Ambient Spatial Intelligence

Matt DUCKHAM 1, Rohan BENNETT,Department of Geomatics, The University of Melbourne, Victoria 3010, Australia

Abstract. Computing in distributed systems increasingly occurs somewhere, withthat location being integral to computational process itself. Ambient spatial intel-ligence (AmSI) is concerned with embedding the intelligence to respond to spa-tiotemporal queries and monitor geographical events in built and natural environ-ments. The emergence of AmSI is enabled by new spatial computing technology:geosensor networks (wireless networks of sensor-enabled, location-aware comput-ers monitoring environmental change). This chapter argues that decentralized spa-tial computing (DeSC), where spatial information is partially or completely filtered,summarized, or analysed in a geosensor network, is a fundamental technique re-quired to support AmSI. By contrast, existing models for complex spatiotempo-ral analysis and queries almost always adopt a centralized approach to computa-tion, where global spatial data is collated and processed, for example in a spatialdatabase or GIS. The chapter identifies four main research challenges facing DeSC(dealing with uncertainty, dynamism, information integration, and interfaces), andillustrates the importance of DeSC to AmSI with reference to three key AmSI ap-plications: management of the environment, infrastructure, and people. Finally, alegislative analysis of one specific domain of human activity, government acts inAustralia, illustrates the potential for increasing importance of AmSI in the nearfuture.

Keywords. geosensor network; decentralized spatial computing;

1. Introduction

Ambient spatial intelligence (AmSI) is concerned with embedding in built and naturalenvironments the intelligence to respond to spatiotemporal queries and monitor geo-graphical events. Recent technological advances in mobile computing, wireless commu-nications networks, and MEMS (microelectromechanical systems) sensors are resultingin many new types of miniaturized computing devices that can be implanted in everydayobjects and environments. In particular, geosensor networks (networks of sensor-enabledcomputers tasked with monitoring geographic phenomena [33]) are a key technology forimplanting spatial computing capabilities in geographical environments. Technologieslike geosensor networks can provide spatial information capture and processing servicesto assist in a wide range of applications, from assisted living to traffic management, envi-ronmental monitoring to emergency response. As a result, computing increasingly occurssomewhere; the location where information is generated and processed is increasinglyintegral to the use of that information in diverse human activities.

1Corresponding Author: Matt Duckham, Department of Geomatics, The University of Melbourne, Victoria3010, Australia; E-mail: matt@duckham.org.

For example, understanding the high-level patterns of movement for groups of mo-bile individuals is important in the domain of behavior monitoring. A range of sophis-ticated tools and algorithms for identifying movement patterns such as flocking, lead-ership, and convergence have been proposed and developed (see Laube, and Wood andGalton, this volume). These tools can help to support many different applications, suchas identifying flows and jams in traffic management; coordination and identification ofpatterns of movements in crowds for reasons of security and safety; and monitoring ofstock movements in smart farming. However, today most of these techniques rely on cen-tralized models of spatial computing, where movement data is collected, collated, andprocessed in traditional spatial databases or GIS. Embedding the intelligence to monitorand identify salient movement patterns in the environment itself can help in developingmore efficient, robust, and responsive information systems, where ad hoc collaborationbetween nearby devices and individuals forms the basis for high-level knowledge gener-ation [25].

This paper explores the foundations of AmSI, and the impact of AmSI in support-ing existing, and developing new spatial applications. In particular, the paper argues thatdecentralized spatial computing is a key technique for realizing AmSI applications. Sec-tion 2 surveys the background work, in particular focusing on existing research into am-bient intelligence and geosensor networks. Section 3 then defines and explores the role ofdecentralized spatial computing in AmSI, with four major DeSC research themes iden-tified and explored in Section 4. Section 5 then introduces the range of applications thatcan be supported by AmSI, highlighting the importance of spatial computing in thoseapplications. Finally, Section 6 concludes the chapter, looking forward at the shape of anew generation of AmSI applications.

2. Related work

The vision of AmSI has its roots in the visions of ubiquitous computing [44] and morerecently ambient intelligence (AmI) [9]. There are many different, related definitionsof AmI, but all are concerned with the idea of embedding context-sensitive computingdevices in our environment to allow individuals to access information services through“natural” interactions with the environment. The key features of AmI include embed-ded computing (unseen computing devices integrated with everyday objects and envi-ronments); ease of interaction (allowing users a range of natural ways to interact withinformation without display screens and keyboards); and context-awareness (automati-cally sensing the immediate environment, adapting to changes in that environment andto habits of users, and anticipating future user requirements) [1, 14].

