Effective power management has become a key concern in the design of wireless sensor networks. Dynamic power management refers to strategies which selectively switch between several power states of a device during the runtime in order to achieve a tradeoff between power consumption and performance. In this article, we present a novel methodology that exploits current model-checking technology for automatic synthesis for dynamic power management. The generic system model for dynamic power management is modeled as a network of timed games. And the synthesis objectives are expressed as synthesis queries. Subsequently, automatic synthesis of power management strategies is performed by UPPAAL-STRATEGO with respect to the synthesis queries. Once a strategy has been constructed, its performance can be analyzed through statistical model-checking using the same tool. The modeling and synthesizing procedures are illustrated with a running example. Finally, the applicability of the methodology is assessed by synthesizing and evaluating a range of power management strategies for a concrete sensor node. Our methodology can be employed to help designers in constructing dynamic power management strategies for wireless sensor networks in practical applications.

Energy saving is a crucial concern in embedded real time systems. Many RT-DVS algorithms have been proposed to save energy while preserving deadline guarantees. This paper presents a novel approach to evaluate RT-DVS algorithms using statistical model checking. A scalable framework is proposed for RT-DVS algorithms evaluation, in which the relevant components are modeled as stochastic timed automata, and the evaluation metrics including utilization bound, energy efficiency, battery awareness, and temperature awareness are expressed as statistical queries. Evaluation of these metrics is performed by verifying the corresponding queries using UPPAAL-SMC and analyzing the statistical information provided by the tool. We demonstrate the applicability of our framework via a case study of five classical RT-DVS algorithms.

Energy saving and high reliability are two key concerns in the design of real time systems. However, high reliability and low energy consumption are conflicting objects, and they are generally contrasted with temporal correctness. In this paper, we propose comparing reliability-ignorant and reliability-aware power management schemes with statistical model checking approach using UPPAAL-SMC. The power management schemes and the relevant components are modeled in the form of stochastic timed automata. And the analysis objectives are expressed as verification queries. With the model and queries as inputs, UPPAAL-SMC returns the probability of system failure and the expected value of energy consumption. In this analysis, we have considered three reliability-ignorant power management schemes and two reliability-aware power management schemes. Based on the comparative study, we provided guidelines for choosing the suitable scheme for a given system. One thing that should be emphasized is that our methodology is not limited to the schemes involved in this paper. The modeling and evaluating procedure can be applied to analyse other energy management schemes in practical application.

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