Adaptive Distributed Sensing and ControlMethods

Zhenhua Huang, Fangxu Dong, and Arthur C. Sanderson

ContentsSensing, Estimation, and Control in Smart Lighting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Distributed Lighting Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Distributed Sensing of the Light Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Adaptive Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Light Field Estimation and Interpolation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Optimal Source Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Adaptive Control Using Sensor Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Future Directions for Smart Lighting Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Sensing, Estimation, and Control in Smart Lighting

The rapid development of solid-state lighting in recent years has enabled thepossibility of creating high-quality light to meet diverse requirements and providebetter controllability than conventional light sources. Smart lighting technology maybe designed for energy efficiency, using automated control such as automaticdimming integrated with centralized building control systems. Energy consumptionis always one of the major concerns. Statistics reveal that lighting utilizes 22 % ofthe energy consumed by commercial and industrial markets, which consumes asmuch as 65 % of the total energy in the USA (Veitch et al. 1998). Smart lightingsystems can sense and communicate and are integrated with novel control systemsthat meet the expectations and requirements of different people and different appli-cations. These can be implemented in both open-loop mode and closed-loop mode.Research has been done on the sampling and control of smart lighting systems

Z. Huang (*) • F. Dong • A.C. SandersonNSF Engineering Research Center for Smart Lighting, Department of Electrical, Computer andSystems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USAe-mail: sandea@rpi.edu

# Springer International Publishing Switzerland 2016R. Karlicek et al. (eds.), Handbook of Advanced Lighting Technology,DOI 10.1007/978-3-319-00295-8_30-1

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(Thapan et al. 2001; IESNA 2000). In open-loop mode, the requirements for energyconsumption and the desired light field are utilized to calculate the configurationsbefore sending commands to light sources. In closed-loop mode, the targets areintegrated with feedback from sensor readings, and updated configurations are sentto light sources. In both modes, the process of determining optimal light sourceconfigurations from the target is necessary. The difference between them is howfrequently to implement this process.

In this chapter, we address the broad problems of adaptive sensing of lightingfields and the determination of desired light source configurations to meet therequirements of a specific target light field. In this approach, a lighting domainmay be defined by a discrete set of light sources, where each light source has alocation (in three dimensions), an orientation, and an illumination model thatdescribes the distribution of light rays in space (and, in general, the spectralcharacteristics of the light). Two key challenges occur in this process. First, thedesign space may be very large. The number and range of candidate solutions for thelight sources may include many alternatives in the search space regions. Second,there may be more than one satisfactory solution to the design problem. The searchspace may have many local extrema, and several alternative design configurationsmay adequately meet the system performance requirements. In this case, the costfunction is termed “multimodal,” and a design strategy that can explore many ofthese alternatives has distinct advantages.

In recent years, the goals of lighting design for both commercial and residentialbuildings have begun to shift from “visibility,” that is, providing necessary illumi-nation for occupant functions, to “lighting quality” that includes a broader range ofoccupant needs; economic, energy, and environmental constraints; and architecturalintegration. Occupant needs include the wider perspective of human health, produc-tivity, interpersonal communication, aesthetic quality, as well as visibility and taskperformance. These broader considerations have led to studies of human response tolighting and their broader physiological and psychological implications (Veitchet al. 1998). Such studies suggest that many different aspects of lighting quality(IESNA 2000) should be considered in good lighting design including colors andreflectances, brightness and glare, controlled use of daylighting, and local control ofartificial and natural light including temporal and spatial variations and contrasts.Quantification and metrics for lighting conditions may need to be extended asexperience with lighting perception is developed. In addition, user behaviors ondifferent tasks influence the light reaching the subject, and changing focus, headmotion, eye blinks, etc., may result in different exposure.

Previous studies have explored these issues and have attempted to quantify theperceptions and effects of luminance distributions on productivity and health ofoccupants (Cetegen et al. 2008; Newsham et al. 2004; Mahdavi and Eissa 2002;Veitch and Newsham 1998). From these studies, it is clear that a wide variety offactors affect the preferred choice of lighting conditions, and an adaptive “smart”approach to the synthesis of lighting conditions may offer opportunities tomarkedly improve the effectiveness of lighting and its role in the productivityand health of occupants. While the prior work is based on studies of fixed lighting

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conditions, the use of adaptive distributed control will require several additionalcapabilities:

1. Distributed light sources with adjustable (controllable) parameters, includingintensity, spectral distribution, spatial pattern, and others.

2. Adaptive smart lighting control methods will need sensory feedback indicatingthe actual lighting conditions in the space and algorithms for modification ofdistributed sources to achieve alternative spatial displays.

3. Sensing of room occupancy and task use will be important to the adaptationintroduced. As discussed above, these considerations might also be individual-ized and depend on the group of users, the time of day, and the characterization ofthe tasks involved.

4. Sensors and light sources will be networked to share information and compute thecontrol functions required.

The opportunities for distributed smart lighting systems control also encompassthe goals of energy efficiency and environmental impact. Recent studies (Selkowitz2008; Griffith et al. 2006) have attempted to assess the needs of building design toachieve improved energy and environmental performance. These studies emphasizethe broad needs of cooperative economic, social, and political initiatives, in additionto technological advances. Within the technology sector, it is also clear that a newlyeducated workforce is an important component of these initiatives. More familiaritywith new technologies will be required to clarify performance, reduce complexity ofdesign, and integrate new technologies with practice and regulation.

Distributed Lighting Systems

Recent advances in lighting sources, sensors, and systems have opened up opportu-nities for novel approaches to lighting systems that integrate component technolo-gies in a distributed architecture. Development of integrated adaptive intelligentsystems for control of energy utilization, including lighting, will reduce the barriersto custom design, construction, and maintenance for these new technologies. Adap-tive smart control of distributed lighting systems will be one important component ofsuch systems. The education of new generations of engineers and technical supportworkers with skills to design and implement smart energy management systems willbe needed to accomplish these goals.

In recent data from the Lawrence Berkeley National Laboratory (Selkowitz2008), Selkowitz indicates that in 2001, 39 % of total energy and 71 % of USelectricity have been used in building energy use. In addition, building energy useis dominated by lighting, heating, and cooling, with lighting (28 %) the largest use ofenergy in commercial buildings. He indicates that major savings of lighting energycould be achieved by the application of currently available lighting control strategiesincluding vacancy detection, dimming with daylight, demand response, and personalcontrols. He cites studies that show savings of 40–60 % through the use of advanced

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lighting controls. He also notes that the addition of wireless lighting controls toballasts would enable more flexibility and adaptability for these systems.

