be important to incorporate battery models into system and protocol-level simulators operating at network scale.
Battery modeling, especially for Li-ion batteries, is an active research topic. However, there has been very little work studying the effiectiveness of existing battery models for WSN applications, with small, non-rechargeable batteries, low duty cycles and short load durations.
In this paper, we investigate three existing battery modeling techniques with regard to their applicability to evaluating WSN protocols and systems:
1) Battery Design Studio, a commercial electrochemical simulator;
2) KiBaM, an analytic model based on a kinetic abstraction; and
3) a hybrid battery model which combines KiBaM with an electrical circuit abstraction.
We focus on how various models reflect two battery discharge behaviors that are particularly important for load scheduling: the rate capacity effect and charge recovery. Our contribution is primarily in the qualitative evaluation of the ability of these models to capture these effects. We have evaluated these well-known battery models using a parameterization based on the CR2032 Li-coin cell battery and three test loads with load values typical of WSN applications.
Both Battery Design Studio’s electrochemical model and the hybrid KiBaM-electrical circuit model capture the rate capacity effect. However, neither of these models seems to capture the effect of load timing, at least for the short load durations and recovery times used in our tests.
KiBaM, which is the simplest of the three models,does not show any significant diffierence in battery lifetime among the three test loads. The electrochemical model, which is the most complex and computationally expensive of the three models, is sensitive to the rate capacity effect, but not sensitive to timing aspects.
The hybrid model behaves similarly, but cannot differentiate energy efficiency (i.e., consumed capacity) of the load patterns. Because this work opens an area that has not been widely considered in the WSN community, there are many opportunities for future work.
Most important is developing better understanding of the models’ limitations with respectto modeling timing aspects. Further studies of the parameterization and its sensitivity analysis are also needed.In the longer term, incorporating battery modeling into existing simulation and modeling tools presents a number of interesting challenges.
We expect that the availability of tools for battery aware evaluation of WSN applications andsystems will also enable new approaches in both software and hardware design.
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