Quality-of-information modeling and adapting for delay-sensitive sensor network applications

Mathew, Mini; Weng, Ning; Vespa, Lucas

doi:10.1109/pccc.2012.6407659

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…In this paper, we focus on sleep scheduling mechanisms; hence, emech here is the energy consumed by a sleep mechanism (emech) and (ebase) gives the energy consumed by a node if it is not switched into a sleep state. Further, we briefly explain our previously proposed quality and energy models [3] for completeness.…”

Section: Modeling Quality-of-experience and Per Packet Latencymentioning

confidence: 99%

“…But quantifying qr, such that it addresses the impact of all these different factors on it, is difficult. In our previous work [3], we presented a fundamental metric for information quality based on signal-to-noise ratio (SNR). The following equation gives the information quality of an individual sensor: where SNRir is the signal quality received by an application and SNRie represents the expected SNR.…”

Section: Modeling Quality-of-experience and Per Packet Latencymentioning

confidence: 99%

“…In particular, these works have focused on maximizing the network lifetime together with improving either the Quality-of-Service (QoS) or qualityof-information [3][4][5][6]. In recent past, however, the focus has been changing to more of a user-centric approach.…”

Section: Introductionmentioning

confidence: 99%

“…So far, the extensive research carried out in sensor networks has considered a network-centric approach to address the various challenges. In particular, these works have focused on maximizing the network lifetime together with improving either the Quality-of-Service (QoS) or quality-of-information [3–6]. In recent past, however, the focus has been changing to more of a user-centric approach.…”

Section: Introductionmentioning

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: Modeling Quality-of-experience and Per Packet Latencymentioning

confidence: 99%

Section: Modeling Quality-of-experience and Per Packet Latencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…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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