QoI-aware energy-efficient participant selection

Song, Zheng; Zhang, B.; Liu, Chi Harold; Vasilakos, Athanasios V.; Ma, Jian; Wang, Wendong

doi:10.1109/sahcn.2014.6990360

Cited by 36 publications

(20 citation statements)

References 24 publications

(35 reference statements)

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“…Section 7 extensively evaluates the performance of the proposed strategy by real-trace-driven simulations, and finally, Section 8 concludes the article. This article largely extends Song et al [2014], by proposing the event boundary detection framework (see Section 4), and giving completely new experimental results (see Section 7).…”

Section: Introductionmentioning

confidence: 88%

“…Theorem 1 has been proved in Song et al [2014]. Based on Theorem 1, when a participant's initial location and sensing capabilities are registered to the central server, his/her expected amount of collected data for all tasks can be calculated.…”

Section: Theorem 1 Given the Position Matrix P( T) A Participant M'mentioning

confidence: 99%

“…In each simulation, we randomly generate some different parameter sets, and then analyze the results of the total number of selected candidate participants, and QoI for all four approaches. The results of the energy consumption index and the collected amount of data can be found in Song et al [2014].…”

Section: Event Boundary Detectionmentioning

confidence: 99%

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An Event-Driven QoI-Aware Participatory Sensing Framework with Energy and Budget Constraints

Zhang

Song

Liu

et al. 2015

ACM Trans. Intell. Syst. Technol.

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Participatory sensing systems can be used for concurrent event monitoring applications, like noise levels, fire, and pollutant concentrations. However, they are facing new challenges as to how to accurately detect the exact boundaries of these events, and further, to select the most appropriate participants to collect the sensing data. On the one hand, participants' handheld smart devices are constrained with different energy conditions and sensing capabilities, and they move around with uncontrollable mobility patterns in their daily life. On the other hand, these sensing tasks are within time-varying quality-of-information (QoI) requirements and budget to afford the users' incentive expectations. Toward this end, this article proposes an event-driven QoI-aware participatory sensing framework with energy and budget constraints. The main method of this framework is event boundary detection. For the former, a two-step heuristic solution is proposed where the coarse-grained detection step finds its approximation and the fine-grained detection step identifies the exact location. Participants are selected by explicitly considering their mobility pattern, required QoI of multiple tasks, and users' incentive requirements, under the constraint of an aggregated task budget. Extensive experimental results, based on a real trace in Beijing, show the effectiveness and robustness of our approach, while comparing with existing schemes. . 2015. An event-driven QoI-aware participatory sensing framework with energy and budget constraints.

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Section: Introductionmentioning

confidence: 88%

Section: Theorem 1 Given the Position Matrix P( T) A Participant M'mentioning

confidence: 99%

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An Event-Driven QoI-Aware Participatory Sensing Framework with Energy and Budget Constraints

Zhang

Song

Liu

et al. 2015

ACM Trans. Intell. Syst. Technol.

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“…END FOR (15) END FOR (16) = effect/total. (17) / * clear the parameters for calculating next weight.…”

Section: The Algorithmmentioning

confidence: 99%

A Context-Driven Worker Selection Framework for Crowd-Sensing

Wang

Helal

et al. 2016

International Journal of Distributed Sensor Networks

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Worker selection for many crowd-sensing tasks must consider various complex contexts to ensure high quality of data. Existing platforms and frameworks take only specific contexts into account to demonstrate motivating scenarios but do not provide general context models or frameworks in support of crowd-sensing at large. This paper proposes a novel worker selection framework, named WSelector, to more precisely select appropriate workers by taking various contexts into account. To achieve this goal, it first provides programming time support to help task creator define constraints. Then its runtime system adopts a two-phase process to select workers who are not only qualified but also more likely to undertake a crowd-sensing task. In the first phase, it selects workers who satisfy predefined constraints. In the second phase, by leveraging the worker's past participation history, it further selects those who are more likely to undertake a crowd-sensing task based on a case-based reasoning algorithm. We demonstrate the expressiveness of the framework by implementing multiple crowd-sensing tasks and evaluate the effectiveness of the case-based reasoning algorithm for willingness-based selection by using a questionnaire-generated dataset. Results show that our case-based reasoning algorithm outperforms the currently practiced baseline method.

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“…Eq. (16) indicates that the energy consumption of WiFi communication is divided into three different levels. Eq.…”

Section: Problem Formulationmentioning

confidence: 99%

Energy-Efficient Collaborative Localization for Participatory Sensing System

Xi¹,

Wang²,

Ngai³

et al. 2014

2015 IEEE Global Communications Conference (GLOBECOM)

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Location based services are getting increasingly popular in participatory sensing systems. They make use of location information on the mobile devices to support applications that improve personal health, object search, and entertainment. However, GPS positioning consumes a lot of energy, which can drain a mobile device's battery. Although WiFi localization and cell tower localization have been suggested as alternatives, they have lower localization accuracy and limited coverage. In this paper, we suggest a novel solution for multiple mobile devices to perform collaborative localization to reduce energy consumption and provide accurate localization. We divide the mobile devices into two groups, the aggregator group and the collector group. The aggregator group turns on their GPS periodically, while the collector group uses the locations of the aggregators to estimate their own locations. We formulate the aggregator set selection problem and propose two novel algorithms to minimize the energy consumption in collaborative localization. Simulations with real traces showed that our proposed solution can save up to 88% of the energy of the entire network.

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QoI-aware energy-efficient participant selection

Cited by 36 publications

References 24 publications

An Event-Driven QoI-Aware Participatory Sensing Framework with Energy and Budget Constraints

An Event-Driven QoI-Aware Participatory Sensing Framework with Energy and Budget Constraints

A Context-Driven Worker Selection Framework for Crowd-Sensing

Energy-Efficient Collaborative Localization for Participatory Sensing System

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