Sensors, models and platform for ambient control - IEEE Xplore

Sensors, models and platform for ambient control Denis Stein*, Matthias Lehmann*, Joern Ploennigs t* and Klaus Kabitzsch* *Institute of Applied Computer Science, Technische UniversiUit Dresden, OlO62 Dresden, Germany Email: {Denis.Stein.Matthias_Lehmann.Joern.Ploennigs.Klaus.Kabitzsch}@tu-dresden.de t Smarter Cities Technology Centre, IBM Research, Dublin, Ireland

Abstract-The future of ambient intelligence (AmI) brings new challenges in designing adequate control systems able to handle a diversity of sensors, and actuators. The paper analyzes on the example of a personalized climate control the classification of sensor, actuator and control approaches and derives a system architecture for an AmI-based control system.

I. INTRODUCTION The building of the future welcomes you "by name with a personal greeting and [. . . ] without human intervention". This vision was stated in 2008 [1]. The realization of such ambient personalized services requires intelligent sensors for identification and localization of users and for measuring the environment. While building automation systems (BAS) constantly improve, they are still far from that goal. C Olmnon BAS are usually installed as designed and operate unchanged afterwards for years. This situation will change in future when BAS will develop to ambient intelligence (AmI) systems. One key element of AmI systems is the strong equipment with sensors in the environment as well as near the users. This includes not only fixed, dedicated sensors in the BAS such as temperature sensors. Also sensors in mobile personal communication equipment complete the system. For example, a webcam can operate as a personalized occupancy sensor and may be dynamically integrated in the AmI system. This flexibility bears much more opportunities for the sys­ tems such as improving the robustness by added redundancy. Furthermore it allows data-based services such as virtual sensors [2], sensor fusion [3] and model-predictive control [4]. However, it also increases complexity from systems with a static structure to systems that have to dynamically adjust to the available sensor information. This requires adaption in used control approaches. While ambient intelligence is discussed since years in science [5], the topic of ambient control is still open [6]. This paper proposes a system architecture that is able to handle the dynamic of AmI to create an ambient control system. The architecture is illustrated for a personalized AmI­ based climate control. With the help of a varying set of sensors, controllers and actuators we want to control the climate around users in offices, labs and similiar rooms using model-based approaches. Our vision is a localized and personalized climate at every workplace that prevents damages to the user's health, caused by inappropriate environments such as dry air followed by dry eyes in winter, and in consequence reduce costs for employers and the general public.

978-1-4673-2421-2/12/$31.00 ©2012 IEEE

The following section describes the classification of sensors and actuators. It analyzes the problem complexity, especially regarding localization, identification and thermal comfort. The other relevant steps towards a indoor climate control are shown in sections III (pre- and post-processing) and IV (controller). A validation concept on an event-based software architecture is stated in section V. Section VI concludes this paper and gives an outlook. II. SENSOR AND ACTUATOR CLASSIFICATION

A. Sensor and actuator classification in general For indoor climate control various sensors are needed rang­ ing from sensor for thermal comfort, such as temperature and humidity, to sensors for occupancy, identity and location. Also the integration of outdoor meteorological data and forecasts from web services is feasible to add a predictive component. A good system architecture should be able to handle these various sensors independent of their specific implementation. Therefore, the sensors need to be abstracted in their semantics and significance for the control, which is done by a classifi­ cation in different dimensions. There are different approaches to classify sensors [7], [8]. We define five main dimensions that centre on the human, which is the focus of ambient intelligence. They are often not generalizable in their elements for all sensor types, but provide means to capture sensor properties in AmI environments. The dimensions are explained for the example of thermal comfort.

1) Semantics: The semantics dimension classifies the kind of physical property measured by a sensor. One sensor may measure different properties. A camera may provide video information, but also work as occupancy sensor depending on the pre-processing. A multi-sensor device may provide tem­ perature, humidity, and occupancy. The devices are classified based on a taxonomy defined by the German VDI 3813 [9]. 2) Significance: The significance dimension defines how well the physical property is expressed. It is related to the hierarchy of the semantics class. Air temperature and radiation temperature are for example both temperatures and strongly related. It also may represent complex, combine semantics. For example, thermal comfort depends on air and radiant temperature, humidity, air velocity, occupants' clothing and ac­ tivity [lO]. Measuring all these elements is very complex [11]. Often only air temperature is measured as main component, maybe also humidity, rarely air velocity [3]. The significance definition has to be refined for each semantics class.

