Abstract: An ever growing emphasis is put nowadays in developing personalized journey planning and renewable mobility services in smart cities. 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: A number of infrastructures are being deployed for Future Internet experimentation purposes, providing access to large-scale IoT resources to researchers and industry. SmartSantander is among the largest ones, deployed at the city center of Santander. We discuss SmartSantanderĒs augmentation using smartphones provided by volunteers, in order to increase sensing resources and ubiquity. Our system allows developers to write code for Android and automatically deploy their experiments to Android devices, alongside the SmartSantander platform. Initial results produced by experiments with a small number of volunteers show that the system provides meaningful extensions to the existing platform.
Abstract: In this work we discuss Kafebook, a system combining popular social networking platforms
with activity input from the physical domain inside public spaces. The system is envisioned as
a means for users to augment and communicate their activities to other people in their physical
proximity through a public display, while catering for anonymity issues. We developed a
client for Android smartphones that is used as the user interface and the enabling platform,
with which users connect to the system infrastructure and interact with it. Apart from
providing access to input from social networking sites, the Android client allows for chat
between the users and music selection polls. Wireless networking is based on Bluetooth, a
widespread technology in smartphones, which enables a more pervasive mode of operation,
while utilizing it also as a proximity sensor. We deployed our system in a two-day public
event as part of an undergraduate theses showcase, receiving positive feedback from visitors.
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 volunteersĒ 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: We briefly present the design and architecture of a system that aims to simplify the process of organizing, executing and administering crowdsensing campaigns in a smart city context over smartphones volunteered by citizens. We built our system on top of an Android app substrate on the end-user level, which enables us to utilize smartphone resources. Our system allows researchers and other developers to manage and distribute their “mini” smart city applications, gather data and publish their results through the Organicity smart city platform. We believe this is the first time such a tool is paired with a large scale IoT
infrastructure, to enable truly city-scale IoT and smart city experimentation.
