Reuse potential assessment framework for gamification-based smart city pilots

The paper proposes a unified framework for assessing the re-use potential for the Smart Engagement Pilot currently being realized in the city of Ghent (Belgium). The pilot aims to stimulate the digital engagement in users (citizens) by involving them in online and offline communities, and increasing the social capital through the use of ICT (Information and Communications Technology). To engage the citizens, the pilot makes use of Gamification based entities (intelligent wireless sensors) embedded in public hardware, through which innovative games are organized in places of interest (neighbourhood, parks, schools, etc.). Once finished, this pilot will be re-used in other European cities under the context of CIP SMART IP project. Since, the success of a pilot in one city doesn't guarantee its success in the other, an objective socio-economic-organizational reuse assessment becomes critical. To do this assessment, we propose a framework, which uses a Key Performance Indicator (KPI) based scorecard to determine the roadblocks and battlefields that could deter such a transition

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Borgs in the Org? Organizational Decision Making and Technology

Data warehousing and the development of the World Wide Web both augment information gathering (search) processes in individual decision making by increasing the availability of required information. Imagine, for example, that one wanted to buy new golf clubs. Thirty years ago, the cost of information gathering would likely have limited an individual\u27s search process to geographically proximal vendors and the golf clubs they stocked. Today, a prospective purchaser can log onto the World Wide Web to find out what types of golf clubs are available anywhere; consult databases, chat rooms, and bulletin boards (e.g., epinions.com) to gather product information and user opinions; and compare prices across vendors around the world

improving parking availability prediction in smart cities with iot and ensemble based model

Abstract Smart cities are part of the ongoing advances in technology to provide a better life quality to its inhabitants. Urban mobility is one of the most important components of smart cities. Due to the growing number of vehicles in these cities, urban traffic congestion is becoming more common. In addition, finding places to park even in car parks is not easy for drivers who run in circles. Studies have shown that drivers looking for parking spaces contribute up to 30% to traffic congestion. In this context, it is necessary to predict the spaces available to drivers in parking lots where they want to park. We propose in this paper a new system that integrates the IoT and a predictive model based on ensemble methods to optimize the prediction of the availability of parking spaces in smart parking. The tests that we carried out on the Birmingham parking data set allowed to reach a Mean Absolute Error (MAE) of 0.06% on average with the algorithm of Bagging Regression (BR). This results have thus improved the best existing performance by over 6.6% while dramatically reducing system complexity