Mobile Crowd Sensing for Internet of Things: A Credible Crowdsourcing Model in Mobile-Sense Service

Zhang

Second International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2023)

et al. 2023

An embedded real-time environmental noise monitoring device is designed for long-term monitoring of environmental noise and noise information processing. The STM32F407 is used as the processing core of the device. A microphone signal conditioning circuit is designed for signal amplification. The ADC embedded in the STM32 is used for signal acquisition. Simultaneously, a BeiDou GNSS module and a 5G module are integrated on the device, and these modules allow the device to upload noise location information, time information, and noise monitoring calculation results to a cloud server. The designed acquisition circuit's sensitivity is calibrated with a reference sound source. The findings of the tests indicate that the noise monitoring device that is designed has the capability to gather and analyse noise data in real-time while also satisfying the practical demands of environmental noise monitoring.

“…In order to determine the sensitivity of the acquisition circuit, the Root Mean Square (RMS) of the samples is calculated based on Equation (3).…”

Section: The Sensitivity and The Thd Of The Acquisition Circuit Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The design of embedded environmental noise real-time monitoring device

Zhang

Second International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2023)

et al. 2023

“…So Just by getting the data from one device is not sufficient for accurately analyzing the daily routine and human behaviour. Reference [9] tells us about the work that has been done with various aspects of mobile crowd sensing to predict human behaviour and their routine habits.…”

Section: Literature Surveymentioning

confidence: 99%

Field Worker’s Routine Behaviour for Efficient Time Utilization

2019

IJITEE

In this paper, we are proposing a monitoring technique that can predict the appropriate time to meet the field officer with more accuracy as compared to general human prediction. To get the routine behaviour of an individual we will keep a track of his online presence on WhatsApp by the “Active Now” status shown beneath the user’s name in the profile. This will help the Personal assistants, secretaries and receptionists to provide right appointment schedule to clients or to those who want to meet a field officer. This all will be done by scraping data from WhatsApp web portal using python modules, storing the judging parameters like unique identification number, date and time stamp, duration of continuous presence and number of sessions in a day, afterwards analyzing the dataset and providing an appropriate routine behaviour prediction. For the sake of efficient utilization of time and resources.

2017 ITU Kaleidoscope: Challenges for a Data-Driven Society (ITU K)

“…In this section, we propose a possible implementation scenario of our findings based on air pollution crowd sensing use case, aimed at collecting and monitoring pollution data. The air pollution sensing requires active citizen participation by carrying wearable sensors as they traverse the city based on opportunistic crowd sensing application [35]. However, monitoring such air pollution via crowd sensing requires that the data being provided are trustworthy and can be relied upon by city authority or government to make an immediate decision.…”

Section: Implementation Modelmentioning

confidence: 99%

Data centric trust evaluation and prediction framework for IOT

Jayasinghe

Otebolaku

et al. 2017

Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas.