To appear in SenSys’04November 3-5, 2004, Baltimore, Maryland

Call and Response: Experiments in Sampling theEnvironment

Maxim A. Batalin1,2, Mohammad Rahimi1,3, Yan Yu1,3,Duo Liu1,4, Aman Kansal1,4, Gaurav S. Sukhatme1,2,

William J. Kaiser1,4, Mark Hansen1,5, Gregory J. Pottie1,4,Mani Srivastava1,4, Deborah Estrin1,3

1Center for Embedded Networked Sensing, UCLA, Los Angeles, CA 900952Department of Computer Science, USC, Los Angeles, CA 900893Department of Computer Science, UCLA, Los Angeles, CA 90095

4Department of Electrical Engineering, UCLA, Los Angeles, CA 900955Department of Statistics, UCLA, Los Angeles, CA 90095

{maxim@sagan.usc.edu, mhr@cens.ucla.edu, yanyu@cs.ucla.edu,duoliu@ee.ucla.edu, gaurav@robotics.usc.edu, kaiser@ee.ucla.edu,cocteau@stat.ucla.edu, pottie@ee.ucla.edu, destrin@cs.ucla.edu}

ABSTRACTMonitoring of environmental phenomena with embedded net-worked sensing confronts the challenges of both unpredictablevariability in the spatial distribution of phenomena, coupledwith demands for a high spatial sampling rate in three di-mensions. For example, low distortion mapping of criticalsolar radiation properties in forest environments may re-quire two-dimensional spatial sampling rates of greater than10 samples/m2 over transects exceeding 1000 m2. Clearly,adequate sampling coverage of such a transect requires animpractically large number of sensing nodes. This paperdescribes a new approach where the deployment of a com-bination of autonomous-articulated and static sensor nodesenables sufficient spatiotemporal sampling density over largetransects to meet a general set of environmental mappingdemands.To achieve this we have developed an embedded networked

sensor architecture that merges sensing and articulation withadaptive algorithms that are responsive to both variability inenvironmental phenomena discovered by the mobile sensorsand to discrete events discovered by static sensors. We be-gin by describing the class of important driving applications,the statistical foundations for this new approach, and taskallocation. We then describe our experimental implementa-tion of adaptive, event aware, exploration algorithms, whichexploit our wireless, articulated sensors operating with de-terministic motion over large areas. Results of experimental

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measurements and the relationship among sampling meth-ods, event arrival rate, and sampling performance are pre-sented.

Categories and Subject DescriptorsI.2.9 [Artificial Intelligence]: Robotics—autonomous ve-hicles, sensors; C.3 [Computer Systems Organization]:Special-Purpose and Application-Based Systems—real timeand embedded systems; C.2.4 [Computer Communica-tion Networks]: Distributed Systems—distributed applica-tions; J.7 [Computer Applications]: Computers In OtherSystems—command and control, process control, real time

General TermsAlgorithms, Design, Experimentation, Management, Mea-surement, Performance, Reliability

Keywordsmobile robotics, sensor network, adaptive sampling, task al-location, distributed

1. INTRODUCTIONA broad class of environmental monitoring objectives in

fundamental science, environmental resource management,and public health protection demand distributed sensing ca-pabilities [1]. For example, the potential global impact ofclimate change creates a requirement for understanding theinteraction between the forest canopy and the atmosphere.Understanding critical phenomena, for example the natureof carbon flux from the atmosphere to forest biomass, re-quires direct experimental characterization of spatiotempo-rally distributed phenomena [2]. This includes measurementof solar radiation (driving fundamental photosynthesis andgrowth), atmospheric water vapor, temperature, and chem-ical composition. Distributed measurements are required

since these phenomena are sensitive to (and in turn influ-ence) the heterogeneous structure of the natural environ-ment.

1.1 High Fidelity Environmental FieldMonitoring

Many environmental monitoring applications share thechallenge for achieving high fidelity characterization capa-bility for environmental field variables. This is convention-ally achieved with high spatiotemporal sampling rate. Forexample, solar radiation and atmospheric properties mustbe mapped in natural environments over a spatial extentcomparable to those of the forest structure (where a mea-surement transect height may exceed 50m and the width re-quired to adequately span the heterogeneous structure mayexceed 100m) . At the same time, some phenomena dis-play a characteristic spatial variability on the scale of cen-timeters, reflecting the fine scale of natural structure andimportant phenomena. Thus, in considering high fidelitysampling for these phenomena over a two-dimensional planewith the required spatial extent of over 1000 m2 and resolu-tion of greater than 10 sample/m2 requires an impracticallylarge number of sensing elements with 10,000 measurementpoints.Measurement distortion may also result from an improper

spatial-sampling distribution (specifically due to mismatchbetween the spatial structure of the phenomena and sensornode placement). This introduces yet further challenges forenvironmental monitoring by limiting the capability for dis-tributed sensor node deployment planning. Specifically, theinherent unpredictability and variability of environmentalstructure and phenomena precludes the possibility of achiev-ing adequate spatial sampling density by advance planning.The conventional solution for reducing this source of dis-tortion has been to increase spatiotemporal sampling rate.However, this results in excessive disturbance to the sensedenvironment. Clearly, fixed sensor distributions, alone, arenot adequate for many important environmental monitoringphenomena.Since it is the presence of dynamic physical structures (for

example the growth of foliage or environmental erosion) thatlead to unpredictable and variable sensor coverage require-ments, sensor networked systems must exploit wide-rangingand high-spatial-resolution sensor node mobility in order toensure adequate coverage.

1.2 Coordinated Fixed and Mobile SensingNodes for High Fidelity Coverage

This paper describes a new Networked InfomechanicalSystems (NIMS) architecture that combines both fixed andmobile sensor nodes to achieve a spatiotemporal environ-ment coverage that is dramatically advanced over that ofeither system alone. Mobility allows the networked sensorsystem to always seek the most efficient spatiotemporal sam-pling distribution to achieve a specified accuracy of environ-mental variable reconstruction. Further, mobility also per-mits the NIMS system to respond to initially unpredictableand variable environmental evolution.While it is shown here that this architecture enables mo-

bile sensors that adapt to variable environments, their cov-erage at any instant is restricted to their effective area ofregard. Thus, temporal measurement distortion will appearin the presence of rapidly changing phenomena.

Figure 1: The NIMS system exploits suspendedaerial infrastructure to enable sustainable and pre-cise transport of mobile nodes within complex three-dimensional environments. Phenomena are mappedwithin a spatially extensive transect where sensingnodes have precise command over horizontal andvertical motion.

The NIMS architecture introduces coordination betweenfixed and mobile devices. Sparsely distributed fixed sensornodes, each remaining vigilant over a localized area of re-gard, provide a distributed event detection service for thecombined network. We experimentally demonstrate thatproper task allocation of mobile devices permits both highspatial sampling rate and low event response time.The NIMS architecture coordinating mobile and fixed de-

vices provides the ability to accommodate high fidelity sens-ing with limited resource constraints. For example, typi-cal environmental monitoring characterization tasks requirecostly sensing modalities where the cost is measured in termsof energy, mass, volume, monetary expense, or other value).Examples in natural environmental monitoring are tasksthat require gas phase analysis or multi-spectral imaging.Thus, while the demand for dense spatial coverage is high,the resource cost associated with supporting a vast num-ber of individual sensor elements for adequate spatiotempo-ral coverage may be excessively high and the disturbanceto the environment due to their introduction may be pro-hibitive. However, it is demonstrated here that the com-bination of low spatial-density fixed sensors (providing therequired assets for mobile node task allocation) and highlycapable mobile devices results in both adequate spatiotem-poral coverage as well reduced overall resource cost.The NIMS architecture for mobile and adaptive sampling

must include the attributes of 1) wide range mobility withinthree-dimensional volumes, 2) precise and high resolutionposition and orientation control, 3) long term autonomous,sustainable operation, and 4) mobile operation with minimaldisturbance to the environment with regards to physical sur-faces or structures or to emitted acoustic noise. The combi-nation of these requirements are met by NIMS infrastructure-supported mobility, as shown in Figure 1. Here, infrastruc-

Figure 2: Map of solar radiation intensity obtainedin a forest ecosystem by a NIMS system transport-ing a light intensity sensor within the canopy. So-lar radiation intensity is indicated in contours as itvaries spatially according to horizontal and verticalsensor displacement.

ture is adapted to the environment and uniquely enableseach of the attributes above.

1.3 Motivating ApplicationAn example of a fundamental phenomenon that controls

processes within natural ecosystems is the flux of solar ra-diation. Solar radiation is spatially filtered by the complexecosystem structure and ultimately controls photosynthesisand growth [3]. The characterization of solar radiation spa-tiotemporal patterns is of primary interest to understandinggrowth, evolution, and global change trends [2].NIMS provides the first method for extensive spatiotem-

poral mapping of phenomena, including solar radiation inthe ecosystem. Figure 2 displays a map of the photosyn-thetically active radiation (PAR) spectral region of solar il-lumination. This two dimensional PAR map was acquiredby a NIMS system operating in a mixed conifer forest withinthe James San Jacinto Mountain Reserve [4]. Note the char-acteristic scale of variability for solar radiation is less than1m with a need to characterize a spatial extent that equalsthe dimensions of the forest canopy itself.Figure 3 shows a NIMS system deployed in the forest re-

serve for continuous operation. This system includes sup-porting cable infrastructure, a horizontally mobile embed-ded computing platform payload, image sensing, and a ver-tically mobile meteorological sensor system carrying watervapor, temperature, and PAR sensing capability. Wirelessnetworking is incorporated to link fixed nodes (distributedon the surface or suspended) with the vertically and horizon-tally mobile elements. The NIMS infrastructure is elevatedin the environment and thus lies above environmental ob-stacles to solar radiation. NIMS systems have exploited thisand have been deployed with battery energy sources suppliedsolely by solar photovoltaic cell energy harvesting. Energyis transported as needed along the NIMS cable infrastruc-ture by an articulated cable system. The NIMS system isdeployed in a transect of length 70m and average height of15m with a total area of over 1,000 m2.The experimental NIMS system operates with a linear

speed range for node motion of 0.1 to 1 m/second. Thus, the

Figure 3: A NIMS system deployed atthe James San Jacinto Mountain Reserve(http://www.jamesreserve.edu). This image showsthe NIMS cable infrastructure, horizontal transportnode (carrying an embedded computing platform,image sensor, and vertical transport control, andvertically mobile meteorological sensor node.

time required to map an entire 1,000 m2 transect with 0.1m2 resolution with a simple ’raster’ regular scanning sched-ule will exceed 104 to 105 seconds. Phenomena that varyat a characteristic rate exceeding this scanning rate maynot be accurately mapped. However, without knowledge ofthe spatial variability of measured phenomena, the samplingsystem must sample at high spatial rate in order to achievelow measurement distortion.

1.4 Event-Aware Adaptive Sampling SystemsThus, as will be described below, the requirements for the

measurement system to respond to variable environmentalstructure and to accurately map environmental variables atminimum time and resource cost has lead to the introduc-tion of a NIMS architecture that combines mobile adaptivesampling with task allocation methods controlling node mo-tion. These rely on fixed sensor networks as well as mobileelements to ensure that measurement is most efficient andthen to focus exploration and mapping at the point in atransect for which change in environmental variables hasoccurred.This paper first describes the Adaptive Sampling, Task

Allocation, and also Event-Aware Adaptive Sampling meth-ods and algorithms. A new Fidelity Driven Sampling adap-tive method is introduced and tested with experimental data.Then, experimental methods including system hardware andsoftware architecture are discussed. This includes descrip-tion of a laboratory-scale NIMS system (NIMS-LS) employedfor algorithm and verification of combined software and mo-bile embedded hardware system implementations. Finally,experimental results obtained with NIMS-LS demonstrat-

ing the marked benefits of combined adaptive sampling andtask allocation are described. It is important to note thatRelated Work is discussed in each Section below.

2. ADAPTIVE SAMPLING ALGORITHMSampling configurations (e.g. sample density) for em-

bedded networked sensor systems are generally limited byconsiderations related to the availability of communicationchannel resources or energy constraints. This has spawneda number of recursive estimation techniques that make de-cisions in a hierarchical fashion [5]. For example, local clus-ter heads accumulate information and propagate this ac-cording to a value metric. By introducing infrastructure-supported mobility, it is now possible to characterize envi-ronmental variable fields with much greater spatiotemporaldensity than was previously practical. While this enablesnew applications, it also creates new choices for estimatorsand for optimizing sampling. A new algorithm, FidelityDriven Sampling will be described that exploits mobile sam-pling to first stratify the environment into regions requiringvarying degrees of sample density, then samples in these re-gions while tracking estimated sampling error (fidelity). Fi-delity Driven Sampling has the advantage over conventionalraster (or other pre-planned sampling strategies) of activelyseeking to minimize error without prior knowledge of thevariable field.