2.1. Relationship between AmI and AmSI

The vision of AmSI differs in two important respects from AmI. First, AmSI is morenarrowly focused on spatial intelligence. Many of the problems of AmI are not explic-itly geospatial, such as how to monitor muscle activity or manage medication regimesin telemedicine [19, 23]. However, location is critical to the meaning of information ina wide range of AmI applications, such as stray prevention systems for the elderly [27].Further, beyond simple tracking of individuals’ movements, AmSI applications often re-

quire the generation of higher-level spatiotemporal information about geographic phe-nomena, like the evolution of regions of high temperature, or the emergence of flocks.Integrating such spatial intelligence in ambient intelligence environments remains an im-portant gap in the existing AmI literature, and a key challenge for AmSI.

Second, AmSI is more broadly focused on communication of information not onlyto individual users in the environment, but integration with other spatial data sources andwidespread dissemination of information to both local and remote users. Spatial infor-mation is by its nature a global framework with broad application to diverse applications.Thus, it is to be expected that the information services for an individual draw on widerspatial data sources, as well as being relevant to wider application domains. For example,when tracking the movements of a group of people at a large public meeting or event,such as a soccer match, the formation of crowds (flocks) is likely to be of interest to in-dividuals in that group, as well as event organizers, security, and safety managers (espe-cially when combined with other spatial information about the layout of the event venue,and links to other spatiotemporal information sources, such as public transportation in-formation). Thus, where AmI focuses specifically on issues of provision of personalizedinformation to users, AmSI requires a broader focus on the entire range of spatial datasources and users, including remote data sources and users.

2.2. Geosensor networks: Technolgy for AmSI

A central technology for AmSI is geosensor networks. A geosensor network is a wire-less sensor network—a digital wireless network of miniaturized, sensor-enabled com-puting devices (called sensor “nodes” or “motes”)—that monitors changes in geographicspace [33, 51]. This technology can already be embedded in a range of built and nat-ural environments (see Figure 1), to monitor local changes to those environments andcommunicate that information to other nearby nodes, in turn processed and relayed toremote spatial databases and GIS. While today’s technology means sensor nodes are stillrelatively large (a few cubic centimeters), in the future much smaller nodes are expectedto be developed, of less than cubic centimeter in size (e.g., so-called “SmartDust” or“PicoNodes,” [17,34]). Further, these nodes can be equipped with the capability to posi-tion themselves, for example using GPS or RF (radio frequency) or ultrasound range- ordirection-finding [22].

Thus, there exists both the technology (embedded geosensor networks) and the need(generating and processing real-time spatiotemporal information about dynamic envi-ronments, integrated with wider spatial information systems and applications) for AmSI.The following section examines in more detail a key hurdle facing any AmSI application:the role of tools and algorithms for decentralized spatial computing.

3. Decentralized spatial computing (DeSC)

Traditional spatial computing systems, like spatial databases and GIS, provide power-ful tools for spatial data storage and analysis, developed and refined over the past fourdecades. Underlying the design of all these systems is centralized architecture, wherelarge-footprint spatial data repositories for collation, integration, and processing of rele-vant spatial data. In effect, such systems assume global knowledge, in the sense that all

Figure 1. iMote2 sensor node deployed in urban parkland (without housing) as part of a geosensor network

data relevant to the application are available in one (logical) location. However, theseconventional centralized architectures cannot hope to satisfy all the requirements ofAmSI for at least three reasons:

1. Information overload: With the development of spatial data capture technolo-gies like remote sensing, aerial photogrammetry, LIDAR, and GPS (in particu-lar personal positioning systems), society today has already moved from a spa-tial information-poor to a spatial information-rich state. However, the rate of in-formation capture is set to accelerate as AmSI technologies, like geosensor net-works, have the capability to generate overwhelming amounts of data. More im-portant than the data volumes per se, is that individual data items generated bya geosensor network (for example, a specific temperature reading from amongstan array of sensors; mounted on a particular node out of thousands in a network;and at a particular second out of weeks or months of continuous monitoring) areoften almost meaningless. Only when this data is aggregated and processed domeaningful spatial patterns emerge. Thus, techniques for filtering, summarizing,and processing spatial data in the network are essential to avoiding informationoverload. Centrally collating, storing, and processing all spatiotemporal data isneither practical nor desirable, and risks information overload.

