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Prediction-based Dynamic Energy Management in Wireless Sensor Networks

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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.

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Target trajectory in the sensing field of WSN.
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f5-sensors-07-00251: Target trajectory in the sensing field of WSN.

Mentions: Assume that the sensing field of WSN is 400 m x 400 m, in which there are 256 wireless sensor nodes equipped by peroelectric infra-red (PIR) sensors with sensing range rsensing = 60 m and root mean square (RMS) of DF error variance σΘ = 20. We set α1 = 50nJ /b □ α2 =100pJ /b/m2 and n0 = 3 in the energy consumption model. The sensing period is set as 0.5 s, and then the sample period of PF is 0.5 s accordingly. The performing time for node selection optimization is set as 0.15s for all the following simulations. According to Section 4.1, we generate a trajectory of 120 points as shown in Figure 5, in some part of which the target moves on its maximum acceleration amax and maximum velocity vmax for generalization. Here, amax = 10m/s2, vmax = 40m/s, and sensing error threashold A0 = 0.6 m.


Prediction-based Dynamic Energy Management in Wireless Sensor Networks
Target trajectory in the sensing field of WSN.
© Copyright Policy
Related In: Results  -  Collection

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

f5-sensors-07-00251: Target trajectory in the sensing field of WSN.
Mentions: Assume that the sensing field of WSN is 400 m x 400 m, in which there are 256 wireless sensor nodes equipped by peroelectric infra-red (PIR) sensors with sensing range rsensing = 60 m and root mean square (RMS) of DF error variance σΘ = 20. We set α1 = 50nJ /b □ α2 =100pJ /b/m2 and n0 = 3 in the energy consumption model. The sensing period is set as 0.5 s, and then the sample period of PF is 0.5 s accordingly. The performing time for node selection optimization is set as 0.15s for all the following simulations. According to Section 4.1, we generate a trajectory of 120 points as shown in Figure 5, in some part of which the target moves on its maximum acceleration amax and maximum velocity vmax for generalization. Here, amax = 10m/s2, vmax = 40m/s, and sensing error threashold A0 = 0.6 m.

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.