2.1 MethodologyOptimal designs for estimators have been studied exten-

sively in the statistics literature; see [6, 7, 8, 9]. Theseapproaches often assume a parametric form for the esti-mate. Given a generic learning algorithm, the problem ofoptimal design is much harder. Optimal designs for sim-ple kernel methods were studied by [10] where it was foundthat the best placement of points depends on the secondderivative of the function; places with high curvature shouldhave relatively more points than flat regions. [11] studydesigns for local linear smoothing, ultimately proposing abatch-sequential algorithm. It should be noted that imagecompression methods are not directly applicable here sincethese are based on post-processing of acquired data, whereasfidelity-driven adaptive sampling specifically selects actualmeasurement sample sites according to data characteristics.The NIMS adaptive sampling algorithms are designed with

the goal of sequentially constructing a sampling pattern whereat each step, samples are chosen that improve the estimateof the sensed field. [12] studies such sampling plans in thecontext of kernel smoothing. Points are added one at a time,chosen so as to reduce an estimate of the integrated squarederror. [13, 14] study the same problem, but with neuralnetworks as their estimators. They use the term “activelearning” to describe the adaptive sampling process. [13]selects points to minimize an information criterion, while[14] studies the mean squared error. In each case, it is as-sumed the bias is negligible and attention is focused on thevariance of the estimator. [14] also introduces the notion ofpath constraints on the samples, a topic that will be impor-tant in our ultimate deployment of adaptive sampling.The Adaptive Sampling algorithm reported here, relies

on a mean squared error estimation approach and an under-lying learning algorithm based on a local linear smoother.Further, a variable bandwidth is assumed based on nearestneighbors. This algorithm, termed Fidelity Driven Sampling

Algorithm 1 Pseudocode description of the Fidelity DrivenSampling algorithm

C: rectangular cell (scanning area)

PREDICT FRAME(C)while there is a strata with error higher than thresholddo

extract strata with highest bias erroradd sampling pointsif current strata mean square error > threshold errorthen

compute vertical and horizontal mean square errorif horizontal bias error > vertical bias error then

divide strata horizontallyadd stratum to the working queue

elsedivide strata verticallyadd stratum to the working queue

return local polynomial fit

(FDS), attempts to reduce mean square error at each sam-pling point by adjusting point density and location. It willbe further described and evaluated below.Mean squared error can be decomposed into a bias com-

ponent and a variance component. In our application ofsolar illumination mapping, measurement noise is a negli-gible fraction of the overall signal, and hence our greatestconcern will be bias. In other applications, bias may notbe dominant, and other estimation methods will be needed.In these other regimes, attention will likely focus on tasksother than field estimation; or if a snapshot of the field is de-sired, a longer learning process involving repeated measuresin time will be required. This is all the subject of futurework.Throughout the sampling process, FDS maintains an es-

timate of the field being observed. In this paper, a locallinear fitting routine is chosen, with its bandwidth varyingto include a fixed number of nearest neighbors. Using thisestimate, the FDS loop identifies regions or strata exhibit-ing a high degree of misfit. At each step in the samplingprocess, FDS adds points to that stratum with the largesterror. In so doing, the FDS algorithm reduces the distancebetween neighbors and effectively lowers the bandwidth ofthe local linear fit within the stratum. The algorithm con-tinues adding points to poor fitting strata until either anoverall sample budget is exhausted or a desired fidelity limitis achieved.Local linear fits have been chosen because these can tie

the notion of sampling with the resolution of structures ex-pected in the variable field. In principle, any nonparametricprocedure could be employed including thin plate splines orother radial basis functions.

2.2 The Fidelity Driven Sampling SystemAlgorithm 1 shows pseudo code description of the Fidelity

Driven Sampling adaptive algorithm. The NIMS roboticsystem continues adaptive sampling for the entire period ofoperation in the environmental mapping state. The FDS al-gorithm then calls the procedure PREDICT FRAME(C)which returns the estimate of the environmental variablefield. The algorithm follows a procedure of stratifying a

l

(x,y,z)

l

r

(a) The 2D-case

l(x,y,z)’’ ’

(b) The 2D-case projectiononto the transect

Πr

(x,y,z)

(c) The 3D-case

(x,y,z)’’ ’ Πr

(d) The 3D-case projectiononto the transect

Figure 4: Different SN topologies and corresponding projections onto the transect.

sampling region and according to observed measurementsin the region and for each strata, adjusting the number ofsampling points.In addition, in the discussion below, the performance of

Raster Scanning, Stratified Random Sampling, and the newFidelity Driven Sampling algorithms are compared throughsimulation of each sampling method with actual experimen-tal data. It is important to note that Fidelity Driven Sam-pling provides an autonomous system that seeks to assignsampling points to achieve a specified threshold error value(or a specified stratification rank). By estimating error mag-nitudes, Fidelity Driven Sampling can actively adapt to achievea sampling fidelity objective. This differs fundamentallyfrom raster scan methods that are not informed of resid-ual errors. It is most important to note that Fidelity DrivenSampling, being adaptive, requires no prior knowledge of theenvironmental variable field characteristics and will ratherreport these characteristics. The successful demonstrationof this is enclosed in Section VI.This PREDICT FRAME(C) procedure starts by insert-

ing a root stratum in a queue. Here, the root stratumcorresponds to the entire scanning area C (transect) - theentire region of study. PREDICT FRAME(C) then ini-tiates a loop that extracts strata with highest product ofmean square error and area. It determines the samplingpoints to be added to the strata and then the mobile sen-sor moves to visit those points and sample correspondingdata. After sampling points in the strata, it performs a lo-cal linear kernel regression and reevaluates the estimate ofthe phenomenon. Error is computed in terms of the abso-lute difference between estimated and sampled values. Ifthe computed error in the strata falls below a threshold,PREDICT FRAME(C) exits the inner loop and proceedsto examine more strata or otherwise divides the strata intohorizontal or vertical substrata depending on which divisionleads to the greatest reduction in error.Following Fidelity Driven Sampling operation (or raster

scanning data acquisition) the returned variable field withits set of sample points was then supplied to a standard

estimation algorithm (that performs an interpolation) andreturns an environmental field map. This final result isreferred to as a reconstruction of the variable field. Ex-perimental results and evaluation of reconstructions will bediscussed below for varying field characteristics. Note thatthroughout the paper error metric used is integrated squareerror across the strata which represents the degree misfit ofthat strata but one can use more complex error functionsthat incorporate the cost of mobility.

3. TASK ALLOCATION ALGORITHMTask Allocation (TA) is the problem of assigning available

resources to tasks. For a comprehensive overview of TA for-mulations please refer to [15]. There are two major subdi-visions: offline and online. The offline TA is the problem ofassigning resources to different tasks if certain information(e.g. the distribution of task arrival times, relative task pri-ority) is known a priori. The assignment process proceedsoffline. The offline TA problem, in its most general form, isequivalent to the conjunctive planning problem [16] whichis NP-Complete.Our focus here is on online task allocation. In online TA,

all information about the tasks becomes available only upontask arrival. The assignment of resources to tasks must becomputed in real time. It has been shown [17] that greedy al-gorithms provide good approximate solutions to online TA.It has also been shown [17] that in some cases the greedyonline TA solution is within a bounded limit of the optimalsolution obtained by offline TA. Following the model in [18],we think of task assignment occurring in decision epochs.A decision epoch is a period of time during which only thetasks which have arrived since the end of the previous epochare considered for assignment. Increasing the decision epochto infinity converts the online TA into the offline TA prob-lem. We model the NIMS system as an online TA problem,since it is designed for real-life autonomous field applicationsin dynamic environments.Our work is related to the body of work on the problem of

online multi-robot task allocation (MRTA). For an overview

t = 1t = 2

t = 3

. . .

pp

p

1

2

3

(a) (b)

e1 2

3

e

e

(c)

