UbiPaPaGo: Context-aware path planning
Graphical abstract
Research highlights
► UbiPaPaGo is a context-aware path planning mechanism in a ubiquitous network. ► UbiPaPaGo adopts SCM to present map context and uses GA to find the optimal path. ► UbiPaPaGo can efficiently achieve the shortest path that guarantees QoS parameters. ► UbiPaPaGo should be scalable and possible to implement in a distributed manner. ► Experimental results validate the effectiveness and the efficiency of UbiPaPaGo.
Introduction
The demand for intelligent navigation applications in path finding, such as PaPaGo (PAPAGO) and TomTom (TomTom International), has risen significantly in recent years, helped by the rapid development of wireless and communication technologies. These systems provide mobile users, such as car drivers, with intelligent path planning, routing and navigation services, and direct users to their destination using electronic maps and Global Position System (GPS). However, these systems are computer-centric, and thus are unable to achieve the vision of Mark Weiser (Weiser, 1991) of a ubiquitous environment with human-centric applications. Context-awareness is a key to human-centric applications. A context-aware application extracts, interprets and uses context, and automatically adapts its functionality to the current context of an individual user. Consequently, mobile users can adopt context to plan the “best-fitting” path automatically.
To help address the problem and develop the solution, a scenario is described for finding John’s optimal path that satisfies his requirements. John needs to drive to the Taipei Arena as quickly as possible to watch a show, because he is late. The automobile assistant connects to a streaming server to watch the live show remotely before John arrives at Taipei Arena. In this case, John needs to find the shortest path to the Taipei Arena, while keeping connectivity with the streaming server. Therefore, the optimal path must consider the path distance, accessibility of WiFi Access Point (AP) or 3G Base Station (BS), accessibility of the streaming service and quality of service (QoS) of the required service. The problem considered herein is formally defined as how to plan an optimal (shortest) path for an individual user based on his requirements and the context of his surrounding environment, such as user preference, location, network connectivity, bandwidth, service availability and quality of service (QoS). The objective is to find an optimal (shortest) path given constraints, such as higher bandwidth, available service and QoS guarantee. These constraints ensure that the shortest path is also the one that best fits the user requirements.
The context is very important for inferring the optimal path. The path planning framework relies on various contexts acquired from various sensing sources. The collected contexts are stored in an open and distributed context management server (Lu, Wang, & Hwang, 2009), called Ubiquitous Gate (U-gate), which is responsible for context acquisition, context representation, context retrieval, context protection and context inference. The path planning framework adopts an ontology as the underlying context model. The ontology model is implemented using RDF (Resource Description Framework) ([RDF Primer] and [RDF Schema]) and OWL (Web Ontology Language) (OWL). The context model includes user contexts, such as personal information and active applications, as well as environmental contexts, such as map information and network connectivity.
The proposed solution, called UbiPaPaGo, is based on spatial conceptual map (SCM) (Kettani & Moulin, 1999) and genetic algorithm (GA) (Holland, 1975). A modified SCM is proposed to model the real world map and related environment context, and a path planning map based on this model is then created. In addition, a GA is proposed to find the optimal path. Chromosomes are encoded based on the method proposed in Inagaki, Haseyama, and Kitajima (1999). The fitness function is carefully designed to fit the user requirements. Prototyping and simulations are performed to demonstrate the feasibility and the efficiency of the proposed UbiPaPaGo.
The remainder of this paper is structured as follows. Section 2 describes related works on path planning and context-aware path prediction. Section 3 introduces the SCM model. Section 4 describes the design of UbiPaPaGo based on GA. Section 5 provides implementation details of the prototyping system. Experimental results are discussed in Section 6. Conclusions are finally drawn in Section 7, along with our future research.
Section snippets
Related work
Path planning problems have been studied extensively in the past, but mostly focusing on mobile robots (Bodhale et al., 2009, Castilho and Trujilo, 2005, Lei et al., 2006, Li et al., 2006, Liu et al., 2008, Tu and Yang, 2003), transportation systems (Li, Liu, et al., 2006) and game systems (Arikan et al., 2001, Wan et al., 2003). These proposed path planning algorithms emphasize features, such as collision avoidance and ease of tracking. Although these solutions address information about roads,
Design of UbiPaPaGo
This section introduces the design goal of UbiPaPaGo. Section 3.2 presents the context model of UbiPaPaGo. Section 3.3 describes the architecture of UbiPaPaGo. Finally, the privacy policy of UbiPaPaGo is introduced in Section 3.4.
UbiPaPaGo based on SCM model
This section represents the trajectory map and environment context using a SCM model. The SCM is an abstraction of real map representation containing of a set of landmark objects (Oj), a set of medium objects (Wy) and the influence areas of those objects. The landmark objects define those areas, such as buildings or user’s target destination. The medium objects (also called “ways”) define areas, such as streets, roads, trajectories and virtual connections between objects (Kettani & Moulin, 1999
UbiPaPaGo based on GA
This section describes our context-aware path planning mechanism based on GA. The GA procedure is shown in Fig. 5. Before applying GA, the SCM model obtained in Section 4 is first converted to path planning map, as shown in Fig. 6, where node αi denotes a crossing in Fig. 3. Restated, to reduce the length of chromosome and to increase the scalability of UbiPaPaGo, the path planning map considers only crossing nodes. Additionally, the path planning map needs to consider the source or destination
Prototyping and implementation
This section demonstrates the feasibility of the UbiPaPaGo by prototyping a real system in U-gate. Because U-gate is an open, efficient and distributed context management architecture and communication model based on standard protocols, UbiPaPaGo can be implemented in a distributed manner, enabling any service provider to deploy its service into the ubiquitous environment easily. For easy deployment, UbiPaPaGo was implemented in a small scope environment. Therefore, the demonstration scenario
Experimental results and discussion
This section evaluates the proposed UbiPaPaGo through computer simulation. The proposed UbiPaPaGo was implemented in a Java (JDK 6) program on a PC with a Core 2 Quad 2.4 GHZ CPU and 2G MB memory. Fig. 12 shows the road map of some part of Taipei city that was used in the experiments. In the experiment, the road map with 1481 nodes was first converted into a graph with 582 crossing nodes.
The parameters are set as follows. The experiment tries five population sizes (N): 100, 200, 300, 400, and
Conclusions and future work
This study proposes an intelligent navigation application, UbiPaPaGo, which focuses on providing human-centric context-aware path planning mechanism in a ubiquitous network. Specifically, UbiPaPaGo considers requirements and contexts of users and environment when planning the best-fitting path. UbiPaPaGo adopts the SCM model to present map context and uses GA to find the optimal path. The map simplifies conversion to graph, thus reducing the computational complexity and increasing scalability.
Acknowledgment
The research was supported by the NSC97-2221-E-194-011-MY3 and NSC97-2221-E-194-012-MY3, National Science Council, ROC.
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