Dataset Information

Improving Signal-Strength Aggregation for Mobile Crowdsourcing Scenarios.

ABSTRACT: Due to its huge impact on the overall quality of service (QoS) of wireless networks, both academic and industrial research have actively focused on analyzing the received signal strength in areas of particular interest. In this paper, we propose the improvement of signal-strength aggregation with a special focus on Mobile Crowdsourcing scenarios by avoiding common issues related to the mishandling of log-scaled signal values, and by the proposal of a novel aggregation method based on interpolation. Our paper presents two clear contributions. First, we discuss the misuse of log-scaled signal-strength values, which is a persistent problem within the mobile computing community. We present the physical and mathematical formalities on how signal-strength values must be handled in a scientific environment. Second, we present a solution to the difficulties of aggregating signal strength in Mobile Crowdsourcing scenarios, as a low number of measurements and nonuniformity in spatial distribution. Our proposed method obtained consistently lower Root Mean Squared Error (RMSE) values than other commonly used methods at estimating the expected value of signal strength over an area. Both contributions of this paper are important for several recent pieces of research that characterize signal strength for an area of interest.

SUBMITTER: Madariaga D

PROVIDER: S-EPMC7914496 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Improving Signal-Strength Aggregation for Mobile Crowdsourcing Scenarios.

Madariaga Diego D Madariaga Javier J Bustos-Jiménez Javier J Bustos Benjamin B

Sensors (Basel, Switzerland) 20210205 4

Due to its huge impact on the overall quality of service (QoS) of wireless networks, both academic and industrial research have actively focused on analyzing the received signal strength in areas of particular interest. In this paper, we propose the improvement of signal-strength aggregation with a special focus on Mobile Crowdsourcing scenarios by avoiding common issues related to the mishandling of log-scaled signal values, and by the proposal of a novel aggregation method based on interpolati ...[more]

PMID: 33562430

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Project description:This work investigates how different forms of input elicitation obtained from crowdsourcing can be utilized to improve the quality of inferred labels for image classification tasks, where an image must be labeled as either positive or negative depending on the presence/absence of a specified object. Five types of input elicitation methods are tested: binary classification (positive or negative); the (x, y)-coordinate of the position participants believe a target object is located; level of confidence in binary response (on a scale from 0 to 100%); what participants believe the majority of the other participants' binary classification is; and participant's perceived difficulty level of the task (on a discrete scale). We design two crowdsourcing studies to test the performance of a variety of input elicitation methods and utilize data from over 300 participants. Various existing voting and machine learning (ML) methods are applied to make the best use of these inputs. In an effort to assess their performance on classification tasks of varying difficulty, a systematic synthetic image generation process is developed. Each generated image combines items from the MPEG-7 Core Experiment CE-Shape-1 Test Set into a single image using multiple parameters (e.g., density, transparency, etc.) and may or may not contain a target object. The difficulty of these images is validated by the performance of an automated image classification method. Experiment results suggest that more accurate results can be achieved with smaller training datasets when both the crowdsourced binary classification labels and the average of the self-reported confidence values in these labels are used as features for the ML classifiers. Moreover, when a relatively larger properly annotated dataset is available, in some cases augmenting these ML algorithms with the results (i.e., probability of outcome) from an automated classifier can achieve even higher performance than what can be obtained by using any one of the individual classifiers. Lastly, supplementary analysis of the collected data demonstrates that other performance metrics of interest, namely reduced false-negative rates, can be prioritized through special modifications of the proposed aggregation methods.

Dataset Information

Improving Signal-Strength Aggregation for Mobile Crowdsourcing Scenarios.

Publications

Improving Signal-Strength Aggregation for Mobile Crowdsourcing Scenarios.

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