Figure 5: Dynamic phenomenon P and a static sensor network. a) Rapidly changing spatially and temporallyphenomenon P = {p1, p2, ...pn}, occurring forward in time; b) Static predeployed sensor network (SN), whereeach node is equipped with relatively cheap, low fidelity sensor capable of detecting pi; c) SN detects p1, p2, p3and creates tasks;

and comparison of the key MRTA architectures see [19],which subdivides MRTA architectures into behavior-basedand auction-based. For example, ALLIANCE [20] is a behavior-based architecture that considers all tasks for (re)assignmentat every iteration based on robots’ utility. Utility is com-puted by measures of acquiescence and impatience. Auction-based approaches include the M+ system [21] and Mur-doch [22]. Both systems rely on the Contract Net Protocol(CNP) that puts available tasks for auction, and candidaterobots make ’bids’ that are their task-specific utility esti-mates. The highest bidder (i.e., the best-fit robot) wins acontract for the task and proceeds to execute it.The proposed TA algorithm differs from the above MRTA

approaches. It relies on a static network, and communica-tion, sensing and computation are distributed. The motiva-tion for the TA algorithm derives from the need to efficientlysample the phenomena instead of sampling the entire envi-ronmental space. As has been discussed in the Introduction,it is impractical to deploy enough fixed sensors to achieverequired sensing fidelity. As will be shown, the system com-bining fixed and mobile nodes enables efficient sampling.TA becomes the primary driver of efficient data collectionin this system, since it allows the user to select a portion ofthe environment for sampling, as opposed to sampling theentire environment. In addition, TA manages system re-sources, so that resources are not consumed unless assignedmost effectively.

3.1 MethodologyThe general online TA system functions in the following

way. Suppose at a given decision epoch the system maintainsa set of resources R = {r1, ..., rn} and tasks T = {t1, ..., tk}.Tasks are prioritized based on a criterion C. C is an applica-tion dependent function and can combine such parametersas task arrival time, task importance, etc. A set of assign-ments A = (l = min(n, k) : {a1, ..., al}) is computed asfollows.

∀a∈Aa = argmaxj=(1,...,|R|)(U(rj , t)) (1)

where t is the next unassigned task according to C andU(rj , t) is the j-th resource utility value for accomplishing t.The assigned resource and corresponding task are removedfrom R and T respectively, before the next assignment. Theutility function is chosen to be application and resource de-pendent. In our model, once assigned, resources cannot bereallocated. After a resource has completed its task it be-comes available for a new assignment. In the terminologyof [23] we adopt a commitment strategy as opposed to op-portunism.The system consists of a mobile node suspended on a ca-

ble and a static sensor network. We assume that the net-work is predeployed where each node knows its location ina global coordinate system. The network monitors the en-vironment for events of interest (motion, change in lightintensity, etc). The problem then is to prioritize the events,and drive the mobile node to a vantage point from which aparticular event is better observed. Once the node arrives,the local phenomenon is measured. In TA terminology, arobot is a resource and a detection by a sensor node of anevent requiring perusal by a robot is a task.Figure 4 shows two network topologies that we define -

positioned on the ground (the 2D-case) and more generally,in the volume surrounding the transect (the 3D-case). Inorder for TA system to plan node’s motion the goal pointsshould lie in the transect plane. Hence, we project the nodeslocations onto the transect plane. As a result we get a setof points on a line l (2D-case, Figure 4a) or a plane Πr (3D-case, Figure 4c), both of which lie in the transect plane. Inthe 2D-case, l is the line where the transect plane intersectsthe ground plane. Since, the mobile node cannot move alongthat line, we translate l to a parallel line lr on the transect.We define the projection function in the 2D-case PROJlr

and 3D-case PROJΠr.

Based on tasks projected locations TA divides the transectinto slices (2D-case, Figure 4b), or generally cells (3D-case,Figure 4d). With every projected node k we associate a cellCn.

Algorithm 2 Event-Aware Adaptive Sampling Algorithm

(xt, yt, zt): coordinates of current taskCt: a cell corresponding to current taskAS ON : flag, turns Adaptive Sampling Module on/off

INSERT (Ta, t) : insert task t into unassigned tasks setTa based on time of arrivalCOMPUTE TASK INFO(t) : determines goal positionand corresponding cell for tMOV E TO(xt, yt, zt) : delivers the robot to given posi-tionPREDICT FRAME(Ct): Algorithm 1

Task Allocation Module:

if there is a new task t thenINSERT (Ta, t)

if Ta 6= 0andAS ON == false thent = Ta(0)(xt, yt, zt, Ct) = COMPUTE TASK INFO(t)MOV E TO(xt, yt, zt)AS ON = true

Adaptive Sampling Module:

if AS ON == true thenPREDICT FRAME(Ct)AS ON = false

Note that a 2D system is sometimes preferred because itis easier to setup in the field and for some applications a2D perspective is enough. As an example, consider study-ing sunlight intensity shining through a forest canopy. Inthis case a sensor network with illumination detectors canbe placed on the ground. Suppose node k discovered aninteresting reading (say an abnormal light value). The TAsystem then would guide the robot towards the goal pointon lr computed by PROJlr . The mobile node then canstudy appropriate slice Ck. The general 3D-case system isinvestigated in this paper.

3.2 The Task Allocation SystemOur system consists of two algorithms - one running on

the robot and another within the network. First we describethe algorithm that runs on every static node. It consists oftwo parts - Task Generation and Task Management.

1. Task generation. If node’s sensor reading is abovea threshold a task is created and a notification mes-sage NEW TASK is broadcasted. The notification mes-sage contains the notifying node’s ID, location, sensorreading, and time stamp (time when the task was gen-erated).

2. Task management. Each node maintains a set ofcurrently active tasks Ta and non-active tasks Tna. Ifa node receives NEW TASK message and the task is new(it is neither in Ta nor in Tna) then Ta is updatedand the message is redirected to the network. As wediscuss later, when the robot fulfills its assigned taskit sends a TASK DONE message containing the id of thenode that generated the task and task’s time stamp.

serial

serial

AdaptiveSampling

Real-Timevisualization of AS operation in Matlab

TaskAllocation

TaskAS

Doneactuators

MICA-2Mote

MICA-2Mote

MICA-2Mote

MICA-2Mote

sensorInterface

driver

actuatordriver

packetdevice driver

FixedSensorNetwork

MobileSensor

Event Inputs to Task Allocation

Ethernet

MobileSensor

RF link

serial

serial

AdaptiveSampling

Real-Timevisualization of AS operation in Matlab

TaskAllocation

TaskAS

Doneactuators

MICA-2Mote

MICA-2Mote

MICA-2Mote

MICA-2Mote

sensorInterface

driver

actuatordriver

packetdevice driver

FixedSensorNetwork

MobileSensor

Event Inputs to Task Allocation

Ethernet

MobileSensor

RF link

Figure 6: The NIMS system architecture combinesthe adaptive sampling and Task Allocation (TA) al-gorithms providing interfaces to actuation and sens-ing. While the adaptive sampling relies on mo-bile sensor inputs, TA responds to events emanatingfrom distributed fixed sensors. The adaptive sam-pling and TA algorithms are coordinated and hostedby the embedded Stargate platform shown in Fig-ure 7

If the task is in Ta, then it is removed from Ta, addedto Tna and the message is redirected to SN.