Current efforts in distributed lighting systems research extend well beyond thebasic control mechanisms currently available and will introduce a new formulationof the problem in terms of multiscale sensor-based control. This approach requiresconsideration of multisource/multisensor distributed systems and distributed algo-rithms that address these problems. There has been a limited amount of priorresearch that addresses this general problem in the context of lighting. A notablepatent was issued to Lyons at Philips (Lyons 1998) in 1998. The patent describes amethod for optimizing energy efficiency of a multisource lighting system specifi-cally using a linear programming technique to adjust source intensities and satisfy atotal energy consumption constraint. The problem is formulated using a set of linearmodels for energy allocation to each of the sources. It may be solved in severaldifferent related formats including minimum and maximum brightness or optimalbrightness constraints. As presented in the patent, the approach does not use sensorsor feedback in the implementation. It formulates a model-based, not a feedbackcontrol, solution to the optimization problem.

A more recent series of publications (Park et al. 2007; Singhvi et al. 2005;Granderson et al. 2004; Wen et al. 2006, 2008; Wen and Agogino 2008) addressesthe problem of wireless networked lighting systems used to optimize energy savings.Wen and Agogino (2008) describe a system of lighting sources linked by wirelessnetwork technologies with controlled source intensities. The basis of their approachis an illuminance model generator that predicts the workplane-level illuminance inan office space with known configuration and surface reflectance properties. Theworkplane-level illuminance is treated as a linear summation of model outcomes.The occupant’s preferred light settings are also specified on the workplane surfacegrid, and the problem is formulated as a linear programming problem. By minimiz-ing the norm of the vector of light output levels subject to the occupant constraints,the overall energy may be minimized. In the case that feasible solutions are notavailable, the settings are relaxed to form inequality constraints and solved accord-ingly. In this work (Wen and Agogino 2008), a hardware implementation of theproposed system was described using a wireless link to a basic actuation model forthe light dimmers. In this experiment, 12 luminaires were used to provide light to4–7 occupants. In these experiments, 50–70 % of the light energy was saved usingthe controlled system. This approach is also model based, not sensor based, andrestricted to linear predictive models of planar illumination.

This chapter describes an approach based on the general representation of a lightfield incorporating spatial and spectral properties of lighting systems, includingangular distribution of light rays in the field. This approach integrates sensors intothe illumination space, such that the sensors will be networked directly to thesources. The resulting ad hoc network of sources and sensors provides a frameworkfor distributed systems control. A multiresolution approach to the illumination fieldrepresentation will be introduced to efficiently represent this formulation. Thisapproach is based on previous work (Hombal et al. 2009a, b) on adaptive samplingin distributed sensor networks and has been shown to be efficient and effective for

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non-homogeneous function models. The optimal configuration of lighting sources issolved by evolutionary algorithms and not limited to linear programming solutions.These algorithms (Zhang and Sanderson 2009a, b) have been shown to perform wellin high-dimensional multimodal search spaces. The optimization criteria for thesesystems may be extended to energy efficiency, environmental constraints, as well asproductivity and health goals. These goals and constraints could be expressed interms of spatial mapping properties and extended to the evolutionary optimizationprocess.

Distributed Sensing of the Light Field

While the smart lighting system aims to produce the right light, a desired light field isusually designed to satisfy the light field requirements in a specific lighting appli-cation. This desired light field may be considered the target of lighting control, andthus the generated light field will be a set of fields similar to the target one. In thiscase, it is reasonable to deploy sensors based on the target field, and the objective isto generate samples to maximize the information obtained from the deployed sensorarray.

In previous research, approaches have been proposed for light field sampling.Levoy and Hanrahan (1996), while introducing the light field for image basedrendering in computer graphics, proposed to use a camera to describe the phenomenaaround scenes without creating a 3D geometry model of these scenes and to computeviews of scenes without knowing their surface properties. Since the radiance doesnot change along a line unless blocked, the light field of interest may be consideredas a 4D function, rather than a 5D function, between two planes. By capturingimages from multiple perspectives of a single translating camera, the authors suc-cessfully represented the 4D light field. Wilbrun (Wilburn 2005) extended this ideato high-performance imaging by using large video camera arrays. The authors useddifferent configurations of camera arrays for different purposes. Moreover, a newlyreleased light field camera invented by Ng (2006) is also inspired by this idea. Thelight field camera captures the entire light field traveling in every direction in everypoint on the camera plane, which permits refocusing of pictures at any time aftertaking the pictures. The author reconstructed the light field inside the camera bodyby inserting an array of microlenses in front of the photosensor. Each microlenscovers multiple photosensor pixels and separates the light rays from differentdirections. Using this information throughout the entire light field, the refocusedimage can be computed and displayed.

These approaches enable reconstruction of the 4D light field between two planesin free space without obstacles. In photographs taken by a single camera, the amountof light traveling along individual rays is not recorded. Instead, only the sum total oflight rays striking each point in the image is recorded. In general illuminationapplications, the entire light field cannot be obtained by a limited number of cameras.Thus, the use of localized sensors (“point” sensors with known spectral response)instead of cameras has two advantages. First, at a specified position and viewing

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angle, a camera cannot provide more information about the light field than a colorsensor does. In this sense, if a camera serves the same purpose as a localized sensor, asingle image from a camera under a single exposure is not sufficient to provide exactlight field information. A high-performance camera using multiple exposures and thesynthesis of a high-dynamic range image is required. Moreover, it would be moreexpensive and inconvenient to deploy cameras than localized sensors. The secondadvantage is that additional computation is needed to obtain the light field data, sincewe do not need the high resolution of the camera image.

Distributed sensor networks (DSN) integrate advanced sensor technology withlocal communications and computing and support a growing range of applicationssuch as observation, monitoring, and tracking of complex distributed processes(Curtin and Bellingham 2001; Estrin et al. 2001; Sanderson et al. 2006). Distributedsensor networks were originally applied in the military area such as for battlefieldsurveillance (He et al. 2004). However, in recent years, because of its importance inacquiring and processing information, this technology has been extended to otherareas, such as industrial and civilian applications. One such scenario involves thedeployment of multiple underwater vehicles such as AUVs (Curtin and Bellingham2001) as a part of a distributed sensor network for ecological and environmentalmonitoring of large bodies of water, such as oceans, harbors, lakes, rivers, andestuaries. Autonomous underwater vehicles (AUVs) have been incorporated indistributed sensor networks enabling pervasive in situ observation of such processesin a wide range of spatial and temporal sampling resolutions (Sanderson et al. 2006).

Although the components in a sensor node network may differ in differentapplications, the network consists of four basic parts, sensor unit, processing unit,data transfer unit, and power supply, as shown in Fig. 1. With these components,each sensor node will perform three main tasks: sensing, computing, and commu-nicating. These sensors are then networked together to share the information and areconnected to a base station. To access the information in the sensor network, queriescan be generated from the base station and routed to regions of interest with a clusterof sensors in the network. These related sensors will process data collaboratively andaggregate information locally within the cluster to reduce communications.

Sensor Processor

Power Supply

Base Station

Sensor Nodes

TransceiverFig. 1 Components of asensor node and the structureof a sensor network

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Because the sensor nodes in the network are embedded devices, they carry limitedhardware resources including limited memory storage and battery power. There arealso resource constraints on the sensor network as a whole, such as limited commu-nication bandwidth. Therefore, efficiently utilization of resources to achieve givensensing tasks is of great importance and typically a major objective in sensornetwork design. Figure 1 shows the components of and a sensor node and thestructure of a sensor network.

Given a set of sensor nodes, it is important to find an optimal deployment of thesenodes to maximize the sensing accuracy while minimizing resource consumption.Much research has focused on the development of wireless sensor networks withlocal communications capability (Popa et al. 2004; Batalin and Sukhatme 2004; Isleret al. 2004). Communications networks are expected to be autonomous and ad hocsuch that the node can enter and leave the network freely. Thus, when deployingsensor nodes, a set of communications and topology control problems need to beconsidered to maintain the network connectivity (Dharne and Jayasuriya 2006).Certain self-deployment methods such as the virtual force algorithm (Zou andChakrabarty 2004) and potential field algorithm (Howard et al. 2002) have beendeveloped to measure, repair, and complete the network connectivity. Furthermore,since the phenomena are usually not uniformly distributed in the environment, thedensity of the sensor nodes is not fixed and should adapt to this distribution. In anextremely dynamic environment, the locations of the sensors will also need to beadaptively adjusted to track the dynamic process. In these cases, dynamic andadaptive deployment of sensing resources is required to achieve the sensing task.Vieira et al. (2004) proposed an efficient incremental deployment algorithm whichuses the information of the current node density, energy level, and sensing coverageto guide the new sensor deployment. Willett et al. (2004) proposed an adaptivepreview refinement approach for sensor deployment. In the preview step, by using asparse set of sensors, an initial environment estimate is formed. This initial estimateis then utilized for determining locations of additional sensors in the refinement step.While maintaining high accuracy, this adaptive two-step approach can significantlyreduce the energy and communication consumption.

Adaptive Sampling

Sampling is a broad methodology for gathering statistics about physical phenomena.It is a fundamental area of scientific activity, which generates empirical evidence forscientific models. In general, sampling refers to the methodology of selecting a finitesubset from a larger set representing the total population toward an assumedscientific objective. In signal processing, such sampling is employed to select asubset of bases from a family of bases toward an efficient representation of the signal(Meyer 2001).

There are some standard sampling methods such as uniform sampling andrandom sampling. Uniform sampling is a classical sampling strategy, which acquiresmeasurement at uniform spatiotemporal intervals. Two design variables are the

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length of transects and the separation between them. In general, there exists atradeoff between the sampling coverage and the time taken to sample a given areawhich represents the efficiency of spatial monitoring. In general, it is straightforwardto design a uniform sampling algorithm and easy to implement it in practice.However, due to the uniform sampling resolution, this algorithm has low samplingefficiency when the observation space (environment) has uneven features. Densesamples should be placed in regions with important features in order to haveimproved sampling resolution. In addition, since the sampling density is uniformeverywhere, regions lacking features are probably oversampled, which results in awaste of sampling resources. On the other hand, when sampling a periodic process,there will be a potential error because of lack of randomization in the samplingalgorithm. These disadvantages limit the applicability of the uniform samplingalgorithm.

It is important to find adaptive sampling algorithms that can intelligently adapt toenvironment features and improve the estimation under resource constraints.Starting with a coarse sampling distribution, adaptive sampling algorithms incorpo-rate environment knowledge from previous samples and utilize it to refine thesampling distribution. In contrast to uniform sampling, good adaptive samplingdesign should be able to allocate different sampling resolutions to regions withdifferent features. Figure 2 shows the comparison of a uniform sampling deploymentand a variation-sensitive adaptive sampling solution, in which the sample densityincreases in regions with high variation and decreases in regions with low variation(Hombal 2009).

The features in a practical environment are often highly nonlinear and dynamic,and there are high spatial and temporal variations in the distributions of the variables.Traditional sampling algorithms such as uniform sampling and random samplinghave poor performance in these circumstances, requiring extensive sample resourcesto guarantee an adequate sampling resolution. For these complex environments with

Fig. 2 Coverage and resolution of sample distributions (Hombal 2009). (a) Uniform sampling and(b) variation-sensitive sampling

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nonuniform features, an efficient sampling design should incorporate incrementalknowledge of the environment through previous measurement to guide an adaptivesampling strategy.

Adaptive sampling refers to iterative feedback sampling algorithms in which anestimate of the underlying function is constructed from previous measurements.Usually this estimate, along with the feedback from the real environment measure-ments, guides the deployments of the sampling resources in the next iteration. In thisapproach, it is important to choose an appropriate model to estimate the underlyingfunction based on initial samples. For complex underlying functions in the obser-vation space, usually it is very difficult to find exact models, and thus surrogatemodels are considered to achieve the approximation. Surrogate models are approx-imation models which can approximate the characteristics of the underlying phe-nomena. Some popular surrogate models include response surfaces (Box and Draper1986), support vector machines (Mangasarian 2003), artificial neural networks(Smith 1993), and radial basis functions (Duchon 1977; Unser 2000). Constructedusing a data-driven approach, surrogate models are computationally feasible andconvenient to implement and are widely used in data representation, optimization,and sensitivity analysis.

In adaptive sampling, it is important to choose an appropriate field model tointegrate initial samples into an estimation of the underlying function. For complexunderlying functions, usually it is very difficult to find exact models, and thussurrogate models are considered to achieve the approximation. Hombal (2009)introduced a multiscale surrogate model for sampling and estimation of the unknownunderlying process based on localized radial basis functions. To be consistent withthis general sampling regime, Dong (2012) employs hierarchical radial basis func-tions (HRBF) to implement coarse-to-fine modeling of the underlying light field.The HRBF network may be viewed as a neural model for multiscale approximationof a function through multilayer decomposition of the approximation error space.Each layer of the model is approximated by a radial basis function (RBF) networkwith a different scale. The structural parameters in the HRBF model can be deter-mined by a hierarchical analysis grid constructed in the problem domain.

