wiper: leveraging the cell phone network for emergency response
TRANSCRIPT
WIPER: Leveraging the Cell Phone Network for
Emergency Response ∗†
Timothy SchoenharlUniversity of Notre DameDept of Computer Science
and EngineeringNotre Dame, IN 46556
Ryan BravoUniversity of Notre DameDept of Computer Science
and EngineeringNotre Dame, IN 46556
Greg MadeyUniversity of Notre DameDept of Computer Science
and EngineeringNotre Dame, IN 46556
September 10, 2006
Abstract
This paper describes the Wireless Phone-basedEmergency Response (WIPER) system. WIPERis designed to provide emergency planners andresponders with an integrated system that will helpto detect possible emergencies, as well as to suggestand evaluate possible courses of action to dealwith the emergency. The system is designed as adistributed system using web services and the serviceoriented architecture. Components of the system fordetecting and mitigating emergency situations canbe added and removed from the system as the needarises. WIPER is designed to evaluate potentialplans of action using a series of GIS-enabled Agent-Based simulations that are grounded on realtimedata from cell phone network providers. The systemrelies on the DDDAS concept [9], the interactiveuse of partial aggregate and detailed realtime datato continuously update the system, which ensuresthat simulations always present timely and pertinentdata. WIPER presents information to users througha web-based interface of several overlaid layers of
∗The research presented in this paper is based in partupon work supported by the National Science Foundation,CISE/CNS-DDDAS, Award #0540348.
†A preliminary version of this paper appeared in the Pro-ceedings of ISCRAM 2006.
information, allowing users rich detail and flexibility.
Key Words:Emergency Response System, GIS, Agent-BasedSimulation, DDDAS
1 Introduction
Current uses of realtime cell phone data centeraround traffic management [1], as the data collectedfrom current cell phones is limited to location, move-ment and call activity information. In the near term,we can determine higher order information on crowdmovement and behavior from this information. Inthe future, cellular networks may provide more var-ied information to emergency response planners. Re-searchers at UC Berkeley are currently developingsmall sensor packages that could be integrated intocell phone handsets to turn cell phone networks intosophisticated mobile sensor networks[18]. In order tofully utilize the large volume of dynamic informationthat will flow in from cell phone networks, emergencyplanners will need new, sophisticated tools. We be-lieve that WIPER is one important tool in that arse-nal.
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Numerous software tools have been developed toaid emergency responders. Several recent exam-ples are EVResponse and the COMBINED project[32, 31]. These tools provide methods of gatheringinformation on the current status of crisis situations.They provide emergency response planners with de-tailed, high-quality information, but require a highcost in terms of personnel and deployment. (PDAsand wireless infrastructure must be purchased, per-sonnel trained and both need to be sent to crisissites.) WIPER would act as a low-cost, highly avail-able monitoring system. Its deployment would be au-tomatic, as anyone with a cell phone in the area is aparticipant. No special training would be required forphone users, but balancing this, the quality of infor-mation from each person is low. Limited to locationand activity information, it may not be clear whattype of crisis is occurring. We use machine learn-ing techniques to infer information about the state ofthe area (i.e. to distinguish a fire from a traffic jam)from the location and call activity information thatwe collect. WIPER would convey three distinct anduseful pieces of information to emergency respondersvia the web-based console:
• It provides near-real time information on the lo-cation of cell phone users in an area, plotted ona GIS-based map of the area.
• It detects potential anomalies, such as trafficjams, roving crowds and call patterns indicativeof a crisis.
• It can evaluate custom-tailored mitigationstrategies, such as potential evacuation routesor barricade placement, through the use of com-puter simulations.
The WIPER system is designed to address specificneeds in the Emergency Response community, specif-ically the ability to view the development of a crisisin realtime, the ability to propose and evaluate re-sponse in near-real time and the ability to collect andanalyze streaming information from an ad-hoc sensornetwork. This capability positions WIPER as an im-portant component is an overall emergency responseworkflow. The WIPER system uses dynamic data
from cellphones and analyzes the data in realtime,providing the ability to detect a crisis as it emerges.An online classification system is designed to predictcrises before they happen by recognizing familiar pat-terns in group behavior. Responding to events fromthe anomaly detection system, GIS-based simulationsof the region are launched and results collated andpresented to planners. Finally, the web-based con-sole allows Emergency Planners to quickly examinethe current state of the environment, see possible pre-dicted outcomes from the simulations and evaluatecourses of action.
WIPER is designed to work with the current levelof information available from the cell phone network,yet it aims to provide a set of functionality far moreadvanced than is currently available. The system uti-lizes dynamic streaming information from cell phoneproviders to monitor and detect anomalies and crisisevents. The simplest form of potential crisis eventswould be traffic disturbances, but by utilizing tempo-ral data mining, historical knowledge of crisis eventsand call patterns and realtime social network calcu-lations, WIPER should be able to predict, detect andpropose responses to a wide range of emergency sit-uations. WIPER would detect crowds and demon-strators at public events, monitoring such events todetermine if they are degenerating into riots. Thismonitoring will consider both location informationand call activity.
2 Background
In this section we describe relevant background to theWIPER project and related work in the EmergencyManagement field.
2.1 Agent-Based Modeling and Simu-lation
Agent-Based Modeling and Simulation is a modelingparadigm that is well established for studying com-plex systems with emergent behavior. Examples ofthis type of system are biological, physical and so-cial systems where both the principal actors (agents),their surrounding environment and the modes of in-
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teraction form the basis for the emergent behavior.Agent-Based Simulations are closely related to Cel-lular Automata, which are often used in modelingspatial phenomena, such as traffic flow[34]. An exam-ple of the application of Agent-Based Modelling andSimulation to the area of crisis response are the Tran-Sims and EpiSims projects [20, 29]. The TranSimsproject was created to accurately model the trans-portation system of an entire city, including personalautomobiles, pedestrians, public transportation andcommercial vehicles. The system is used to providecity planners with a way of accurately gauging theimpact of infrastructure changes on a city’s trans-portation system. The EpiSims system was an out-growth of TranSims and is able to model the trans-mission of infectious agents through a city. EpiSimsmakes it possible to empirically evaluate methods ofinhibiting the spread of biological warfare agents inan urban setting.
2.2 Emergency Management
The use of Information Systems in the EmergencyManagement field is well established [32, 31, 16]. Ifdesigned and implemented properly, Information Sys-tems can enable Emergency Management profession-als to deal with increasingly complex crisis scenariosand coordinate effective inter-organizational response[33]. However, in order to be useful certain designconsiderations must be met[7].
2.3 GIS Enabled Simulations
Geographic Information Systems can be used to pro-vide added realism in Agent-Based Simulations [14].Agents can interact with terrain and roads represen-tative of the real world, enhancing the credibility ofsuch simulations. GIS systems have been success-fully integrated with simulations in scenarios wherean explicit spatial representation is important to thevalidity of the simulation[4, 15]
Computational Grid
SimulationsSimulationsSimulationsSimulationsSimulations
Dynamic Data Injection
Data Source
Simulation Controller
Create Simulation Ensemble
Evaluate Simulation Output
Figure 1: A fundamental concept of DDDAS systems:integrating simulations with the sensors. Here we seethat simulations receive a stream of real-time sensorinformation .
2.4 Real-Time Sensing in Urban En-vironments
Several projects similar to WIPER already exist.The most important project is MIT’s SENSEableCity[24]. The aim of the SENSEable City projectis to allow city officials, urban planners and peopleat large the ability to follow the trends in populationmovement and activity around the city. Initially theproject mapped the real time activity in the city ofGraz, Austria, but now it has been expanded to coverRome, Italy as well [25].
2.5 DDDAS
Recently the National Science Foundation has cre-ated a program to spur the development of Dynamic-Data Driven Application Systems[30]. A DDDAS is asoftware system that tightly couples simulations withsensors and data collection devices, a process that en-ables simulations to more quickly adapt to changingdata and even control the collection of data[9, 11].
The DDDAS approach has been implemented innarrowly-focused crisis management platforms, suchas weather monitoring [6] and fire monitoring [19]applications. These examples demonstrate how theDDDAS approach is beneficial in crisis scenarios, assimulations are constantly being updated and refined
3
Real World
Call Activity
Dynamic Social Network
Anomaly?