Note that in practice the sizes of Ta and Tna are fixed (20and 30 in our experiments). Both sets can be overwritten,so where is no overflow problem. At the same time, the sizeof Ta should be set with care to avoid loss of data aboutcurrently active tasks and potentially failing to propagatethis data to the rest of the system.Our system is a special case of the TA methodology de-

scribed above - with only one resource (mobile node) for taskassignment. We consider the problem of assigning tasks oneat a time. In this case the greedy assignment is obviouslyoptimal. Consider task assignment Equation 1. Since thereis only one mobile node, the next task with highest priority(according to criterion C) is assigned to the mobile node,no matter what the mobile node’s utility function might be.The task prioritization criterion selected here is C based onthe time stamp associated with every task. The algorithmrunning on the mobile node is as follows.All incoming new tasks (specified by NEW TASK message)

are sorted according to the criterion C (tasks with smallertime stamp get priority - a FIFO policy) and stored in aset of currently active tasks Ta. Note that since the systemdoes not have any prior knowledge about the spatiotemporalvariation of event arrival, the priority of events are taken asuniform. Therefore, FIFO scheduling is appropriate. In ourfuture work, as we will learn more about the nature of thephenomenon, we plan to incorporate that knowledge intothe task allocation process. When the mobile node becomesavailable for reassignment, the task of highest priority isextracted from Ta and assigned to it. Next, based on thetask information the mobile node needs to compute a goalpoint.If the task’s position is p then the goal position will be

PROJlr (p) in 2D-case and PROJΠr(p) in 3D-case (see Fig-

ure 4). After the robot completes its last task it sends aTASK DONE message containing the id of the sensor node thatgenerated the task and task’s time stamp.The TA algorithm computes projections of the static net-

work onto the transect and separates the transect into slices(in the 2D-case) or more refined cells (in the 3D-case). Thisis the cornerstone for Event-Aware Adaptive Sampling Al-gorithm discussed next.

4. EVENT-AWARE ADAPTIVE SAMPLINGThe Adaptive Sampling approach we proposed in Sec-

tion 2 (Fidelity Driven Sampling or FDS) is designed tocapture static phenomena with an adjustable level of accu-racy. On the other hand, consider a dynamic scene whenthe phenomenon to be observed changes spatially and tem-porally. If this change is faster than the time it takes adap-tive sampling to complete, the algorithm would not obtaina correct result - the final “sensor picture” will consist ofsuperimposed phenomena.

4.1 MethodologySuppose the task is to observe a dynamic phenomenon P

which changes rapidly in time (Figure 5a). Thus, P con-sists of {p1, p2, ...pn}. One solution is to deploy many highfidelity sensors so that every point of the environment issampled. This is impractical. Consider a hybrid approachwhere a large number of static low-fidelity sensors capable ofdetecting pi are used in conjunction with the mobile NIMSnode which carries the high fidelity sensor. The adaptivesampling algorithm can then focus on a portion of a tran-sect containing pi that TA system provides. As shown inFigure 5b, the sensor network effectively discretizes the en-vironment, allowing to localize pi and limit the adaptivesampling to a part of the transect. Note that multiple nodescan detect the same phenomenon pi. For simplicity we as-sume that only the node with highest sensor reading of thephenomenon detects it. In principle, there are two ways toaddress that problem. One is to let the mobile node clustertasks and then create a combined slice of the transect torun the adaptive sampling. Another way is to let the SNlocally determine the cluster, and the ’leader’ of the clusterwill create a combined task.

4.2 Event-Aware Adaptive Sampling SystemThe following describes the Event-Aware Adaptive Sam-

pling (EAAS) System. The system consists of two modules:Task Allocation Module and Adaptive Sampling Module.Algorithm 2 shows pseudo-code of EAAS system.TA module monitors the environment for new tasks. If

unassigned tasks set Ta 6= 0, TA assigns the robot to thenext task in order, say task t. Then TA computes the ap-propriate goal position and a corresponding transect cell asdiscussed in previous section. The robot is delivered nextto the computed goal position of the cell to sample. Onceat the goal position, TA sends request to the adaptive sam-pling system with (x, y, z) of the center of the scan anddimensions of the scanning cell. TA module pauses, whilemonitoring the environment for new events. The adaptivesampling scans designated area and returns to TA. The pro-cess repeats.In summary, when a node detects a phenomenon (sensor

reading reaches a predetermined threshold) it notifies the SNand the mobile node. We say that a task is created. The

notification message contains the node ID, its location andthe sensor reading. Given this information we can use theTA algorithm to assign robot to the task, navigate the robotto that task and start the adaptive sampling on a limitedcell provided by TA. Figure 5c shows nodes that detectedphenomena of Figure 5a and created corresponding tasks.

5. SYSTEM PROTOTYPE ANDEXPERIMENTAL SETUP

The combined Adaptive Sampling and Task Allocationmethods have been introduced to both enable efficient mea-surement (for a specified accuracy estimate) while also en-abling fast response to events. This section describes theenabling architecture and design methods.

5.1 System Hardware PrototypeAs described in the Introduction, a NIMS system has re-

cently been deployed in the field and characterization offorest canopy phenomena using NIMS methods may begin.However, the rapid development of NIMS algorithms and theverification of NIMS software, embedded hardware, and sen-sor systems benefit from characterization and testing withcomplete systems that operate in an indoor laboratory-scalefacility where all inputs may be controlled. This laboratory-scale system, NIMS-LS, has been developed and is shown inFigure 6.NIMS-LS includes a mobile node system suspended by a

cable network (see Figure 7a) that is articulated by a step-per motor control system (see Figure 7b) controlled by aStargateTM embedded node. The motor control system al-ternately winds and unwinds cable length from a pair ofcable spools thereby causing a horizontal and vertical trans-lation of the mobile node. The actuator system includes ac-celeration and deceleration control as well as odometer sens-ing of motion on the NIMS infrastructure cable. Throughproper calibration, this system provides less then 1 cm res-olution for localization of the node at any point within thetransect plane. While configurable in height and width, thetransect used for the experiments reported here was 8m inlength and 2.5m in height. Fixed sensor nodes were dis-tributed on the surface of the transect region and also ele-vated in the transect as well, as will be discussed below.The mobile node is a standard wireless mote sensor sys-

tem. The MICA-2 mote is used as a standard wireless motein both the mobile node and in a wireless network with boththe Stargate platform as well as the distributed fixed sen-sors.

