Harnessing battery recovery effect in wireless sensor networks: Experiments and analysis

Chau, Chi-Kin; Qin, Fei; Sayed, Samir G.; Wahab, Muhammad Husni; Yang, Yang

doi:10.1109/jsac.2010.100926

Cited by 75 publications

(53 citation statements)

References 13 publications

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“…In practical engineering especially networked control systems, the sensor saturation often occurs in a probabilistic way due to the random abrupt changes. For example, it has been shown in [5] that the battery recovery effect exhibited in wireless sensor networks is subject to the saturation threshold dependent on the random sensing activities, and a Markov chain model has been established to capture the random sensor saturations and further study the effectiveness of duty cycling and buffering. In [10], the problem of distributed average consensus has been investigated for sensor networks with quantized data and random link failures leading to random sensor saturations, and the quantizer parameters have then been designed based partially on the desired probability of saturation occurrence.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

H∞ filtering with randomly occurring sensor saturations and missing measurements

2012

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In this paper, the H∞ filtering problem is investigated for a class of nonlinear systems with randomly occurring incomplete information. The considered incomplete information includes both the sensor saturations and the missing measurements. A new phenomenon of sensor saturation, namely, randomly occurring sensor saturation (ROSS), is put forward in order to better reflect the reality in a networked environment such as sensor networks. A novel sensor model is then established to account for both the ROSS and missing measurement in a unified representation by using two sets of Bernoulli distributed white sequences with known conditional probabilities. Based on this sensor model, a regional H∞ filter with a certain ellipsoid constraint is designed such that the filtering error dynamics is locally mean-square asymptotically stable and the H∞-norm requirement is satisfied. Note that the regional l2 gain filtering feature is specifically developed for the random saturation nonlinearity. The characterization of the desired filter gains is derived in terms of the solution to a convex optimization problem that can be easily solved by using the semi-definite programme method. Finally, a simulation example is employed to show the effectiveness of the filtering scheme proposed in this paper.

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Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

H∞ filtering with randomly occurring sensor saturations and missing measurements

2012

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“…Chau et al [10] attempted to exploit the battery recovery effect in WSN. They empirically studied the gain at which the battery recovery effect prolongs commercial sensors lifetime.…”

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confidence: 99%

“…This effect has also been studied analytically corroborated by simulation. The outcome of [10] revealed that there is a saturation threshold at which the battery recovery resulted from idle listening will contribute less in improving the behaviour of the battery. Authors in [10] proposed a distributed battery aware duty cycle protocol and measure the battery runtime under both deterministic and randomized schedules.…”

mentioning

confidence: 99%

“…The outcome of [10] revealed that there is a saturation threshold at which the battery recovery resulted from idle listening will contribute less in improving the behaviour of the battery. Authors in [10] proposed a distributed battery aware duty cycle protocol and measure the battery runtime under both deterministic and randomized schedules. The authors in [10] studied also the trade-off between both delay and harnessing the recovery effect and suggested that we can perfectly harness battery recovery without increasing delay if we carefully adjust the sleep time period before reaching the saturation threshold.…”

mentioning

confidence: 99%

“…Authors in [10] proposed a distributed battery aware duty cycle protocol and measure the battery runtime under both deterministic and randomized schedules. The authors in [10] studied also the trade-off between both delay and harnessing the recovery effect and suggested that we can perfectly harness battery recovery without increasing delay if we carefully adjust the sleep time period before reaching the saturation threshold. The authors in [11] derived upper bounds of battery lifetime and proposed a more energy-efficient algorithm that is aware of battery recovery effect and this is by extending the pseudo-random duty cycling scheme proposed in [11] by a forced sleep.…”

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confidence: 99%

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