A Wsn Based Intelliegent Light Control System Considering User Activities and Profile

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  1710 IEEE SENSORS JOURNAL, VOL. 8, NO. 10, OCTOBER 2008 A WSN-Based Intelligent Light Control SystemConsidering User Activities and Profiles Meng-Shiuan Pan, Lun-Wu Yeh, Yen-Ann Chen, Yu-Hsuan Lin, and Yu-Chee Tseng  , Senior Member, IEEE   Abstract— Recently, wireless sensor networks (WSNs) havebeen widely discussed in many applications. In this paper, wepropose a WSN-based intelligent light control system for indoorenvironments. Wireless sensors are responsible for measuring cur-rent illuminations. Two kinds of lighting devices, namely,  wholelighting  and  local lighting  devices, are used to provide backgroundand concentrated illuminations, respectively. Users may havevarious illumination requirements according to their activitiesand profiles. An illumination requirement is as the combination of background and concentrated illumination demands and users’locations. We consider two requirement models, namely,  binary satisfaction  and  continuous satisfaction  models, and propose twodecision algorithms to determine the proper illuminations of devices and to achieve the desired optimization goals. Then, aclosed-loop device control algorithm is applied to adjust the illu-mination levels of lighting devices. The prototyping results verifythat our ideas are practical and feasible.  IndexTerms— Intelligentbuildings,lightcontrol,pervasivecom-puting, wireless communication, wireless sensor network. I. I NTRODUCTION W IRELESS sensor networks (WSNs) have made a lot of progress recently. Extensive research works have ded-icated to energy-efficient media access control (MAC) proto-cols [22], sensor deployment and coverage [12], and localiza-tion [17]. Applications of WSN include habitat monitoring [3],wildfire monitoring [2], and navigation [13], [20].In this paper, we propose a WSN-based intelligent light con-trol system that considers users’ activities and profiles in indoorenvironments. Fig. 1 shows the network scenario. The network field is divided into regular grids. Each grid has a fixed sensor.Together, these sensors form a multihop ad hoc network. One Manuscript received February 9, 2008; accepted March 19, 2008. Thiswork was supported in part by Taiwan MoE ATU Plan, by the Na-tional Science Council (NSC) under Grants 93-2752-E-007-001-PAE,95-2221-E-009-058-MY3, 95-2221-E-009-060-MY3, 96-2219-E-009-007,96-2218-E-009-004, 96-2622-E-009-004-CC3, and 96-2219-E-007-008, byRealtek Semiconductor Corporation, by the Ministry of Economic Affairs(MOEA) under Grant 94-EC-17-A-04-S1-044, by the Industrial TechnologyResearch Institute (ITRI), Taiwan, by Microsoft Corporation, and by IntelCorporation. The associate editor coordinating the review of this manuscriptand approving it for publication was Dr. Subhas Mukhopadhyay.M.-S. Pan, L.-W. Yeh, Y.-A. Chen, and Y.-H. Lin are with the Depart-ment of Computer Science, National Chiao-Tung University, Hsin-Chu300, Taiwan (e-mail:;;; Tseng is with the Department of Computer Science, National Chiao-TungUniversity,TaiwanandalsowiththeDepartmentofInformationandCom-puter Engineering, Chung-Yuan Christian University, Chung-Li 200, Taiwan(e-mail: versions of one or more of the figures in this paper are available onlineat Object Identifier 10.1109/JSEN.2008.2004294 of the nodes is designated as the  sink   of the network and is con-nected to a  control host  . The control host can issue light controlcommands via powerline or UPnP communication protocols.In our system, there are two kinds of lighting devices, called whole lighting  and  local lighting devices . A whole lighting de-vice is one such as a fluorescent light, which can provide illu-minations for multiple grids. For example, in Fig. 1, the light inis a whole lighting device, which covers grids , , ,, , , , , and . A local lighting device isone such as a table lamp, which can only provide concentratedillumination.In our system, we assume that the location of each user isknown and each user carries a wireless sensor, which can de-tect its local light intensity. Users are considered to have var-ious illumination requirements according to their activities andprofiles. For example, in Fig. 1, user is watching television inand user is reading in . Both and require