2. Resource limitations: Technologies for AmSI, like geosensor networks, are typ-ically highly resource-limited computing environments. Embedded computingdevices must often rely on their own power supplies, either from batteries orharvested from the environment, placing major constraints on energy resources.Low power wireless communication from small-footprint embedded devices alsopresents major resource constraints, for example on communication distances aswell as available bandwidth for data. Given these resource limitations, a central-ized architecture where all data is communicated to a small number of centraldata stores also leads to unscalable systems, with single points of failure. Pro-cessing data in the network can help to reduce the communication resources re-quired by a network, and spread the resource usage across the network, makingthe system more scalable and more robust to failures in individual nodes.

3. Sensor/actuator networks: In an AmSI application, the information generatedabout the environment will often be required by users or other systems close tothose locations where the information was generated (termed sensor/actuator net-works). A farmer requesting information about the soil salinity in a particularfield, or a police office requiring information about crowd movement at an event,for example, will often be physically interacting with the environment at the sametime as requesting information about that environment. In such situations, com-municating information to a remote centralized repository, processing it, and thenreturning it to the particular user is inefficient, potentially significantly increasingresource usage as well as latency in query responses. Here again, locally process-ing data in the network, close to the source of a query, can improve efficiency androbustness of AmSI applications.

As a result, a key research question for AmSI is how to achieve decentralized mod-els of spatial information processing. A decentralized system is a special case of a dis-tributed system (where multiple computing units must cooperate via a digital communi-cations network in order to complete some task [48]). Specifically, a decentralized systemis a distributed system where no single computing unit has access to knowledge aboutthe entire system state [29]. For example, in computing the area of a region of high soilmoisture monitored by a geosensor network, a centralized architecture would commu-nicate all the individual sensor node locations and sensor readings back to a centralizedGIS or spatial database, which would then construct the boundary of the region and com-pute its area. However, in a decentralized system, computing the area might only involvecommunication and collaboration between those nodes that can locally determine theyare the boundary of the region. In such a decentralized system, it is never necessary forany node to know the entire system state, only boundary nodes need to be involved in thequery.

Decentralized computing is already an important research topic in the areas of wire-less sensor networks (e.g., [15,30]) and AmI (e.g., [14]). However, decentralized spatialcomputing (DeSC) presents particular challenges and opportunities distinct from broaderwork in decentralized computing. More specifically, geographical entities have spatial(and temporal) extents. Dynamic geographic entities, like evolving regions or crowds,typically require coordination between nodes that are spatially far apart. Therefore, de-centralized spatial algorithms must provide the capability to monitor zonal or global ge-ographic phenomena based only on locally sensed data and local collaboration betweennearby sensor nodes. Consequently, the need to provide information about global geo-graphic phenomena based on local spatiotemporal knowledge is the central challengefacing decentralized spatial computing.

Despite this fundamental challenge, DeSC also provides an important opportunity.The structure of information about geographic phenomena is inherently autocorrelated;nearby things are more alike than spatially distant things [42]. Thus, DeSC informationsystems and algorithms can potentially take advantage of this underlying structure, inmany cases through an expectation that spatially (and temporally) nearby informationis more relevant to the query or analysis at hand than more distant information. To il-lustrate, in the earlier example of decentralized computation of the area of a region, theinherent spatial structure of the region means that a node at the boundary of a region canexpect in its immediate neighborhood other nodes that are also at the boundary of theregion. Similarly, when designing a decentralized algorithm for finding parking spaces in

busy urban environments, spatially and temporally proximal vehicles are more likely topossess information relevant to each other about open parking spaces [46]. In short, theopportunity for DeSC is to use the inherent structure of spatial information to structurecommunication and computation within a decentralized network.

4. Research issues in DeSC

While the structure of spatial information provides the “grand” challenge and opportu-nity to DeSC, it is possible to identify four major research themes within DeSC, eachconnected to established research issues in geographic information science: uncertainty,dynamism, integration, and interaction issues. This section describes in turn each of theseissues and how they relate to DeSC, illustrating the importance of the particular issue toAmSI with a specific use case.

4.1. Uncertainty

Uncertainty is an unavoidable feature of spatial information, caused by imperfectionsin information. The two most important categories of imperfection in spatial informa-tion are inaccuracy (which concerns a lack of correctness in information) and impreci-sion (which concerns a lack of detail in information) [48]. A long history of researchinto uncertainty in spatial information has yielded a range of techniques for managingand reasoning about imperfect spatial information (e.g., [43]). However, AmSI presentsparticular problems with respect to inaccuracy and imprecision because:

• unlike conventional spatial data capture systems, usually subject to careful qualitycontrols, AmSI makes use of a wide range of poorly calibrated, low-cost, andinherently inaccurate embedded sensors; and

• the high spatial density and high temporal sampling frequency of sensor nodesmeans that AmSI data sources, like geosensor networks, often generate informa-tion at different, and usually finer levels of spatial and temporal detail (precision)than conventional spatial data sources, like ground survey and remote sensing.

