Abstract

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.

Highlights

  • Wireless sensor networks (WSNs) are one of the technologies that are gaining considerable attention

  • The scientific contribution of this article can be summarized as follows: 1. We present a novel methodology for dynamic power management (DPM), which is based on current model-checking technology for automatic synthesis

  • We proposed a novel methodology for synthesizing near-optimal power management strategies for individual sensor node

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Summary

Introduction

Wireless sensor networks (WSNs) are one of the technologies that are gaining considerable attention. We find the mean value of the metrics from a number of runs N(N = 10, 000 in our experiments) under the strategy (here, we use strategy str[1] as an example) by time BOUND using the following queries: Average power consumption: E1⁄2\ = BOUND; N Š (max : Energy=BOUND) under str[1]; Figure 8. We add the ‘‘UPPAAL-Con’’ strategy in the experiments, which is generated with ‘‘minimizing the power consumption, guaranteeing the average queue size is smaller than Q and the service lost ratio is smaller than 0.005,’’ that is, done = (avQuS\ = Q)&&(loSeR\ = 0:005) in the synthesis query. It can be observed that when the average queue size is small (smaller than 0.02), the performance difference among the DPM strategies is not obvious This is because, in order to achieve the desired performance metric, the SP seldom moves to the low-power states (Standby and Sleep states).

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