Information quality model and optimization for 802.15.4-based wireless sensor networks

Weng, Ning; Li, I-Hung; Vespa, Lucas

doi:10.1016/j.jnca.2010.12.013

Cited by 13 publications

(7 citation statements)

References 20 publications

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“…Here, we have assumed a quality requirement of 0.5. The measured SNR for a corresponding sampling rate is obtained from [14]. From Figure 4(a), we can see that at smaller rates (e.g., 30 Hz) the quality obtained is low, because the measurement SNR [14] at these rates are lower than the expected (27 dB) value.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The measured SNR for a corresponding sampling rate is obtained from [14]. From Figure 4(a), we can see that at smaller rates (e.g., 30 Hz) the quality obtained is low, because the measurement SNR [14] at these rates are lower than the expected (27 dB) value. As the sampling rate increases, the quality starts improving, but energy consumption also increases, as shown in Figure 4(b).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Therefore, the impact on QoI if this packet is lost is not considered. = quality management( , , V, SNR ); (5) conflict resolver( ); (6) assign updated to node ; (7) end (8) end (9) quality management ( , , V, SNR ) (10) begin (11) calculate the initial via (4); (12) while | − | > V do (13) if − > V then (14) //improving energy efficiency (15) repeat (16) = + ; (17) recalculate via (4) There is processing overhead involved when performing on-line scheduling. However, this overhead is not significant in our case due to several reasons.…”

Section: Adaptive Sleep Scheduling Algorithmmentioning

confidence: 99%

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Adaptive Sensor Node Sleep Scheduling for Quality-of-Experience Enhancement

Mathew

Weng

2016

International Journal of Distributed Sensor Networks

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Focusing on a user's quality-of-experience (QoE) has become important, because of the growing space of sensor-dependent applications and low-cost sensor design. QoE is typically affected by two quantities: the quality-of-information (QoI) received and the lifetime-of-service. Therefore, QoE is defined as a sensor network's ability to consistently offer assured QoI for an expected lifetime when operating on a limited energy resource, such as a battery. However, dynamic factors, such as varying user requirements, unpredictable sensor environment, unreliable network conditions, and limited energy resource, affecting both QoI and lifetime-of-service, make it challenging to achieve a good QoE. In our previous work, we presented a SNR-based QoI metric which addresses the impact of several of these factors on QoI. In this paper, we design a QoE metric that quantifies the relationship between energy conservation, QoI received by a user, and an application's quality expectation. Further, we develop an adaptive sleep schedule mechanism to demonstrate the usefulness of this metric. Finally, simulation results presented show the effectiveness of our mechanism in achieving QoE improvement.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Adaptive Sleep Scheduling Algorithmmentioning

confidence: 99%

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Adaptive Sensor Node Sleep Scheduling for Quality-of-Experience Enhancement

Mathew

Weng

2016

International Journal of Distributed Sensor Networks

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“…Supposing that each channel is independent, the busy/idle state of the channel can be modeled as the Markov random process with ON/OFF type [18]. Therefore to the channel j for j ∈ [1, L ] the persistent time of ON/OFF state obeys the exponential distribution with mean u j / v j , which is given by:

{\begin{matrix} f_{B_{j}} (t) = u_{j} e^{- u_{j} t}, & O N \\ f_{I_{j}} (t) = v_{j} e^{- v_{j} t}, & OFF \end{matrix}

where u j and v j are the transition rates from busy to idle and idle to busy, respectively.…”

Section: Cooperative Spectrum Sensingmentioning

confidence: 99%

Optimal Periodic Cooperative Spectrum Sensing Based on Weight Fusion in Cognitive Radio Networks

Liu

Jia

et al. 2013

Sensors

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The performance of cooperative spectrum sensing in cognitive radio (CR) networks depends on the sensing mode, the sensing time and the number of cooperative users. In order to improve the sensing performance and reduce the interference to the primary user (PU), a periodic cooperative spectrum sensing model based on weight fusion is proposed in this paper. Moreover, the sensing period, the sensing time and the searching time are optimized, respectively. Firstly the sensing period is optimized to improve the spectrum utilization and reduce the interference, then the joint optimization algorithm of the local sensing time and the number of cooperative users, is proposed to obtain the optimal sensing time for improving the throughput of the cognitive radio user (CRU) during each period, and finally the water-filling principle is applied to optimize the searching time in order to make the CRU find an idle channel within the shortest time. The simulation results show that compared with the previous algorithms, the optimal sensing period can improve the spectrum utilization of the CRU and decrease the interference to the PU significantly, the optimal sensing time can make the CRU achieve the largest throughput, and the optimal searching time can make the CRU find an idle channel with the least time.

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“…Mathew et al proposed a SNR based metric which incorporates packet loss and network delay and covers accuracy and timeliness for QoI modeling in delay-sensitive sensor network applications. They analyzed and demonstrated the impact of network delay and application deadlines [12] apart from sensor sampling rate and loss rate [10, 11] on the QoI. Bahjat et al [13] specifically considered accuracy and timeliness and addressed the quality measure trade-offs in constrained communication networks for image applications.…”

Section: Introductionmentioning

confidence: 99%

A Flexible Framework for Assessing the Quality of Information in Wireless Sensor Networks

Guo¹,

Pan²,

Huang³

et al. 2015

International Journal of Distributed Sensor Networks

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As wireless sensor networks (WSNs) often provide incorrect and outdated information about the events in a monitored environment, quality of information (QoI) assessment is invaluable for users to manage and use the information in particular applications. In this paper, we propose a flexible framework to dynamically assess the QoI in different WSN applications, with focus on accuracy and timeliness. Our framework is constructed on the infrastructure of an information aggregation procedure under some assumptions about the network. Based on information fusion theory, two processing models are adopted to assess the accuracy of low-level measurement data and high-level decision information without the need for Ground Truth (GT). Meanwhile, our framework generally exploits two respective models according to the specific category of the information timeliness in different delay-sensitive applications. To quantify the timeliness, we utilize a practical measurement method by means of timestamp to determine the information acquisition time. The framework is evaluated by simulations, including accuracy assessment in two environmental monitoring application scenarios, and timeliness assessment in two delay-sensitive application scenarios. The simulation results show that our framework is effective and flexible for quantitative assessment of the QoI in different WSN applications.

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Information quality model and optimization for 802.15.4-based wireless sensor networks

Cited by 13 publications

References 20 publications

Adaptive Sensor Node Sleep Scheduling for Quality-of-Experience Enhancement

Adaptive Sensor Node Sleep Scheduling for Quality-of-Experience Enhancement

Optimal Periodic Cooperative Spectrum Sensing Based on Weight Fusion in Cognitive Radio Networks

A Flexible Framework for Assessing the Quality of Information in Wireless Sensor Networks

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