Call and Response: Experiments in Sampling the Environment

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 90089

3Department of Computer Science, UCLA, Los Angeles, CA 900954Department of Electrical Engineering, UCLA, Los Angeles, CA 90095

5Department 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}

Abstract— Monitoring of environmental phenomena withembedded networked sensing confronts the challenges ofboth unpredictable variability in the spatial distributionof phenomena, coupled with demands for a high spatialsampling rate in three dimensions. For example, low dis-tortion mapping of critical solar radiation properties inforest environments may require two-dimensional spatialsampling rates of greater than 10 samples/m2 over transectsexceeding 1000 m2. Clearly, adequate sampling coverage ofsuch a transect requires an impractically large number ofsensing nodes. This paper describes a new approach wherethe deployment of a combination of autonomous-articulatedand static sensor nodes enables sufficient spatiotemporalsampling density over large transects to meet a general setof environmental mapping demands.

To achieve this we have developed an embedded networkedsensor 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 beginby describing the class of important driving applications, thestatistical foundations for this new approach, and task allo-cation. We then describe our experimental implementation ofadaptive, event aware, exploration algorithms, which exploitour wireless, articulated sensors operating with deterministicmotion over large areas. Results of experimental measure-ments and the relationship among sampling methods, eventarrival rate, and sampling performance are presented.

I. INTRODUCTION

A broad class of environmental monitoring objectivesin fundamental science, environmental resource manage-ment, and public health protection demand distributedsensing capabilities [1]. For example, the potential globalimpact of climate change creates a requirement for under-standing the interaction between the forest canopy andthe atmosphere. Understanding critical phenomena, forexample the nature of carbon flux from the atmosphereto forest biomass, requires direct experimental charac-terization of spatiotemporally distributed phenomena [2].

This includes measurement of solar radiation (driving fun-damental photosynthesis and growth), atmospheric watervapor, temperature, and chemical composition. Distributedmeasurements are required since these phenomena aresensitive to (and in turn influence) the heterogeneousstructure of the natural environment.

A. High Fidelity Environmental Field Monitoring

Many environmental monitoring applications share thechallenge for achieving high fidelity characterization ca-pability for environmental field variables. This is conven-tionally achieved with high spatiotemporal sampling rate.For example, solar radiation and atmospheric propertiesmust be mapped in natural environments over a spatialextent comparable to those of the forest structure (wherea measurement transect height may exceed 50m andthe width required to adequately span the heterogeneousstructure may exceed 100m) . At the same time, somephenomena display a characteristic spatial variability onthe scale of centimeters, reflecting the fine scale of naturalstructure and important phenomena. Thus, in consideringhigh fidelity sampling for these phenomena over a two-dimensional plane with the required spatial extent of over1000 m2 and resolution of greater than 10 sample/m2

requires an impractically large number of sensing elementswith 10,000 measurement points.

Measurement distortion may also result from an im-proper spatial-sampling distribution (specifically due tomismatch between the spatial structure of the phenomenaand sensor node placement). This introduces yet fur-ther challenges for environmental monitoring by limitingthe capability for distributed sensor node deploymentplanning. Specifically, the inherent unpredictability andvariability of environmental structure and phenomenaprecludes the possibility of achieving adequate spatialsampling density by advance planning. The conventional

Fig. 1. The NIMS system exploits suspended aerial infrastructureto enable sustainable and precise transport of mobile nodes withincomplex three-dimensional environments. Phenomena are mapped withina spatially extensive transect where sensing nodes have precise commandover horizontal and vertical motion.

solution for reducing this source of distortion has beento increase spatiotemporal sampling rate. However, thisresults in excessive disturbance to the sensed environment.Clearly, fixed sensor distributions, alone, are not adequatefor many important environmental monitoring phenomena.

Since it is the presence of dynamic physical structuresthat lead to unpredictable and variable sensor coverage re-quirements, sensor networked systems must exploit wide-ranging and high-spatial-resolution sensor node mobilityin order to ensure adequate coverage.

B. Coordinated Fixed and Mobile Sensing Nodes for HighFidelity 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 spatiotemporalsampling distribution to achieve a specified accuracy ofenvironmental variable reconstruction. Further, mobilityalso permits the NIMS system to respond to initiallyunpredictable and variable environmental evolution.

While it is shown here that this architecture enablesmobile sensors that adapt to variable environments, theircoverage at any instant is restricted to their effective areaof regard. Thus, temporal measurement distortion willappear in the presence of rapidly changing phenomena.

Fig. 2. Map of solar radiation intensity obtained in a forest ecosystem bya NIMS system transporting a light intensity sensor within the canopy.Solar radiation intensity is indicated in contours as it varies spatiallyaccording to horizontal and vertical sensor displacement.

The NIMS architecture introduces coordination betweenfixed and mobile devices. Sparsely distributed fixed sensornodes, each remaining vigilant over a localized area ofregard, 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 fixeddevices provides the ability to accommodate high fidelitysensing with limited resource constraints. For example,typical environmental monitoring characterization tasksrequire costly sensing modalities where the cost is mea-sured in terms of energy, mass, volume, monetary ex-pense, or other value). Examples in natural environmentalmonitoring are tasks that require gas phase analysis ormulti-spectral imaging. Thus, while the demand for densespatial coverage is high, the resource cost associated withsupporting a vast number of individual sensor elementsfor adequate spatiotemporal coverage may be excessivelyhigh and the disturbance to the environment due to theirintroduction may be prohibitive. However, it is demon-strated here that the combination of low spatial-densityfixed sensors (providing the required assets for mobilenode task allocation) and highly capable mobile devicesresults in both adequate spatiotemporal coverage as wellreduced overall resource cost.

The NIMS architecture for mobile and adaptive sam-pling must include the attributes of 1) wide range mo-bility within three-dimensional volumes, 2) precise andhigh resolution position and orientation control, 3) longterm autonomous, sustainable operation, and 4) mobileoperation without disturbance to the environment. Thecombination of these requirements are met by NIMS

Fig. 3. A NIMS system deployed at the James San Jacinto MountainReserve [4]. This image shows the NIMS cable infrastructure, horizontaltransport node (carrying an embedded computing platform, image sensor,and vertical transport control, and vertically mobile meteorologicalsensor node.

infrastructure-supported mobility, as shown in Figure 1.Here, infrastructure is adapted to the environment anduniquely enables each of the attributes above.

C. Motivating Application

An example of a fundamental phenomenon that controlsprocesses 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 spa-tiotemporal mapping of phenomena, including solar ra-diation in the ecosystem. Figure 2 displays a map ofsolar illumination intensity in the photosynthetically activespectral region (PAR). This two dimensional PAR mapwas acquired by a NIMS system operating in a mixedconifer forest within the James San Jacinto MountainReserve [4]. Note the characteristic scale of variability forsolar radiation is less than 1m with a need to characterizea spatial extent that equals the dimensions of the forestcanopy itself.

Figure 3 shows a NIMS system deployed in the forestreserve for continuous operation. This system includessupporting cable infrastructure, a horizontally mobile em-bedded computing platform payload, image sensing, anda vertically mobile meteorological sensor system carrying

water vapor, temperature, and PAR sensing capability.Wireless networking is incorporated to link fixed nodes(distributed on the surface or suspended) with the verti-cally and horizontally mobile elements. The NIMS infras-tructure is elevated in the environment and thus lies aboveenvironmental obstacles to solar radiation. NIMS systemshave exploited this and have been deployed with batteryenergy sources supplied solely by solar photovoltaic cellenergy harvesting. Energy is transported as needed alongthe NIMS cable infrastructure by an articulated cablesystem. The NIMS system is deployed in a transect oflength 70m and average height of 15m with a total areaof over 1,000 m2.

The experimental NIMS system operates with a linearspeed range for node motion of 0.1 to 1 m/second. Thus,the time required to map an entire 1,000 m2 transect with0.1 m2 resolution with a simple ’raster’ regular scanningschedule will exceed 104 to 105 seconds. Phenomena thatvary at a characteristic rate exceeding this scanning ratemay not be accurately mapped. However, without knowl-edge of the spatial variability of measured phenomena, thesampling system must sample at high spatial rate in orderto achieve low measurement distortion.

D. Event-Aware Adaptive Sampling Systems

Thus, as will be described below, the requirements forthe measurement system to respond to variable environ-mental structure and to accurately map environmental vari-ables at minimum time and resource cost has lead to theintroduction of a NIMS architecture that combines mobileadaptive sampling with task allocation methods controllingnode motion. These rely on fixed sensor networks as wellas mobile elements to ensure that measurement is mostefficient and then to focus exploration and mapping atthe point in a transect for which change in environmentalvariables has occurred.

This paper first describes the Adaptive Sampling, TaskAllocation, and also Event-Aware Adaptive Samplingmethods and algorithms. A new Fidelity Driven Samplingadaptive method is introduced and tested with experimen-tal data. Then, experimental methods including systemhardware and software architecture are discussed. Thisincludes description of a laboratory-scale NIMS system(NIMS-LS) employed for algorithm and verification ofcombined software and mobile embedded hardware sys-tem implementations. Finally, experimental results ob-tained with NIMS-LS demonstrating the marked benefitsof combined adaptive sampling and task allocation aredescribed. It is important to note that Related Work isdiscussed in each Section below.

II. ADAPTIVE SAMPLING ALGORITHM

Sampling configurations (e.g., sample density) for em-bedded networked sensor systems are generally limited by

considerations related to the availability of communicationchannel resources or energy constraints. This has spawneda number of recursive estimation techniques that makedecisions in a hierarchical fashion [5]. For example, localcluster heads accumulate information and propagate thisaccording 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 mobilesampling to first stratify the environment into regionsrequiring varying degrees of sample density, then samplesin these regions while tracking estimated sampling error(fidelity). Fidelity Driven Sampling has the advantageover conventional raster (or other pre-planned samplingstrategies) of actively seeking to minimize error withoutprior knowledge of the variable field.

A. Methodology

Optimal designs for estimators have been studied ex-tensively in the statistics literature; see [6], [7], [8], [9].These approaches often assume a parametric form for theestimate. Given a generic learning algorithm, the problemof optimal design is much harder. Optimal designs forsimple kernel methods were studied by [10] where itwas found that the best placement of points depends onthe second derivative of the function; places with highcurvature should have relatively more points than flatregions. [11] study designs for local linear smoothing,ultimately proposing a batch-sequential algorithm.

The NIMS adaptive sampling algorithms are designedwith the goal of sequentially constructing a samplingpattern where at each step, samples are chosen thatimprove the estimate of the sensed field. [12] studiessuch sampling plans in the context of kernel smoothing.Points are added one at a time, chosen so as to reducean estimate of the integrated squared error. [13], [14]study the same problem, but with neural networks astheir estimators. They use the term “active learning” todescribe the adaptive sampling process. [13] selects pointsto minimize an information criterion, while [14] studiesthe mean squared error. In each case, it is assumed thebias is negligible and attention is focused on the varianceof the estimator. [14] also introduces the notion of pathconstraints on the samples, a topic that will be importantin our ultimate deployment of adaptive sampling.

The Adaptive Sampling algorithm reported here, relieson a mean squared error estimation approach and anunderlying learning algorithm based on a local linearsmoother. Further, a variable bandwidth is assumed basedon nearest neighbors. This algorithm, termed FidelityDriven Sampling (FDS), attempts to reduce mean square

Fig. 4. Pseudocode description of the Fidelity Driven Samplingalgorithm.

error at each sampling point by adjusting point density andlocation. It will be further described and evaluated below.

Mean squared error can be decomposed into a biascomponent and a variance component. In our applicationof solar illumination mapping, measurement noise is anegligible fraction of the overall signal, and hence ourgreatest concern will be bias. In other applications, biasmay not be dominant, and other estimation methods willbe needed. In these other regimes, attention will likelyfocus on tasks other than field estimation; or if a snapshotof the field is desired, a longer learning process involvingrepeated measures in time will be required. This is all thesubject of future work.

Throughout the sampling process, FDS maintains anestimate 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. Usingthis estimate, the FDS loop identifies regions or strataexhibiting a high degree of misfit. At each step in thesampling process, FDS adds points to that stratum withthe largest error. In so doing, the FDS algorithm reducesthe distance between neighbors and effectively lowers thebandwidth of the local linear fit within the stratum. Thealgorithm continues adding points to poor fitting stratauntil either an overall sample budget is exhausted or adesired fidelity limit is achieved.

Local linear fits have been chosen because these can tiethe notion of sampling with the resolution of structuresexpected in the variable field. In principle, any nonpara-metric procedure could be employed including thin platesplines or other radial basis functions.

B. The Fidelity Driven Sampling System

Figure 4 shows pseudo code description of the FidelityDriven Sampling adaptive algorithm. The NIMS roboticsystem continues adaptive sampling for the entire periodof operation in the environmental mapping state. TheFDS algorithm then calls the procedure predict-frame()which returns the estimate of the environmental variablefield. The algorithm follows a procedure of stratifying asampling region and according to observed measurementsin the region and for each strata, adjusting the number ofsampling points.

In addition, in the discussion below, the performanceof Raster Scanning, Stratified Random Sampling, and thenew Fidelity Driven Sampling algorithms are comparedthrough simulation of each sampling method with actualexperimental data. It is important to note that FidelityDriven Sampling provides an autonomous system thatseeks to assign sampling points to achieve a specifiedthreshold error value (or a specified stratification rank).By estimating error magnitudes, Fidelity Driven Samplingcan actively adapt to achieve a sampling fidelity objective.This differs fundamentally from raster scan methods thatare not informed of residual errors. It is most importantto note that Fidelity Driven Sampling, being adaptive,requires no prior knowledge of the environmental variablefield characteristics and will rather report these character-istics. The successful demonstration of this is enclosed inSection VI.

This predict-frame() procedure starts by inserting a rootstratum in a queue. Here, the root stratum corresponds tothe entire transect - the entire region of study. predict-frame() then initiates a loop that extracts strata with high-est product of mean square error and area. It determinesthe sampling points to be added to the strata and thenthe mobile sensor moves to visit those points and samplecorresponding data. After sampling points in the strata, itperforms a local linear kernel regression and reevaluatesthe estimate of the phenomenon. Error is computed interms of the absolute difference between estimated andsampled values. If the computed error in the strata fallsbelow a threshold, predict-frame() exits the inner loop andproceeds to examine more strata or otherwise divides thestrata into horizontal or vertical substrata depending onwhich division leads to the greatest reduction in error.

Following Fidelity Driven Sampling operation (or rasterscanning data acquisition) the returned variable field withits distribution of sample points was then supplied to astandard estimation algorithm (that performs an interpola-tion ) and returns an environmental field map. This finalresult is referred to as a reconstruction of the variable field.Experimental results and evaluation of reconstructions willbe discussed below for varying field characteristics.

III. TASK ALLOCATION ALGORITHM

Task Allocation (TA) is the problem of assigning avail-able resources to tasks. There are two major subdivisions: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,is equivalent to the conjunctive planning problem [15]which is NP-Complete.

Our focus here is on online task allocation. In onlineTA, all information about the tasks becomes available onlyupon task arrival. The assignment of resources to tasksmust be computed in real time. It has been shown [16] thatgreedy algorithms provide good approximate solutions toonline TA. It has also been shown [16] that in some casesthe greedy online TA solution is within a bounded limitof the optimal solution obtained by offline TA. Followingthe model in [17], we think of task assignment occurringin decision epochs. A decision epoch is a period of timeduring which only the tasks which have arrived since theend of the previous epoch are considered for assignment.Increasing the decision epoch to infinity converts theonline TA into the offline TA problem. We model theNIMS system as an online TA problem, since it is designedfor real-life autonomous field applications in dynamicenvironments.

Our work is related to the body of work on theproblem of online multi-robot task allocation (MRTA).For an overview and comparison of the key MRTA ar-chitectures see [18], which subdivides MRTA architec-tures into behavior-based and auction-based. For example,ALLIANCE [19] is a behavior-based architecture thatconsiders all tasks for (re)assignment at every iterationbased on robots’ utility. Utility is computed by mea-sures of acquiescence and impatience. Broadcast of LocalEligibility [20] is also a behavior-based approach, withfixed-priority tasks. For every task there exists a behaviorcapable of executing the task and estimating the utilityof robot executing the task. Auction-based approachesinclude the M+ system [21] and Murdoch [22]. Bothsystems rely on the Contract Net Protocol (CNP) that putsavailable tasks for auction, and candidate robots make’bids’ that are their task-specific utility estimates. Thehighest bidder (i.e., the best-fit robot) wins a contract forthe task and proceeds to execute it.

The proposed system differs from the above MRTAapproaches. Our system relies on a static network, andcommunication, sensing and computation are distributed.The motivation for this system derives from the need toefficiently sample the entire environmental space. As hasbeen discussed in the Introduction, it is impractical todeploy enough fixed sensors to achieve required sensingfidelity. As will be shown, the system combining fixed and

mobile nodes enables efficient sampling. TA becomes theprimary driver of efficient data collection in this system,since it allows the user to select a portion of the envi-ronment for sampling, as opposed to sampling the entireenvironment. In addition, TA manages system resources,so that resources are not consumed unless assigned mosteffectively.

A. Methodology

The general online TA system functions in the followingway. Suppose at a given decision epoch the system main-tains a set of resources R = {r1, ..., rn} and tasks T ={t1, ..., tk}. Tasks are prioritized based on a criterion C.C is an application dependent function and can combinesuch parameters as task arrival time, task importance, etc.A set of assignments A = (l = min(n, k) : {a1, ..., al})is computed as follows.

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

where t is the next unassigned task according to Cand U(rj , t) is the j-th resource utility value for ac-complishing t. The assigned resource and correspondingtask are removed from R and T respectively, beforethe next assignment. The utility function is chosen tobe application and resource dependent. In our model,once assigned, resources cannot be reallocated. After aresource has completed its task it becomes available fora new assignment. In the terminology of [23] we adopt acommitment strategy as opposed to opportunism.

The system consists of a mobile node suspended on acable and a static sensor network. We assume that the net-work is predeployed where each node knows its locationin a global coordinate system. The network monitors theenvironment 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 5 shows two network topologies that we define- positioned on the ground (the 2D-case) and more gener-ally, in the volume surrounding the transect (the 3D-case).In order for TA system to plan node’s motion the goalpoints should lie in the transect plane. Hence, we projectthe nodes locations onto the transect plane. As a resultwe get a set of points on a line l (2D-case, Figure 5a)or a plane Πr (3D-case, Figure 5c), both of which liein the transect plane. In the 2D-case, l is the line wherethe transect plane intersects the ground plane. Since, themobile node cannot move along that line, we translate l toa parallel line lr on the transect. We define the projectionfunction in the 2D-case PROJlr and 3D-case PROJΠr

.

Based on tasks projected locations TA divides thetransect into slices (2D-case, Figure 5b), or generally cells(3D-case, Figure 5d). With every projected node k weassociate a cell Cn.

Note that a 2D system is sometimes preferred becauseit is 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 systemis investigated in this paper.

B. The Task Allocation System

Our system consists of two algorithms - one running onthe 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 above athreshold a task is created and a notification messageNEW TASK is broadcasted. The notification messagecontains the notifying node’s ID, location, sensorreading, and time stamp (time when the task wasgenerated).

2) Task management. Each node maintains a set ofcurrently active tasks Ta and non-active tasks Tna.If a node receives NEW TASK message and the taskis new (it is neither in Ta nor in Tna) then Ta

is updated and the message is redirected to thenetwork several times. As we discuss later, when therobot fulfills its assigned task it sends a TASK DONEmessage containing the id of the node that generatedthe task and task’s time stamp. If the task is in Ta,then it is removed from Ta, added to Tna and themessage is redirected to SN several times.

Note that in practice the sizes of Ta and Tna arefixed (20 and 30 in our experiments). Both sets can beoverwritten, so where is no overflow problem. At the sametime, the size of Ta should be set with care to avoid lossof data about currently active tasks and potentially failingto propagate this data to the rest of the system.

