# Urban Open Platform for Borderless Smart Cities
## 1 Introduction
In addition, [PERSON] & [PERSON] [5] argue that data interoperability and integration is the most challenging smart city problem. In general, the literature on big data classifies sources under three broad categories: opportunistic sensing, purposely sensing and crowd sensing [8]. Opportunistic sensing leverages data running on existing systems, such as a telecommunication network, but can be used to better understand different systems. In other words, data is collected for one purpose and used for another. This approach to data collection is made possible largely by the fact that mobile phones have become ubiquitous (www.itu.int/ITU-D/ict/material/Telecom09_flyer.pdf, accessed on 9 November 2021)--citizens replace the need for purposeful sensors, contributing real-time data through their portable devices. Other typical data providers include credit card companies recording user transactions, taxi fleets reporting vehicle GPSs, etc. In contrast to opportunistic sensing, purposely sensed datasets are derived from ad hoc sensor networks configured to study a specific phenomenon. Thanks to advances in microelectronics sensors, computations are becoming increasingly affordable and distributed, a phenomenon often referred to as \"smart dust\". Hence, networks of remote sensing agents can now be embedded in the city fabric to extract large amounts of information. This data is channelled into central control stations where it is aggregated, analysed and used to make decisions on how the monitored terrain should be regulated and actuated. Here, the resulting datasets tend to be more uniform, with the stated use and actual end-use scenarios better aligned to decode various flows within the city. In every city, the complete story cannot be told by figures and data alone. To adequately assess a situation, the voice of the citizen must be heard. Each urbanite can be thought of as a human sensor, capable of reporting on their experience of the city through content-sharing platforms such as Flickr, Twitter, Facebook or Wikipedia [9]. These actions offer a unique view of how citizens navigate their environment, bringing clarity to points of attraction or spontaneous migration. This approach describes the third data source known as crowd sensing. The crowd becomes a distributed network of sensors that allows understanding the dynamic patterns of the city and the experiences of its citizens at a quasi-real-time rate. In the absence or failure of top-down sensor networks, this grassroots approach to sensing leverages the millions of newly cyber-connected citizens to coordinate human activity on an unprecedented scale. Integrated cities perform with unparalleled efficiency (whetherresources, transportation or infrastructure), enabled by digitally controlled circuitry and virtual operating systems, ultimately transforming urban space into an open living lab. However, the question remains to what extent Urban Platforms attract participation of citizens in the provision of better services. In one study, citizen e-participation was positively associated with the clearance rate of urban service requests, although the effect size of participatory service performance varies between different types of city services--there was more involvement in complex problems compared to simple routine services [10]. Importantly, Urban Platforms can also be independent from the direct role of the city government. [PERSON], [PERSON], & [PERSON] [11] focus on the concept of \"smartness\" in the context of service provision, reflecting the ambition of the public sector to become more agile and resilient when applying novel technologies such as the Internet of Things--also central to Urban Platforms. The Internet of Things (IoT) is a concept for connecting various sensors to the Internet using big data analytics [12] with applications in smart cities [13]; it can, at least theoretically, improve the urban services and reduce resources consumption [14].