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TABLE I ApPROACHES FOR DETERMINING THE SPATIAL (LOCALIZATION) AND SIGNIFICANCE DIMENSION (IDENTIFICATION) (CIRCLES DESIGNATE DIRECTLY MEASURED APPROACHES, SQUARES INDIRECTLY ONES; THE NUMBERS REFER TO THOSE IN SECTIONS II-C AND II-D) significance -I- spatial

--+

[IJ

building or area room

(])

presence switch; passive infrared sensor; light beam or laser sensor

[]]

room door or window sensors; activity or consumption monitoring

place

CD @]

pressure sensor voice detection

barrier or turnstile

passive infrared individual customer counting

@

[l] [I]

building access control system

l

room access system; computer monitoring

[i]

air quality monitoring; weighing office chair

@

floor sensor; indoor locating and positioning systems via radio frequency, optics or acoustics; video or stereo camera [ZJ smartphone's pedometer

3) Accuracy: The accuracy dimension describes the quality of the measurement. It is also specific for the semantics class. Simple embedded temperature or humidity sensors, for example, measure only in 1 K or 10 % respectively and are often not calibrated. Laboratory equipment has a much better accuracy [11]. 4) Spatial: The spatial dimension specifies the area the sensor information is (and can be) generalized for. For ex­ ample, a room temperature sensor measures only a specific spot, but is generalized for the room. An occupancy sensor is also often generalized for a room, even if it has hidden spots. The dimension stretches from building wide, enclosed area, room, place around a person, to on-body. 5) Temporal: The temporal dimension defines the availabil­ ity of information, such as rarely, often or permanently. A tem­ perature sensor can for example be a permanent room sensor that samples every minute, or one in a mobile device (e. g. smartphone or clockI) that its owner may carry. Spatial and temporal dimension are independent of sensor semantics. There are several secondary dimensions to sensors. Such as privacy, reliability, practicability, maintainability and costs. The cost dimension is the balancing dimension. It is usually possible to equip each room and user with several high­ tech sensors to reach the maximum in all other dimensions. Measuring for example thermal comfort under laboratory conditions comes definitely not cheap [11], [12]. Virtual sensor concepts (cf. also section III) on the other hand may reduce the equipment requirements with a reduced accuracy. These secondary dimensions have less influence on the architecture and the selection of adequate algorithms. The same dimensions can also be used to classify actua­ tors. Consider for example a HVAC (heating, ventilation, air­ conditioning) system for changing thermal comfort. A radiator covers a smaller area than a floor heating system, but both provide only heating. Server rooms often provide an additional ventilation and cooling. Some buildings also have an air­ conditioning with humidifying action. Few buildings even have personalized HVAC systems for each workplace. I Vivago clock: http://www.vivago.com

identity

unique

occupancy

target of the paper

The next subsection will exemplarily discusses the classi­ fication dimensions for the semantics classes of localization and identification in more detail. B.

Sensor classification for localization and identification

Indoor localization and identification of humans is a com­ mon, but still challenging task. Typical application areas are localization of patients and elderly people in hospitals and nursing homes as well as observation in stores or in industrial applications. Our application focus lies in office buildings. For classifying the corresponding approaches, we primary look at the significance dimension to describe the detection of a human (classes: none, occupancy, unique, identity) as well as the spatial dimension to ideally detect the human's actual workplace as specified above (classes: none, building or area, room, place). 'None' in all dimensions means, that no adequate sensor information is available. 'Occupancy' has the lowest significance in the identification dimension. It specifies that one or several people are in a space, however, it does not provide the number of persons, relevant for example for climate control or evacuation. This is accomplished by the next level labeled 'unique'. The highest level in the significance dimension is 'identity', allowing to identify persons by name or other unambiguous characteristics. The lowest value in the spatial dimension is the 'building or area' class. It provides very general information, such as that a person is in a building or enclosed area. More specific is the 'room' class, that someone is in a specific room. 'Place' is the area around a person, like a workplace. Due to the inertia of indoor climate, tracking of humans (i. e. combination of spatial and temporal dimension with high resolution) is not essential in our work. Considering stationary work for example on a desk in front of a computer is sufficient. In this survey we do not limit on specific technological aspects such as interfaces and cOlmnunication protocols (e. g. wired or wireless), power requirements (e. g. energy manage­ ment, harvesting and storing) or the computation strategy (e. g. central or decentral). We even want to discuss alternative and novel approaches.

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TABLE II RANGE AND ACCURACY OF DIRECT LOCALIZATION APPROACHES (ADAPTED FROM [13], [14]; italic entries ARE ESPECIALLY FOR OU TDOOR LOCALIZATION AND FOR COMPARISON ONLY) range

approach

I

CD 'place, occupancy': Pressure sensors used in office chairs8 can detect occupancy of specifc workplaces.

accuracy

acoustic (e. g. ultrasound)

3-20 m

0.01-0.2m

optical (e. g. infrared)

0.2-2m

S-lOm 3-10 m 20-200m j-20m

RFID

10-100 m lOO-25·JoJ m j9·jrf'-24·jrf'm 0.01-20 m

UWB

lO-SOm

0.1-0. 2S m

Wi-Fi

SO-200m

I-20m

ZigBee

lO-lOOm

3-S m

radio frequency bluetooth

cellular CPS

I-10m

Table I gives a compact overview on different approaches for localization (vertical) and identification (horizontal). Each dimension is divided in classes, as described above. Normally higher classes include the lower ones. For example, if the location of a human is known, its presence is derivable. The following subsections describe the corresponding entries line by line and denote their dimension by 'spatial, significance'. The definition of the temproal dimension is straight for­ ward. If significant, it will be distinguished. To simplify the explanation we further want to distinguish, if the information can be derived directly or indirectly from the sensor. Direct approaches are marked with circles (cf. section II-C), indirect ones with squares (section II-D). The category 'place, identity' has the highest significance. This survey will be concluded in section II-E by an extensive discussion of applicable technolo­ gies especially on accuracy, privacy, and costs. C.

Direct approaches for localization and identification

'none, none': Most offices do not provide any useful sensor detecting at least occupancy. The absence of sensor informa­ tion is not explicitely listed in table I.

CD 'room, occupancy': Presence switches and passive in­ frared sensors2 belong to this category. Mounted on ceilings the latter cover a conic field with a diameter up to 20 m. They recognize occupancy by detecting motions of heat sources be­ tween different sensor zones. This is also their main drawback. When remaining in one zone, nothing will be detected. Single visible3 or invisible4 light or laser sensors5 can be used to detect occupancy. Multiple sensors6 can detect move­ ment directions. A drawback is, that their beams should not be obstructed by doors or placed near reflective materials (e. g. glass), otherwise detection fails. Even the usage of a lighting grid is feasible and would lead to higher accuracy. But, all lack identifying individuals. 2 Servodan Sensor PIR 3600 Wireless 41-301: http://www.servodan.com

en 'room, unique': Passive infrared individual customer counting systems7 detect body heat and differ individuals, but cannot give a human's identity. @ 'place, unique': Sensor mattresses on the floor can usu­ ally locate people or at least count in case of the entry zone. An indoor localization via GPS (global positioning sys­ tem) [15] or cellular frequencies like UMTS (universal mobile telecommunications system) [16] has only limited perfor­ mance (e. g. best results under unobstructed sky for GPS) or is even not possible. However, this information as well as proximity of known wireless local area networks or bluetooth devices can be used as initial information [16]. Different techniques and requirements for localization in wireless sensor networks are studied in [17]. As radio frequency spreads with light velocity, which is much faster than acoustic velocity, these approaches cover wider ranges (cf. table II). But, acoustic ones are more accurate. As devices can be left by persons at their workplace, it is not a reliable information about their owner's position. Using video9 or stereo cameras [18] human tracking is pos­ sible. Individuals can be identified by face detection, but their recognition rate is still insufficient. The ability of tracking can produce negligible information, that bears covetousness, see also section II-E. The next section reviews indirect approaches, that can help to improve the detection accuracy. D. Indirect approaches for localization and identification

ITJ 'building or area, unique': Barriers or turnstiles, that permit only one person to pass, allow counting them, too. m 'building or area, identity': Access control systems such as RFID (radio frequency identification) systems [19] installed at central entrances provide information about the occupancy of a building or an enclosed area including the identity of the users. While this information may be more useful on room level, on building level it still allows to shut down the systems after the last occupant left. In case of emergency it is of high relevance for rescue worker. Assuming common workplaces it may also be used for contro!' [I] 'room, occupancy': If the state of room door or window sensors to changes, occupancy can be inferred with some probability, but no identity. Activity monitoring by analyzing usage of devices such as light switches or energy consumption on smart meters provide another mean of measuring occupancy. Consumption data based device detection allows a 98 % true-positive-rate [20]. Maybe this can be also used for computer and monitor. With increasing time, absence becomes more and more likely, but absence is not detectable (e. g. still lights on in case of absence, or never despite occupancy) and especially no identity.

3TotaiCount TC-WL2S0: http://www.tota!count.com.au 4eastek Counteasy Wireless: http://www.eastek.de sLeuze PRKL 2SB: http://www.leuze.de 6TrafSys Directional Wireless Beam Customer Traffic Counters: http://www.trafsys.com

7Traf-Sys Thermal Sensor: http://trafsys.com 8IQfy Funkstuhl: http://www.iqfy.de geastek Cognimatics: http://www.eastek.de I OYerve Living Systems Recessed Door Sensor: http://www.vervelivingsystems.com

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W 'room, unique': Occupancy and the number of humans can be detected by analyzing CO2 concentration [21]. Office chair senors detect occupancy, but can not distinguish between a human or a box. With some probability an individ­ ual can be identified, especially if an user is assigned to a desk. Identification via user's weight is feasible. But, standing work, for example during a call, is not detectable that way. [IJ 'room, identity': Another possibility is the usage of infor­ mations of security or building access systems for rooms [22]. So activity, and with some probability occupancy, can be inferred. But, in some cases, access cards are only necessary to enter a door, not in case of leaving. If one unlocks the door, some can pass tail or leave unrecognized. Pre- or absence can be detected by analyzing computer network traffic [23], [24], especially if an user is assigned to a desk or when using fixed IP (internet protocol) addresses. However, computer are switched on in the morning, and may be left behind during meetings. So, traffic caused by still running applications may lead to incorrect detections. An user identification can be done via personal devices, logins, fingerprint sensors, or face detection via a webcam. Also the way typing on a keyboard can be used [25]. However, offline-work infers no presence. This can be compensated by utilizing usage information of other devices such as photo­ copier, phone, or fax that may require personalized logins. � 'place, occupancy': Occupancy, the number of humans and their location may be detected by analyzing voices [25]. [ZJ 'place, unique ': Using a smartphone, its location and track can be estimated from acceleration measurements. So the number of steps and their length can be inferred [16]. Miscellaneous: User's characteristics can also help, for example his weight, conductance, smell or even his radiation. An electronic time-punch machine can detect, whether an user is present (or should be). Even the usage of game controllers such as Microsoft Kinect is feasible for localization [26]. E.

Discussion

1) Accuracy: Accuracy defines the precision of the sensor measurement. Table II compares exemplarily indoor local­ ization approaches. The range of radio frequency is higher than acoustic one's, their accuracy is contrary. But, precise measurements are often only possible outdoors [27]. The accuracy of location also depends on the number of sen­ sors, with a common minimum of three sensors. By increasing the number of sensors, higher accuracy is achievable [17], [19]. All in all, occupancy in a room is detectable. Identity can often only be derived indirectly and lacks total certainty. 2) Privacy concerns: In AmI systems also aspects of privacy, personal freedom and security should be considered. There is a risk of surveillance of individuals, as shown in the outdoor context of GPS [28]. One could register, if and how long has an employee been on his workplace. An increased potential for misuse is given, if data is assignable to individuals such as body temperature, heart or transpiration rate [28], while other data usage is accepted such as finger print information in an access control system.

The usage of this information also concerns legal aspects, particularily in Germany where privacy laws are strong. All in all, psychological concerns have to be taken seriously, but should not be exaggerated.

3) Costs, effort and energy requirements: The review showed that various technologies for measuring identity and location are available on the market. The general rule is, the higher the wanted accuracy, the higher costs and effort. RFID for example provides good accuracy, but is normally not installed in offices, even though RFID tags are not that expensive. Mobile Wi-Fi devices instead are cOlmnonly used and the corresponding infrastructure already installed. Some approaches (e. g. ultra wide band, UWB) require a comprehen­ sive calibration of their antennas. Furthermore, energy demand increases due complex computations and maybe additional hardware. An intelligent solution can help to harvest (e. g. solar panel) and store (e. g. battery) energy and manage its consumption [1], [17]. 4) Conclusion: No ultimate all-purpose solution for sensing identity and location exists. When looking for a non-intrusive approach, which causes no additional effort for the user, uti­ lization of already existing devices is interesting. For example, smartphones are already widespread and accepted by the user. Their owners even tend to share personal information such as their place on social networks. Smartphones are mobile, can be integrated in heterogenous networks dynamically and entail no further power usage. They cause no further costs and can be used in buildings, where no relevant sensor for localization or identification exists. However, smartphone sensors are not highly accurate, can only observe their narrow environment and are only temporarily available. So the control system must be fault tolerant and able to reorganize itself. Combining different, sometimes inprecise und partially re­ dundant information can improve the results. This requires a flexible approach supporting maybe a multi-stage detection. Only if the occupancy sensor detects movements, a camera for example in a notebook will be used to identify the user. This approach has benefits especially for flexible workplaces. To prevent unethical use, labour unions and works council have to be incorporated. There should be no duty to use these personalized services. Users have to decide on their own (i. e. opt-in). Furthermore, they must be able to outvote the system, for example when changed climatic desires are necessary in case of illness.

III. PRE - AND POST- PROCESSING CLASSIFICATION

The analysis of identification and localization sensors in section II illustrates that single sensor approaches are often not sufficient. Modern data processing approaches provide new ways of improving raw sensor information as well as improving actuation.

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A. Virtual sensors

A. Open loop control

Virtual sensors are applicable if significant sensors are not available, due to physical or cost limitations. They use a model (analytic, stochastic, or simulative) to either compute a required sensor value out of readings of other sensors [29], or out of previous readings of the same sensor to eliminate for example dead times [30]. They can also be used for confining sensor failures by using comparable sensors in the same or nearby rooms or a predictive model out of historical data.

This is a potential control approach if only sensors with low significance (no feedback loop) or accuracy are available. This also includes the case that no sensor information is available and a virtual sensor is used. The control quality (e. g. occupant comfort) is usually low. One example is switching the light or HVAC on or off based on occupancy.

B.

Sensor fusion

Sensor, data or information fusion [31], [32] are related approaches that use many, sometimes only temporal available data to complement and sUlmnarize information of the same semantics. An use is the combination of multiple sensors to achieve higher quality. For example, a combination of an occupancy sensor with the interpretation of computer usage and a smartphone can lead to the user's presence at a certain place, that is at his desk. Therefore a higher amount of data has to be stored, pre-processed and interpreted. Conflicts in the information need to be resolved. C.

B.

On/off control

One-point, two-point or bounce controls are preferably used with a sensor feedback loop and low accuracy of the sensors or low actuation capabilities. They are commonly used in buildings as they are also easy to program and parameterize. C.

PID control

PID (proportional plus integral plus derivative) controller may be used with a sensor feedback loop and better sensor accuracy and good actuation capabilities. They can reach a good control quality if correctly parameterized or an adaptive control is used [33].

D. Model-based control

Personalization

Personalization is also an aspect of pre-processing, as the basic sensor information of a person's identity need to be enriched by profile information from a data base to provide individual set-points for control. Issue is often the merging of heterogenous profiles of users in the same area.

D. Virtual and defusion actuators They transfer the concepts of virtual sensors and sensor fusion to actuators and are often complementary used. Let us consider a thermal comfort control that bases on a virtual sensor using only temperature [2]. A controller working on the virtual measurement 'thermal comfort' is then usually also providing a 'virtual' actuation cOlmnand that needs to be translated back to the HVAC system. Depending on the available actuators (heating, ventilation, or an air-conditioning with humidification), the correct action needs to be performed. This also may include an optimization decision, for example in terms of energy-efficiency. IV. CONTROLLER CLASSIFICATION In an AmI control the controller must be able to adapt to different availability of sensors and actuators flexibly. This results in a classification of controllers comparable to sensors. Controller have a spatial dimension, describing the area they are initiated for. It deduces from the value in the spatial dimension of the sensors and actuators. But, even if a body sensor may provide significant and accurate data about thermal comfort of a person, it requires no specific controller, if the oc­ cupied room has only one large floor heating system. However, if the room provides a personalized HVAC system and a good localization, but only one unsignificant temperature sensor, a virtual sensor could be used to improve the information [2]. Controllers also have a significance dimension, as discussed in the following subsections.