Simulation Prediction
System
Yes
Figure 2: A visual representation of the WIPER sce-nario. As real world data streams into the system,we examine call activity by location and social net-work of the users to detect potential anomalies. Inthe image, orange circles represent cell phone users.
based on streams of incoming data.
3 WIPER System Overview
As proposed in several previous projects, the exist-ing cell phone network can be used both as a toolfor detecting the state of the environment [1, 24] aswell as communicating directly with those affected bycrisis events [37, 38, 8]. WIPER is intended to pushthe boundary of crisis detection and monitoring withthe current cell phone network. The WIPER systemwill receive a feed of realtime information from cellphone providers. This is expected to be a sample ofthe incoming data, as the full data stream would beprohibitively difficult to transmit. The incoming datawould be monitored for anomalies, which include theobvious spatial and temporal aggregation, as well ascall patterns and movement discrepancies that cansignal the impending onset of a crisis event.
A visual description of the WIPER scenario is pre-sented in Figure 2.
Figure 3 shows the overall system architectureof WIPER. The WIPER system is a distributedsystem combining traditional methods of composi-
Decision Support System (DSS)
Decision Support System (DSS)
Decision Support System (DSS)
Decision Support System (DSS)
Simulation and Prediction System
(SPS)
Simulation and Prediction System
(SPS)
Simulation and Prediction System
(SPS)
Simulation and Prediction System
(SPS)
Simulation and Prediction System
(SPS)
Simulation and Prediction System
(SPS)
Mobile Agent for Data
Preprocessing
Mobile Agent for Data
Preprocessing
Mobile Agent for Data
Preprocessing
Data Source Cellular Service
Provider
Historical Data Store
Data Stream
Data Stream
Training
DataDetection and Alert System
(DAS)
Potential Anomaly?
Raw Data Stream
Dyna
mic
Data
Req
uest
Dynamically
Injected Data
Service Provider Network
WIPER Network
Web Console Clients(Internet)
Figure 3: An overview of the prototype WIPER sys-tem.
tion (RMI) with newer, more robust methods (Ser-vice Oriented Architecture, Web Services and MobileAgents). WIPER is composed of three layers:
• Data Source and Measurement
• Detection, Simulation and Prediction
• Decision Support
The Data Source and Measurement layer handlesthe acquisition of realtime cell phone data, as wellas the fixed transformations on the data, such as thecalculation of triangulation information for providingmore accurate location information on legacy hand-sets. The Detection, Simulation and Prediction layeranalyzes incoming data for anomalies, attempts tosimulate the anomaly to predict possible outcomesand suggests actions to mitigate the event. Finally,the Decision Support layer presents the informationfrom the other layers to end users, in terms of sum-maries of traffic information for commuters, real timemaps and simulations on the anomaly to first respon-ders and potential plans for crisis planners.
These layers are further divided into componentsthat handle highly specific functions, as described inthe following sections.
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3.1 Data Source and MeasurementLayer
This layer contains three modules, all of which havefunctionality related to the management of the realtime cell phone data. The Real Time Data Source(RTDS) collects information from one cell phoneprovider, performs filtering and aggregation as neces-sary and redirects the data stream into componentsin the Detection, Simulation and Prediction (DSP)layer. The RTDS is composed of several mobile soft-ware agents that are dispatched to the cell phoneprovider. The software agent removes personalizedinformation such as phone number and customer idand replaces it with a coded value that is internallyconsistent within the WIPER system but cannot beused to identify the user. For training purposes, snap-shots of the data are occasionally stored on a serverand become part of the Historical Data (HIS) mod-ule. The HIS streams historical data in the sameformat as the RTDS for training and testing the De-tection and Simulation modules in the DSP layer.A Triangulation Information module (not pictured)handles converting the rough location information as-sociated with a cell phone into a more precise loca-tion which is needed by the Simulation and Predic-tion System. On newer handsets, GPS sensors canprovide the cell phone provider with precise locationinformation, but only if the feature is enabled andthe cell phone provider is equipped to monitor it.
