Haosheng Huang

A Survey of Mobile Indoor Navigation Systems

With the gradual maturating of ubiquitous computing and the rapid advances in mobile devices and wireless communication, indoor Location Based Services have gained increasing interests as an important application of indoor ubiquitous computing. In this paper, an evaluation framework which combines the key aspects of indoor navigation for investigating mobile indoor navigation systems is proposed. Based on this evaluation framework, we give a comparison and analysis of the current mobile indoor navigation systems, and conclude that “indoor navigation systems are still on an early development stage”. We also identify some challenges which require further research and development, such as sensor fusion, context-awareness, route communication, seamless switch between indoor and outdoor navigation, and ubiquitous indoor computing.

Smart Environment for Ubiquitous Indoor Navigation

Mobile navigation service is one of the most important Location Based Services. With the rapid advances in enabling technologies for ubiquitous computing, more and more active or passive devices/sensors are augmented in the indoor environment, indoor environment has become smarter. This paper proposes that by introducing the notions of Smart Environment and Ambient Intelligent, a ubiquitous indoor navigation service can be built to provide an adaptive smart wayfinding support and enhance users with a new experience during indoor navigation. In this paper, we set up a smart environment with a positioning module and a wireless module. Based on this smart environment, we design a ubiquitous indoor navigation system with interaction and annotation module (for user generated content), user tracking module (for collaborative filtering) and context-aware adaptation to illustrate some potential benefits of combining indoor navigation and Smart Environment.

Using Context-Aware Collaborative Filtering for POI Recommendations in Mobile Guides

Mobile guide is one of the most popular Location Based Services. Currently, providing context-aware services/information is still very challenging in mobile guides. Collaborative filtering (CF), known as “Amazon-like recommendations”, is a promising solution for providing context-aware recommendations. The paper investigates how context-aware CF (CaCF) can be introduced into mobile guides. Specifically, we focus on applying CaCF methods on the highly available GPS trajectories to enhance visitors with context-aware POI (Point of Interest) recommendations.

Spatial Knowledge Acquisition with Mobile Maps, Augmented Reality and Voice in the Context of GPS-based Pedestrian Navigation: Results from a Field Test

GPS-based pedestrian navigation systems have become increasingly popular. Different interface technologies can be used to communicate/convey route directions to pedestrians. This paper aims to empirically study the influence of different interface technologies on spatial knowledge acquisition in the context of GPS-based pedestrian navigation. A field experiment was implemented to address this concern. Firstly, the suitability of the evaluation methods in assessing spatial knowledge acquisition was analyzed empirically (focusing on the ability of differentiating “familiar” and “unfamiliar” participants). The suitable methods were then used to compare the influence of mobile maps, augmented reality, and voice on spatial learning. The field test showed that in terms of spatial knowledge acquisition, the three interface technologies led to comparable results, which were not significantly different from each other. The results bring some challenging issues for consideration when designing mobile pedestrian navigation systems.

AffectRoute – considering people’s affective responses to environments for enhancing route-planning services

Humans perceive and evaluate environments affectively. Some places are experienced as unsafe, while some others as attractive and interesting. These affective responses to environments influence people’s daily behavior and decision-making in space, e.g., choosing which route to take, or which place to visit. In this article, we report on a methodology of using people’s affective responses to environments for enhancing computer-based route planning. More specifically, we explore a crowdsourcing approach to model and collect people’s affective responses to environments; an Affect-Space-Model and a mobile application are developed to facilitate this crowdsourcing approach; a routing algorithm (named AffectRoute) is then proposed to aggregate and integrate the collected data for automatic route planning. Evaluation with human participants shows that the routes generated by considering people’s affective responses to environments are significantly preferred over the conventional shortest ones, which are employed in car navigation systems and many online route planners. In conclusion, considering people’s affective responses to environments contributes to the improvement of automatic route planning. The proposed method can be integrated into existing route-planning services (e.g., location-based services) to provide users with more satisfying routing results.

Using Activity Theory to Identify Relevant Context Parameters

One of the most important aspects of ubiquitous computing is context-awareness. In this paper, we adopt an interactional perspective on context: 1) Something is context because it is used for adapting the interaction between the human and the current system. 2) Activity is central to context. 3) Context differs in each occasion of the activity. Based on this understanding, this paper proposes an Activity Theory based method which attempts to answer the following questions: how to analyze activity for context-awareness, and how to identify relevant context parameters. This method includes two steps: 1) Decomposing activity into actions, which we take as units for identifying context parameters, by using Activity Theory’s hierachical structure of activity. 2) Identifying relevant context parameters for each action by our extended Activity Theory’s framework. Finally, this paper gives an outlook how this method can be used in designing context-aware pedestrian wayfinding services.