The structural parameters of HRBF are set according to an ordering imposed by ahierarchical analysis grid defined on the problem domain. The analysis grid is suchthat each layer of the grid is a dyadic partition of the previous layer. The intersectionsof such partitions form the nodes of the analysis grid. Each layer of the analysis gridcorresponds to a layer in the HRBF. The number and position of nodes at thecorresponding layer in the grid determine the number of basis functions and thelocations of the centers in each HRBF layer. Further, the scale parameter is setaccording to the density of the nodes. Figure 3 shows an example of the 1D analysisgrid.

Multiscale adaptive sampling algorithm (MSAS) (Homabl 2009) is an adaptivesampling approach which achieves variation-sensitive sampling and generates amultiscale functional representation of the underlying process using localized basisfunctions. Starting with a sparse sample distribution, the MSAS algorithm constructsan estimate of the underlying process from existing measurements and utilizes it to

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guide the selection of subsequent samples for refinement. The implementation ofMSAS can be facilitated by the integration of sensors on mobile robots, enablingadaptive and dynamic redeployment of sensors for new sensing tasks. This chapterprovides a brief survey of MSAS as well as new results on experimental evaluationof its performance.

The multiscale adaptive sampling (MSAS) algorithm generates a variation-sensitive sample distribution and is capable of obtaining accurate multiresolutionrepresentations of the underlying functions with low sampling costs. In practice,MSAS is especially useful in guiding sensor deployment strategy for exploration ofunknown regions. However, the basic MSAS algorithm is only valid for samplingstationary underlying phenomena. Samples in MSAS are iteratively generated, andplanning of sensor locations to visit the sample points is suboptimized in eachiteration, but not optimized globally. In practice, subject to constraints on sensoravailability, it may take a long time to deploy sensors on generated sample points formeasurements. In a dynamic environment, MSAS has to globally redeploy thesample resources in response to underlying phenomenon changes, which may betime inefficient and cannot guarantee the imposed temporal sampling resolutionsubject to the process constraint. Since many environments are highly dynamic,this property limits the practical applications of the basic MSAS algorithm.

The adaptive sampling approach guides the systematic selection of sensors tosample the generated light field and fusion of sensor information for lighting control.This adaptive sampling and sensor fusion approach could significantly reduce theerror in representation of the light field. A series of experiment results also demon-strate that the adaptive lighting control system with this adaptive light field samplingregime can consistently reduce the control error by more than 65 % relative to thatwith uniform sampling. However, this adaptive sampling approach may be onlyvalid for a pristine (no disturbances) environment. Since the sample locations arepredetermined and fixed based on the target field, the deployed sensors may not beable to fully capture the light field disturbance due to user activities or the presence

a

k rc4,2i

c3,i c3,i+1

c2,2j–2 c2,2j–1 c2,2j

c1,1 c1,j c1,31

2

3

4

a+b2

b

r4 = r18

r3 = r14

r2 = r12

r1 = b – a

2

Fig. 3 The 1D analysis grid

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of natural light. Among various disturbances in a smart room, daylight is a major oneand the understanding of its characteristics is of great importance for the develop-ment of an energy-efficient lighting system. For an environment subject to dynamicdaylight disturbance, real-time monitoring requires incorporation technologies toselect or redefine the sensor network and real-time reallocation of sensor locations toadaptively sample the dynamic light field.

Dong (2012; Dong and Sanderson 2013) proposed a dynamic multiscale adaptivesampling (DMSAS) approach based on MSAS for unknown dynamic. In DMSAS,sensors deployed at the last time step first take samples of the new underlyingfunction and employ the sensing data with corresponding RBFs to construct a coarsemultiscale estimation. This estimation can be viewed as a measurement of the newunderlying process and is corrupted with sampling noise. With this measurement andprior knowledge of the process evolution model, a Kalman filter may be designed toderive a refined estimate of the new underlying function and determine the newposition of sensors for multiresolution sampling. In this way, the route of currentlydeployed sensors to their destinations can be optimally planned with an objective tominimize the traveling time. Different from globally redeploying all the sensors inMSAS, DMSAS involves locally moving related sensors to capture local features ofthe underlying process. The relocation of sensors in DMSAS is much more timeefficient and consistent with dynamic sampling. The resulting approach maintains alinear evolving model of the dynamic daylight and recursively identifies the modelparameters based on previous daylight estimation. To sample a new daylight field, amodel-based prediction is first achieved through Kalman filtering, and new samplelocations are generated based on this prediction. The mobile sensor can then berepositioned to take all the samples and refine the daylight estimation. These pro-cedures are repeated to plan and manage mobile sensors to track and estimate thedynamic daylight field.

Light Field Estimation and Interpolation

Currently, in general illumination applications, the measurement of light distribu-tions and control of light sources are typically conducted with respect to atwo-dimensional reference plane in the illumination field. Light sensors are placedeither on a plane or pointing at the same angle, and much of the light distributioninformation from different viewing angles is neglected. The results of light sourceanalysis, design, or control will no longer be valid if the viewing position or theviewing angle is changed. Therefore, it is necessary to take into consideration lightdistributions from all directions for better design and control of light sources to meetdiverse target light field requirements. In order to address these design and controlissues in the more general case, a consistent representation and set of analysis toolsfor the five-dimensional field are needed.

For analysis and design purposes, it is desirable to reconstruct (interpolate) thelight field based on a set of discrete sample points of the simulated data or experi-mental measurements. In this case, the sampled data occur in the five-dimensional

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space, three dimensions in spatial position and two dimensions in direction. Thesampled data may include the property of anisotropy. That is, there may be differentmodels for estimation in different directions. Adaptive sampling of the light field(Dong 2012) and adaptive control of color-tunable lighting systems (Afshariet al. 2012) are examples where such an interpolated model may be required.

Most previous research on light field acquisition has been applied to the problemof image rendering in computer graphics (Foley et al. 1995; Hill 2001). Renderingmay be viewed as a reconstruction of the light field forming an estimate of theperceived image around the objects (Goral et al. 1984; Phong 1975). However, thesetechniques are not suitable for the study of the general illumination applicationsconsidered here. First, the goal in image rendering is to achieve realistic imageswhen viewed from a specified viewing angle. The goal in general illuminationapplications is to achieve a full description of the light field in space rather thanthe light field viewed from a specified angle. Second, in applications to imagerendering, there is a requirement for a high-resolution representation of the imagelight field. In the case of general illumination, the required resolution of the desiredlight field is usually greatly reduced. The goals of general illumination, such asambient, task, and accent lighting, require a lower spatial resolution but span a largerspatial volume and range of angles.

For image synthesis in computer graphics, Greger et al. (1998) proposed the ideaof irradiance volume to calculate the global illumination. They sampled the radianceat sample points and directions and computed the irradiance distribution function tobuild the irradiance volume (Greger 1996; Greger et al. 1998). However, the queryand estimation of the irradiance volume is completed by trilinear interpolation,without considering the anisotropic characteristic of light transport. Huang (2013)proposed a spatial estimation techniques based on Kriging techniques to address theproblem of anisotropy.

Kriging (Matheron 1963) is a technique developed for spatial interpolation ofsampled data and has often been applied to fields of environmental science(Bayraktar and Turalioglu 2005), hydrogeology (Chiles and Delfiner 1999), andmining (Richmond 2003). Kriging techniques estimate the value of an unknownfunction expressed by a combination of two components: (1) a deterministic com-ponent related to the estimate of a localized mean and (2) a stochastic componentwhich is dependent on the covariance of the function.

A five-dimensional function of the light field, which is a subset of the seven-dimensional plenoptic function of the light field widely accepted in computergraphics (Gershun 1936; Levoy and Hanrahan 1996; Adelson and Bergen 1991),is utilized to describe the light field. The light field can be represented as a five-dimensional plenoptic function in terms of spatial position (x, y, z) and spatial angle(θ, φ). The sample space is therefore expressed as a five-dimensional vector x ¼x, y, z, θ, φð Þ . Figure 4 shows an example of spherical coordinates. The functionvalue L(x) is the plenoptic function of spatial position (x, y, z) viewed at angle (θ, φ).

For the illumination application, the light field is sampled at a finite set of points,and the plenoptic function is estimated at other locations and angles using thetechnique of universal Kriging. A second-order polynomial is used for the trend,

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and there are no cross-terms considered between different independent dimensions.In order to account for the anisotropic properties of light, a three-dimensionalanisotropic model based on the spherical semivariogram model is used. The spher-ical semivariogram is defined by a parameter, and the anisotropic model is defined byadditional three parameters: two angle variables and the fraction of anisotropy. Thus,the range of variance changes with direction.

Figure 5 shows a 3D view of the five-dimensional light field distribution in thespace along a horizontal plane. Each volume pair (upper and lower) in this figurerepresents the light field that can be detected at the specified position, which is thepoint of intersection of the upper and lower volume. Any point on a volumerepresents the light that can be detected from a viewing angle, which is determinedby the direction from the point of intersection to the point on the volume, and thedistance between represents the value of light measurement such as illuminance.

Optimal Source Configuration

Optimal light source configurations are needed to meet the requirements of lightperformance and energy consumption. Figure 6 shows a schematic example of lightsource configurations and the target light field. The search process can be formulatedas an optimization problem based on analytical models of light sources, lightpropagation characteristics, and the user requirements.

To formulate this optimization problem, the mathematical definition of the targetrequirements is important. In previous illumination applications, the target isrepresented by a one-dimensional array or a two-dimensional matrix of illumination

x

z

y

r

(r, q, j)

q

j

Fig. 4 Spherical coordinateswith two angle variables

Adaptive Distributed Sensing and Control Methods 13

values which can be detected based on two-dimensional sensor sampling. However,this is not sufficient to achieve the goals of smart lighting, because there is a lack ofspatial information including the directional characteristics of light sources. A five-dimensional function of the light field is utilized to describe the target. The fivedimensions include the appropriate values for spatial position (x, y, z) of thespecified point and the azimuth angle and zenith angle (θ, φ) of the specifieddirection. The light field at any position at any viewing angle is expressed as L(x, y, z, θ, φ), which can express the target accurately. Figure 7 shows a schematicdrawing of five dimensions of a light field detected at a single location in space.

Since the angular information is included in the target function, the angularinformation in the light source configurations is part of the corresponding targetlight field. The light source is characterized by the following parameters: the spatialposition (x, y, z), which can also be expressed by the spherical coordinate (R, θl,φl), the directional angle (θd, φd), the power (intensity) of the light source I, and

Fig. 5 A 3D view of the five-dimensional light fieldsampled in a horizontal planein three-dimensional spacewith diffuse reflective surface

Fig. 6 Schematic example oflight source and target lightfield

14 Z. Huang et al.

the beam angle of the light source ω. These seven parameters constitute the basicseven-dimensional search space of a single light source. The estimation of the five-dimensional light field in the computation is accomplished by spatial sampling andspatial estimation using the Kriging technique described above.

Consider the case of a desired light field Ld(q) at each spatial position q with Nlight sources and the light field generated from the i th light sources Li(q). Theoptimization problem can be expressed as

minXq

Ld qð Þ �XNi¼1

Li qð Þ�����

�����

!

According to the light transport characteristics, the result of multiple light sourcesis the superposition of the fields generated by all the light sources. On one hand, foreach light source, there is a seven-dimensional search space. The dimensionincreases dramatically with the increase in the number of light sources. On theother hand, there may be more than one satisfactory configuration to the problem.There is therefore a need to optimize with respect to problems of high dimensionwith highly multimodal objective functions.

The Speciated Parameter Adaptive Differential Evolution (SPADE) algorithm isone of the optimization algorithms developed for this purpose (Huang 2013). TheSPADE algorithm is a novel multiple population DE algorithm proposed to identifydifferent species which occur as distinct subpopulations of the evolutionary process.In this approach, the population is partitioned using an unsupervised clusteringmethod, and the subpopulations are able to track alternative solutions associatedwith local minima of the objective function. These species evolve separately basedon parameter adaptive differential evolution with occasional crossover interactionsacross subpopulations. Therefore, each species can be tracked separately by thecorresponding population to achieve multimodal optimization.

In the optimization of light source configurations, the SPADE algorithm utilizes afive-dimensional light field estimation. A model of the light source and the light

Sensor

fq

Vy

Vz

Vx

Fig. 7 Schematic drawing offive dimensions of a light field

Adaptive Distributed Sensing and Control Methods 15

sensor response according to the light source is predefined by an eight-dimensionalmatrix. This eight-dimensional matrix is generated in simulation by importingluminous intensity models of the light source and applying light sensors. Themodel variables include the distance between the light source and the light sensor,the spread cone angle of the light source, the two spatial angles of light sensors withrespect to the light source, the two orientation angles of the light source, and the twoorientation angles of the light sensor.

Compared to general ray tracing methods, this eight-dimensional matrix model-based approach saves a lot of computation time. However, this model-basedapproach has the problem of sensitivity to model variation and model errors.First, the optimization procedure is sensitive to the luminous intensity model ofthe light source, which defines the entire the light distribution of the light sources. Ingeneral, there are different types of light sources, such as point light sources, spotlight sources, and area light sources. If the luminous intensity model is verydifferent from what is used in practice, there would be a large error caused to theresult of optimization. In this thesis, an approximation of the luminous intensitymodel of the light source is provided by the designer. Second, among the modelparameters that can be adjusted in the procedure, the sensitivity to the parameter ofsource spread angle is high, while the sensitivity to the distance between the lightsource and the light sensor is low. For better performance of the optimization, it mayalso be worthwhile to take the optimization of the luminous intensity model of thelight source into the procedure in future work. In addition, a more extensivequantitative evaluation of model sensitivity and its impact on optimization maybe carried out.

For the optimization of light source configurations, the SPADE algorithm offerssubstantial advantages. The computational time is affected by the dimension of thesearch space and the number of species generated. The dimension of the search spacemakes a larger impact on the computational time than the number of speciesgenerated. In illumination applications, additional computation is required for thelight sensor response caused by the light source. In order to save computational time,an approximation of the model was used to integrate the ray tracing program into theoptimization procedure. However, the model is still of high dimension, and there areadditional requirements for the computation of applying the model to generate theresults and provide a comparison with the five-dimensional target light field. Thisadditional computation cost is relatively small compared with the effect of additionaldimensions added in the search space.

Figure 8 shows an example of a light source configuration optimization. In thisexample, the target light field is obtained with the condition of three predefined lightsources, as shown in the figure on the right. The search space is a 15-dimensionalspace, including the distance away from the center, the angular position in thespherical coordinate, and the orientation angle of all three light sources. Theoptimization results are determined with the average error less than 25 %. The figureon the left shows the light field distribution after optimization. Similarly, eachvolume represents the light field that is detected at the specified position.

16 Z. Huang et al.

Adaptive Control Using Sensor Feedback

The multiscale functional approximation of the light field can be achieved by fusingmultiple sensor measurements, and it can be utilized in a feedback mode for lightingcontrol. Dong and Sanderson (2013) incorporated the sampling methodology andadaptive lighting control in a smart space testbed, as shown in Fig. 9. The lightingsystem consists of multispectral LED modules as light sources and a set of colorsensors used for light field sampling and estimation.

Spatial angular distribution in 3D space, z=0.4

1

0.5

02

1

0

–1

–2 –2

–1

01

2

1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

Fig. 8 An example of the light source configuration optimization

Fig. 9 Smart space testbedfor lighting control

Adaptive Distributed Sensing and Control Methods 17

Effective feedback control in a lighting system requires knowledge of the lightpropagation under given conditions. For an LED lighting system, (Afshariet al. 2012) introduced a linear model to characterize the light propagation fromthe system input to the generated light field on discrete sensors. A similar model canbe constructed for mapping the LED input to the generated light field on the discretedomain.

fRGB ¼ GuRGB þ ω

where fRGB is the generated light field represented in RGB color space, uRGB is theRGB LED lighting input, G is the light transport matrix (LTM), and ω denotes lightdisturbances in the system. The LTM depends on the room configuration and can beidentified using a least square approach.

In this lighting system, light field sampling is conducted by a distributed sensornetwork. Based on the sampling tasks, the sensors are divided into two subsets. Inone of the subsets, the sensors are set static and adaptively deployed based on thetarget light field. The main task is to sample and capture features of generated lightfield. The other subset of sensors is mobile, which are routed and repositioned withtime by the proposed dynamic adaptive sampling approach to track and sample thedynamic disturbance. The actual light field is measured by all sensors, and afunctional light field approximation can be derived by an HRBF interpolation onthe sensor measurements. As a fusion of all the sensor measurements, this light fieldapproximation should be capable of capturing features of the actual light field,which is a combination of the generated field by light sources and the dynamicdisturbance.

Once the approximation of the light field is constructed, it can be used in feedbackmode for adaptive lighting control. The objective of the control is to minimize theperceptual difference between the estimated light field and target light field.

min J uð Þ ¼X

ftarLab xð Þ � fLab xð Þ�� ��22

st: fRGB ¼ Guþ dRGB þ ωfLab ¼ h fRGBð Þ

where J(u) is the cost function, fLabtar (x) is the target light field represented in CIELAB

color space, fLab xð Þ is the estimation of target light field represented in CIELABcolor space, fRGB is the generated light field represented in RGB color space, u is theRGB LED lighting input, G is the light transport matrix (LTM), dRGB represents thedaylight in RGB color space, ω denotes light disturbances in the system, and h �ð Þ is anonlinear mapping from the RGB color space to the CIELAB color space.

With this formulation, a gradient-based control method is developed to iterativelyobtain the optimal LED input to iteratively obtain the optimal LED input to generatethe target light field. The control input at the ith time step is calculated as

18 Z. Huang et al.

uiþ1 ¼ ui þ ϵ �∇u

�Ltar � L uð Þ

∇u

�atar � a uð Þ

∇u

�btar � b uð Þ

24

35þ

i

Ltar � L� �atar � að Þbtar � b� �

24

35i

where ui is the ith time-step input, ϵ is the tunable step size, �½ �þ is the pseudo-inverse

matrix, and ∇u �ð Þ is the gradient with respect to the input.An experiment has been implemented to evaluate the dynamic adaptive sampling

approach in lighting control with dynamic daylight disturbance. The light field isobserved on a 2D horizontal plane in the smart space, and a 2D target light field isspecified on the observation plane. Initially the lighting control system is testedwithout presence of daylight, and a distribution of 12 samples is generated byadaptive sampling based on the target field. The light field estimation is utilized infeedback mode by the control system, which adaptively tunes the light sources toreproduce the target field. Figure 10a shows the distribution of the steady-statelighting control error over the observation plane, which is defined as the Euclideandistance between the target and actual light field in the CIELAB color space. Thelighting control error is very small in the region of observation, which implies thatwith accurate feedback of the light fields, the lighting system is capable ofreproducing the target field with minimal perceptual distortion. For comparison,Fig. 10b shows the distribution of the lighting control error with uniform light fieldsampling. In this case, the lighting system fails to reproduce the target field in theregion due to the lack of sensors and incomplete feedback of the generated lightfield. The mean control error on the problem domain is five times higher than that ina system with adaptive sampling.

At the following time steps in the experiment, the lighting system is tested withdynamic daylight disturbance. At each time step, eight samples are generated basedon the target field by adaptive sampling, and their locations are fixed during the

40

30

20

10

0

–10

–20

–30

–40

80

70

60

50

40

30

20

10

0

80

70

60

50

40

30

20

10

0–60 –40 –20 0

adaptive sampling uniform sampling

a40

30

20

10

0

–10

–20

–30

–40

b

20 40 60 –60 –40 –20 0 20 40 60

Fig. 10 Control error comparison between lighting systems with adaptive and uniform samplingmethods. (a) Adaptive sampling and (b) uniform sampling

Adaptive Distributed Sensing and Control Methods 19

experiment. Another four new samples are generated by the dynamic adaptivesampling approach to track the daylight field. Based on the measured light field,the control system aims to compensate the daylight disturbance and maintain theactual light field as the target one. For comparison, a parallel experiment isconducted to test the lighting system with 12 static sensor samples, whose locationsare predetermined to sample the target light field by the adaptive sampling approach.Figure 11 shows the mean control errors of the lighting control systems withdynamic and static sampling versus time. The lighting system with dynamicapproach is capable of tracking the dynamic circumstances and reproducing thetarget light field with little perception distortions. In comparison, the lighting systemwith static approach fails to adequately reproduce the target light field due to the lackof sensors and insufficient feedback of dynamic information from surroundings. Thelighting system with dynamic adaptive light fielding sampling reduces the controlerror by 20 % relative to that with static sampling.

Future Directions for Smart Lighting Systems

The rapid development of new technologies for lighting sources, sensors, networkinfrastructure, and architecture underlies a broad exploration of novel systemsconcepts and new models for lighting design and deployment. The principles ofadaptive distributed sensing described in this chapter will be central to these newapproaches and will lead to opportunities for adaptive and intelligent environments.Such environments will require integrated sensing capability without intrusion intothe living space. In addition, lighting sources are expected to continue to becomemore diverse and innovative. Sources that provide more adaptive access to spectraland spatial characteristics will be important. Flexible networking of sources andsensors will provide the backbone of infrastructure to support adaptive changesresponding to use, occupancy, health, and task-related needs.

05

10

15

20

5 10Time Step k

15

Dynamic SamplingStatic Sampling

Fig. 11 An example of lightcontrol comparison betweendynamic and static methods

20 Z. Huang et al.

Acknowledgments This research was supported in part by the Engineering Research CentersProgram of the National Science Foundation under NSF Cooperative Agreement No. EEC-0812056and in part by New York State under NYSTAR contract C090145.

References

Adelson E, Bergen J (1991) The plenoptic function and the elements of early vision. In: Landy M,Movshon JA (eds) Computation models of visual processing. MIT Press, Cambridge

Afshari S, Mishra S, Julius A, Lizarralde F, Wen JT (2012) Modeling and feedback control of color-tunable LED lighting systems. Paper presented at 2012 American control conference, Montreal

Batalin MA, Sukhatme GS (2004) Coverage exploration and deployment by a mobile robot andcommunication network. Telecommun Syst J 26(5):181–196

Bayraktar H, Turalioglu FS (2005) A Kriging-based approach for locating a sampling site – in theassessment of air quality. SERRA 19(4):301–305

Box GE, Draper NR (1986) Empirical model-building and response surface. Wiley, New YorkCetegen D, Veitch JA, Newsham GR (2008) View size and office luminance effects on employee

satisfaction. Proceedings of Balkan Light 2008, Ljubljana, 7–10 Oct 2008, pp 243–252Chiles JP, Delfiner P (1999) Geostatistics, modeling spatial uncertainty. Wiley Interscience,

New YorkCurtin TB, Bellingham JG (2001) Guest editorial-autonomous ocean-sampling networks. IEEE J

Ocean Eng 26(4):421–423Dharne AG, Jayasuriya S (2006) A new protocol for the development and maintenance of

autonomous mobile sensor networks. Paper presented at American control conference 2006,pp 3494–3499

Dong F (2012) Dynamic adaptive sampling for environmental and smart lighting distributed sensorapplications. PhD dissertation, Rensselaer Polytechnic Institute

Dong F, Sanderson AC (2013) A dynamic adaptive light field and daylight sampling approach forsmart lighting control. Light Res Technol. doi:10.1177/1477153513502030

Duchon J (1977) Splines minimizing rotation-invariant semi-norms in Sobolev spaces. In: SchemppW, Zeller K (eds) Constructive theory of functions of several variables. Oberwolfach, 1976.Springer, Berlin, pp 85–100

Estrin D, Borriello G, Colwell R, Fiddler J, Horowitz M, Kaiser W, Leveson N, Liskov B, Lucas P,Maher D (2001) Embedded, everywhere: a research agenda for networked systems of embeddedcomputers. Computer Science and Telecommunications Board (CSTB) Report

Foley JD, Dam AV, Feiner SK, Hughes JF (1995) Computer graphics: principle and practice.Addison Wesley, Reading

Fung G, Mangasarian OL (2004) A feature selection newton method for support vector machineclassification. J Comput Optim Appl 28(2)

Gershun A (1936) The light field, Moscow (trans: Moon P, Timoshenko G). Math Phys, 193918:51–151

Goral CM, Torrance KE, Greenberg DP, Battaile B (1984) Modeling the interaction of light betweendiffuse surfaces. Comput Graph 18:213–322

Granderson J, Wen YJ, Agogino AM, and Goebel K (2004) Towards demand-responsive intelligentlighting with wireless sensing and actuation. In: Proceedings of the IESNA (IlluminatingEngineering Society of North America) 2004 annual conference, Tampa

Greger G (1996) The irradiance volume. Computer science. Cornell UniversityGreger G, Shirley P, Hubbard PM, Greenberg DP (1998) The irradiance volume. Comput Graph

Appl 18:32–43Griffith B, Torcellini P, Long H, Crawley D, Ryan J (2006) Assessment of the technical potential for

achieving zero-energy commercial buildings. In: Proceedings of the 2006 ACEEE summerstudy on energy efficiency in buildings. American Council for an Energy-Efficient Economy,Washington, DC

Adaptive Distributed Sensing and Control Methods 21

He T et al (2004) Energy efficient surveillance system using wireless sensor networks. In: 2ndinternational conference mobile system, application and services, wide-area monitoring ofmobile objects, pp 270–283

Hill FS (2001) Computer graphics. Prentice Hall, Upper Saddle RiverHombal VK (2009) Adaptive sampling in robotic sensor networks for environmental applications.

PhD dissertation, Rensselaer Polytechnic InstituteHombal V, Sanderson AC, Blidberg DR (2009a) Adaptive multiscale sampling in robotic sensor

networks. In: Proceedings 2009 IEEE/RSJ conference on intelligent robots and systems,St. Louis

Hombal V, Sanderson AC, Blidberg DR (2009b) Adaptive sampling in robotic sensor networks forenvironmental robotics. In: Proceedings 2009 conference on unmanned undersea systems andtechnology, Lee

Howard A et al (2002) Mobile sensor network deployment using potential fields: a distributed,scalable solution to the area coverage problem. In: 6th international symposium distributedautonomous robotics system, Fukuoka

Huang Z (2013) Parameter adaptive multimodal optimization and its application in smart lighting.PhD dissertation, Rensselaer Polytechnic Institute

Huang Z, Sanderson AC (2014) Light field modeling and interpolation using Kriging techniques.Light Res Technol 46(2):219–237

Illuminating Engineering Society of North America (IESNA) (2000) Lighting handbook: reference& application, 9th edn. Illuminating Engineering Society of North America, New York

Isler Vet al (2004) Sampling based sensor-network deployment. In: Intelligent Robots and Systems,2004 (IROS 2004). Proceedings. 2004 IEEE/RSJ

Levoy M, Hanrahan P (1996) Light field rendering. In: Proceedings of the Association of ComputerMachinery’s Special Interest Group on computer graphics and interactive technology (ACMSIGGRAPH). ACM Press, pp 31–42

Lyons D (1998) Computer software for optimizing energy. US Patent 5,812,422, Philips ElectronicsNorth America Corporation, 22 Sept 1998

Mahdavi A, Eissa H (2002) Subjective evaluation of architectural lighting via computationallyrendered images. J Illum Eng Soc 31(2):11–20

Mangasarian OL (2003) Support vector machine classification via parameterless robust linearprogramming. Data mining institute technical report

Matheron G (1963) Principles of geostatistics. Econ Geol 58:1246–1266Meyer Y (2001) Oscillating patterns in image processing and nonlinear evolution equations: the

fifteenth dean Jacqueline B. Lewis memorial lectures. American Mathematical SocietyNewsham GR, Marchand RG, Veitch JA (2004) Preferred surface luminances in offices, by

evolution. J Illum Eng Soc 33(1):14–29Ng R (2006) Digital light field Photography. PhD dissertation, Stanford UniversityPark H, Burke J, Srivastava MB (2007) Design and implementation of a wireless sensor network for

intelligent light control. In: Proceedings of the 6th international conference on informationprocessing in sensor networks, Cambridge, MA, pp 370–379

Phong B (1975) Illumination for computer generated pictures. Commun ACM 18(6):311–317Popa DO, Stephanou HE, Helm C, Sanderson AC (2004) Robotic deployment of sensor networks

using potential fields. In: IEEE international conference on robotics and automationRichmond A (2003) Financially efficient ore selection incorporating grade uncertainty. Math Geol

35(2):195–215Sanderson AC, Hombal VK, Fries DP, Broadbent HA, Wilson JA, Bhanushali PI, Ivanov SZ,

Luther M, and Meyers S (2006) Distributed environmental sensor network: design and exper-iments. multisensor fusion and integration for intelligent systems, 2006 I.E. internationalconference, Sept 2006, pp 79–84

Selkowitz S (2008) Energy efficiency perspectives: intelligent networks and the challenge of zeroenergy buildings. Presentation to connected urban development global conference

22 Z. Huang et al.

Selkowitz SE, Granderson J, Haves P, Mathew PA, Harris JP (2008) Scale matters: an action planfor realizing sector-wide “zero energy” performance goals in commercial buildings. In: Pro-ceedings of the 2008 ACEEE summer study on energy efficiency in buildings, Pacific Grove,pp 17–22

Singhvi V, Krause A, Guestrin C, James H, Garrett JR, Matthews HS (2005) Intelligent light controlusing sensor networks. In: Proceedings of the 3rd international conference on embeddednetworked sensor systems. ACM, San Diego

Smith M (1993) Neural networks for statistical modeling. Wiley, New YorkThapan K, Arendt J, Skene DJ (2001) An action spectrum for melatonin suppression: evidence for a

novel non-rod, non-cone photoreceptor system in humans. J Physiol 535:261–267Unser M (2000) Sampling 50 years after Shannon. Proc IEEE 88(4):569–587Veitch JA, Newsham GR (1998) Lighting quality and energy-efficiency effects on task perfor-

mance, mood, health, satisfaction and comfort. JIES 27:107–129Veitch JA, Julian W, Slater AI (1998) A framework for understanding and promoting lighting

quality. In: Veitch JA (ed) Proceedings of the first CIE symposium on lighting quality. CIECentral Bureau, Vienna, pp 237–241

Vieira LF et al (2004) Efficient incremental sensor network deployment algorithm. In: ProceedingsBrazilian symposium computer networks, pp 3–14

Isler V, Kannan S, Daniilidis K (2004) Sampling based sensor-network deployment. In: IntelligentRobots and Systems (IROS 2004). Proceedings. 2004 IEEE/RSJ

Wen YJ, Agogino AM (2008) Wireless networked lighting systems for optimizing energy savingsand user satisfaction. In: Proceedings of 2008 I.E. wireless hive networks conference, Austin

Wen YJ, Granderson J, Agogino AM (2006) Towards embedded wireless-networked intelligentdaylighting systems for commercial buildings. In: Proceedings of the IEEE internationalconference on sensor networks, ubiquitous, and trustworthy computing (SUTC’06), vol. 1.IEEE Computer Society, Taichung

Wen YJ, Bonnell J, and Agogino AM (2008) Energy conservation utilizing wireless dimmablelighting control in a shared-space office. In: Proceedings of the 2008 annual conference of theIlluminating Engineering Society, Savannah

Wilburn B (2005) High-performance imaging using large camera arrays. In: Proceedings of theACM computer graphics, pp 765–776

Willett R, Martin A, Nowak R (2004) Backcasting: adaptive sampling. In: Information Processingin Sensor Networks, IPSN 2004. Third international symposium, 26–27 Apr 2004, pp 124–133

Zhang J, Sanderson AC (2009a) Adaptive differential evolution. A robust approach to multimodaloptimization. Springer

Zhang J, Sanderson AC (2009b) JADE: Adaptive differential evolution with optional externalarchive. IEEE Trans Evol Comput 13(5):945–958

Zou Y, Chakrabarty K (2004) Sensor deployment and target localization in distributed sensornetworks. ACM Trans Embed Comput Syst (TECS) 3(1):61–91

Adaptive Distributed Sensing and Control Methods 23