suf-ficient background illuminations in their surroundings, andneeds concentrated illumination for reading. In this paper, wemodel an illumination requirement as the combination of back-groundandconcentratedlightingaccordingtotheuser’scurrentactivity.Anilluminationrequirementconsistsofan illuminationinterval anda coverage range .Auserissaidtobe satisfied  iftheprovided light intensity is in the specified interval for all gridsin the coverage range. We further consider a  binary satisfac-tion  and a  continuous satisfaction  models. In the former, a userwho is satisfied returns a satisfaction value of one; otherwise, azero is returned. In the latter model, a satisfaction value that isa function of the specified illumination interval and the sensedlight intensity is returned. For the binary model, our goal is tosatisfy all users such that the total power consumption is mini-mized. For the continuous model, our goal is to satisfy all userssuch that the total satisfaction value is maximized. However, inboth models, it may not be possible to satisfy all users simulta-neously.In thiscase, we will gradually relaxusers’ illuminationintervals until all users are satisfied. We design illumination de-cision algorithms for both models. Then, the outputs are sentto a closed-loop device control algorithm to adjust the illumi-nations of lighting devices. Our prototyping results and systemdemonstrations verify that our ideas are practical and feasible.Several works [15], [16], [19], [21] have investigated usingWSNs in light control for energy conservation. O’Reilly andBuckley [15] and Wen  et al.  [21] introduce light control usingwireless sensors to save energy for commercial buildings.Lighting devices are adjusted according to daylight intensity.Park   et al.  [16] define several kinds of user requirements andtheir corresponding cost functions. The goal is to adjust lightsto minimize the total cost. However, the result is mainly for 1530-437X/$25.00 © 2008 IEEE Authorized licensed use limited to: National Chiao Tung University. Downloaded on August 4, 2009 at 03:08 from IEEE Xplore. Restrictions apply.  PAN  et al. : A WSN-BASED INTELLIGENT LIGHT CONTROL SYSTEM CONSIDERING USER ACTIVITIES AND PROFILES 1711 Fig. 1. Network scenario of our system. media production. Singhvi  et al.  [19] model the light controlproblem as a tradeoff between energy conservation and userrequirements. Each user is assigned a utility function with re-spect to light intensity. The goal is to maximize the total utility.However, it does not consider the fact that people need differentilluminations under different activities. Also, some users maysuffer from very low utilities, while others enjoy high utilities.In [16] and [19], it is necessary to measure all combinations of dimmer settings of all devices and the resulting light intensitiesat all locations. If there are interested locations, dimmerlevels, and lighting devices, the complexity is .Moreover, the above works only consider one type of lightingdevices. In real life, lighting devices can be classi fi ed as wholelighting and local lighting ones.The rest of this paper is organized as follows. Preliminariesare given in Section II. Sections III and IV introduce our illu-mination decision algorithms under binary and continuous sat-isfaction models, respectively. Section V presents our devicecontrol algorithm. Section VI reports our prototyping results.Section VII presents some performance evaluation results. Fi-nally, Section VIII concludes this paper.II. P RELIMINARIES In this system, there are grids, users, whole lightingdevices, and local lighting devices. All lighting devices areadjustable. The grids represent the network area and are la-beled as , and . In each grid , , thereis a  fi xed sensor , and each user , , also carriesa portable wireless sensor . Users can specify their currentactivities to the control host via their portable devices. We alsoassume that via a localization scheme (such as [9]), users ’  cur-rent grid locations are known to the control host.The whole lighting devices are named ,and the local lighting devices are named . The fi xed sensor that is closest to , , is denoted as. However, because users are mobile, we use a function, , to denote the association betweenusers and local lighting devices. This function restricts a locallighting device