Conserving energy amenable to the activities of occupants in public buildings is a particularly challenging objective that includes associating energy consumption to particular individuals and providing them with incentives to alter their behavior. This paper describes a gamification framework that aims to facilitate achieving greater energy conservation in public buildings. The framework leverages IoT-enabled low-cost devices, to improve energy disaggregation mechanisms that provide energy use and—consequently—wastage information at the device, area and end-user level. The identified wastages are concurrently targeted by a gamified application that motivates respective behavioral changes combining team competition, virtual rewards and life simulation. Our solution is being developed iteratively with the end-users’ engagement during the analysis, design, development and validation phases in public buildings located in three different countries: Luxembourg (Musée National d’Histoire et d’Art), Spain (EcoUrbanBuilding, Institut Català d’Energia headquarters, Barcelona) and Greece (General Secretariat of the Municipality of Athens).

Through this research the authors communicate the lessons learned during the deployment of two RFID retail applications—namely dynamic pricing of fresh products and management of promotions in the supermarket floor—in two real-life pilot sites in Greece and Ireland. The lessons are presented in three inter-related perspectives; engineering challenges and associated solutions, individual adoption factors, and organizational success indicators. The authors argue that these findings might inform the design community to develop better RFID instantiations.

The arrival of the Internet of Things (IoT) paradigm has opened the door to a variety of services for building users. Considering the long-lasting issue of high energy use by buildings and low-energy literacy, it is tempting to use this new technology for increasing the literacy of users. This paper shows the results of a study performed in two pilot buildings with real users that have interacted with a series of energy educational interventions that encourage them in a timed and personalised way to reduce their energy consumption. The interventions aimed at reducing the consumption of energy and a close follow-up of the intervention from a behavioural aspect has been performed. The results show that the users, when interacting with the intervention and staying active, can reduce the energy consumption in the building by more than 30%, but the average savings are of 20%. This is in consensus with the literature, but in our case, the intervention has been one showing that personalised methods can result in energy reductions as large as those of more standard interventions.

This paper has developed and deployed a Bluetooth Low Energy (BLE) beacon-based indoor positioning system in a two-floor retail store. The ultimate purpose of this study was to compare the different indoor positioning techniques towards achieving efficient position determination of moving customers in the retail store. The innovation of this research lies in its context (the retail store) and the fact that this is not a laboratory, controlled experiment. Retail stores are challenging environments with multiple sources of noise (e.g., shoppers’ moving) that impede indoor localization. To the best of the authors’ knowledge, this is the first work concerning indoor localization of consumers in a real retail store. This study proposes an ensemble filter with lower absolute mean and root mean squared errors than the random forest. Moreover, the localization error is approximately 2 m, while for the random forest, it is 2.5 m. In retail environments, even a 0.5 m deviation is significant because consumers may be positioned in front of different store shelves and, thus, different product categories. The more accurate the consumer localization, the more accurate and rich insights on the customers’ shopping behavior. Consequently, retailers can offer more effective customer location-based services (e.g., personalized offers) and, overall, better consumer localization can improve decision making in retailing.