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Prediction-based Dynamic Energy Management in Wireless Sensor Networks

View Article: PubMed Central

ABSTRACT

Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a target state, which was adopted to awaken wireless sensor nodes so that their sleep time was prolonged. With the distributed computing capability of nodes, an optimization approach of distributed genetic algorithm and simulated annealing was proposed to minimize the energy consumption of measurement. Considering the application of target tracking, we implemented target position prediction, node sleep scheduling and optimal sensing node selection. Moreover, a routing scheme of forwarding nodes was presented to achieve extra energy conservation. Experimental results of target tracking verified that energy-efficiency is enhanced by prediction-based dynamic energy management.

No MeSH data available.


Flow chart of distributed genetic algorithm and simulated annealing.
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f2-sensors-07-00251: Flow chart of distributed genetic algorithm and simulated annealing.

Mentions: Figure 2 presents the framework of DGASA. Assigned with the population of solutions from GA, each available node in current period runs SA for a specified time. Then solutions of SA will perform crossover and modulation to generate a new population of GA. An optimal solution for energy consumption is obtained by iteration.


Prediction-based Dynamic Energy Management in Wireless Sensor Networks
Flow chart of distributed genetic algorithm and simulated annealing.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3756720&req=5

f2-sensors-07-00251: Flow chart of distributed genetic algorithm and simulated annealing.
Mentions: Figure 2 presents the framework of DGASA. Assigned with the population of solutions from GA, each available node in current period runs SA for a specified time. Then solutions of SA will perform crossover and modulation to generate a new population of GA. An optimal solution for energy consumption is obtained by iteration.

View Article: PubMed Central

ABSTRACT

Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a target state, which was adopted to awaken wireless sensor nodes so that their sleep time was prolonged. With the distributed computing capability of nodes, an optimization approach of distributed genetic algorithm and simulated annealing was proposed to minimize the energy consumption of measurement. Considering the application of target tracking, we implemented target position prediction, node sleep scheduling and optimal sensing node selection. Moreover, a routing scheme of forwarding nodes was presented to achieve extra energy conservation. Experimental results of target tracking verified that energy-efficiency is enhanced by prediction-based dynamic energy management.

No MeSH data available.