The need to use DeSC in AmSI systems can lead to more subtle uncertainty ef-fects. For example, [36] show how a selection of different neighborhood structures usedin geosensor networks can affect the accuracy of topological and metric decentralizedspatial algorithms for monitoring regions. Network granularity effects can lead to dif-ferent results for decentralized spatial algorithms because the neighborhood structure istypically used as the basis for inferring the location of the boundary.

Further, the constraints of local processing in DeSC means that in some cases usingapproximate rather than exact algorithms may be beneficial. For some DeSC algorithms,using local knowledge may be able to approximate centralized spatial algorithms withglobal knowledge, but with the benefit of improved efficiency and scalability. For ex-ample, [25] explore a decentralized spatial algorithm for detecting movement patternsin mobile geosensor networks. The work shows how even approximate algorithms canproduce useful information about movement patterns like flocks.

As a result, building robust decentralized spatial algorithms that can generate usefulknowledge about the environment in the presence of uncertainty is important capabilityfor AmSI systems, and an ongoing research theme.

4.2. Dynamism

AmSI is by definition explicitly dynamic, and concerns the generation and use of spa-tiotemporal information about geographical events and changes in built and natural en-vironments. Traditional approaches to information about dynamic geographic environ-ments typically represent change as a series of static “snapshots” of the state of the world.However, more recent research interest has focused strongly on the explicit representa-tion and processing of dynamic entities, like geographical events and processes [16, 49].For example, [50] investigate the development of formal models of DeSC for monitoringtopological changes in the evolution of regions. The approach focuses explicitly on thedetection of events, like the appearance, disappearance, merging, and splitting of regions.

In addition to dynamism in the environment monitored using AmSI, the technologi-cal infrastructures for AmSI themselves may also be dynamic. For example, researchersare already investigating the importance of mobility in geosensor networks (e.g., [2,25]),where individual sensor nodes are attached to mobile entities like vehicles, animals, orindividuals. In addition to mobility, another form of dynamism in geosensor networksis volatility, where individual nodes within a network can turn themselves on or off de-pending on their environmental or network context. In [11], for example, an algorithmfor “georesponsive” sensor networks is proposed, in which individual nodes can coordi-nate turning themselves on or off depending on environmental changes in their vicinity.In effect, a georesponsive sensor network can behave like a spatial “zoom,” increasingthe spatial density of the network in locations where interesting changes are occurring,and decreasing spatial density in stable areas to preserve network resources.

Consequently, systems and algorithms to support AmSI are needed that can generatespatiotemporal information about highly dynamic environments, even in situations wherethe AmSI infrastructure itself may be in flux.

4.3. Integration

New combined spatial information systems and data sources, like geosensor networks,are enabling technologies for AmSI. However, such technologies complement, ratherthan replace more traditional spatial information systems, like GIS and spatial databases,and data sources, like remote sensing and ground survey. Geosensor networks are ca-pable of generating information at fine spatial and temporal granularities, but typicallyover limited spatial extents when compared with other spatial data capture systems, likeremote sensing. Notwithstanding the inevitable future advances in size, cost, and powerof sensor node technology, the idea of global-scale monitoring using geosensor networksremains a medium- to long-term objective, decades rather than years away. Further, al-though today’s geosensor networks often comprise homogeneous sets of nodes, eachwith the same basic architecture and capability, it is to be expected that many futureAmSI applications will demand much greater intra- and inter-network heterogeneity,with data combined from different geosensor networks, each comprising many differenttypes of nodes and sensors.

As a result, the capability for flexible and efficient integration of heterogeneous spa-tial information are basic requirements of AmSI. There are two different classes of inte-gration commonly distinguished in the literature: conflation and fusion. Conflation con-cerns the simple combination of information regarding the same geographic attributes or

phenomena from different sources [21]. Fusion, on the other hand, concerns the confla-tion of two or more data sets to produce new information with added value, reliability, orutility [13]. In the context of AmSI, the two main barriers to conflation and fusion are:

• Semantics: spatial data is generated by diverse information communities usingmany different data capture systems, and as a result integration many need toovercome substantial semantic heterogeneity.

• Granularity: the differing spatial and temporal granularities of spatial data rel-evant to AmSI presents substantial problems in integration, for example in thefusion of fine-grained geosensor network data over limited extents with coarser-grained remote sensing data over larger spatial extents.

For example, [26] describe a sensor web architecture, designed to enable the integra-tion of information from heterogeneous spatial information systems, in particular sensornetworks. Sensor webs provide a nexus for integration of many different spatial infor-mation sources, and as a result are themselves an important technology for AmSI. It isnot only integration of spatial data that is required by AmSI; the central role of DeSC inAmSI means that integration of spatial computation is also required. As an example, [12]discuss the integration of environmental modeling capabilities into geosensor networks,arguing that such capabilities can help improve system robustness by providing a basisfor cross-validating the predictions from environmental models with the actual sensorobservations.

In summary, the capability to integrate heterogeneous spatial data and informationprocessing capabilities are basic functions of any AmSI system.

4.4. Interaction

As already identified, new spatial data capture technologies like geosensor networkshave the potential for information overload: generating overwhelming volumes of data inwhich individual data items are almost meaningless. Making sense of this data at source,by providing techniques and algorithms for in-network processing of spatial data in adhoc groups of nearby sensor nodes, is one of the primary motivations for DeSC (seeSection 3). However, there will always be cases where it is still necessary for users tointeract with highly voluminous, multidimensional, raw data from geosensor networks.For example, in connection with scientific and exploratory data analysis, it may be im-portant to discover previous unspecified or unknown patterns in data where algorithmsfor detecting the patterns of interest (centralized or decentralized) do not yet exist. Insuch cases, DeSC can aim only to filter, rather than process spatial data, reducing datavolumes by screening out data that is definitely not of interest.

As a result, an important facet of AmSI is to enable communication to users of a widevariety of information (both processed and merely filtered) about geographic phenomena.Most current approaches to visualization of geosensor network data and the sensor web(e.g., [4, 40]) adopt conventional cartographic paradigms (the “map metaphor,” see [7]).Visualization tools for AmSI, however, do not necessarily need to be bound by the mapmetaphor. Indeed there are four main reasons why more broad interactions paradigmsare potentially useful (cf. [20, 48]):

• Dynamic data: The map metaphor is essentially static, while all AmSI applica-tions are inherently dynamic (see Section 4.2). Consequently, the map metaphoris poorly equipped to represent change and evolution in geographic phenomena.

• Multidimensional data: The map metaphor is primarily two dimensional, whilethe data generated by geosensor networks has very high dimensionality, makingit especially difficult for humans to interpret. The data typically comes from alarge numbers of sensors, each of which is generating data about an array ofsensed phenomena (e.g., temperature, light, humidity, CO2 concentrations, ...) intwo or three spatial dimensions with frequent updates over time. Maps make poorchoices for representing such multidimensional data.

• Multimodal interfaces: Maps are primarily monomodal, visual interfaces. How-ever, a range of different interaction modes are appropriate with AmSI, includ-ing modes based on text-based, speech, haptic, or other input/output channels.For example, in many applications purely textual SMS messages might form animportant component of an AmSI interface, alerting users to a salient spatiotem-poral event (like the emergence of a region of high sea temperature for marinescientists, or high bush fire susceptibility to home owners in rural areas).

• Context-aware: Aside from simple re-presentation of mapped data (for example,zooming, panning, and changing symbolization) maps and conventional spatialinformation systems based on the map metaphor, like GIS, offer very limitedcapabilities for interacting with data. By contrast, in AmSI users may be activelyinteracting with the environment they are currently receiving information about.An AmSI system should be able to adapt the information presented to the specificcontext of the user (see Section 2), including their current location.

The last of these, context awareness, has an especially important role to play in AmSI(and AmI) in the form of implicit input (where an information system interprets as inputuser actions that are not primarily aimed at interacting with the system [37]). Locationis a fundamental component of a user’s context that can often be automatically sensedusing positioning systems (like GPS). In many cases, users may be interacting with theenvironment about which they wish to receive information. In such cases, knowledge ofa user’s location allows information about the environment to be carefully tailored to theuser, providing information connect with their current or recent locations, or comple-menting information they may already be able to sense directly from the environment.

5. AmSI applications

The previous sections identify the key research challenges facing the development ofAmSI, and in particular DeSC. As the sections illustrate, current research is beginningto address these challenges, as well as advancing the technological aspects of wirelesssensor and geosensor networks (e.g., [18, 33]). However, applications of AmSI have thepotential to reshape how society functions in a many different domains of human activ-ity. This section examines in more detail the range of potential applications of AmSI,including applications within the realm of assisted living. Specifically, we examine threedistinct classes of AmSI application: environmental management, infrastructure manage-ment, and people management.

5.1. Environmental management with AmSI

A key application area for AmSI is environmental monitoring and management. AmSIcan potentially be used to generate information about sensitive, hazardous, and remote

environments at fine levels of detail, using flexible, low-cost technology. Many environ-mental applications are already beginning to be investigated using small geosensor net-works. For example, [41] detail a prototype for flood hazard monitoring in Manitoba.A small (five-sensor) prototype provides real-time monitoring capabilities that improvedecision-making during emergencies. More ambitious plans for the sensor web to sup-port monitoring of drought severity and crop vigor are also discussed, as conventionalinformation gathering techniques (usually requiring a human presence) are not capa-ble of providing the necessary information to support such applications. In a landmarkstudy, [39] describe a network of roughly 100 sensors for monitoring the habitat condi-tions of a rare species of petrel in Maine, USA. In particular, the study highlights thepotential for using geosensor networks for monitoring sensitive environments, as thepresence of human field researchers has been shown to significantly reduce bird nestingsuccess.

Other environmental monitoring applications have been explored in the marine envi-ronment. Large scale, real-time monitoring of coastal areas and oceans remains an ongo-ing challenge: the remoteness and vast distances involved make conventional studies byhuman field officers hazardous and expensive. Instead, [28] describe how geosensor net-works and sensor web technologies could help enlist coastal buoys in providing timely,specialist marine data for public safety, weather monitoring, and oceanography. Simi-larly, the impact of using sensor networks for scientific studies on the environmentallyand economically important Australian Great Barrier Reef are described in [8]. Whilemost current studies focus primarily on designing and building technologies like geosen-sor networks and the sensor web, some studies are already looking further into the fu-ture, and equipping these technologies with more spatial intelligence. For example, inthe marine environment [3] proposes an infrastructure for real-time coastal nowcasting,forecasting, and long-term coastal erosion prediction.

As an example scenario, work is ongoing in using AmSI to assist with conservationcontracts management. Conservation contracts are contracts issued by government to re-imburse private landowner for conservation activities and environmental improvementson private land. In Victoria, Australia, a pioneering pilot study called EcoTender, legis-lated through the Conservation, Forests and Lands Act 1987 (Vic), has designated largeareas of the state where landowners can bid for contracts to pay them for environmen-tal improvements to their land. The improvements include weed and pest control, nativevegetation, protection of gullies and wetlands, and stock control (DSE, 2008). Currently,determination of the most suitable land for contracts is based on a statewide 20-metergrid that models phenomena such as groundwater, carbon, forestry, cropping, and ero-sion. Monitoring compliance using self-assessment or site visits can be unreliable andcostly, especially as the number of agreements grows. Current studies are developinggeosensor networks that can assist in automatically detecting environmental changes,in particular spatial changes such as increases in areas of high carbon sequestration orconnectivity of sensitive habitats. The objective is to develop AmSI systems that canintegrate information from both geosensor networks and traditional spatial data sourcesin order to support wider use of cheaper and more flexible conservation contracts. Theinformation generated in an AmSI system could be provided to contract managers tomanage compliance, as well as to landowners to provide detailed feedback on the effectsof management practices. Decentralized spatial computing is required to help filter large

volumes of data and to identify salient patterns, such as increases in area or changes inconnectivity.

5.2. Infrastructure management with AmSI

The potential of geosensor networks to enable AmSI is not limited to land and marinemanagement. AmSI also has the potential to contribute to the management of spatial andmobile infrastructures for business and personal applications, such as precision farming,utilities management, asset protection, and traffic monitoring. The possibility of utilizingAmSI in traffic management is considered in [38]. The article explores using camerasensors deployed across a metropolitan environment to track objects, including vehiclesand people. Similar systems have been considered to to detect potential threats, such asthe spread of a chemical agent through a subway.

Other potential urban AmSI applications include the development of flexible infras-tructures for sharing travel resources. In [35, 45], the idea of a shared-ride trip planningsystem is explored, allowing individuals to plan and execute trips using spare capacity inmany different transportation hosts, from trains and buses to taxis and private cars. Theapproach requires the integration of data about the urban transportation network and real-time movement of vehicles. In addition, the system must have the capacity to processingtransportation data and plan efficient routes for large numbers of mobile individuals. Al-though current work on shared-ride trip planning involves extensive use of simulationsof mobile geosensor networks, a practical implementation would be a clear applicationfor DeSC within an AmSI system.

AmSI has the potential radically reform many such social and governmental func-tions, uniting apparently disparate information sets in real-time, and enabling outputs ina range of accessible and easy to understand formats. As such, AmSI provides an im-portant practical component of the “spatially enabled society” vision as defined by [31].This vision relies on the existence a spatial data infrastructure (SDI), which includes sys-tems for generating and communicating up-to-date information about geographic envi-ronments and events in those environments.

5.3. People management with AmSI

The use of AmSI can also be extended into the realm of people management: crowdcontrol, health care, and policing, for example, might all benefit from spatial informationcaptured and processed in real-time. In the health care domain, most research to date hasfocused on the use of WSNs to enable patient monitoring and elderly care [5]. Researchis focusing on intelligent systems that collect information about many different aspectsof a patient’s health and surrounding environment, gathered by a mix of static and mobiledevices worn on the patient and placed around their home or places of care (e.g., [47]).Such systems can potentially support a range of activities including continuous out-carepatient monitoring, elderly care, and automated clinical trials.

Despite this existing work, spatial issues have thus far received less attention inhealth care, most likely as a result of the small-scale of many of the spaces being mon-itored (for example, a patient’s house or room) and the limited number of devices usedin today’s systems. However, as the use of these assisted-living devices increases andthe range of activities being monitored extends, more attention will need to be given to

potential AmSI applications. For example, monitoring the spatiotemporal activities ofpatients or the elderly moving more widely outside homes and institutions requires spa-tial intelligence. In particular, building from monitoring single individuals to monitor-ing patterns of movement and interactions between groups of individuals requires spa-tial intelligence. In [25], decentralized algorithms are presented for detecting flockingbehavior, where a number of individuals stay within close spatial proximity for a certainperiod of time. Such pattern detection algorithms have wide application across peoplemanagement, and are examples of fundamental capabilities required by AmSI.

People-related applications also bring into focus a range of complex ethical con-siderations, more so than environmental and urban infrastructure applications. In par-ticular, location privacy (the claim of individuals to control information about their lo-cation [10]) can potentially be compromised by surveillance and tracking systems likegeosensor networks. Thus, an important function of any AmSI system for people man-agement must be the inclusion of privacy safeguards. Although increased monitoring ofindividuals provides challenges to privacy, DeSC provides an opportunity to protect pri-vacy, since local, in-network processing of spatial information can potentially assist inpreventing the collation and storage of personal location information. For example, [24]describe and compare decentralized algorithms for generating routing information whileprotecting the location privacy of the people involved.

5.4. Example: AmSI in government

The diversity of AmSI applications can be illustrated by examining potential for AmSIin current government activities, based on legislative analysis. In [6], the statute booksof both the Australian and Victorian (State) governments were assessed with respect tospatial “footprints,” administrative arrangements, and technological components. In Aus-tralia, statues are laws created by parliament: they provide the basis for the administra-tive tasks of government. Figure 2 illustrates the number of statues legislated in eachjurisdiction and the number of those having a spatial footprint (i.e. the legislation appliesto a particular geographical areas). The figure also lists the number of statutes that couldpotentially be supported by AmSI, judged based on whether a statute satisfies the fol-lowing criteria: the statute must promote or limit an activity within the environment; thestatute must have a monitoring or enforcement element requiring timely information; thestatute’s application could be either ongoing, seasonal, or ad-hoc; and the statute neededto apply to a specific set of spatial points, networks, polygons, or land parcels. Examplesof statutes with a potential AmSI application include monitoring the use of chemicalson agricultural land (Agricultural and Veterinary Chemical, Control of Use, Act 1992,Vic); observing the use and condition of culturally significant places (Archaelogical andAborginal Relics Preservation Act 1972, Vic); examining the success of introduced pestcontrol agents (Biological Control Act 1986, Vic); and ensuring water catchments areprotected (Catachment and Land Proection Act 1994, Vic).

There are a number of points to make regarding the graph. Firstly, the total numberof statutes legislated is significant: 1427 at the federal level and 1045 at the state level.This number has been growing steadily in the last three decades [6]. As the drive forsustainability increases, the number of environmental activities requiring control throughlegislation is likely to increase. This will result in increased demands for administration,monitoring, and enforcement. Secondly, roughly half of all legislation has a very strong