3.2 Detection, Simulation and Predic-tion Layer
The Detection, Simulation and Prediction (DSP)layer contains modules that monitor the streamingdata, and generates computer simulations to deter-mine whether perceived anomalies represent poten-tial crisis events and what actions can be taken tomitigate these events. The Detection and Alert Sys-tem (DAS) will use a combination of established tech-niques for detecting anomalous patterns of spatialactivity, as well as new methods of real time socialnetwork analysis to detect call patterns that may in-dicate emerging crisis activity. Upon detection of apotential anomaly, the DAS will transfer information
Figure 4: The proposed Service Oriented Architec-ture of the WIPER system.
about the event to the Simulation and Prediction Sys-tem (SPS). The SPS will use the information to cre-ate a GIS-based computer simulation that will at-tempt to model the outcome of the event. The SPSwill create an ensemble of Agent-Based simulationsthat are run on a computational grid. The simula-tions will be monitored by the SPS and ranked ac-cording to their ability to correctly predict the pro-gression of the actual event. The SPS and each of thesimulations will interact with the RTDS to acquiremore detailed information concerning the potentialanomaly area. For more information on the SPS see[17].
3.3 Decision Support System Layer
The Decision Support System (DSS) acts as a frontend for the WIPER system. It is the main portal fordisseminating the information from WIPER to crisisplanners and responders, public safety personal andthe general public. A picture of the web-based con-sole is shown in Figure 5. The DSS will aggregateinformation from the SPS and present the real timesystem status and any predicted anomaly informa-tion in a web based interface. There will be optionsfor crisis planners to specify and evaluate mitigationplans through the web interface. These plans willbe evaluated with Agent-Based simulations and theresults will be accessible from the web based inter-
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Figure 5: The WIPER DSS web-based console. Theconsole provides easy, standards-compliant access toall of the components of the WIPER system, allowingemergency planners access to the real time data, bothoverall activity and spatially aggregated, simulationoutput and information on system status.
face. This web interface will only allow access toauthorized users and will use encryption to preventsnooping. The DSS may also be configured to allowcertain information to be publicly accessible. Thiscould mean providing a near-real time picture of thetraffic situation or predictions of traffic congestion.
3.4 Technologies
3.4.1 Web Services
The use of Web Services and the Service Ori-ented Architecture allows WIPER to be composed ofstandards-compliant modules and simplifies the de-velopment of the system. The proposed SOA for theWIPER system is demonstrated in Figure 4. Thisalso allows the system to easily incorporate new in-formation sources such as GIS, weather monitoringor news feeds, as well as giving the end user optionsfor customizing the display formatting.
Figure 6: A 2D view of activity in the cell system.
3.4.2 Mapping and Visualization
Accurate, informative visualizations are crucial tothe WIPER system. A properly designed visualiza-tion system can present geographic information moreclearly and coherently than a textual description. Inthe WIPER system, we present geographic data fromthe cell phone provider, representing a recent snap-shot of the activity and location of individuals in theaffected area, as well as GIS-based simulations whichcan be used to provide various scenarios about thedevelopment and outcome of certain crisis events.
Our data source currently provides us with dataon user locations and activity at a cell-sized level ofresolution. The size of a cell can vary widely anddepends on many factors, but these can be gener-alized in a simple way using a Voronoi diagram [36](also called Thiessen polygons). A Voronoi lattice is atiling of polygons in the plane constructed in the fol-lowing manner: Given a set of points P (in our case, aset of towers) construct a polygon around each pointin P such that for all points in the polygon aroundp0, the point is closer to p0 than to any other pointin P . Thus we can construct a tiling of a GIS spaceinto cells around our towers, as shown with activityin Figure 6.
We currently have two methods for visualizing thelocation data. The first method is to color the
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Figure 7: A 3D view of activity in the cell system.
Voronoi cells in the area of interest based on the levelof activity. This method is demonstrated in Figure6. In this image the color scale ranges from green(low activity) to red (high activity). Alternately, wecan build a 3D image based on the activity at thesite of interest, as shown in Figure 7. This 3D viewgives a better conceptual picture of the comparativeactivity levels in the cells. However, viewing the ac-tivity in this manner may not enable Emergency Re-sponse planners to evaluate the current activity levelsor compare them to historic activity information. Weare currently considering other methods of attenuat-ing the display to account for the varying size of thecells, as shown in Figure 8, or perhaps normalizingthe cell activities to historical values for this area atsimilar times.
4 Implementation Details
4.1 GIS and Mapping
In the WIPER system, it is a design goal to utilizeFree and Open-Source software whenever possible.
