Introduction and definition

Participatory sensing refers to the usage of sensors, usually embedded in personal devices such as smartphones to allow citizens to feed data of public interest. This could include anything from photos to passive monitoring of movement in the traffic. Participatory sensing involves higher commitment from citizens, contrary to opportunistic sensing where user may not be aware of active applications.

The diffusion of mobile phones significantly lowers the barriers of participation and data input by citizens, with automated geo-tagging and time-stamping: given the right architecture, they could act as sensor nodes and location-aware data collection instruments. While traditional sensor nodes are centralised, these sensors are under the owners’ control. This would give way to data availability at an unprecedented scale.

Why it matters in governance

Participatory sensing radically improves the data availability for evaluating the effect of public policies and how individual behaviour is changing, provided adequate privacy provisions are in place. Devices should assure enhanced users’ control over data, i.e. which data is being sent, when and how it is treated, as well as possibility for enhanced data anonymisation.

Furthermore, design of participatory sensing should be placed in the framework of policy contexts, allowing inference of policy impact from data. Future platforms should combine participatory sensing, mass moderation, personalised feedback and social network analysis to assess the interplay between perception, data and social interaction.

Participatory sensing is already used in “public sphere” activities such as environment and health. However the specific issue of evaluating public policies has been so far little researched, with particular regard to the implications for privacy, large-scale deployment and bias management on citizens sensing.

Recent trends

Small-scale experiments are being carried out in different domains, mainly dealing with environmental and health data. Applications in the field of urban planning are particularly promising, yet there is no structure link between participatory sensing and policy models. Larger scale deployment would require more granular privacy compliance and user-control, adequate incentives to participation and deriving business models. There is no formalisation of the requirements and the design of opportunistic versus participatory sensing, including sampling design for participants recruitment.

Advantage of Application in Public Policy

Participatory sensing can be used to gather and collect the following kinds of information:

1. Civic data: neighborhood maintenance issues, power outage documentation
2. Environmental data: data providing hints on pollution levels, climate-change related data
3. Transportation: commutation habits, location and movement data, condition of the road, connections to public transportation, incidence of traffic, accidents occurrence
4. Health: vital signs, info providing early warnings of diminishing health, info on epidemic spread, self-administered diagnostic tests

The advantages for policy making are:

1. Possibility to collect data at an otherwise unachievable scale and geographic range
2. Virtually costless data collection
3. Reveal and highlight behavioural patterns and routines which can be accordingly changed
4. Engage common citizens in sensitive issues
5. More pervasive monitoring capacity in fields such as environment and health

Inspiring cases of policy making related applications

1. PEIR3 (Personal Environmental Impact Report), which is a system allowing users to determine their exposure to environmental pollution by using a sensor in their mobile phone able to determine the location and the mean of transport
2. eHealthSense4, automatically detects health related events which are not directly observed by current sensor technology, like pain, tow conditions, depression
3. SenSay5, which is able to alert the medical staff when the user falls or in case of suspect behaviour
4. MobAsthma6, which monitors the exposure to pollution affecting asthma. Both the volume of air inhaled and the pollution rate are collected by sensors interfaced to the mobile phone
5. Haze Watch7, in which the concentration of carbon monoxide, ozone, sulphur dioxide, and nitrogen dioxide is measured by embedding pollution sensors in mobile phones
6. NoiseTube8, which registers noise levels used to monitor noise pollution, which can affect human hearing and behaviour
7. EpySurveyor9, used by the Red Cross to evaluate anti-malarial bednet distribution and use throughout sub-Saharan Africa, as well as the coverage achieved by vaccination campaigns
8. CarTel10, which is a mobile sensing and computing system making use of mobile phones carried in vehicles to collect information about traffic or WIFI access points
9. NoiseSpy11, which is a sound-sensing system able to log data for monitoring environmental noise. Users can explore a city area while at the same time visualize noise levels in real time

Key challenges and gaps

1. Preserve the privacy of the users which are required to provide extremely personal data
2. Create new mobile device interfaces which are engaging and efficient and can be used
3. Ensure security, as the current and past citizen’s position might be spotted
4. Provide new sensors capable of increasing the range of information that individuals can track and use
5. Create network infrastructures aimed at supporting participatory sensing services
6. Provide incentive for participation to data collection
7. Develop analytical techniques to carry out more accurate inference with mobile phone supplied data such as geo-data and images
8. Develop visual analytics and data analysis techniques which provide relevant and easy to interpret information for the general public
9. Create engaging and efficient mobile device interfaces to support effective, real-time user interaction
10. Provide quality data, temporal and geo-graphical availability, and ability to cover the phenomena

Current research

1. Aggregating and validating citizens generated and government data resource discovery,
2. Selective sharing, and context verification mechanisms, as well as application-level support for data gathering campaigns,
3. Incentives for participatory sensing,
4. Evaluation of human agents as sensors

Disciplines of research: sensor networks, location services; psychology, economics of participation; privacy

Research instruments: testbeds and living labs, STREPs

Future research: long term and short term issues

Short-term research

1. Sensing coverage, sensor calibration and sensor context for opportunistic sensing.
2. Quality verification for participatory sensing
3. Privacy-compliant sensing and sharing
4. Business models for participatory sensing
5. Intelligently recruiting collaborators and deploying data collection protocols.
6. Anonymous, transparent use of human-carried sensing devices
7. Evaluating behavioural change through participatory sensing

Long-term research

1. Enhanced analytical techniques to make more accurate inferences from mobile phone-supplied data such as location and images and to automatically detect and respond to subtle events;
2. New personal-scale sensors to expand the range of information that individuals can track and use
3. Privacy by design in participatory sensing