Abstract: A considerable part of recent research in smartcities and IoT has focused on achieving energy savings in buildings and supporting aspects related to sustainability. In this context, the educational community is one of the most important ones to consider, since school buildings constitute a large part of non-residential buildings, while also educating students on sustainability matters is an investment for the future. In this work, we discuss a methodology for achieving energy savings in schools based on the utilization of data produced by an IoT infrastructure installed inside school buildings and related educational scenarios. We present the steps comprising this methodology in detail, along with a set of tangible results achieved within the GAIA project. We also showcase how an IoT infrastructure can support activities in an educational setting and produce concrete outcomes, with typical levels of 20% energy savings.
Abstract: Smartcities are becoming a vibrant application domain for a number of science fields. As such, service providers and stakeholders are beginning to integrate co-creation aspects into current implementations to shape the future smart city solutions. In this context, holistic solutions are required to test such aspects in real city-scale IoT deployments, considering the complex city ecosystems. In this work, we discuss OrganiCity˘s implementation of an Experimentation-as-a-Service framework, presenting a toolset that allows developing, deploying and evaluating smart city solutions in a one-stop shop manner. This is the first time such an integrated toolset is offered in the context of a large-scale IoT infrastructure, which spans across multiple European cities. We discuss the design and implementation of the toolset, presenting our view on what Experimentation-as-a-Service should provide, and how it is implemented. We present initial feedback from 25 experimenter teams that have utilized this toolset in the OrganiCity project, along with a discussion on two detailed actual use cases to validate our approach. Learnings from all experiments are discussed as well as architectural considerations for platform scaling. Our feedback from experimenters indicates that Experimentation-as-a-Service is a viable and useful approach.
Abstract: An ever growing emphasis is put nowadays in developing personalized journey planning and renewable mobility services in smartcities. These services combine means of scheduled-based public transport and electric vehicles or bikes, using crowdsourcing techniques for collecting real-time traffic information and for assessing the recommended routes. The goal is to develop an information system that will allow the fast, real-time computation of best routes.
The main challenges in developing such an information system are both technological and algorithmic. The technological challenge concerns the collection, storage, management, and updating of a huge volume of transport data that are usually time-dependent, and the provision (through these data) of personalized renewable mobility services in smartphones. This challenge is typically confronted by creating a cloud infrastructure that on the one hand will support the storage, management, and updating of data, while on the other hand it will handle the necessary data feed to the smartphone applications for providing the users with the requested best routes.
The algorithmic challenge concerns the development of innovative algorithms for the efficient provision of journey planning services in smartphones, based on data they will receive from the cloud infrastructure. These services guarantee the computation of realistic and useful best routes, as well as the updating of the precomputed (route and timetable) information, in case of delays of scheduled public transport vehicles, so that the users can online update their routes to destination. The goal is to develop an algorithmic basis for supporting modern renewable mobility services (information systems), such as "mobility on demand'' (where the next leg of a journey is decided in real-time) and "door-to-door'' personalized mobility, in urban scheduled-based public transport environments. Scheduled-based public transport information systems should not only compute in real-time end-user queries requesting best routes, but also to update the timetable information in case of delays.
The core algorithmic issues of mobility and journey planning (regarding the computation of optimal routes under certain criteria) in scheduled-based public transport systems concern the efficient solution of the fundamental earlier arrival (EA) problem (compute a journey from station S to station T minimizing the overall traveling time required to complete the journey), the minimum number of
transfers (MNT) problem (compute a journey from station S to station T minimizing the number of times a passenger is required to change vehicle), and the efficient updating of timetable information system in case of vehicle delays. The EA and MNT problems have been extensively studied in the literature under two main approaches: the array-based modeling (where the timetable is represented as an array) and the graph-based modeling (where the timetable is represented as a graph). Experimental results have shown so far that the array-based approaches are faster in terms of query time than graph-based ones, as they are able to better exploit data locality and do not rely on priority queues. On the other hand, the array-based approaches have not been theoretically or experimentally studied as far as the efficient updating of timetable information, in case of delays, is concerned.
In this thesis, new graph-based models are being developed that solve efficiently the aforementioned fundamental algorithmic mobility problems in urban scheduled-based public transport information systems, along with a mobile application (journey planner) running on Android-based smartphones that includes a service for the evaluation of the recommended routes by the users. In particular:
(a) An extensive comparative evaluation was conducted on graph-based dynamic models that represent big data volumes regarding their suitability for representing timetable information. The study confirmed that the realistic time-expanded model is the most suitable for representing timetable information.
(b) Two new graph-based models have been developed for representing timetable information (in a timetable information system), the reduced time-expanded model and the dynamic timetable model (DTM), both of which are more space-efficient with respect to the realistic time-expanded model. For both of the new models, new efficient algorithms were developed for fast answering of EA and MNT queries, as well as for updating the timetable information representation in case of delays.
(c)An experimental evaluation was conducted with the new graph-based models and their associated query and update algorithms on a set of 14 real-world scheduled-based transportation systems, including the metropolitan areas of Berlin, Athens, London, Rome, and Madrid. The experimental results showed that the query algorithms of the reduced time-expanded model are superior to those of the DTM model, while the reverse is true regarding the update algorithms. In addition, the experimental study showed that the query algorithms of the new graph-based models compete favorably with those of the best array-based models.
(d) A mobile, cloud-based, journey planner (information system) was developed whose core algorithmic engine builds upon the new graph-based models. The mobile application is accompanied by a service that allows the users to assess the recommended journeys. The journey planner demonstrates the practicality of the new graph-based models and their associated query and update algorithms.