Figure 8: A transformed view of the activity over anurban area. The activity values are normalized bythe area of the cell.
To that end we have used GRASS GIS [10], PostGIS[26], GDAL [12] and Shapelib [35] in our workflowto generate images, both interactively and as part ofour automated workflow. We also use OpenMap [5]and Geotools [13] to enable GIS functionality in oursimulations.
In generating our images, we primarily usedGRASS. First we created a spatial-relationaldatabase using PostgreSQL [23] and PostGIS. Thisdatabase contained both reference information on ourarea of interest, including geographic features, polit-ical boundaries (cities, counties, zip codes, etc) andsome information on major roads. Using GRASS wecould combine the cellular phone users’ activity data,aggregated at a particular time scale (images in thispaper are aggregated at 10 minute intervals) with thehistoric information in the PostGIS database, allow-ing us to view several layers of information in oneimage. We also used GRASS to generate images thatshow the change in phone activity in an area over thecourse of a day.
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Figure 9: An example of overlaying activity informa-tion on a satellite photo. Satellite image taken fromGoogle Earth.
4.1.1 Anomaly Detection on Streaming Data
We are currently developing our anomaly detec-tion system to deal with multiple types of potentialanomalies. A full treatment of this topic is beyondthe scope of this paper. Those interested should readPawling et al [22].
4.1.2 Integrating GIS Data with Agent-Based Simulations
In the WIPER system Agent-Based Simulations areused to explore potential anomalies and to evaluatethe efficacy of various mitigation strategies. In orderto improve the realism of the simulation and increaseits relevance to the crisis, we build our simulations ontop of a Geographic Information System. A screen-shot of a sample simulation is shown in Figure 10.
Our simulations are built using the RePast Agent-Based Modeling toolkit [21]. RePast includes twoJava APIs that allow easy integration with GIS data.OpenMap [5] and Geotools [13].
Figure 10: An Agent-based Simulation of an urbanarea, initialized from sensor data.
5 Privacy and Ethical Con-cerns
Concern about government monitoring of cell phonelocation and call activity may present a challengefor the deployment of the WIPER system [28, 27].In order to address any concerns about privacy, theWIPER system is designed so that all personallyidentifiable data is removed from the data streambefore it leaves the cell provider’s network, ensur-ing that there is no potential for sensitive data to beabused. The software agents that handle the prepro-cessing reside on the servers of the cellular serviceprovider and ensure that all data that is streamedacross the internet is anonymized and encrypted. TheWIPER system itself uses only aggregate data fromthe data streams and is not designed to allow themonitoring or tracking of individual handsets. Wewill continue to examine the potential impacts of suchsystems on personal privacy, especially in the contextof location-aware systems such as WIPER that utilizeGIS systems and technologies[2, 3]
6 Contributions
We have presented the proposed architecture for theWIPER system. It is designed as a distributed,multi-agent system built on open standards to ad-
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dress events in the real world. WIPER brings cuttingedge social network analysis algorithms, anomaly de-tection on streaming data, sophisticated GIS-enabledAgent-Based Simulations and web-based interactionand visualization tools together in one package toenhance the decision making process of EmergencyManagement professionals. The system will inter-face with the existing cellular telephone network toallow cell phone activity to be monitored in aggre-gate, essentially creating a large scale, ad-hoc sensornetwork. The stream of incoming data will be mon-itored by an anomaly detection algorithm, flaggingpotential crisis events for further automated investi-gation. Agent-Based simulations will attempt to pre-dict the course of events and suggest potential mit-igation plans. And the system will display outputat every level to human planners so that they canmonitor the current situation, oversee the softwareprocess and make decisions. When completed, theWIPER system is designed to integrate into a crisisresponse workflow, adding an important componentto the toolbox of Emergency Response professionals.
7 Future Work
The WIPER system is still under development. Welook forward to presenting more information regard-ing our detection and alert system and simulationand prediction system components. For further in-formation on the WIPER system and up to datedescriptions of the system and its components, visithttp://www.nd.edu/∼dddas/.
8 Acknowledgments
We would like to thank our collaborators on theWIPER project Professor Albert-Laszlo Barabasi,Dr. Gabor Szabo, Dr. Ping Yan and Alec Pawling.
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