5.2 System Software Prototype ArchitectureThe NIMS software architecture (as illustrated in Fig-

ure 6) is based on the Emstar system [24]. This has beenselected for NIMS and provides the following benefits. First,Emstar provides a common set of embedded platform inter-faces for multiple embedded platforms that compose NIMSfield and NIMS-LS systems. This offers the benefit that ap-plications developed for the NIMS-LS system may then bedirectly applied to NIMS field systems. Second, the Emstarevent architecture provides the designer with robust meth-ods for servicing unscheduled events and order of operations.The Adaptive Sampling and Task Allocation systems bothbenefit from this for implementation of their reactive na-ture. Third, the Emstar system provides a regular means

(a) (b)

Figure 7: a) The NIMS-LS system employs a cable network to allow rapid deployment of a NIMS transectwithin an indoor environment. The cable system permits a sensor node to operate over the length and depthof a transect area. Software system interfaces are devised to enable applications to operate both on NIMSfield-scale systems as well as NIMS-LS where development and verification may occur. In addition, reconfig-urable lamp illumination sources and obstacles allow the designer to select a transect environmental variabledistribution for the purposes of testing. Note that both mobile and fixed sensors appear in this architecture.Note that fixed sensors are distributed in the two dimensional transect plane as well. For the experimentalcharacterization reported here, six MICA-2 mote sensors were deployed in two rows, equally spaced alongthe transect (of length 2m) and with the first row at a height of 80cm and second row at a height of 160cmas shown. b) The NIMS-LS system incorporates a standard mote sensor node for mobile measurement andan Intel StargateTM platform for hosting of Adaptive Sampling and Task Allocation algorithms. The platformmanages cable actuator control that, in turn, provides accurate node motion in the transect plane. Thisembedded platform, its motor systems, and cable spools for cable actuation are shown.

for implementing the many complex device drivers that ap-pear in NIMS electromechanical systems. Finally, Emstarhas enabled NIMS emulation that includes rapid verifica-tion of mobile sensing algorithms (on standard x86 worksta-tion platforms) in preparation for testing on the NIMS-LSplatform.The Adaptive Sampling and Task Allocation algorithms

operate on the Stargate platform and communicate oversocket interfaces as shown in Figure 6 Both Adaptive Sam-pling and Task Allocation exploit Emstar device drivers.The degree to which this Emstar-based architecture accom-modates diverse applications is illustrated by this exam-ple. Here, the Adaptive Sampling algorithm (with its inter-faces to motor control systems) is developed in C and C++.However, the Task Allocation algorithm is implemented inJava. The NIMS-LS architecture has incorporated this di-versity and enables robust operation of the combined com-plex and event-ware Adaptive Sampling and Task Allocationprocesses. A visualization tool developed in Matlab com-municates with the Adaptive Sampling system through theethernet connection. This allows scientists to monitor the

Adaptive Sampling system and visualize the sampled fieldreal time.

5.3 Experimental SetupThe environmental variable field is created through use of

a high intensity quartz halogen light source that illuminatesthe transect. Just as in the natural environment, photodiodesensors are employed for light intensity measurement. Inaddition, cylindrical obstacles are available and are placed inthe environment to create a variable field for test purposes.For the experiments reported here, the variable field wasselected to follow that observed in the natural environmentas in Figure 2.

6. RESULTS FOR ADAPTIVE SAMPLINGAs described in Section II, Fidelity Driven Sampling ex-

ploits mobile sensing capabilities to explore the variablefield, stratify this into regions of greatest required sampledensity, and then sample in these regions adaptively to min-imize estimated sampling error. Fidelity Driven Samplingoperates in an iterative architecture seeking to reach a de-

Figure 8: An environmental field map for a complexvariable field with superimposed sampling points asautonomously selected by Fidelity Driven Sampling.The image grey level indicates illumination intensityvariation (varying by a factor of 5.7 from the darkestto lightest regions).

sired threshold error. While multiple error threshold poli-cies may be applied, two are explored here: 1) an errorthreshold defined as a maximum tolerated mean squared er-ror estimate across the entire environmental field, and 2) amaximum allowed stratification rank which is a number ofdivisions that resulted in that strata. Adjustment of thesethresholds permits the mobile sensor node to return an envi-ronmental field map with a specified estimated fidelity with-out the requirement for any prior knowledge of the field char-acteristics. An inability to reach a specified estimated error(within a given time or rank level limit) will be reported bythe Fidelity Driven Sampling algorithm. This then providesthe user with yet further assurance of proper sampling andconfidence in returned data.Fidelity Driven Sampling is evaluated here by subjecting

the algorithm to environmental variable fields having twoextremes in their “curvature” characteristics. For one limit,the environmental variable field was created by placing manyobstacles in the illumination field (see Figure 9) to emulatethe characteristically most complex patterns observed in thenatural environment. In addition, the algorithm was alsosubjected to an environmental variable field that showedlow curvature created by casting only diffuse shadowing onthe transect. This latter case is characteristically similar tothe least complex fields observed under clear forest canopystructure. We call this a smooth phenomena.The performance of the Fidelity Driven Sampling algo-

rithm was evaluated by allowing the algorithm to autonomouslyoperate and return a sample distribution. This distributionthen was supplied to the estimator to return a reconstructedvariable map. Finally, this map was compared with the ac-tual measured data obtained by exhaustively moving thenode at his highest resolution through the variable field.This returned a “ground truth” map of the scene.The results of Fidelity Driven Sampling were then com-

pared with conventional raster scanning data acquisition.Raster scanning is the generally used approach that governs

Figure 9: The reconstructed variable field derivedfrom the Fidelity Driven Sampling points of Figure 8

sampling given no knowledge of the variation of field vari-ables. It provides a reference for performance of the newalgorithms described here that include awareness of bothevents (Section 7) and spatiotemporal variation in field vari-ables.Fidelity Driven Sampling algorithm shows a value of Mean

Squared Error for its reconstruction compared to groundtruth that meets or is superior to that of raster scanning.However, this must be achieved without pre-planning andmust be independent of the nature of the field characteristic.We examined both the returned reconstruction as well as itsmean of squared error over the entire transect.It is important that Fidelity Driven Sampling shows a

monotonic decrease in mean squared error for an increasein stratification rank level. We compared the result of FDSwith raster scanning for both rough and smooth phenomena.

6.1 Fidelity Driven Sampling vs. RoughPhenomena

Figure 9 shows a map of both ground truth and the po-sitions of both strata and actual sample points selected byFidelity Driven Sampling during an experimental session.Note that the sample point density increases in regions ofgreatest field curvature. This experimental result was cap-tured as the Field Driven Sampling system passed throughstratification rank 5 and had selected 489 sample pointswithin the transect. Figure 10 shows the reconstructionresulting from this. Close agreement in field shapes is ob-served.A test of the performance of this approach is shown in

Figure 11. Note that the actual mean squared error (com-puted between the reconstruction and ground truth acrossthe transect) reduces with increasing stratification rank. Also,Figure 12, shows the dependence of mean squared error onsample density for Fidelity Driven Sampling (Adaptive) andraster scanning (Raster) methods.

6.2 Fidelity Driven Sampling vs. SmoothPhenomena

The sample distribution results and reconstruction resultsfor a rank level of 5 are shown in Figures 13 and 14 for an en-

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Figure 11: Mean Squared Error for reconstructionsof the environmental field of Figure 8 as a func-tion of sampling points comparing Fidelity Drivenwith raster sampling. Note that while raster sam-pling may exhibit a large variation in error withchoice of sample density, Fidelity Driven samplingautonomously seeks a low value.

Figure 12: n environmental field map for a low cur-vature variable field with superimposed samplingpoints as autonomously selected by Fidelity DrivenSampling. The image grey level indicates illumina-tion intensity variation (varying by a factor of 1.5from the darkest to lightest regions).

Figure 13: The reconstructed variable field derivedfrom the Fidelity Driven Sampling points of Fig-ure 12

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vironmental field showing dramatically less curvature thanthat of Figure 9. A comparison of mean squared error perfor-mance between Fidelity Driven and raster scanning is shownin Figure 14. Note that for this reduced curvature, in our ex-periments, not only does Fidelity Driven Sampling convergeto a specified mean squared error, it is equal or superiorto raster scanning in efficiency with respect to numbers ofsample points.The combination of these tests provides a successful eval-

uation of the performance of Fidelity Driven Sampling forautonomously adjusting sample density through appropri-ate motion control and sampling of the mobile sensing sys-tem. As has been discussed this sampling method seeks toestablish a reconstructed variable field with a specified max-imum sampling error without requiring prior knowledge orplanning. We plan for further analysis of the algorithm ondifferent test patterns.

7. RESULTS FOR EVENT-AWARE ADAP-TIVE SAMPLING

Both Task Allocation and Event-Aware Adaptive Sam-pling use a notion of a task. In the following experiments wewill compare the cumulative task OnTime across all tasks,over the duration of every experiment. Each task’s OnTimeis computed as the difference between the time the task wasserviced by a robot (TASK DONEmessage is sent) and the timethe task was detected by one of the nodes of the network.As shown on Figure 7, a network of 6 Mica2 Motes was

predeployed in a test environment with predetermined coor-dinates. We use the general 3D topology. Hence, by knowingnodes locations and computing nodes’ projections onto thetransect plane, the TA algorithm produces a subdivision ofthe transect similar to Figure 4b.

7.1 Task Allocation vs. Raster ScanFollowing the same approach as described for characteri-

zation of the Fidelity Driven Sampling system component,experiments were conducted comparing Task Allocation(TA)

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Figure 15: Comparison of event OnTime betweenTA and Raster Scan. Number of events varies be-tween 3 and 20.

methods with conventional Raster Scan methods. The RasterScan method scans every point of the transect with a spec-ified resolution. When the Raster Scan reaches the locationof an event it clears it by sending TASK DONEmessage. RasterScan method proved to be prohibitively low in performance.In particular, experimental results showed that at the max-imum NIMS-LS spatial resolution of 1 cm, with a samplingdwell time of 1 second at each location, OnTime results weredramatically inferior to TA methods. Raster Scan methodwas also characterized at reduced spatial resolution of 5cmwith a corresponding improvement in response time. Thishowever, is still inferior to TA algorithm described in thispaper.In this experiment an artificial event is first generated on

a remote server. Then the server sends an event messageto the node designated for task generation and the nodeproceeds as if this event was detected by the node’s sensor.For this experiment, schedules of 3, 5, 7, 10 and 20 eventswere drawn (in time) from a Uniform distribution to arrivewithin 600 seconds, with uniformly distributed nodes thatdetected the event. Note that for actual applications we donot expect to receive/process more than 1 - 10 events in 10minutes on average. Hence the case of 20 events shows thebehavior of the system at the limit.Figure 15 shows experimental results comparing OnTime

performance of TA and Raster Scan. The number of eventsvaries between 3 and 20. Both algorithms were evaluatedfrom 3 different starting positions of the mobile node on thetransect (drawn from a Uniform distribution). The resultswere averaged. As can be seen from the graph, TA performs9-22 times better on the entire interval of 3-20 events. Notealso that TA is stable, as indicated by error bars, and henceis favored for use in this application since it provides reducedbounds on system run time over Raster Scan method.In addition to response time comparison, it is also impor-

tant to compare mobility requirements for TA and RasterScan methods. Specifically, the use of mobility requires en-ergy. Thus, this can be computed and compared for each

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method. Now, when the density of the events is low, theTA algorithm enables the mobile node system to remain ina static position for extended periods - “in between events”.This occurs when it has serviced all events that have arrivedand is awaiting new events. Raster Scan, however, forces therobot to move constantly. Hence, this method will consumefar greater energy and mobility resources than TA. A mea-sure of energy for mobility is determined for the purposes ofcomparison by computing the total time of mobile node mo-tion. Figure 16 shows comparison of energy consumption inunits of time-in-motion between TA and Raster Scan. As ex-pected, TA outperforms Raster Scan significantly. Howeveras the number of events increases to infinity, the TA shouldapproach Raster Scan energy consumption. Also note, thaton interval [5, 20] the slope of the Raster Scan curve is verysmall and the energy consumption is insensitive to eventarrival rate.

7.2 Event-Aware Adaptive SamplingThe introduction of Event-Aware Adaptive Sampling pro-

duces a potential dramatic improvement in event responsetime. Thus, in this section the results of measurement oftask OnTime is described for the system using the Event-Aware Adaptive Sampling (EAAS) algorithm. A previousinvestigation has evaluated an algorithm approach intendedto enable mobile sensor nodes to perform an adaptive sam-pling [25]. This was evaluated on data from a mobile sensorsystem that was acquired using a regular raster scan. In con-trast, the system architecture reported here allows adaptivesampling algorithms to be hosted directly on mobile embed-ded systems. In addition, this system architecture enablescooperation between adaptive sampling and Task Alloca-tion algorithm. This is demonstrated through direct exper-imental evaluation with the NIMS-LS system. The resultsfor EAAS operation combining task allocation and adaptivesampling are compared directly with results for the adaptivesampling system operating alone.

It is important to note that adaptive sampling does notprovide an accurate sampling of the scene if the frequencyof events is such that events arrive at a rate greater thanthe time required for adaptive sampling to complete. But ifthe event frequency will be sufficiently low then on averageevery task OnTime will be approximately the same. Hence,it suffices to compare the results for OnTime of only onetask.This experimental characterization was performed using

light intensity variations to induce events (exactly as occursin natural environment conditions.) As shown in Figure 7,the mobile and fixed nodes form a network. Also, each nodeis equipped with a light intensity sensor as deployed on themobile node. Here, a node generates an event when thesensor sampled value exceeds a threshold.The task OnTime performance of adaptive sampling is

20524 seconds and 577 seconds for EAAS. Hence EAAS sig-nificantly outperforms adaptive sampling (with an improve-ment by a factor greater than 35). This dramatic advantageof EAAS results from its ability to determine the locationof the task, deliver the mobile node to the proper location,and then reduce the scan area requirements for the adaptivesampling module by focusing attention on the proper eventregion.

8. SUMMARY AND FUTURE WORKSensor network systems are now being applied in criti-

cal science and engineering applications in complex envi-ronments. Such environmental field characterizations, con-fronts the challenge of spatiotemporal evolution of obsta-cles in the sensing environment, introducing an unknownlevel of measurement distortion. This paper describes anew architecture that augments fixed sensor networks withinfrastructure-supported mobile nodes. This architecture in-corporates systems that exploit adaptive mobility to activelyexplore environments and determine sampling point distri-bution, achieving a specified level of sensing fidelity. The co-ordination of fixed and mobile nodes also calls for new taskallocation algorithms and associated systems that direct mo-bile node resources to events. This paper describes the inte-gration of a Fidelity Driven Adaptive Sampling method withnew Task Allocation algorithms and systems. Experimentalmeasurements demonstrate that this combined system offersdramatically improved spatiotemporal coverage over that ofeither fixed or mobile sensors alone.Future work will both develop new Fidelity Driven sam-

pling and exploration systems and also apply these systemsmore broadly to field biology and civil engineering environ-mental monitoring needs. Sensing diversity will also be in-troduced to enhance Fidelity Driven Sampling with mea-surements made available from fixed and mobile sensors ofdiverse modes and locations. It is anticipated that this willpermit Bayesian-style updating or learning that can drawon possibly coarse or noisy auxiliary measurements whenforming an estimate of the variable field.Finally, new architectural features will appear that in-

clude ’sensor strands’ consisting of fixed nodes suspendedfrom the infrastructure in the three dimensional environ-ment and emplaced and reconfigured by mobile NIMS nodesystems. Multiple NIMS mobile node transects will alsobe deployed in large and varied environments and transecttypes. This will lead to investigation and development ofnew utility functions for task allocation that coordinate fixed

node networks and multiple mobile assets for further ad-vances in sensing fidelity for complex environmental fieldcharacterization.

9. ACKNOWLEDGMENTSThis material is based upon work supported by the Na-

tional Science Foundation (NSF) under Grant No. ANI-00331481. Any opinions, findings, and conclusions or rec-ommendations expressed in this material are those of theauthor(s) and do not necessarily reflect the views of theNational Science Foundation. The authors wish to acknowl-edge the development of NIMS systems and technologies byRichard Pon, Rachel Scollans, Lisa Shirachi, the valuableadvice and suggestions of Carl Oberg, the Emstar technol-ogy of Lew Girod and Jeremy Elson, and the science appli-cation guidance of Phil Rundel. Further, the authors wishto acknowledge the advice and expert guidance of MichaelTaggart, Michael Wimbrow, and Michael Hamilton of theJames San Jacinto Mountain Reserve.

10. REFERENCES[1] D. Estrin, G.J. Pottie, and M. Srivastava,

“Instrumenting the world with wireless sensornetworks,” in ICASSP 2001, Salt Lake City, USA,2001.

[2] C. M. P. Ozanne, D. Anhuf, S. L. Boulter, M. Keller,R. L. Kitching, C. Korner, F. C. Meinzer, and A. W.Mitchell, “Biodiversity meets the atmosphere: Aglobal view of forest canopies,” Science, vol. 301, pp.183–186, 2003.

[3] Radiation and Light Measurements, chapter 6, pp.97–116, Physiological Ecology - Field Methods andInstrumentation. Chapman & Hall, London, U.K.

[4] James San Jacinto Mountain Reserve,http://www.jamesreserve.edu.

[5] R. Nowak, U. Mitra, and R. Willett, “Estimatinginhomogeneous fields using wireless sensor networks,”IEEE Journal on Selected Areas in Communications,2004.

[6] J. Kiefer, “Optimal experimental design,” in J. Roy.Statist. Soc., Ser. B, 21, 272-319, 1959.

[7] V. V. Fedorov, Theory of Optimal Experiments, NewYork: Academic Press, 1972.

[8] S. D. Silvey, Optimal Design, London: Chapman &Hall, 1980.

[9] R. Pukelsheim, Optimal Design of Experiments, NewYork: Wiley, 1993.

[10] H Muller, “Optimal designs for nonparametric kernelregression,” in Statistics and Probability Letter, 1984.

[11] M-Y Cheng, P. Hall, and M. Titterington, “Optimaldesign for curve estimation by local linear smoothing,”in Bernoulli 4(1), 3-14, 1998.

[12] J. Faraway and D. Park, “Sequential design forresponse curve estimation,” 1998, pp. 9, 155–164.

[13] D.J.C. MacKay, “Information-based objectivefunctions for active data selection,” in NeuralComput., 1992, pp. 4, 590–604.

[14] D. A. Cohn, “Neural netowrk exploration usingoptimal design,” in MIT AI Lab Memo No. 1491.,1994.

[15] B. Gerkey, “On multi-robot task allocation,” Tech.Rep. Technical Report CRES-03-012, University ofSouthern California, 2003.

[16] D. Chapman, “Planning for conjunctive goals,”Artificial Intelligence, vol. 32, pp. 333–377, 1987.

[17] Bala Kalyanasundaram and Kirk Pruhs, “OnlineWeighted Matching,” J. of Algorithms, vol. 14, pp.478–488, 1993.

[18] M. A. Batalin and G. S. Sukhatme, “Using a sensornetwork for distributed multi-robot task allocation,”in Proc. of IEEE International Conference on Roboticsand Automation (ICRA’04), New Orleans, USA, 2004,pp. 158–164.

[19] B. Gerkey and M. J. Mataric, “Multi-robot taskallocation: Analyzing the complexity and optimalityof key architectures,” in Proc. of the IEEEInternational Conference on Robotics an Automation(ICRA03), Taipei, Taiwan, 2003, pp. 3862–3868.

[20] L. E. Parker, “Alliance: An architecture forfault-tolerant multi-robot cooperation.,” in IEEETransactions on Robotics and Automation, 1998,vol. 14, pp. 220–240.

[21] S. Botelho and R. Alami, “M+: a scheme formulti-robot cooperation through negotiated taskallocation and achievement.,” in Proc. of IEEEInternational Conferenceon Robotics and Automation(ICRA), 2000, pp. 293–298.

[22] B. P. Gerkey and M. J. Mataric, “Sold!: Auctionmethods for multi-robot coordination.,” in IEEETransactions on Robotics and Automation, 2002,vol. 18, pp. 758–768.

[23] E. H. Ostergard, M. J. Mataric, and G. S. Sukhatme,“Distributed multi-robot task allocation foremergency handling.,” in Proc. of the IEEE/RSJ Intl.Conf. on Intelligent Robots and Systems (IROS),2001, pp. 821–826.

[24] L. Girod, J. Elson, A. Cerpa, T. Stathopoulos,N. Ramanathan, and D. Estrin, “Emstar: a softwareenvironment for developing and deploying wirelesssensor networks,” in to appear in Proceedings ofUSENIX 04.

[25] M. Rahimi, R. Pon, W. J. Kaiser, G. S. Sukhatme,D. Estrin, and M. Srivastava, “Adaptive sampling forenvironmental robotics,” in IEEE Int. Conf. onRobotics and Automation, ICRA, New Orleans, LA,2004.