An SVG-based method to support spatial analysis in XML/GML/SVG-based WebGIS

XML/GML/SVG-based approaches are promising for building Web-based geographic information systems (WebGIS). However, current XML/GML/SVG-based WebGISs are lacking in spatial analysis. Some of them are designed for web mapping only. Others adopt a server-side solution for spatial analysis, which suffers from the ‘bottleneck’ problem and results in a high network transmission load. Load-balancing spatial analysis between server side and browser side can be used to solve the above problems. This article focuses on one of the key building blocks of load-balancing spatial analysis, that is, SVG-based spatial analysis which enables spatial querying and analysis directly on SVG (on the browser side). After analyzing the workflow of spatial analysis, we identify and focus on two key issues in providing spatial analysis on SVG: SVG-based spatial information representation and SVG-based spatial extended structured query language (SSESQL). For the first issue, a theoretical foundation is set up to develop an SVG-based spatial information representation model. Some spatial operators are designed and integrated into an SSESQL to support spatial querying on SVG. Finally, we design and implement two case studies. The results of these case studies show that the proposed method is feasible and operable in supporting spatial analysis directly on SVG on the browser side. The proposed method can be easily incorporated with some existing methods (e.g., GML-based spatial analysis on the server side) to provide load-balancing spatial analysis (load balancing between server side and browser side) in XML/GML/SVG-based WebGIS. As a result, users can access high-performance spatial analysis simply via a web browser (such as Internet Explorer and Firefox).

A Technical Survey on Decluttering of Icons in Online Map-Based Mashups

Recent years have witnessed rapid advances in online map-based mashups with Application Programming Interfaces (APIs) and web services. Map-based mashups often display different kinds of information (e.g., POIs, represented as icons) on base maps, such as Google Maps and Bing Maps. The visualization of large number of icons in a map on web browsers or mobile devices often results in the icon cluttering problem with icons touching and overlapping each other. This problem decreases map legibility, and thus prevents users from effectively processing the information presented in the map. It also leads to a dramatic degradation of performance, and a high transmission load. All these problems will greatly decrease the usability of a mashup application.

Collective intelligence-based route recommendation for assisting pedestrian wayfinding in the era of Web 2.0

Mobile pedestrian navigation systems are one of the most popular Location-Based Services. In the era of Web 2.0, current mobile navigation systems often suffer from the following problems: the lack of social navigation support (utilising other peoples experiences) and the challenge of making user-generated content (UGC) useful. In this article, some collective intelligence-based route recommendation methods are designed to address these problems. The proposed methods can make use of UGC (reflecting other users’ navigation experiences), and provide users with the least complex and the length-complexity-optimised (LCO) routes. Some simulations using the street network of the first district of Vienna (Austria) are designed to evaluate the proposed methods. The results show that compared to the shortest route, the collective intelligence-based routes (i.e. the least complex and the LCO routes) have a significant improvement of the route quality (with lower complexity ratings), thereby more effectively supporting users’ navigation tasks (more chances of reaching the destination, fewer mistakes made and shorter distance travelled).

Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques

Tailored routing and navigation services utilized by wheelchair users require certain information about sidewalk geometries and their attributes to execute efficiently. Except some minor regions/cities, such detailed information is not present in current versions of crowdsourced mapping databases including OpenStreetMap. CAP4Access European project aimed to use (and enrich) OpenStreetMap for making it fit to the purpose of wheelchair routing. In this respect, this study presents a modified methodology based on data mining techniques for constructing sidewalk geometries using multiple GPS traces collected by wheelchair users during an urban travel experiment. The derived sidewalk geometries can be used to enrich OpenStreetMap to support wheelchair routing. The proposed method was applied to a case study in Heidelberg, Germany. The constructed sidewalk geometries were compared to an official reference dataset (“ground truth dataset”). The case study shows that the constructed sidewalk network overlays with 96% of the official reference dataset. Furthermore, in terms of positional accuracy, a low Root Mean Square Error (RMSE) value (0.93 m) is achieved. The article presents our discussion on the results as well as the conclusion and future research directions.