Abstract: In recent years, the evolution of urban environments, jointly with the progress of the Information and Communication sector, have enabled the rapid adoption of new solutions that contribute to the growth in popularity of SmartCities. Currently, the majority of the world population
lives in cities encouraging different stakeholders within these innovative ecosystems to seek new solutions guaranteeing the sustainability and efficiency of such complex environments. In this work, it is discussed how the experimentation with IoT technologies and other data sources form the cities can be utilized to co-create in the OrganiCity project, where key actors like citizens, researchers and other stakeholders shape smart city services and applications in a collaborative fashion. Furthermore,
a novel architecture is proposed that enables this organic growth of the future cities, facilitating the experimentation that tailors the adoption of new technologies and services for a better quality of life, as well as agile and dynamic mechanisms for managing cities. In this work, the different components and enablers of the OrganiCity platform are presented and discussed in detail and include, among others, a portal to manage the experiment life cycle, an Urban Data Observatory to explore data assets,
and an annotations component to indicate quality of data, with a particular focus on the city-scale opportunistic data collection service operating as an alternative to traditional communications.
Abstract: The domain of smartcities is currently burgeoning, with a lot of potential for scientific and socio-economic innovation gradually being revealed. It is also becoming apparent that cross-discipline research will be instrumental in designing and building smarter cities, where IoT technology is becoming omnipresent. SmartSantander is an FP7 project that built a massive
city-scale IoT testbed aiming to provide both a tool for the research community and a functional system for the local government to implement operational city
services. In this work, we present key smartcities projects, main application domains and representative smart city frameworks that reflect the latest advances in the smartcities domain and our own experience through SmartSantander. The project has deployed 51.910 IoT endpoints, offering a massive infrastructure to the community, as well as functional system services and a number of end-user applications. Based on these aspects, we identify and
discuss a number of key scientific and technological challenges. We also present an overview of the developed system components and applications, and
discuss the ways that current smart city challenges were handled in the project.
Abstract: Abstract— Numerous smart city testbeds and system deployments have surfaced around the world, aiming to provide services over unified large heterogeneous IoT infrastructures. Although we have achieved new scales in smart city installations and systems, so far the focus has been to provide diverse sources of data to smart city services consumers, while neglecting to provide ways to simplify making good use of them. We believe that knowledge creation in smartcities through data annotation, supported in both an automated and a crowdsourced manner, is an aspect that will bring additional value to smartcities. We present here our approach, aiming to utilize an existing smart city deployment and the OrganiCity software ecosystem. We discuss key challenges along with characteristic use cases, and report on our design and implementation, along with preliminary results.
Abstract: A number of Future Internet testbeds are being deployed around the world for research experimentation and development. SmartSantander
is an infrastructure of massive scale deployed inside a city centre. We argue that utilising the concept of participatory sensing can augment the functionality and potential use-cases of such a system and be beneficiary in a number of scenarios. We discuss
the concept of extending SmartSantander with participatory sensing through the use of volunteers˘ smartphones. We report on our design and implementation, which allows for developers to write
their code for Android devices and then deploy and execute on the devices automatically through our system. We have tested our implementation in a number of scenarios in two cities with the help
of volunteers with promising results; the data collected enhance the ones by fixed infrastructure both quantitatively and qualitatively across the cities, while also engaging citizens more directly.
Abstract: Although we have reached new levels in smart city installations and systems, efforts so far have focused on providing diverse sources of data to smart city services consumers while neglecting to provide ways to simplify making good use of them. In this context, one first step that will bring added value to smartcities is knowledge creation in smartcities through anomaly detection and data annotation, supported in both an automated and a crowdsourced manner. We present here LearningCity, our solution that has been validated over an existing smart city deployment in Santander, and the OrganiCity experimentation-as-a-service ecosystem. We discuss key challenges along with characteristic use cases, and report on our design and implementation, together with some preliminary results derived from combining large smart city datasets with machine learning.
Abstract: The adoption of technologies like the IoT in urban environments, together with the intensive use of smartphones, is driving transformation towards smartcities. Under this perspective, Experimentation-as-a-Service within OrganiCity aims to create an experimental facility with technologies, services, and applications that simplify innovation within urban ecosystems. We discuss here tools that facilitate experimentation, implementing ways to organize, execute, and administer experimentation campaigns in a smart city context. We discuss the benefits of our framework, presenting some preliminary results. This is the first time such tools are paired with large-scale smart city infrastructures, enabling both city-scale experimentation and cross-site experimentation.
Abstract: The Internet of Things (IoT) and smartcities are two of the most popular directions the research community is pursuing very actively. But although we have made great progress in many fields, we are still trying to figure out how we can utilize our smart city and IoT infrastructures, in order to produce reliable, economically sustainable solutions that create public value, and even more so in the field of education.
GAIA1, a Horizon2020 EC-funded project, has developed an IoT infrastructure across school buildings in Europe. Its primary aim has been to raise awareness about energy consumption and sustainability, based on real-world sensor data produced inside the school buildings where students and teachers live and work. Today's students are the citizens of tomorrow, and they should have the skills to understand and respond to challenges like climate change. Currently, 25 educational building sites participate in GAIA, located in Sweden, Italy, and Greece. An IoT infrastructure  is installed in these buildings, monitoring in real-time their power consumption, as well as several indoor and outdoor environmental parameters.