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Two things drive the building and operation of smart cities: gathering data from the environment and allowing people to use that data in a meaningful way—sometimes to change behavior.

While making strange bedfellows of computer scientists and psychologists, the “trans-theoretical model of behavior change” is proving to be key in building smart environments.

“The model has been developed and applied primarily within the field of healthcare, for example, in exercise and addiction treatment,” say researchers from the National University of Ireland, Galway and Ultra4, Greece.

The same principle can be used to get people to conserve energy. For example, a smart environment can gather water and energy usage data from sensors, send that data to smart devices, and allow users to reduce their water and energy use accordingly.

“A key driver in the development of smart environments is the convergence of technologies such as the Internet of Things (IoT) and big data, which are driving the digitization of physical infrastructures with sensors, networks, and social capabilities. Smart environments, leveraging IoT, can support the development of resource management (for example, water/energy) applications for efficient and effective use of the resource within the environment,” say the authors in their article “Internet of Things Enhanced User Experience for Smart Water and Energy Management” in IEEE Internet Computing.

The smart environment is where the Internet of Things (IoT) and Big Data meet

The researchers start with an analysis of how the Internet of Things (IoT) and Big Data converge to form the smart environment.

IoT and big data

A model for IoT-enhanced user experience.

They then designed the user interfaces for kids and adults, personalizing the information according to their needs.

“The activities in this part of the model increase user awareness through the use of targeted information delivery via personalized usage dashboards and task-oriented applications. Users are presented with meaningful and contextual information about usage, price, and availability in an intuitive and interactive way. Different users have different data requirements to manage their water or energy, from home users managing their personal usage, business users managing the consumption of their commercial activities, to municipalities managing regional distribution and consumption at the city level,” the authors say.

The trans-theoretical model of behavior change

Then, in order to design an effective user experience, the researchers gathered information from numerous disciplines, including behavioral science, to build an effective smart environment framework.

“Building effective IoT applications for smart environments requires the combination of technology, techniques, and skills from multiple disciplines, from electronic engineering, data engineering, and data science, to user experience design and behavioral science. A key challenge in delivering smart environments is creating an effective user experience with new digital infrastructures,” say the authors.

They borrowed a 1970s concept from the world of psychotherapy called the “trans-theoretical model of behavior change” to create the human-computer interaction (HCI) framework for smart cities.

trans-theoretical model interface

Smart applications targeting different parts of the user experience and stages of the trans-theoretical model.

There are five stages to behavioral change in the trans-theoretical model.

“As a framework with which to bridge these multiple strands of behavior change theory, the Trans-Theoretical Model (TTM) can be used as a guiding heuristic for high-level user experience design. Developed by Prochaska and DiClemente, the TTM model describes the ‘stages of change’ a person goes through when modifying their behavior,” say the authors.

TTM Stages of behavior change

Stages of behavior change in the trans-theoretical model aligned with interventions.

Testing the trans-theoretical model of behavior change

To test the TTM, the researchers used a variety of smart environments scattered across Italy, Greece, and Ireland. During the testing period, they gathered extensive data and feedback from the different user groups.

high-level research methodology

The high-level research methodology followed during the evaluation of the deployed systems.

The testing period involved gathering extensive data and, in some cases, lasted more than a year.

“During the initial period of the pilots, metering data was collected from existing systems to establish baselines across all pilots. During the control period, the users within the pilots had access to the data generated by the metering infrastructure system though traditional information systems (that is, building management system and basic public dashboards within the airports, office building, and school). The data collection period for each pilot spanned between 6 to 16 months, which also included a range of user interventions such as pre-surveys, focus groups, interviews, feedback cycles, and so on,” say the authors.

The dubbed “Waternomics Project” produced a variety of applications for an even wider variety of users.

“Across the five pilot sites, we developed 25 different applications to support users to optimize resource usage from highly technical leakage detection apps for building managers to personal dashboards for office workers and children at home and at school,” the authors say.

Target end user groups across different pilot sites.

In developing the applications, the researchers discovered several key considerations:

  • Minimize cognitive overload with clear and focused applications and visualizations.
  • Understand your users’ needs and their journey.
  • Remember that social influence and interaction are strong motivators.
  • Close the feedback loop with personalization.
  • Bring your “humans in the loop” of the smart environment.
  • Carefully use targeted alerts and notifications.

Real-time Linked Dataspace manages everything

Within each of the pilots, data management was provided by a Real-time Linked Dataspace (RLD) that links “pay-as-you-go” dataspaces with the needs of different users. The RLD manages the relationships among devices, sensors, datasets and users.

“The RLD goes beyond a traditional dataspace approach by supporting the management of entities within the smart environment as first-class citizens along with data sources, and it extends the dataspace support platform with unified queries across live streams, historical data, and entities,” the authors say.

Architecture of the real-time linked dataspace.

Architecture of the real-time linked dataspace.

Ultimately, the goal is to connect everyone to the smart environment, no matter their age or purpose.

“Smart environments can engage a wide range of end users with different interests and priorities, from corporate managers looking to improve the performance of their business to school children who want to explore and learn more about the world around them. Creating an effective user experience within a smart environment (from smart buildings to smart cities) is an important factor to success,” the authors say.

 

Related research on smart cities and environments in the Computer Society Digital Library: