Ensuring energy efficient coverage for participatory sensing in urban streets

Khan, Adnan; Imon, Sk Kajal Arefin; Das, Sajal K.

doi:10.1109/smartcomp.2014.7043855

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…STREET is a DCF that investigates the problem of the spatial coverage, classifying it between partial coverage, full coverage or k-coverage. It aims to improve the task coverage in an energy-efficient way by requiring updates only when needed [185]. Another approach to investigate the coverage of a task is to estimate the optimal number of citizens needed to meet the sensing task demands in a region of interest over a certain period of time [186].…”

Section: B Data Collection Frameworkmentioning

confidence: 99%

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

Capponi

Fiandrino

Kantarcı

et al. 2019

IEEE Commun. Surv. Tutorials

456

176

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Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas.

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Section: B Data Collection Frameworkmentioning

confidence: 99%

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

Capponi

Fiandrino

Kantarcı

et al. 2019

IEEE Commun. Surv. Tutorials

456

176

View full text Add to dashboard Cite

show abstract

“…erefore, it no longer wants to perceive the IoT operation from the obtained signature. It simply assesses if the received power signature is in the ordinary power intake variety of the comparable IoT operation [28].…”

Section: Proposed Systemmentioning

confidence: 99%

IoT-Based Harmful Toxic Gases Monitoring and Fault Detection on the Sensor Dataset Using Deep Learning Techniques

Praveenchandar

Vetrithangam

Kaliappan³

et al. 2022

Scientific Programming

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One of the main reasons for accidents among workers is harmful gas leakage. Many people die in chemical industries and their surrounding areas. The present invention is responsible for monitoring and controlling hazardous toxic gases like nitrogen dioxide (NO2), carbon monoxide, ozone (O3), sulfur dioxide (SO2), LPG, hydrocarbon gases, silicones, hydrocarbons, alcohol, CH4, hexane, benzine, as well as environmental conditions, such as temperature and relative humidity to prevent industrial accidents. The Arduino UNO R3 board is used as the central microcontroller. It is connected to the Cloud via AQ3 sensor, Minipid 2 HS PID sensor, IR5500 open path infrared gas detector, DHT11 Temperature and Humidity Sensor, MQ3 sensor, and ESP8266 and WIFI Module, which can store real-time sensor data and send alert messages to the industry’s safety control board. Machine learning and artificial intelligence will be used to make an intelligent prediction (AI). The information gathered will be examined in real-time. The real-time data provided through the sensor can be accessed worldwide. Sensor data quality is critical in the Internet of Things (IoT) applications because poor data quality renders them useless. Error detection in sensor data improves the IoT-based toxic gas monitoring, controlling, and prediction system. Live data from sensors or datasets should be analyzed properly using appropriate techniques. Hence, hybrid hidden Markov and artificial intelligence models are applied as an error detection technique in the sensor dataset. This technique outperformed the dataset gas sensor array under dynamic gas mixtures and lived data. Our method outperformed harmful gas monitoring and error detection in sensor datasets compared to other existing technologies. The hybrid HMM and ANN fault detection methods performed well on the datasets and produced 0.01% false positive rate.

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“…Our framework is based on the concept of minimizing server influence in the data collection process [27,47]. It is somewhat close to the first category of applications, as the server does not receive any location update from the participating mobile devices.…”

Section: Real-time Mobile Crowdsourcingmentioning

confidence: 99%

A novel localization and coverage framework for real-time participatory urban monitoring

Khan

Imon

Das

2015

Pervasive and Mobile Computing

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Ensuring energy efficient coverage for participatory sensing in urban streets

Cited by 10 publications

References 27 publications

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

IoT-Based Harmful Toxic Gases Monitoring and Fault Detection on the Sensor Dataset Using Deep Learning Techniques

A novel localization and coverage framework for real-time participatory urban monitoring

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