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Networked Estimation with an Area-Triggered Transmission Method

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ABSTRACT

This paper is concerned with the networked estimation problem in which sensor data are transmitted over the network. In the event-driven sampling scheme known as level-crossing or send-on-delta, sensor data are transmitted to the estimator node if the difference between the current sensor value and the last transmitted one is greater than a given threshold. The event-driven sampling generally requires less transmission than the time-driven one. However, the transmission rate of the send-on-delta method becomes large when the sensor noise is large since sensor data variation becomes large due to the sensor noise. Motivated by this issue, we propose another event-driven sampling method called area-triggered in which sensor data are sent only when the integral of differences between the current sensor value and the last transmitted one is greater than a given threshold. Through theoretical analysis and simulation results, we show that in the certain cases the proposed method not only reduces data transmission rate but also improves estimation performance in comparison with the conventional event-driven method.

No MeSH data available.


Estimation error as δ = 0.9, α = 0.0303 in case 2.
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f7-sensors-08-00897: Estimation error as δ = 0.9, α = 0.0303 in case 2.

Mentions: The simulation results with different threshold values for the two cases are shown in Table 1 and Table 2, where α value is chosen according to δ value such that n (number of sensor data transmissions) is identical in the two methods. In both cases, we see that when δ is small (i.e. δ = 0.1, 0.3), estimation performance of SOD and SOA is almost the same. Rigorously speaking, SOD is slightly better than SOA. However, when δ is increasing, SOA method shows to be outperform significantly. For example as δ = 0.9, SOA outperforms SOD by the D̄1 reduction of 5 times (i.e. 0.0116 vs. 0.0536 in case 1, and 0.0017 vs. 0.0081 in case 2). As illustrated in Fig. 6 and Fig. 7, the estimation error of SOA is much smaller than that of SOD.


Networked Estimation with an Area-Triggered Transmission Method
Estimation error as δ = 0.9, α = 0.0303 in case 2.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3927498&req=5

f7-sensors-08-00897: Estimation error as δ = 0.9, α = 0.0303 in case 2.
Mentions: The simulation results with different threshold values for the two cases are shown in Table 1 and Table 2, where α value is chosen according to δ value such that n (number of sensor data transmissions) is identical in the two methods. In both cases, we see that when δ is small (i.e. δ = 0.1, 0.3), estimation performance of SOD and SOA is almost the same. Rigorously speaking, SOD is slightly better than SOA. However, when δ is increasing, SOA method shows to be outperform significantly. For example as δ = 0.9, SOA outperforms SOD by the D̄1 reduction of 5 times (i.e. 0.0116 vs. 0.0536 in case 1, and 0.0017 vs. 0.0081 in case 2). As illustrated in Fig. 6 and Fig. 7, the estimation error of SOA is much smaller than that of SOD.

View Article: PubMed Central - PubMed

ABSTRACT

This paper is concerned with the networked estimation problem in which sensor data are transmitted over the network. In the event-driven sampling scheme known as level-crossing or send-on-delta, sensor data are transmitted to the estimator node if the difference between the current sensor value and the last transmitted one is greater than a given threshold. The event-driven sampling generally requires less transmission than the time-driven one. However, the transmission rate of the send-on-delta method becomes large when the sensor noise is large since sensor data variation becomes large due to the sensor noise. Motivated by this issue, we propose another event-driven sampling method called area-triggered in which sensor data are sent only when the integral of differences between the current sensor value and the last transmitted one is greater than a given threshold. Through theoretical analysis and simulation results, we show that in the certain cases the proposed method not only reduces data transmission rate but also improves estimation performance in comparison with the conventional event-driven method.

No MeSH data available.