A recommendation of the use of mobile crowdsensing to the address the issue of traffic congestion in

But do we have the political will?

A recommendation of the use of mobile crowdsensing to the address the issue of traffic congestion in

An SNSF-funded project devoted to crowdsensing has found ways to improve privacy and localisation accuracy as well as reduce the impact on hardware. Page Content Connecting data from the world's smartphones could put a global supercomputer into all of our pockets. Tapping into that processing power would improve the real-time collection and analysis of data, but technical hurdles and privacy concerns linger.

The main focus of the project is crowdsensing, in which access to a smartphone's sensors makes it possible to collect information about a particular area.

A typical example are map applications which can infer traffic congestion data from the smartphones' accelerometers.

Smarter use of mobile data - SNF

As our connected devices gather insights about many facets of our environment — motion, sound, people, air quality, etc. Nonetheless, crowdsensing applications face significant challenges. In particular, there is a trade-off between data collection, user impact and privacy.

Transmitting data drains hardware resources, for example, while poor security measures pose risks for identity theft.

Four teams developed new approaches to improve crowdsensing technology and establish best practices for its application. Researchers are exploring four key areas: That is comparable to GPS, but relies only on the device's sensor data and radio signals, reaching areas behind walls and concrete where GPS signals are blocked.

The researchers collect sensor measurements from the smartphones, alongside the Wifi radio's signal strength. This information is then passed through several machine learning algorithms. The resulting mobile app integrates sophisticated localisation algorithms and location-stamped sensor measurements, which are pushed to the cloud.

From there, the information is fed to the Internet of Things, allowing personalised and location-based automation applications across a number of smart objects and products. The experiments showed that they could create rapid outreach on social networks such as Facebook and Twitter, but also in ad hoc physical networks of mobile devices.

These messages could respond to local behaviours, assess feedback in real time and circulate more quickly among targeted users.

A recommendation of the use of mobile crowdsensing to the address the issue of traffic congestion in

The research provides a deeper understanding of social influence in human behaviour, and discovered correlations between physical locations, shared preferences and event-based social communities. A balancing act "A major problem for researchers is balancing data and privacy," explains Braun.

To ensure security, the Chalmers University of Technology team in Sweden has developed machine learning methods for data analysis and automatic decision making that achieve "differential privacy".

This protects the data of individuals by injecting carefully calibrated "noise" random data into information collected from a device. Researchers at the University of Geneva addressed another challenge: If users fear a strain on their phone, they might reject applications which make use of otherwise idle sensors.

This project is investigating game theory models for distributing such burdens among phones and users. In a field experiment, volunteers in San Francisco downloaded apps to map noise levels in the city, collecting useful data for the local government while testing competing methods for distributing loads among devices.

With its interdisciplinary approach, the SwissSenseSynergy project has yielded new techniques with potential benefits for research and applications. The project is developing a novel experimentation architecture, called Vivo, to involve volunteers in the experimental phase to support application development.

The SwissSenseSynergy project The project gathers four partners:Smarter use of mobile data. 12/Jun/ A typical example are map applications which can infer traffic congestion data from the smartphones' accelerometers.

As our connected devices gather insights about many facets of our environment – motion, sound, people, air quality, etc.

– crowdsensing has the potential to guide decisions on where.

Crowdsensing and Vehicle-Based Sensing

A Recommendation of the Use of Mobile Crowdsensing to the Address the Issue of Traffic Congestion in Campuses. 1, words. 3 pages. Cellphones: A Catalyst of Change in Modern Society.

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1, words. The Impact of Cellphone Use in Society. words. 1 page. Infrastructure crowdsensing is used for measuring the public infrastructure (e.g., traffic congestion and road conditions).

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The social crowdsensing is used for measuring data about the social life of individuals (e.g., the cinemas visited by an individual). Table 1 shows the typology of crowdsensing . The issue of traffic congestion around large campuses is becoming chronic and it needs to be immediately addressed.

In a nutshell, the issue is facing many congested area and which calls for a long lasting solution to be sought.

A Recommendation of the Use of Mobile Crowdsensing to the Address the Issue of Traffic Congestion in Campuses. Need writing cause of traffic deaths essay? Use our custom writing services or get access to database of free essays samples about cause of traffic deaths.

Crowdsensing and Vehicle-Based Sensing

Signup now and have "A+" grades! Fighting Traffic Congestion with Information Technology. We now have the technical means to “solve” congestion.

But do we have the political will? Traffic congestion is a vexing problem felt by residents of most urban areas.

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