Future Of Real-Time: Human Sensor Networks

Future Of Real-Time: Human Sensor Networks

Passive feedback systems require minimal infrastructure and transforms people into sensory nodes with little effort on their part.

Nate Graham
  • 1 february 2011

An increasing number of personal technologies are equipped with sensors that have the capacity to collect geographically- tagged data while people simply go about their daily routines. Phones, laptops and cars equipped with sensors such as GPS receivers and accelerometers can collect, share and analyze this data in real-time. This passive feedback system requires minimal infrastructure and transforms people into sensory nodes with little effort on their part.


• Citizens become participants in data collection without having to alter normal routines.

• Passive research seamlessly integrates into daily life; reflects the actual needs and behaviors of communities.

• Increased volume, frequency and type of data enables greater efficiency; ability to tailor products and services.

• The efficiency of existing personal technology is maximized leading to a reduced need to build costly technological infrastructure.

• Value can be derived from nearly any action or activity. A walk down a street can generate meaningful data for an organization.

Supporting Examples for Human Sensor Networks

Converting Bikes into Mobile Sensing Units

The Copenhagen Wheel concept transforms ordinary bicycles into mobile sensing units that can map pollution levels, traf- fic congestion, and road conditions in real-time. As a person cycles, the wheel’s sensing unit captures their effort level and information about immediate surroundings, including: road conditions, carbon monoxide, NOx, noise, ambient temperature and relative humidity. Riders can access this data through their phone and even share the information with their community, contributing to a dynamic database of real-time environmental conditions.

Wearable Device Monitors Environmental Conditions

French technology company, Sensaris, has developed a wearable device that monitors environmental conditions for its user. Sensors detect levels of air quality, noise, and humidity, mapping these alongside accelerometer and GPS data. These devices are intended for use by groups of individuals looking to contribute to larger community- oriented applications, including city noise mapping and urban planning initiatives.

Asthma Inhaler Monitors Air Quality

The Spiroscout is a small GPS-enabled device designed by Asthmapolis that attaches to the end of people’s inhalers, automatically capturing time and location of symptoms each time an inhaler is used. The Spiroscout connects to a user’s PC through a USB, transferring information directly to the organization’s website. By aggregating this anonymous, voluntarily-shared data about asthma, Asthmapolis provides people with the latest information about asthma in their communities, and helps scientists and public health agencies to target interventions designed to reduce the burden of asthma sufferers.

Networked Personal Laptops Provide Earthquake Alerts

Participants simply download free software that runs in the background, notifying a central server when they record tremors above a 4.0 magnitude. The goal is to provide a better understanding of earthquakes, while giving early warning to schools, emergency response systems, and others.

Crowd Sourced Turn-By-Turn Navigation

Waze is a mobile application that uses data from drivers’ mobile phones to create crowd sourced maps and give turn-by-turn driving directions. The system suggests daily routes based on driving patterns and social input to provide a real-time view of traffic conditions such as road accidents, traffic jams, weather hazards and even speed trap locations.

Taxi Drivers Used To Find Fastest Driving Route

Researchers from Microsoft have been testing a new method for generating faster driving path suggestions by tapping into the expertise of local cab drivers and monitoring their GPS trajectories. While current drive-time predictions rely on the length of road and the posted speed limit, cabbies reliably select the fastest path to a destination, even if the route might look longer because it takes unexpected side streets. By ana- lyzing GPS data from 33,000 Beijing taxis over the course of 3 months, researchers were able to determine optimal routes, ultimately reducing drive times by 16%.


Add your thoughts and ideas on Twitter and other social media networks using #HumanSensorNetworks

Watch the PSFK team present the report in New York in February on Feb 10 at Open UN – Engagement in the age of real-time and on Feb 18 at PSFK SALON at Soho House NY.

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