to serve at most one user at one time. If there isno local lighting device near user , ; other-wise, is the ID of the nearest local lighting device.Light intensities sensed by , , and , ,are denoted by and , respectively. Because the valueof may be contributed by multiple sources, we denote by, , the portion of light intensity contributedby to the  fi xed sensor closest to , i.e., . Note thatbecause may be affected by otherwhole lighting devices and sunlight. Similarly, we denote by, , the portion of light intensity contributed byto portable sensor if user satis fi es . If there exists no such that , we let .Note that in reality, the values of and cannot bedirectly known, unless there are no other light sources. We willaddress this issue in Section II-A.Inthesystem, sensorsperiodicallyreporttheir readingstothesink. For simplicity, we de fi ne the following column vectors:Note that, in practice, each has its limitation, so we letbe the upper bound of and letWe make some assumptions about lighting devices. First, weassume that a local lighting device can always satisfy a user ’ s Authorized licensed use limited to: National Chiao Tung University. Downloaded on August 4, 2009 at 03:08 from IEEE Xplore. Restrictions apply.  1712 IEEE SENSORS JOURNAL, VOL. 8, NO. 10, OCTOBER 2008 Fig. 2. System architecture of our light control system.Fig. 3. Experiment for characterizing the degradation of light signals. need when the user is underneath this device. Second, we as-sume that there is no obstacle between whole lighting devicesand  fi xed sensors. Third, the illumination provided by a locallighting device does not affect the measured light intensity of  fi xed sensors.Fig. 2 shows our system architecture. Light adjustments aretriggered by users ’  movements or environment changes. First,the illuminations of whole lighting devices are determined, fol-lowed by those of the local lighting devices. Feedbacks fromsensors are then sent to the sink to decide further adjustment of lighting devices so as to satisfy users ’  demands.  A. Computing and  Earlier, we mentioned that the values of and cannotbe known directly. Below, we  fi rst use an experimental methodto derive . Assuming no other light source existing, Fig. 3(a)shows themeasured intensities of a wholelighting device byand other fi xedsensorsatdifferentdistancesfrom ,under different on-levels of . We see that the measured inten-sitydegradesfollowingasimilartrend.Infact,ifwefurthernor-malize thevalue totheintensitymeasured by ,we seethatthe degrading trends are almost the same, as shown in Fig. 3(b).Therefore, assuming the impact factor of on to be, the impact factor of on any other can bewritten as a weighted factor , where . Putting allimpact factors together, we de fi ne a weight matrix.........Because light intensities are additive [19], the light intensitymeasured by is the sum of intensities from sunlightand neighboring devices. The intensities of the sunlight to all fi xed sensors are written as a column vector , so wehave(1)In(1),thereare unknownsin and equations,where.Anytypical -meansalgorithm[14]cansolve(1)byinducingthe least mean square error. Here, we simply construct a newmatrix by keeping all th rows, , inand removing the other rows, so (1) can be rewrittenas(2)The weight matrix can be measured at the deployment stage,vector can be measured online when all lights are off, andvector can be obtained online, so the calibration complexityis . This is lower than those of [16] and [19].The calculation of is quite straightforward. Due to theproperty of our approach, before a user arrives at a , no mea-surement can be obtained for . At this time, .When a portable sensor, say, is getting close to and boundedwith , the local lighting device may be triggered. Here, wesimply use the reading of the  fi xed sensor, say, located at thesamegridas asthebackgroundlightintensity.Weletthelightintensity provided by to be Authorized licensed use limited to: National Chiao Tung University. Downloaded on August 4, 2009 at 03:08 from IEEE Xplore. Restrictions apply.  