Our system is a special case of the TA methodologydescribed above - with only one resource (mobile node)for task assignment. Consider task assignment Equation1. Since there is only one mobile node, the next task withhighest priority (according to criterion C) is assigned tothe mobile node, no matter what the mobile node’s utilityfunction might be. The task prioritization criterion selectedhere is C based on the time stamp associated with everytask. The algorithm running on the mobile node is asfollows.

l

(x,y,z)

l

r

(a) The 2D-case

l(x,y,z)’’ ’

(b) The 2D-case projection onto thetransect

Πr

(x,y,z)

(c) The 3D-case

(x,y,z)’’ ’ Πr

(d) The 3D-case projection onto thetransect

Fig. 5. Different SN topologies and corresponding projections onto the transect.

All incoming new tasks (specified by NEW TASK mes-sage) are sorted according to the criterion C (tasks withsmaller time stamp get priority - a FIFO policy) and storedin a set of currently active tasks Ta. When the mobile nodebecomes available for reassignment, the task of highestpriority is extracted from Ta and assigned to it. Next,based on the task information the mobile node needs tocompute a goal point.

If the task’s position is p then the goal position will bePROJlr (p) in 2D-case and PROJΠr

(p) in 3D-case (seeFigure 5). After the robot completes its last task it sends aTASK DONE message containing the id of the sensor nodethat generated the task and task’s time stamp.

The TA algorithm computes projections of the staticnetwork onto the transect and separates the transect intoslices (in the 2D-case) or more refined cells (in the 3D-case). This is the cornerstone for Event-Aware AdaptiveSampling Algorithm discussed next.

IV. EVENT-AWARE ADAPTIVE SAMPLING

The Adaptive Sampling approach we proposed in Sec-tion II (Fidelity Driven Sampling or FDS) is designedto capture static phenomena with an adjustable level ofaccuracy. On the other hand, consider a dynamic scenewhen the phenomenon to be observed changes spatiallyand temporally. If this change is faster than the time ittakes adaptive sampling to complete, the algorithm wouldnot obtain a correct result - the final “sensor picture” willconsist of superimposed phenomena.

A. Methodology

Suppose the task is to observe a dynamic phenomenonP which changes rapidly in time (Figure 6a). Thus,P consists of {p1, p2, ...pn}. One solution is to deploy

many high fidelity sensors so that every point of theenvironment is sampled. This is impractical. Consider ahybrid approach where a large number of static low-fidelity sensors capable of detecting pi are used in con-junction with the mobile NIMS node which carries thehigh fidelity sensor. The adaptive sampling algorithm canthen focus on a portion of a transect containing pi thatTA system provides. As shown in Figure 6b, the sensornetwork effectively discretizes the environment, allowingto localize pi and limit the adaptive sampling to a partof the transect. Note that multiple nodes can detect thesame phenomenon pi. For simplicity we assume that onlythe node with highest sensor reading of the phenomenondetects it. In principle, there are two ways to address thatproblem. One is to let the mobile node cluster tasks andthen create a combined slice of the transect to run theadaptive sampling. Another way is to let the SN locallydetermine the cluster, and the ’leader’ of the cluster willcreate a combined task.

B. Event-Aware Adaptive Sampling System

The following describes the Event-Aware AdaptiveSampling (EAAS) Algorithm:

1) TA module monitors the environment for new tasks;2) If unassigned tasks set Ta 6= 0, TA assigns the robot

to the next task in order, say task t;3) TA determines the appropriate goal position and a

corresponding transect cell as discussed in previoussection;

4) TA delivers the robot to the computed goal positionof the cell to sample;

5) TA sends request to the adaptive sampling systemwith (x, y, z) of the center of the scan and dimen-sions of the scanning cell;

t = 1t = 2

t = 3

. . .

pp

p

1

2

3

(a) (b)

e1 2

3

e

e

(c)

Fig. 6. Dynamic phenomenon P and a static sensor network. a) Rapidly changing spatially and temporally phenomenon P = {p1, p2, ...pn}, occurringforward in time; b) Static predeployed sensor network (SN), where each node is equipped with relatively cheap, low fidelity sensor capable of detectingpi; c) SN detects p1, p2, p3 and creates tasks;

6) TA system pauses, while monitoring the environ-ment for new events;

7) The adaptive sampling scans designated area, returnsto TA (sends AS DONE MSG);

8) TA sends TASK DONE message into SN containingid of the node that generated the task and task’s timestamp.

9) TA resumes. Repeat;

In summary, when a node detects a phenomenon (sensorreading reaches a predetermined threshold) it notifies theSN and the mobile node. We say that a task is created. Thenotification 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 6c shows nodes that detectedphenomena of Figure 6a and created corresponding tasks.

V. SYSTEM PROTOTYPE AND EXPERIMENTAL SETUP

The combined Adaptive Sampling and Task Allocationmethods have been introduced to both enable efficientmeasurement (for a specified accuracy estimate) while alsoenabling fast response to events. This section describes theenabling architecture and design methods.

A. System Hardware Prototype

As described in the Introduction, a NIMS system hasrecently been deployed in the field and characterizationof forest canopy phenomena using NIMS methods maybegin. However, the rapid development of NIMS algo-rithms and the verification of NIMS software, embeddedhardware, and sensor systems benefit from characteriza-tion and testing with complete systems that operate in anindoor laboratory-scale facility where all inputs may be

serial

serial

AdaptiveSampling

Real-TimeVisualization

(Matlab)

TaskAllocation

socketTaskAS

Doneactuators

MICA-2Mote

MICA-2Mote

MICA-2Mote

MICA-2Mote

sensorInterface

driver

actuatordriver

packetdevice driver

FixedSensorNetwork

MobileSensor

Event Inputs to Task Allocation

socket

MobileSensor

serial

serial

AdaptiveSampling

Real-TimeVisualization

(Matlab)

TaskAllocation

socketTaskAS

Doneactuators

MICA-2Mote

MICA-2Mote

MICA-2Mote

MICA-2Mote

sensorInterface

driver

actuatordriver

packetdevice driver

FixedSensorNetwork

MobileSensor

Event Inputs to Task Allocation

socket

MobileSensor

Fig. 7. The NIMS system architecture combines the adaptive samplingand Task Allocation (TA) algorithms providing interfaces to actuationand sensing. While the adaptive sampling relies on mobile sensor inputs,TA responds to events emanating from distributed fixed sensors. Theadaptive sampling and TA algorithms are coordinated and hosted by theembedded Stargate platform shown in Figure 8

controlled. This laboratory-scale system, NIMS-LS, hasbeen developed and is shown in Figure 7.

NIMS-LS includes a mobile node system suspended bya cable network (see Figure 8a) that is articulated by a“stepper” motor control system (see Figure 8b) controlledby a StargateTM embedded node. The motor controlsystem alternately winds and unwinds cable length froma pair of cable spools thereby causing a horizontal andvertical translation of the mobile node. Through propercalibration, this system provides less then 1 cm resolutionfor localization of the node at any point within the transectplane. While configurable in height and width, the transectused for the experiments reported here was 8m in lengthand 2.5m in height. Fixed sensor nodes were distributed

(a) (b)

Fig. 8. a) The NIMS-LS system employs a cable network to allow rapid deployment of a NIMS transect within an indoor environment. The cable systempermits a sensor node to operate over the length and depth of a transect area. Software system interfaces are devised to enable applications to operateboth on NIMS field-scale systems as well as NIMS-LS where development and verification may occur. In addition, reconfigurable lamp illuminationsources and obstacles allow the designer to select a transect environmental variable distribution for the purposes of testing. Note that both mobile andfixed 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 along the transect (of length 2m) and with thefirst row at a height of 80cm and second row at a height of 160cm as shown. b) The NIMS-LS system incorporates a standard mote sensor node formobile measurement and an Intel StargateTM platform for hosting of Adaptive Sampling and Task Allocation algorithms. The platform manages cableactuator control that, in turn, provides accurate node motion in the transect plane. This embedded platform, its motor systems, and cable spools for cableactuation are shown.

on the surface of the transect region and also elevated inthe transect as well, as will be discussed below.

The mobile node is a standard wireless mote sensorsystem. In addition, this device is included in a wirelessnetwork with both the Stargate platform as well as thedistributed fixed sensors.

B. System Software Prototype Architecture

The NIMS software architecture (as illustrated in Fig-ure 7) is based on the Emstar system [24]. This hasbeen selected for NIMS and provides the following ben-efits. First, Emstar provides a common set of embeddedplatform interfaces for multiple embedded platforms thatcompose NIMS field and NIMS-LS systems. This offersthe benefit that applications developed for the NIMS-LSsystem may then be directly applied to NIMS field sys-tems. Second, the Emstar event architecture provides thedesigner with robust methods for servicing unscheduledevents and order of operations. The Adaptive Samplingand Task Allocation systems both benefit from this forimplementation of their reactive nature. Third, the Emstarsystem provides a regular means for implementing the

many complex device drivers that appear in NIMS elec-tromechanical systems. Finally, Emstar has enabled NIMSemulation that includes rapid verification of mobile sens-ing algorithms (on standard x86 workstation platforms) inpreparation for testing on the NIMS-LS platform.

The Adaptive Sampling and Task Allocation algorithmsoperate on the Stargate platform and communicate oversocket interfaces as shown in Figure 7 Both AdaptiveSampling and Task Allocation exploit Emstar devicedrivers. The degree to which this Emstar-based archi-tecture accommodates diverse applications is illustratedby this example. Here, the Adaptive Sampling algorithm(with its interfaces to motor control systems) is developedin C and C++. However, the Task Allocation algorithmis implemented in Java. The NIMS-LS architecture hasincorporated this diversity and enables robust operation ofthe combined complex and event-ware Adaptive Samplingand Task Allocation processes.

C. Experimental Setup

The environmental variable field is created through useof an optical lamp system that illuminates the transect.

Fig. 9. An environmental field map for a complex variable fieldwith superimposed sampling points as autonomously selected by FidelityDriven Sampling. The image grey level indicates illumination intensityvariation (varying by a factor of 5.7 from the darkest to lightest regions).

Just as in the natural environment, photodiode sensorsare employed for light intensity measurement. In addition,cylindrical obstacles are available and are placed in theenvironment to create a variable field for test purposes.For the experiments reported here, the variable field wasselected to follow that observed in the natural environ-ment.

Fig. 10. The reconstructed variable field derived from the FidelityDriven Sampling points of Figure 9

VI. RESULTS FOR ADAPTIVE SAMPLING

As described in Section II, Fidelity Driven Samplingexploits mobile sensing capabilities to explore the variablefield, stratify this into regions of greatest required sampledensity, and then sample in these regions adaptively tominimize estimated sampling error. Fidelity Driven Sam-pling operates in an iterative architecture seeking to reach

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a desired threshold error. While multiple error thresholdpolicies may be applied, two are explored here: 1) an errorthreshold defined as a maximum tolerated mean squarederror estimate across the entire environmental field, and2) a maximum allowed stratification rank. Adjustment ofthese thresholds permit the mobile sensor node to return anenvironmental field map with a specified estimated fidelitywithout the requirement for any prior knowledge of thefield characteristics. An inability to reach a specified esti-mated error (within a given time or rank level limit) will

be reported by the Fidelity Driven Sampling algorithm.This then provides the user with yet further assurance ofproper sampling and confidence in returned data.

Fidelity Driven Sampling is evaluated here by subject-ing the algorithm to environmental variable fields havingtwo extremes in their “curvature” characteristics. Forone limit, the environmental variable field was createdby placing many obstacles in the illumination field (seeFigure 8) to emulate the characteristically most complexpatterns observed in the natural environment. In addition,the algorithm was also subjected to an environmentalvariable field that showed low curvature created by castingonly diffuse shadowing on the transect. This latter caseis characteristically similar to the least complex fieldsobserved under clear forest canopy structure.

The performance of the Fidelity Driven Sampling al-gorithm was evaluated by allowing the algorithm toautonomously operate and return a sample distribution.This distribution then was supplied to the estimator toreturn a reconstructed variable map. Finally, this mapwas compared with the actual measured data obtainedby exhaustively moving the node at his highest resolutionthrough 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 acquisitionthat performs the role of a conventional pre-planned andnon-adaptive sampling strategy.

Fidelity Driven Sampling algorithm shows a value ofMean Squared Error for its reconstruction compared toground truth that meets or is superior to that of rasterscanning. However, this must be achieved without pre-planning and must be independent of the nature of thefield characteristic. We examined both the returned recon-struction as well as its mean of squared error over theentire transect.

It is important that Fidelity Driven Sampling shows amonotonic decrease in mean squared error for an increasein stratification rank level. We compared the result ofFDS with raster scanning for both rough and smoothphenomena.

A. Fidelity Driven Sampling vs. Rough Phenomena

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 isobserved.

Fig. 13. n environmental field map for a low curvature variable fieldwith superimposed sampling points as autonomously selected by FidelityDriven Sampling. The image grey level indicates illumination intensityvariation (varying by a factor of 1.5 from the darkest to lightest regions).

Fig. 14. The reconstructed variable field derived from the FidelityDriven Sampling points of Figure 13

A test of the performance of this approach is shownin Figure 11. Note that the actual mean squared error(computed between the reconstruction and ground truthacross the transect) reduces with increasing stratificationrank. Also, Figure 12, shows the dependence of meansquared error on sample density for Fidelity Driven Sam-pling (Adaptive) and raster scanning (Raster) methods.

B. Fidelity Driven Sampling vs. Smooth Phenomena

The sample distribution results and reconstruction re-sults for a rank level of 5 are shown in Figures 13 and14 for an environmental field showing dramatically lesscurvature than that of Figure 9. A comparison of meansquared error performance between Fidelity Driven andraster scanning is shown in Figure 14. Note that for thisreduced curvature, not only does Fidelity Driven Sampling

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converge to a specified mean squared error, it is equal orsuperior to raster scanning in efficiency with respect tonumbers of sample points.

The combination of these tests provides a successfulevaluation of the performance of Fidelity Driven Samplingfor autonomously adjusting sample density through appro-priate motion control and sampling of the mobile sensingsystem. As has been discussed this sampling methodseeks to establish a reconstructed variable field with aspecified maximum sampling error without requiring priorknowledge or planning.

VII. RESULTS FOR EVENT-AWARE ADAPTIVESAMPLING

Both Task Allocation and Event-Aware Adaptive Sam-pling use a notion of a task. In the following experimentswe will compare the cumulative task OnTime across alltasks, over the duration of every experiment. Each task’sOnTime is computed as the difference between the timethe task was turned off by a Robot (TASK DONE messageis sent) and the time the task was detected by one of thenodes of the network.

As shown on Figure 8, a network of 6 Mica2 Moteswas predeployed in a test environment with predeterminedcoordinates. We use the general 3D topology. Hence, byknowing nodes locations and computing nodes’ projec-tions onto the transect plane, the TA algorithm producesa subdivision of the transect similar to Figure 5b.

A. Task Allocation vs. Raster Scan

Following the same approach as described for charac-terization of the Fidelity Driven Sampling system compo-nent, experiments were conducted comparing Task Alloca-

tion(TA) methods with conventional Raster Scan methods.The Raster Scan method scans every point of the transectwith a specified resolution. When the Raster Scan reachesthe location of an event it clears it by sending TASK DONEmessage. Raster Scan method proved to be prohibitivelylow in performance. In particular, experimental resultsshowed that at the maximum NIMS-LS spatial resolutionof 1 cm, with a sampling dwell time of 1 second at eachlocation, OnTime results were dramatically inferior to TAmethods. Raster Scan method was also characterized atreduced spatial resolution of 5cm with a correspondingimprovement in response time. This however, is stillinferior to TA algorithm described in this paper.

In this experiment an artificial event is first generated ona 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 in10 minutes on average. Hence the case of 20 events showsthe behavior of the system at the limit.

Figure 16 shows experimental results comparing On-Time performance of TA and Raster Scan. The numberof events varies between 3 and 20. Both algorithms wereevaluated from 3 different starting positions of the mobilenode on the transect (drawn from a Uniform distribution).The results were averaged. As can be seen from the graph,TA performs 9-22 times better on the entire interval of 3-20 events. Note also that TA is stable, as indicated byerror bars, and hence is favored for use in this applicationsince it provides reduced bounds on system run time overRaster Scan method.

In addition to response time comparison, it is alsoimportant to compare mobility requirements for TA andRaster Scan methods. Specifically, the use of mobilityrequires energy. Thus, this can be computed and comparedfor each method. Now, when the density of the events islow, the TA algorithm enables the mobile node systemto remain in a static position for extended periods - “inbetween events”. This occurs when it has serviced allevents that have arrived and is awaiting new events. RasterScan, however, forces the robot to move constantly. Hence,this method will consume far greater energy and mobilityresources than TA. A measure of energy for mobility isdetermined for the purposes of comparison by computingthe total time of mobile node motion. Figure 17 showscomparison of energy consumption in units of time-in-motion between TA and Raster Scan. As expected, TAoutperforms Raster Scan significantly. However as thenumber of events increases to infinity, the TA shouldapproach Raster Scan energy consumption. Also note, that

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on interval [5, 20] the slope of the Raster Scan curve isvery small and the energy consumption is insensitive toevent arrival rate.

B. Event-Aware Adaptive Sampling

The introduction of Event-Aware Adaptive Samplingproduces a potential dramatic improvement in event re-sponse time. Thus, in this section the results of measure-ment of task OnTime is described for the system usingthe Event-Aware Adaptive Sampling (EAAS) algorithm. Aprevious investigation has evaluated an algorithm approachintended to enable mobile sensor nodes to perform anadaptive samplinnog [25]. This was evaluated on data froma mobile sensor system that was acquired using a regular

raster scan. In contrast, the system architecture reportedhere allows adaptive sampling algorithms to be hosteddirectly on mobile embedded systems. In addition, thissystem architecture enables cooperation between adaptivesampling and Task Allocation algorithm. This is demon-strated through direct experimental evaluation with theNIMS-LS system. The results for EAAS operation com-bining task allocation and adaptive sampling are compareddirectly with results for the adaptive sampling systemoperating 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 than thetime required for adaptive sampling to complete. But if theevent 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 usinglight intensity variations to induce events (exactly asoccurs in natural environment conditions.) As shown inFigure 8, the mobile and fixed nodes form a network.Also, each node is equipped with a light intensity sensoras deployed on the mobile node. Here, a node generates anevent when the sensor sampled value exceeds a threshold.

The task OnTime performance of adaptive samplingis 20524 seconds and 577 seconds for EAAS. HenceEAAS significantly outperforms adaptive sampling (withan improvement by a factor greater than 35). This dramaticadvantage of EAAS results from its ability to determinethe location of the task, deliver the mobile node to theproper location, and then reduce the scan area require-ments for the adaptive sampling module by focusingattention on the proper event region.

VIII. SUMMARY AND FUTURE WORK

Sensor network systems are now being applied incritical science and engineering applications in complexenvironments. Such environmental field characterizations,confronts the challenge of spatiotemporal evolution ofobstacles in the sensing environment, introducing an un-known level of measurement distortion. This paper de-scribes a new architecture that augments fixed sensornetworks with infrastructure-supported mobile nodes. Thisarchitecture incorporates systems that exploit adaptivemobility to actively explore environments and determinesampling point distribution, achieving a specified levelof sensing fidelity. The coordination of fixed and mobilenodes also calls for new task allocation algorithms andassociated systems that direct mobile node resources toevents. This paper describes the integration of a FidelityDriven Adaptive Sampling method with new Task Alloca-tion algorithms and systems. Experimental measurementsdemonstrate that this combined system offers dramatically

improved spatiotemporal coverage over that of either fixedor mobile sensors alone.

Future work will both develop new Fidelity Drivensampling and exploration systems and also apply thesesystems more broadly to field biology and civil engi-neering environmental monitoring needs. Sensing diver-sity will also be introduced to enhance Fidelity DrivenSampling with measurements made available from fixedand mobile sensors of diverse modes and locations. It isanticipated that this will permit Bayesian-style updatingor learning that can draw on possibly coarse or noisyauxiliary measurements when forming an estimate of thevariable 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 NIMSnode systems. Multiple NIMS mobile node transects willalso be deployed in large and varied environments andtransect types. This will lead to investigation and de-velopment of new utility functions for task allocationthat coordinate fixed node networks and multiple mobileassets for further advances in sensing fidelity for complexenvironmental field characterization.

IX. ACKNOWLEDGMENTS

Left out for anonymity.

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