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A grid-based collaborative virtualgeographic environment for theplanning of silt dam systemsLin Hui a , Zhu Jun a b , Gong Jianhua c , Xu Bingli a & Qi Hua ba Institute of Space and Earth Information Science, The ChineseUniversity , Hong Kong, Chinab Surveying Engineering Department , Southwest JiaotongUniversity , Chengdu, Chinac State Key Laboratory of Remote Sensing Science , Institute ofRemote Sensing Applications, Chinese Academy of Sciences ,Beijing, ChinaPublished online: 16 Mar 2010.

To cite this article: Lin Hui , Zhu Jun , Gong Jianhua , Xu Bingli & Qi Hua (2010) A grid-basedcollaborative virtual geographic environment for the planning of silt dam systems, InternationalJournal of Geographical Information Science, 24:4, 607-621, DOI: 10.1080/13658810903012425

To link to this article: http://dx.doi.org/10.1080/13658810903012425

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A grid-based collaborative virtual geographic environmentfor the planning of silt dam systems

Lin Huia, Zhu Juna,b*, Gong Jianhuac, Xu Binglia and Qi Huab

aInstitute of Space and Earth Information Science, The Chinese University, Hong Kong, China;bSurveying Engineering Department, Southwest Jiaotong University, Chengdu, China; cState KeyLaboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Academy

of Sciences, Beijing, China

(Received 19 February 2009; final version received 30 April 2009)

To improve the efficiency of planning and designing silt dam systems, this articleemploys theories and technologies of collaboration and distributed virtual geographicenvironments (VGEs) to construct a collaborative virtual geographic environment(CVGE) system. The CVGE system provides geographically distributed users with ashared virtual space and a collaborative platform to implement collaborative planning.Many difficulties have been found in integrating data resources and model procedures forthe planning of silt dam systems because of their diversity in heterogeneous environ-ments. Unlike most of the current distributed system applications, the proposed CVGEsystem not only supports multi-platform and multi-program-language interoperability inthe dynamically changing network environment, but also shares programs, data andsoftware in the collaborative environment. Based on creating a shared 3D space byvirtual reality technology, agent and grid technologies were tightly coupled to developthe CVGE system. A grid-based multi-agent system service framework was designed toimplement this new paradigm for the CVGE system, which efficiently integrates andshares geographically distributed resources as well as having the ability to build model-ling procedures on different platforms. At the same time, mobile agent computingservices were implemented to reduce the network load, process parallel tasks, enhancecommunication efficiency and adapt dynamically to the changing network environment.Using Java, JMF (Java Media Framework API), Globus Toolkits (GT) core, Voyager,C++, and the OpenGL development package, a prototype system was developed tosupport silt dam systems planning in the case study area, the Jiu-Yuan-Gou watershedof the Loess Plateau, China. Compared with the traditional workflow, the CVGE systemcan reduce the workload by between one third and a half.

Keywords: collaborative virtual geographic environment; grid service; planning; siltdam systems; mobile agent

1. Introduction

The Loess Plateau of northwestern China is one area of the world that has suffered the mostsevere damage from soil erosion. Silt dams are constructed across gullies to trap silt thaterodes during heavy downpours from watersheds or through small river systems. Theconstruction of silt dams is of significance to prevent the Loess Plateau from water and

International Journal of Geographical Information ScienceVol. 24, No. 4, April 2010, 607–621

*Corresponding author. Email: vgezj@163.com

ISSN 1365-8816 print/ISSN 1362-3087 online# 2010 Taylor & FrancisDOI: 10.1080/13658810903012425http://www.informaworld.com

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soil losses and to reduce the amount of sediment flowing into the Yellow River and to realizeeconomic, sustainable development in this area. At present, the dam-land occupies only 9%of the entire farmland areas in the Loess Hill Ravine Region, but dam-land grain yieldscomprise 23.5% or higher of the overall grain production of this region (Xu et al. 2006). Tohelp save its fragile ecosystem, a total of 163,000 silt dams are scheduled to be constructedby 2020, with a tentative budget of US$ 10.027 billion.

The planning and design of silt dam systems play an important role in the whole project.The traditional planning method for silt dam systems needs technical staff to implementlaborious fieldwork investigation and surveys, a large amount of indoor calculations,analyses, drawings, and reports. Such traditional planning procedures are time consumingand long. Meanwhile, under traditional methods, when evaluating the success or otherwiseof planning and designing silt data systems, experts need to look over the physiognomy ofwatersheds and the site condition of each key dam. This increases the workload and is timeconsuming. Therefore, there is a very urgent need to use modern information technologies toimprove the efficiency of planning. There are three important user requirements: (1)integrating current distributed data and model resources, (2) creating a visualization envir-onment to improve computing efficiency, and (3) supporting the collaborative planningwork.

The main objective of this study is to integrate agent, grid, and other collaborativetools for the planning of silt dam systems. Based on creating a shared 3D space byvirtual reality (VR) technology, agent and grid technologies are tightly coupled todevelop a new collaborative virtual geographic environment (CVGE) application para-digm. VR technology used as a medium for geographic visualization and analysis hasconsiderable potential to extend the geo-information visualization methods of 2D mapsor traditional geographic information systems (GIS). The grid platform can help inte-grate distributed data resources and various modelling software. Agent-based computingcan be good for adapting the dynamically changing network environment and improvingwork efficiency.

The remainder of this article is organized as follows. In Section 2, the development of theCVGE and related work is addressed. Features of agent and grid technologies are introducedand their advantages for the CVGE are discussed in Section 3. Section 4 focuses on how todesign a grid-based multi-agent system (MAS) service framework for the CVGE system.Some key problems in implementing the mobile agent (MA) computing service are dis-cussed. Section 5 describes how a prototype system was developed and rudimentaryexperimentation was implemented. Conclusions and future research plans are addressed inthe final section.

2. Related work

From the perspective of geography, Gong and Lin (1999) presented the concept of the VGEand defined VGE as a virtual representation of the natural world that enables a person toexplore and interact, in cyberspace, with vast amounts of natural and cultural information onthe physical and cultural environment (Gong and Lin 1999, Lin and Zhu 2005).Collaborative work among geographically dispersed workers has become very popularand important in both academia and industry. In the geoscience community, however, themethods and tools of cartography and geographic GIS have been developed for use byindividuals (MacEachren et al. 2005). Thus, the CVGE was proposed to support collabora-tive work on geospatial information exploration and analysis by creating a sharing digitallandscape environment.

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The new paradigm has drawn increasing attention in the fields of collaborative GIS andcollaborative virtual environments. Much related effort has been directed towards theintegration of the traditional GIS, geographical application models, 3D modelling tools(e.g. AutoCAD, 3-D Studio Max), collaborative tools (e.g. QICQ in China, Webcam, MSN,Second Life, Google Earth, Virtual Earth), and software development packages(e.g. ActiveX, VRML, Java and Java3D) to construct distributed virtual geo-environmentson the Internet. Using Java and Java3D, Hibbard et al. (2005) designed and developed aVisAD system to create applications that enable many users to implement the visualizationof a shared set of numerical geo-data and geo-computations. Based on Geometrek’sDeepMatrix 1.1, an existing multi-user virtual environment, Manoharan et al. (2002)established a collaborative urban planning prototype system to assist shared analysis ofurban planning proposals by visualization and interaction with spatial data. Maceachren andBrewer (2004) and MacEachren et al. (2005) address geospatial virtual environments anddialogue-assisted assisted visual environments, focusing on visual representation, explora-tory interaction, visually enabled dialogue, and team/group collaboration in the context ofgeo-visualization. Huang and Claramunt (2004) use Java/VRML to develop a 3D virtualenvironment based on a WEB browser, which allows users to assemble modelling andvisualization components with flexibility. Li et al. (2007) developed an integrated onlineGIS-enabled virtual public meeting space for overall municipal environmental assessmentprocesses.

Our VGE research team has also developed some VGE application systems. AnInternet-based virtual studio has been provided for public participation in Country Parkplanning (Lin and Gong 2002). Using Java, Java 3-D, and VRML, Gong and Lin (2006)developed a prototype system supporting some rudimentary applications in the planningof silt dam systems. An intelligent, VGE system has been developed for the simulationof SARS transmission (Gong et al. 2006). By adopting a hierarchy cluster model andP2P technology, a CVGE system has been developed to support large-scale intermittentusers in collaborative work (Zhu et al. 2007). A distributed VGE system-based webservice has been used for the simulation of water pollution in Yangzhou city, JiangsuProvince, China (Zhang et al. 2007).

Most of the above applications cannot be shared and are not interoperable becauseof their heterogeneous environments. Many difficulties have occurred in integratingdata resources and modelling procedures for the planning of silt dam systems becauseof their diversity. In this article, we employ theories and technologies of collaborationand distributed VGEs to construct a CVGE system for the planning of silt damsystems. The CVGE system aims to offer an intuitive, efficient, and interactivevisualization environment through which geographically distributed users can explorecomplicated spatial information and conduct collaborative work. Based on creating ashared 3D space by VR technology, we will focus on how to cope with three corechallenges: (1) sharing the distributed data resources (e.g. DEM data, RS data, modeldata), (2) dealing with the dynamic changing network environment, and (3) integratingthe heterogeneous software and hardware platforms (e.g. previous planning algorithmsand multimedia collaborative tools).

3. Agent-based computing and grid service

A MAS is a loosely coupled network of software agents which interacts to solve problemsthat are beyond the individual capacities or knowledge of each problem solver (Ferber1999). The MAS has been traditionally employed in artificial intelligence to solve highly

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complex distributed planning problems. A MA is an autonomous software entity that cantravel freely among the hosts in a heterogeneous network, interact with other agents or shareresources. The MA is a brand new computing technique promoted for solving the compli-cated and dynamic applications of distributed intelligence (Neo et al. 2005). This technologywill bring new vigour to CVGE system design and maintenance. It can migrate amongresources that are located in different administrative domains. The agents’ tasks can includemaintaining database consistencies, user interactions, scene updating, and graphics render-ing. Moreover, the MA is also tasked with resource discovery, scheduling, and monitoringroles.

Invented in the 1990s, grid technology aims to integrate computational resources, dataresources, and storage resources scattered on the Internet to construct a virtual supercomputer that users all over the world can access through the Internet (Foster andKesselman 1998). Therefore, the grid computing system can provide different kinds ofservices for users. Essentially, grid users see a super virtual computer despite the fact that thepool of resources are geographically distributed and connected over the worldwide net-works. Open grid services architecture (OGSA) represents the next generation of gridarchitecture and has evolved from the early five levels hourglass model architecture.A basic premise of the OGSA is that everything is represented by a service. OGSA canintegrate services across distributed, heterogeneous, and dynamic ‘virtual organizations’formed from disparate resources within an individual enterprise and/or from externalresource sharing and service provider relationships (Foster et al. 2004).

Agent and grid communities both develop concepts and mechanisms for open distributedsystems. The combination of grid and agents can offer useful opportunities for exploiting theCVGE system capabilities. Grid technology can offer a robust, distributed computing platformthat allows the CVGE to discover, acquire, federate, and manage the capabilities necessary toexecute decisions. Adopting an agent-oriented approach means decomposing the CVGEsystem into multiple, interacting, and autonomous agents. These agents may provide avaluable grid function, with the collection of agents implementing a robust global resourcemanagement behaviour that might not otherwise be achieved. An agent-based approach canimprove the CVGE with flexible, decentralized decision-making capabilities. Compared totraditional distributed computing systems, the proposed CVGE architecture will have obviousadvantages.

4. Design of MAS service framework and its key technologies

4.1. Design of MAS service framework

Our approach in this article implements agent systems on top of grid mechanisms. Ourdesign of the CVGE system architecture is based on MAS and grid technologies (Figure 1).TheMAS service framework is divided into three layers: the resource layer, the service layer,and the user layer. These are explained in detail as follows:

4.1.1. The resource layer

The resource layer contains local and remote resources, which are registered into a resourceregistry centre for sharing. This is the foundation of the whole system. The resources serviceenvironment provides a set of resources located within one or more administrative domainsthat can support native facilities for service management. These are managed by resourceagents in the service layer and the user layer.

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4.1.2. The service layer

The service layer is built upon the resource layer. The service layer is the core of the CVGEsystem. It ensures that the whole system runs correctly. Within this layer, a series ofmanagement toolkits and protocol criteria are defined for sharing, integration, and inter-operation of all resources in the resource layer. Meanwhile, transparent and unified accessinginterfaces of resources and services are developed to support applications in the user layer.The service layer is comprised of three kinds of functional component: the MA servicesplatform, the grid basic service platform, and advancedMAS services. By means of messagemechanisms, agents in the service environment with different functions can be organized toimplement collaborative work or tasks. The cooperative agent can control the whole systemand ensure the work of all kinds of agents. Its main task is to take on overall dispatchmanagement and to coordinate all functions. Other agents can take on other, specifiedfunctions and be organized to resolve tasks under the unified deployment of the cooperativeagent.

Grid basic services

Register service Mobile agent service Model service Other application services

Logic service, index, information, security, state monitor, task solving,

metadata, scene, user management, etc,

Route choosing, staying, dynamic route control, mobile agent backup, failure recovery, state checking, etc,

Mobile agent services

Resource agent Function agentIndex service agent Knowledge agent Scene agent

Information agent Cooperative agent Task schedule agent

Resource agent

Function agent

User agent Spatial analysis

Visualization expression

Audio/video collaboration

Data resources, computing resources, storage resources, information resources, knowledge resources, software resources, model and algorithm resources, manpower resources, mobile agent host system, etc,

User interface control

Information query

Model computing

Resources services environment

User layer

Service layer

Resource layer

Figure 1. Grid-based MAS service framework.

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4.1.3. The user layer

The user layer is the interface between the CVGE system and its clients. It provides functionsfor different applications. Its task is to design useful, transparent, reliable, scalable, andsecure applications for users. Agents in the user layer are divided into user agents, resourceagents, and function agents. A user agent helps the client to more conveniently and rapidlysubmit and monitor tasks and obtain final results. The resource agent manages the localdatabase, and the function agent manages local application functions (e.g. scene simplify-ing). These agents can cooperatively help the client implement some basic functionmodules, such as the geographic scene module, spatial layout module, and collaborationwork module.

4.2. Mobile agent computing service

As discussed in Section 4.1, many agents and services are involved in the CVGE system.Considering the basic environment, this section focuses on two areas: first, how to design theMA computing model, and second, a discussion of the two key problems of the dynamicroute control algorithm and the creation of the dynamic MAS cooperative group. All effortsare aimed at implementing the MA computing service, which can adapt dynamically to thechanging network environment and flexibly migrate among hosts.

4.2.1. Mobile agent computing model

The MA computing model is designed as shown in Figure 2, which is composed of theMA, agent host (host’s system), and cooperative agent. The agent host is a basic, mobileagent service environment (MASE) existing on each node of the CVGE system. It iscompatible with the CVGE system and does not influence functions of the original system.According to the client’s demands, the user agent can visit the function information baseand resources lists and implement problem solving and route choosing (Section 4.2.2).Moreover, a MA is created and migrates among corresponding host systems to implement

Sending out

mobile agent

MASE

Host A

Accepting

mobile agent

Computing and

returning results

Cooperative

agent

Host B

online ?

Select new hostNo

Host B

busy ?

Yes

Yes

Return results and

removing task Host A

online ?

Yes

No Save results

Agent initializing

and read task

transmission

?

Yes

No

No

MASE

Host B

Figure 2. Mobile agent computing model.

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tasks. Because the agent host and corresponding resources are placed in the same node,the MA can directly access resources in the local system, which effectively avoids datatransmission within the network. The cooperative agent server is designed as a MAswitching system, which can adapt to a low dependability network and deal with networkcongestion.

4.2.2. Dynamic route control algorithm

The route strategy is the core of the MA computing and ensures it runs correctly. Figure 3shows the flowchart of the dynamic route control algorithm. After one user agent submitsthe task request to the service layer, the cooperative agent decides whether this problem canbe resolved bymeans of other function agents (e.g. using the resource agent to check whetherthe requested data can be found in the CVGE system). If it can, the cooperative agent willorganize all agents related to the task to create a dynamic group, and an initial route path isset up. According to the user node state, the route relation is dynamically adjusted until thetask is finished. In the course of implementing the task, the cooperative agent continuouslymonitors all user nodes’ states and maintains a dynamic MAS cooperative group to ensurecorrect implementation of the task. If one node withdraws from the system, the coopera-tive agent searches for a substitute and the route path is updated. Meanwhile, thecooperative agent also offers the stay service, whereby the MA can remain temporarilyin the server node. The cooperative agent regularly backs up the MA. If the message fromthe MA has not been received under certain limiting conditions (e.g. one predefined time),the route path will be updated and one backup MAwill be sent out to continue executingthe task.

User agent Cooperative agent

Setting up route path

Achieving

task ? Other user agents

Independently

solving ?

Yes

No

No

Yes

State monitoring

Creating MA

Searching replaceable node

Negotiating

Updating route

MA backup

One node

failure ?

YesOne node finishing task

Figure 3. Flowchart of dynamic route control algorithm.

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5. Prototype system implementation and application experiments

5.1. Prototype system implementation

Our study aims to build a grid-based MACVGE system across the Internet to support spatialplanning of silt dam systems. To support different software and hardware environments, it isbetter to choose a development platform that is descriptive and independent of specificlanguages. Java is an ideal language in which to develop grid services and implement mobilecomputing. After the compilation, Java binary code can be executed by any cross-platformJava interpreter. Thus we used Java, JMF (Java Media Framework API), Globus Toolkits(GV) core, Voyager, C++, and the OpenGL development package to develop our prototypesystem for the planning of dam systems. Figure 4 is a snapshot of the client-side interface ofour prototype system.

In geographic environment modelling, we used basic scene data including the SPOT 5remote sensing image and the 1:10,000 scale DEM data. We implemented fast terrain render-ing using a high-efficiency level of detail (LOD) algorithm (Zhu 2007). C++ and OpenGLwere used to deal with a LOD algorithm for improving terrain scene rendering speed, whichcan be compiled as a DLL file to support Java displaying the virtual scene by means of the JNI(Java native interface) methods. Figure 5a shows the virtual 3Dwatersheds with a sub-regionalDEM overlaid by a SPOT 5 image. Figure 5b shows the terrain scene rendered with wireframewhen the LOD screen projection error is 1. In Figure 5b, the terrain scene includes 10,171triangles, and its simplification ratio is 1.8%, the rendering speed ratio is 60 frames per second(Celeron 1.7 GHZ CPU, 256Mmemory, and 32M integrated graphics card). Thus, the abovemethod can improve the rendering efficiency of a large-scale terrain dataset.

We use the Java language to develop an online chat room, which is a way of commu-nicating by sending text messages to people in the same chat room in real time. Meanwhile,

Figure 4. A snapshot of the client-side interface of a CVGE system.

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the JMF technology was used to develop video collaboration so that users may actually seeeach other. Each user can have two or more video windows in the video collaboration,including local video display windows and collaboration. If a user becomes the presidingauthority, he or she can capture the local video image and send it to other members in thecollaborative work. Users who do not have the video device or the authority of presiding canonly monitor the network port to receive and display video images.

Meantime, when a user edits objects in 3D scene space (e.g. different thematic modelscan be added or removed in 3D virtual space), other online users can also see it. Anychanges made by one user will be automatically broadcast to other collaborators and theirdisplays will be updated as well. To avoid the concurrency of 3D objects operating atthe same time, the method of token mechanism combined with priority is used to controlthe concurrency of the authority of presiding. The token mechanism has several basicfunctions: reclaiming or obtaining a token and maintaining a request queue. The user withthe highest priority firstly obtains the token and other users form a waiting queue accordingto priority.

Each grid service in the CVGE system implements one or more interfaces, each of whichdefines a set of operations. Some graphic interfaces have been developed to shield compli-cated physical details and help clients more conveniently implement their task. In ourprototype system, three resource registry interfaces are offered. The interface of the userinformation registry service uploads some information parameters including user name, usercode, memory, IP address, CPU, and bandwidth. The interface of the data informationregistry service can register data name and type, data path, and attribute description. Theinterface of the function information registry service is responsible for sharing software andprocedures. The cooperative agent can specify the resource agent to set up the user manage-ment list as shown in Table 1. This includes user’s name and password, user’s IP, andresource attributes (Table 2). According to different data resource information characteristics,corresponding resource identification is set up to describe data and information resources. Alldata distributed in different user nodes can be registered into a shared resource. All theinformation about the state of these resources is stored as service data. By means of the abovemethod, all data in the CVGE system environment can be integrated and form a virtualuniform database.

(a) (b)

Figure 5. Three-dimensional terrain scene. (a) Terrain scene after using texture. (b) Wireframeterrain scene.

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The implementing mechanism of the MA computing is shown in Figure 6. Voyager isused to implement MA development. Voyager, from Recursion Software, Inc., is a pure Javadistributed computing platform that can be utilized to rapidly produce distributed applica-tions of high capability. The Voyager agent system platform offers the class Agent, whichcan convert any serializable objects into an object of the type IAgent. MoveTo, a method, isoffered to realize the movement of an object to a remote host. The moving object is called theMA. After the migration of the MA, all its local references can be released, although it is notdeleted on the remote host. In the course of implementing the task, the MA executes severalsteps on every host, including initialization, execution of task, computation of the remainingroute, and migration to the next host.

5.2. Application experiments

In the case study area of the Jiu-Yuan-Gou watershed, Suide County, Shanxi Province,China, with a total area of 70.7 km2 and a length of 18 km of the major channel, there are 202

Table 1. User management list.

User Code IP address Information ID

Zhu 123 159.226.117.115 A1, B1, . . .Du abc 159.226.117.119 A2, B2, . . .Huang ABC 159.226.88.22 A3, B3, . . .

Table 2. User information list.

Information ID CPU speed Memory size Bandwidth Other Information

A1 2.4 GHz 512 MB 10 M . . . . . .A2 1.8 GHz 256 MB 10 M . . . . . .A3 2.0 GHz 256 MB 512 KB . . . . . .

Internet

MASE

Interactive computing

MA

Local resources

MASE

Interactive computing

MA

Local resources

Migrating

Figure 6. Implementation mechanism of the mobile agent computing.

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silt dams, of which 17 are backbone dams (big dams) and 185 are ordinary dams (middleand small dams). The whole dam system is typical of the massive silt dam constructionprojects on the Loess Plateau. The planning flowchart for the silt dam system can be shownas in Figure 7. The VGE provides functionalities for silt dam systems planning.Geographically distributed participants can design the planning scheme of silt dam systemsand share their opinions or ideas by means of collaborative tools (e.g. text, video, shared3D space).

For example, with regards to the spatial distribution of dam systems, users can firstset up some dams based on their own knowledge. According to the practical production,the backbone silt dam should be responsible for an area of 3–10 km2, with the middleand the small silt dams less than 3 km2. The CVGE system supports distributedresources sharing by means of grid services. Some planning models, such as topo-graphic properties calculation and ideal distribution of dams, have been packaged intothe MA to be automatically calculated for the whole network. The initial spatial settingof dams will be set up. According to different rules and experiments, such as theshortest time of silt-deposited land, other participants in the CVGE system can thenuse the collaborative working environment to discuss and modify this scheme to obtainthe ideal distribution. Figure 8a shows an ideal distribution of 2D dams and Figure 8bshows the distribution of dams in 3D space.

Some specialized tasks, such as the drainage network extraction, can be packaged intothe MA that can migrate to the data client and implement local data accessing. Someexperiments on drainage network extraction were implemented to test the MA computingefficiency (Celeron 1.7 GHZ CPU, 256 M memory, and Windows XP Operating System).

Database supporting

The initial layout scheme

Benefit calculating Investment budget

Single dam design

Water level calculating, Reservoir characteristic

Scheme design

Dam layout Hydrological featuresPotentiality for dams

Final layout scheme

Yes

No

Sediment analysis

Optimum scheme?

Figure 7. Planning flowchart for the dam system.

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Table 3 displays test results of the performance comparison (data value is the average of threetimes) between the traditional client/server (C/S) computing mode and the mobile comput-ing mode. In the C/S computing mode, only the centralized management server is respon-sible for data and calculation tasks. After the client finished calculations, the results can bereturned to the server. This mode results in a large amount of data transmission between theclient and the server. However, the MA can automatically migrate to the remote host andimplement the specified computation in the MASE. The movement and the execution of theconfiguration of the task will continue until all hosts listed for configuration are visited. Inthe course of implementing the task, the cooperative agent keeps monitoring the wholecourse of the MA computation, carries on the dynamic route adjusting and offers the stayservice for the MA. After the task is finished, the result is conveyed to the user and the MAunloads all tasks and finishes the task cycle. Test results demonstrate that the mobilecomputing mode can reduce the flow of data in the network. As the network bandwidth isreduced, the advantage of MA computing becomes more obvious.

For a practical application of dam system planning, other factors, such as safety, socialconsiderations (e.g. population distribution), and economic (e.g. investment) elements,are also considered. Based on the ideal distribution of dam systems, participants in theCVGE system may adjust different parameters to optimize the distribution of dam systemsand create multi-tasks. MAs can create a cascade of clones in the network when admin-istering parallel processing tasks, which will help increase efficiency and reduce proces-sing time. Multiple MAs have the unique ability of distributing themselves among thehosts in the network to maintain the optimal configuration for solving the above problems.

Table 3. Performance comparison of mobile agent-based computing with C/S mode.

Test results Data transmission time (ms)

Computingmode

Amount of datatransmitted

10 M(bandwidth)

512KB(bandwidth)

52KB(bandwidth)

Mobile computing 29.9 KB 12.643 (ms) 214.6 (ms) 1929 (ms)C/S computing 2.863 MB 2210 (ms) 29238 (ms) 280686 (ms)

(a) (b)

Figure 8. Computation of ideal dam systems. (a) Ideal distribution of 2D dams. (b) The distribution of3D dams.

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Figure 9 shows experimental results of parallel processing based on MAs (Celeron1.7 GHZ CPU, 256 M memory, and Windows XP Operating System). The bandwidthof the experimental network is 10 M and the CVGE system has 10 computers. The resultsindicate that the computing efficiency can be improved when the number of MAsincreases. But the efficiency improves slowly after the number of MAs exceeds 10.This may be caused by the network bandwidth or the number of computers in theCVGE system. The above experiments illustrate that parallel computing based on MAscan save time when processing multi-tasks and improve collaborative work efficiency.

Moreover, MAs based on a grid service environment can react dynamically tounfavourable situations and events, making our system more robust and fault-tolerant.The MA with intelligent routing can sense the execution environment and react auton-omously to changes. It dynamically determines its next move based on the load ofservers and networks. If a host in the CVGE system is being shut down, all agentsexecuting on that machine are warned and given time to dispatch and continue theiroperation on another host in the network. If the message from the MA has not beenreceived under certain limiting conditions (e.g. a host suddenly shutting down at onepredefined time), the route path will be adjusted and one backup MA will be sent out tocontinue executing the task.

The above experimental results show that the scheme developed in this article is efficientand feasible. The CVGE System has been applied to the Jiu-Yuan-Gou watershed, SuideCountry, China. It can integrate current distributed data and model resources, support thecollaborative planning work, and use a visualization environment to improve computingefficiency. Compared with the traditional workflow, the practice application has illustratedthat it can reduce the workload by between one third and a half. Thus, the efficiency of theplanning of silt dam systems has been noticeably improved.

6. Conclusions and discussion

The study of geo-collaboration involves diverse aspects ranging from participants andorganizations to mediated tools, geo-problem contexts, and supportive environments.Our research presents an effective way to implement a CVGE system based on agentand grid technologies, which allows distributed users to implement the collaborative

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Figure 9. Parallel computing based on mobile agents.

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planning of silt dam systems. The prototype system was successfully applied to aproject of collaborative silt dam planning and design in the Loess Plateau. As a result,our study can offer a paradigm that will help promote the application of informationtechnologies to geographic information sciences. Our main contributions can be sum-marized as follows.

Firstly, grid technology was used to enhance the overall performance. A grid-basedMAS service framework can support multi-platform, multi-architecture, and multi-program-language interoperability on the Internet. It can be unrestricted in specifiedoperation platforms and system environments and some existing visualization algorithmsand programs can be efficiently utilized. All users can be allowed to register theirresources and publish resource sharing services. This helps integrate geographicallyseparated resources and platform heterogeneous model programs into a unified CVGEsystem for resources sharing.

Secondly, the MA-based computing mode was used in our collaborative planningenvironment. The mobility in the heterogeneous network environment of software andhardware brings new vigour to the CVGE system design and maintenance and providesmany benefits over those of traditional distributed computational models. It is able toeffectively reduce the network loading, enhance communication efficiency, and adaptdynamically to the changing network environment in an experimental environment.

Finally, a prototype system was designed and developed. The implementation schemewas discussed in detail. The complete dam system in the Jiu-Yuan-Gou watershed repre-sents a typical example of a massive silt dam construction project on the Loess Plateau,Shanxi Province, China. The study employs the example of the watershed to explore themethodologies of collaborative spatial planning of silt dam systems. Using the prototypesystem, participants can implement communication with each other via media tools, mainlyin the virtual collaborative space, and execute 3D editing of shared dams, calculations oftopographic properties, and view the ideal spatial distribution of dam systems in the VGE.

The development of CVGE systems is still ongoing. The CVGE will resolve somepotential challenge such as resource tracking when autonomous MAs are given system-wide access, and coordination of agent construction and destruction in dynamic,unstable network environments. In fact, there are still some potential drawbacks of themobile system relative to a client–server approach. Firstly, we have to set up a runtimeenvironment in clients to support correctly implementing mobile computing and also useredundant servers to avoid a single point of failure and a potential bottleneck. These willresult in increased complexity and cost, especially in the stage of system developmentand update. Secondly, another drawback is the security risk involved in using MAs.An agent platform typically limits the capability of an agent to execute code createddynamically, as it is considered an unsafe operation. Finally, Java language does notprovide any means to control over the resources consumed by the Java objects. It meansthat if an agent starts looping indefinitely, it will waste processor cycles and startconsuming memory resources.

With continuous development, agent and grid technologies will play more importantroles in the implementation of CVGE systems. In fact, more applications should beimplemented to support the planning of silt dam systems. For example, the field workerscooperate with indoor experts by means of mobile devices such as mobile phones andpersonal digital assistants. Current computations in mobile equipment rely heavily onexpensive but fragile network connections and require an uninterrupted connectionbetween the mobile devices and the fixed network. This is not economically andtechnically ideal. MA-based computing can solve this problem.

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AcknowledgementsThis research is supported by CUHK RGC Project No. 447807, the National High Technology Plan(863) of the People’s Republic of China, Project No. 2006AA12Z204, and Project No.2009AA12Z207. The authors thank three anonymous reviewers and editors whose comments havenotably improved the manuscript. Finally, the authors are particularly grateful to Dr. Huang Bo for hisvaluable and helpful comments on the original manuscript.

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