PAN  et al. : A WSN-BASED INTELLIGENT LIGHT CONTROL SYSTEM CONSIDERING USER ACTIVITIES AND PROFILES 1713 III. S OLUTION FOR THE  B INARY  S ATISFACTION  M ODEL Eachuserpro fi leconsistsofanumberofactivity-requirementpairs. Given an activity, the system should try to satisfy the cor-respondingrequirement.Eachrequirementofauser hasthreeparts.1) Expected illumination interval of whole lighting:(in lux), where andare the lower and the upper bounds, respectively.2) Expected illumination interval of local lighting:, where and are thelower and the upper bounds, respectively.3) Coverage range of whole lighting:, where for each, if grid is expected to receivea light intensity within for user ;otherwise, . This array de fi nes the range of grids, which should meet the whole lighting requirement.Forexample,apossiblerequirementofareadinguser inFig.1canbe ,, andLet and be the current intensity vectors provided bywhole and local lighting devices, respectively. To facilitate thepresentation, let be a row vector,and a matrix such that......We formulate our problem P as a linear programming problemwith inputs , , , , and , and user requirements. Ourgoal is to  fi nd the adjustment vectorsfor whole and local lighting devices, respectively, where ,, and , , are the amounts of ad- justment required for and , respectively, such that the fol-lowing two objectives are satis fi ed:(3)(4)subject to(5)(6)if (7)Equations (3) and (4) mean that the total power consumptionsof both whole and local lighting devices after the adjustmentshould be minimized. Equation (5) imposes the whole lightingrequirement,where isthelightintensityvectorafteradjustment and matrix is to  fi lter out those grids not in thecoverage range of whole lighting. Equation (6) is to con fi ne theadjustment resultwithinthemaximumand theminimumcapac-ities of devices, where is a zero vector. Equation (7) is to im-pose the requirement of each local lighting if a user is boundedto it. Here, we assume that local lighting can always provideextra illuminations to satisfy users ’  requirements, so we do notspecify upper bounds as that in (6).Becauseweassumethattheilluminationsoflocallightingde-vices do not affect the measured light intensity of   fi xed sensors,thedecision ofwholelighting levelscan be madeindependentlyof the decision of local lighting levels. (However, the reverse isnot true because the decision of whole lighting levels does af-fect the decision of local lighting levels.) This allows us to solveproblem P in two stages as formulated below.P1: Given , ,and , and userrequirements, solvefor (3), (5), and (6).P2: Given and , and user requirements, solve for(4) and (7). Theorem 1:  Problem P is equivalent to the joint problems P1and P2.Problem P1 is a linear programming problem, which can besolved by the Simplex method [11], unless the problem itself isinfeasible, which may happen when two users have con fl ictingrequirements on the same grid. When no feasible solution canbe found, our system will try to eliminate some constraints tomake P1 feasible. Sankaran [18] already showed that  fi nding afeasible subsystem of a linear system by eliminating the fewestconstraints is NP-hard. Hence, we propose a heuristic below.The idea is to gradually relax some requirements until a fea-sible solution appears. We  fi rst de fi ne some notations. Giventhe current values of , , and , it is easy to computethe minimum and maximum possible illuminations of grids byand .Also, consider intervals on (the set of reals), which de fi neusers ’  requirements on whole lighting. We say that an intervalhas an  overlapping degree  of if for each point, fallsinatleast oftheabove intervals.Anintervalis said to be a  max-interval  if there exists no other interval, which has a higher overlapping degree than andis a superset of . It is not dif  fi cult to see that givenany intervals, there must exist a max-interval. Also, it is easyto design a polynomial-time linear search algorithm to  fi nd amax-interval (we omit the details here). Our algorithm works asfollows.1) Foreachgrid , , fi ndthesetofusers whosecoverage ranges contain , i.e.,. For each user , check if . If so,the requirement cannot be satis fi ed, so we setand update .2) Again, for each grid , , consider the set .Check if there is a common overlapping interval for therequirements of all users in . If not,  fi nd a max-interval,say, for the requirements of all users in . For eachuser ,checkif .Ifso, Authorized licensed use limited to: National Chiao Tung University. Downloaded on August 4, 2009 at 03:08 from IEEE Xplore. Restrictions apply.
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