Limits...
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.


Comparison of awakening mechanisms: (a) Event-driven on s3; (b) Awakening periodically to s1; (c) Awakening periodically to s2; (d) Prediction-based dynamic awakening.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3756720&req=5

f1-sensors-07-00251: Comparison of awakening mechanisms: (a) Event-driven on s3; (b) Awakening periodically to s1; (c) Awakening periodically to s2; (d) Prediction-based dynamic awakening.

Mentions: Event-driven on s3: As shown in Figure 1(a), when there is no target in the sensing range, the node keeps its state on s3. Once any target moves into the range, the sensing module will generate an interrupt to awaken the node to state s1. Node will go back to state s3 after sensing and transmitting.


Prediction-based Dynamic Energy Management in Wireless Sensor Networks
Comparison of awakening mechanisms: (a) Event-driven on s3; (b) Awakening periodically to s1; (c) Awakening periodically to s2; (d) Prediction-based dynamic awakening.
© Copyright Policy
Related In: Results  -  Collection

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

f1-sensors-07-00251: Comparison of awakening mechanisms: (a) Event-driven on s3; (b) Awakening periodically to s1; (c) Awakening periodically to s2; (d) Prediction-based dynamic awakening.
Mentions: Event-driven on s3: As shown in Figure 1(a), when there is no target in the sensing range, the node keeps its state on s3. Once any target moves into the range, the sensing module will generate an interrupt to awaken the node to state s1. Node will go back to state s3 after sensing and transmitting.

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.