Tianyi Ma

Personal Mobility Service System in Urban Areas: The IRMA Project

We present an ongoing research project, namely Integrated Real-time Mobility Assistant (IRMA). IRMA is a software system that targets the personal mobility in a near future scenario, oriented to green, shared and public transports. IRMA aims to be an extensible, easy-to-implement and sustainable modular platform based on the combined use of multiple information sources (crowd, open, social, and sensor data) and on array of value propositions, each serving a class of stakeholders, which include municipality, users, transport providers. Hence, IRMA supports users in the entire lifecycle of mobility, and municipalities and transport providers in the whole cycle of mobility management. IRMA is deployed on both smartphone and web, and is built on a hierarchy of re-usable web services, that are based on SOA/EDA (Service Oriented Architecture / Event Driven Architecture).

Service Level Management (SLM) in Cloud Computing - Third Party SLM Framework

The key issue in cloud computing in enterprises is the management of the Quality of Service (QoS), by an appropriate SLM (Service Level Management). Cloud Service Provider are offering SLMs to their users. We think that a third party SLM can be a better way to assure a robust and equal SLM. This paper presents the elements of third party SLM, namely Cloud Service Registration Agent, Negotiation Agent, Compensation Agent, Comment Agent, Billing Agent and Service Monitoring Engine. Such framework has been tested in a real life case. Results show that third party SLM is not only equal, but also dependable and reasonably easy to implement.

CITY FEED: A Crowdsourcing System for City Governance

In city governance, participation and engagement of stakeholders are a critical issue. Many researches use crowdsourcing to foster such participation and collaboration. To make sure that such support is complete, an evaluation grid shall be developed. To serve such need, we introduce a framework to assess the maturity of crowd-sourced municipality services. To show a structured example of crowd based city governance, we present CITY FEED, a system consisting of transactional and analytical modules. CITY FEED has been designed, implemented and deployed as a pilot in Pavia, Italy.

Mobility Service Systems: Guidelines for a possible paradigm and a case study

This paper discusses a framework for the design of business services, called Service System (SS). SSs are a layer built on the top of Internet of Things (IoT) and Internet of Services (IOS). SSs integrate two components, Internet of Business (IOB) and Internet of Data (IOD). IOB delivers complex business services that combine services from IOS and IOT. In turn, IOD links semantically the information that is extracted from IOS and IOT and that is processed in IOB; also, it provides a repository for future applications. The association of SS architecture with Open Source drives a twofold roadmap, with a top down design and a bottom implementation. The SS concept is exemplified on a IRMA, namely a project on urban mobility, with pilot cities in Europe and China, where a demo SS is being developed by using Open Source software.

CITY FEED: A Pilot System of Citizen-Sourcing for City Issue Management

Crowdsourcing implies user collaboration and engagement, which fosters a renewal of city governance processes. In this article, we address a subset of crowdsourcing, named citizen-sourcing, where citizens interact with authorities collaboratively and actively. Many systems have experimented citizen-sourcing in city governance processes; however, their maturity levels are mixed. In order to focus on the service maturity, we introduce a city service maturity framework that contains five levels of service support and two levels of information integration. As an example, we introduce CITY FEED, which implements citizen-sourcing in city issue management process. In order to support such process, CITY FEED supports all levels of the maturity framework (publishing, transacting, interacting, collaborating, and evaluating) and integrates related information relationally and heterogeneously. In order to integrate heterogeneous information, it implements a threefold feed deduplication mechanism based on the geographic, text semantic, and image similarities of feeds. Currently, CITY FEED is in a pilot stage.

Delivering Real-Time Information Services on Public Transit: A Framework

Public transit is described by a wide range of data, which include sensor data, open data, and social network data. Data come in large real-time streams, and are heterogeneous. How to integrate such data in real time? We propose MOBility ANAlyzer (MOBANA), a distributed stream-based framework. MOBANA deals with the integration of heterogeneous information, processing efficiency, and redundancy reduction. As far as integration is concerned, MOBANA integrates data at different layers, and converts them into exchangeable data formats. Specifically, to integrate feed information, MOBANA uses an improved incremental text classifier, based on Kullback Leibler distance. As far as efficiency is concerned, MOBANA is implemented by distributed stream processing engine and distributed messaging system, which enable scalable, efficient, and reliable real-time processing. Specifically, within the transport domain, MOBANA identifies the real-time position of vehicles by an as-needed adjustment of planned position against the real-time position, thus dropping network load. As far as redundancy is concerned, MOBANA filters tweets through a three-fold similarity analysis, which encompasses geo-location, text, and image. In addition, MOBANA is a complete framework, which has been tested as a pilot with real data in the city of Pavia, Italy.

Smart cities, urban sensing, and big data: mining geo-location in social networks

Location-based social networks offer spatiotemporal information which can be accessed through public application programming interfaces and have drawn the interest of researchers with diverse scientific backgrounds. This availability of data enables the potential use of geo-located content as an additional, low-cost and infrastructureless source of information for urban sensing in smart cities. All these aspects along with the need for real-time analytics for urban sensing, take us to Big Data management and its related issues. Real-time urban sensing uses citizens as active and passive sensors and can reveal important insights about human behavior in the city. A systematic literature review outlines related works and gaps in current research. In this chapter, we propose a reference model to exploit Big Data for urban sensing and we validate it using a case study. Finally, we give recommendations for future research about location and mobility mining of social network data.

XYZ Indoor Navigation through Augmented Reality: A Research in Progress.

We present an overall framework of services for indoor navigation, which includes Indoor Mapping, Indoor Positioning, Path Planning, and En-route Assistance. Within such framework we focus on an augmented reality (AR) solution for en-route assistance. AR assists the user walking in a multi-floor building by displaying a directional arrow under a camera view, thus freeing the user from knowing his/her position. Our AR solution relies on geomagnetic positioning and north-oriented space coordinates transformation. Therefore, it can work without infrastructure and without relying on GPS. The AR visual interface and the integration with magnetic positioning is the main novelty of our solution, which has been validated by experiments and shows a good performance.

Multi-floor Indoor Navigation with Geomagnetic Field Positioning and Ant Colony Optimization Algorithm

We here illustrate a new indoor navigation system. It is an outcome of creativity, which merges an imaginative scenario and new technologies. The system intends to guide a person in unknown building by relying on technologies which do not depend on infrastructures. The system includes two key components, namely positioning and path planning. Positioning is based on geomagnetic fields, and it overcomes the several limits of WIFI and Bluetooth, etc. Path planning is based on a new and optimized Ant Colony algorithm, called Ant Colony Optimization (ACO), which offers better performances than the classic A* algorithms. The paper illustrates the logic and the architecture of the system, and also presents experimental results.

A Threefold Similarity Analysis of Crowdsourcing Feeds

Crowdsourcing is a valuable social sensing for the smarter city. We present an approach for classifying crowd sourced feeds from a threefold point of view, namely image, text, and geography. The main idea is to extract feeds within a specific geographic range, and then analyze similarity of image color and text semantic. The approach enables to identify feeds that report the same issue, hence filtering redundant information. Based on proved methods and algorithms, such approach has been implemented in a software application, called CITY FEED, that is used by the Municipality of Pavia.