A crowdsensing platform for real-time monitoring and analysis of noise pollution in smart cities

Jezdović, Ivan; Popović, Snežana; Radenković, Miloš; Лабус, Александра; Bogdanović, Zorica

doi:10.1016/j.suscom.2021.100588

Cited by 17 publications

(7 citation statements)

References 35 publications

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“…Nonetheless, smartphone can be calibrated computationally out of the laboratory. For example, it can done by comparing smartphone measurements to a reference calibrated sound level meter [27] or computationally by applying a correction vector to sound level measurements based on collected database of similar device model [24,28].…”

Section: Related Workmentioning

confidence: 99%

Efficient Mobile Crowdsourcing for Environmental Noise Monitoring

Alashaikh

Al-Hazemi

2022

IEEE Access

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Governments and commissions all around the globe have worked on developing policies and rules as well as actions and measures for continuous assessment and remedies for environmental noise pollution. All of which requires continuous and accurate measurements of noise levels. The advent of smart city and IoT-based monitoring makes it possible for different technologies to cooperate in collecting and reporting environmental noise levels for longer duration and wider geographical region. Among others, mobile crowdsourcing (MCS) appears to be a promising technique for environmental noise monitoring with minimal upfront cost and almost no recurrent cost for regulators. This is due to the assumption of voluntary participation from mobile device owners who may be reluctant to participate if the recurrent cost of mobile resource consumption is high. In this work, an approach to improve mobile device resources efficiency for mobile crowdsourcing application in environmental noise monitoring is proposed. That is, noise samples from a device are collected only if it is significant to the accuracy of the measurements. Also this paper defines and mathematically formalizes the problem under study and develops two algorithms to optimize both resource and measurements efficiency. The results demonstrate the efficiency of the proposed solution. INDEX TERMS Environmental Noise monitoring, Mobile crowdsourcing (MCS), measurement samplingThis article has been accepted for publication in IEEE Access.

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Section: Related Workmentioning

confidence: 99%

Efficient Mobile Crowdsourcing for Environmental Noise Monitoring

Alashaikh

Al-Hazemi

2022

IEEE Access

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“…Therefore, attracting a large number of users to participate and incentivizing them to submit high-quality data are two crucial factors for the success of crowdsensing [2]. Numerous studies have been conducted on motivating users to participate in perception tasks to improve participation rates [3][4][5][6]. For instance, Ref.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Ref. [3] analysed urban noise pollution in real-time by encouraging numerous mobile sensing users to participate in tasks that enhance sensing data. Several studies have been conducted on data quality indicators [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

TB-HQ: An Incentive Mechanism for High-Quality Cooperation in Crowdsensing

Zhao,

Zeng,

Wang

et al. 2024

Electronics

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Crowdsensing utilizes a range of sensing resources and participants, including mobile device sensors, to achieve collaborative sensing and information fusion. This enables it to handle complex social sensing tasks and provide more intelligent and real-time environment sensing services. Incentive mechanisms in crowdsensing are employed to address issues related to insufficient user participation and low-quality data submission. However, existing mechanisms fail to adequately consider reference points in user decision-making and uncertainty in the decision-making environment. This results in high incentive costs for the platform and limited effectiveness. On the one hand, the probabilities and utilities in the actual decision environment are defined based on user preferences, and uncertainty can lead to unpredictable impacts on users’ future gains or losses. On the other hand, users identify their choices based on certain known values, namely reference points. The factors influencing user decisions are not solely the absolute final result level but rather the relative changes or differences between the final result and the reference point. Therefore, to resolve this problem, we propose TB-HQ, an incentive mechanism for high-quality cooperation in crowdsensing, which simultaneously considers the reference points adopted by users in decision-making and the uncertainty caused by their preferences. This mechanism includes a task bonus-based incentive mechanism (TBIM) and a high quality-driven winner screening mechanism (HQWSM). TBIM motivates users to participate in tasks by offering task bonuses, which alter their reference points. HQWSM enhances data quality by reconstructing utility functions based on user preferences. Simulation results indicate that the proposed incentive mechanism is more effective in improving data quality and platform utility than the comparative incentive mechanisms, with a 32.7% increase in data quality and a 77.3% increase in platform utility.

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“…The inherent mobility nature of people has empowered and inspired the people to take part in ubiquitous sensing, and the rich sensing capabilities of sensor-enhanced devices make pervasive computing possible, which stimulates the emergence and promotes the development of an appealing paradigm named Mobile CrowdSensing (MCS) [4]. MCS enables and inspires a vast number of people to sense and contribute data; therefore, it has become a convenient method for many Internet of Things (IoT) applications, such as smart cities [5][6][7], environmental monitoring [8][9][10][11][12], smart transportation [13,14] and intelligent medicine [15,16], which improves work efficiency and quality of our life.…”

Section: Introductionmentioning

confidence: 99%

A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing

Liu,

Li,

Cheng

et al. 2023

Applied Sciences

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Mobile CrowdSensing (MCS) has become a convenient method for many Internet of Things (IoT) applications in urban scenarios due to the full utilization of the mobility of people and the powerful capabilities of their intelligent devices. Nowadays, edge computing has been introduced into MCS to reduce the time delays and computational complexity in cloud platforms. To improve task completion and coverage rates, how to design a reasonable user recruitment algorithm to find suitable users and take full advantage of edge nodes has raised huge challenges for Mobile CrowdSensing. In this study, we propose a Reputation-based Collaborative User Recruitment algorithm (RCUR) under a certain budget in an edge-aided Mobile CrowdSensing system. We first introduce edge computing into MCS and build an edge-aided MCS system in urban scenarios. Moreover, we analyze the influence of user reputation on user recruitment. Then we establish a user reputation module to deduce the user reputation equation by combining the user’s past reputation score with an instantaneous reputation score. Finally, we utilize the sensing ability of edge nodes and design a collaborative sensing method. We use the greedy method to help choose the appropriate users for the tasks. Simulation results compared with the other three algorithms prove that our RCUR approach can significantly achieve better performance in task completion rate and task coverage rate.

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A crowdsensing platform for real-time monitoring and analysis of noise pollution in smart cities

Cited by 17 publications

References 35 publications

Efficient Mobile Crowdsourcing for Environmental Noise Monitoring

Efficient Mobile Crowdsourcing for Environmental Noise Monitoring

TB-HQ: An Incentive Mechanism for High-Quality Cooperation in Crowdsensing

A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing

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