Figure 2. Australian and Victorian Government activities that could potentially be supported by AmSI

spatial component: 514 statutes at the federal level and 620 at the state level. Today, theadministration of these spatial acts often makes minimal use of spatial technologies. Ascosts reduce and spatial technologies become ubiquitous, it is fair to suggest that spatialtechnologies generally and AmSI specifically will play an increasingly prominent role insupporting the administrative tasks of government. Finally, there already appears to besignificant opportunities for AmSI in assisting the administration of existing laws: AmSIwas found to be directly applicable to 59 statutes at the federal level and 84 at the statelevel. That is, AmSI could be used in the administration, enforcement, and monitoringof over 143 statutes. While this appears to be a small number in comparison to the totalnumber of statutes, the activities they administer certainly are not. Table 1 provides someexamples of the statutes potentially utilizing AmSI. These statutes constitute some ofthe most significant environmental management functions of government. In these cases,AmSI has the potential to greatly improve the timeliness, accuracy and granularity ofinformation involved in the monitoring and administrative processes

6. Conclusions

This chapter has set out a vision of ambient spatial intelligence (AmSI). AmSI is con-cerned with embedding the intelligence to respond to spatiotemporal queries and mon-itor geographical events within built and natural environments. AmSI is distinct fromambient intelligence (AmI) in two ways. First AmSI is more narrowly focused on spa-tial intelligence, monitoring and processing to spatial events and changes. Second, ac-

Statute Jurisdiction PurposeArchaeological and Aboriginal RelicsPreservation Act 1972

State Provides for the registration, monitoring andenforcement of Aboriginal relic protection

Biological Control Act 1986 State Enables the release and monitoring of pestcontrol agents

Catchment and Land Protection Act1994

State Declares and enables the monitoring andmanagement of Victoria’s catchment areas

Control of Genetically ModifiedCrops Act 2005

State Enables the planning and monitoring of GMcrops in particular areas

Environmental Protection Act 1970 State Provides measures and powers for the pro-tection and monitoring of the environment

Fisheries Act 1995 State Manages and enforces fishing license quotasand levies

Flora and Fauna Guarantee Act 1988 State Establishes a range of measures for the con-servation, management and control of floraand fauna

Forests Act 1958 State Creates powers and permits for the manage-ment and utilization of forests

National Parks Act 1975 State Establishes creation, monitoring and en-forcement processes for national parks

Planning and Environment Act 1987 State Provides a framework for the use, develop-ment and protection of land

Border Protection (Validation andEnforcement Powers) Act 2001

Federal Provides powers to monitor and protect Aus-tralia’s borders

Environment Protection andBiodiversity Conservation Act 1999

Federal Enables the protection of the environmentand conservation of biodiversity

Fisheries Management Act 1991 Federal Relates to the registration, regulation andenforcement of Australian fisheries

Table 1. Examples of legislation potentially utilizing AmSI for their administration

knowledging wide range of applications of spatial information, AmSI is more broadlyfocused on the entire range of users and sources of spatial intelligence, including remotedata sources and users. In realizing the vision of AmSI, the chapter argues that decen-tralized spatial computing (DeSC) is a fundamental technique required in order to avoidpotential information overload, make efficient use of system resources, and support sen-sor/actuator networks. The chapter set out four key research themes in DeSC: dealingwith uncertainty, modeling dynamism, supporting information integration, and providingeffective user interfaces. Finally, the chapter identifies three main classes of applicationarea for AmSI—management of environments, infrastructure, and people—and exploresthe specific example of the role of AmSI in current government activities in Australia.

The direction for future work is clear: as the technology develops, there are manyopportunities for geographic information science to develop new techniques, algorithms,and applications for AmSI. In particular, the emergence of DeSC has thrown into starkrelief the assumption of global knowledge inherent in almost all of today’s spatial algo-rithms and spatial information systems. While centralized spatial information systems,like GIS and spatial databases, will undoubtedly form an important technology for a longtime to come (see section 4.3), it is an interesting thought experiment to imagine a futurewithout centralized information systems: without GIS and spatial databases. Could ourfuture information requirements be satisfied solely by AmSI, where the environment it-

self has the embedded spatial intelligence to monitor spatial events? For example, imag-ine a future without GIS or spatial databases, where decentralized AmSI systems andalgorithms support applications including:

• A farmer is alerted that a flock of sheep has formed in a sensitive area, requiringmanagement interaction to prevent soil nutrient leaching;

• A marine biologist is alerted that two regions of high sea temperature havemerged, presenting increased risk of coral reef bleaching; or

• A navigation system automatically updates routing information based on othervehicles encountered.

We believe it is possible to envision such “pure” AmSI applications, which rely en-tirely on decentralized information processing requiring no centralized control or infor-mation storage. Indeed we think the seeds of such applications are already germinatingin the literature (cf. [2, 12, 25, 32, 45, 46, 50]).

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