They may be used with a sensor feedback loop and high sensor accuracy and good actuation capabilities. In contrast to virtual sensors, which model a sensor, the model represents the controlled system and is used to optimize the control [30]. They can reach a good control quality if the model is of a good quality. In case of adaptive models, this requires also a high quality of the sensor significance and accuracy. E.

Model-predictive control

Model-predictive control may provide high up to optimal control quality [4]. Those systems can handle even multivari­ able systems without difficulty. By using an internal model for prediction they can optimize future behaviour. But, this requires high quality data, where required even external data such as weather data and/or simulation models. V. PL ATF ORM ARCHITECTURE After discussing all relevant parts in the former sections, an architecture is proposed that realizes this AmI control system. Fig. 1 shows the generic architectural concept. The control system is depicted on the lower half by white boxes. It contains sensors, that measure variables of the controlled system. After pre-processing they form the inputs of a controller. The actua­ tor takes post-processed controller output variables, stimulates the controlled system and closes the loop. In contrast to common control systems, available sensors and actuators can be integrated dynamically. As no control approach is able to handle this diversity by default, also con­ trollers, pre- and post-processors need to be adapted flexibly. This adaptation is realized by a model coordinator (middle gray box), which bases on an ontology-based reasoning en­ gine. It uses the gained knowledge from the classification of sensors, actuators and the controlled system introduced in section II (in each case light gray boxes).

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•

OSGi

..

I

,

pre-processing (virtual sensor, fusion sensor, personalization)

Fig. 1.

From the analysis in sections II to IV follow different requirements for implementation:

•

•

•

..

I

Main components of the platform architecture

The architecture also contains a data framework, that stores actual and historic data for pre- and post-processing. A module repository provides implementations of the individual control and processing algorithms (central and right dark gray box).

•

..

post-processing (virtual actuator, fusion actuator)

Depending on the classifiers' output the model coordinator selects one component for pre- and post-processing as well as the controller from sections III and IV. The classifiers are based on an ontology model of the installed BAS, called BAS­ ont (left dark gray box), that refers to a pre-classification of the devices' type [34]. Sensors and actuators are dynamically detected by a middleware [35].

•

..

Flexibility: Due the diversity of the components (e. g. sen­ sors and actuators) it should be possible to add, modify and delete them without restarting the system. In this case, the shown classifiers have to update automatically. Scalability: The system should be adaptable to single rooms as well as entire buildings, The software has to be able to handle varying complexity. Maintainability: The system should be serviced without restarting. Autonomy: The system should not require user input, but operate automatically and autonomously in background. Stability: The system should provide a good stability in terms of availability as well as control quality. This requires a robust design as well as approaches for smooth transition when components change.

There are a lot of platforms for AmI systems, for example multi-agent systemII or service-oriented architectures 1213. I l OASIS: http://www.oasis-project.eu 12 Amigo: http://www.hitech-projects.com/euprojects/amigo 13LinkSmart: http://sourceforge.net/projects/linksmart

Especially event-driven architectures (EDA) are suitable for the requirements mentioned above. Their main benefit is, that the software system only works, if an event raises. Otherwise it remains in an idle state and requires less system resources. The OSGi specification [36] describes an event-based, lay­ ered architecture for all periods of the software life cycle. The user can (un-)deploy, start and stop components without shutting down the whole system, If one component fails, the rest of the system still works, which also benefits system's robustness. Another great advantage of EDA-oriented software is their loose coupling of several components. One plugin does not need to know the other ones. However, the modules in the control loop have to conununicate via the eventing mechanism. VI. CONCLUSION AND FUTURE WORK The main focus of this paper lies on a generic classification of potential elements in an AmI control system and the proposition of an adequate system architecture. Our approach focusses on AmI control systems and defines and classifies the required elements. This general classification was adopted to different technologies for localization and identification and discussed extensively in section II. A classi­ fication for the other parts (pre- and post-processing elements and controllers) was presented in sections III and IV. These sections also discussed rules for selecting adequate modules depending on available sensor and actuator information. While the idea of ambient intelligence is promising, its implementation requires a flexible system architecture. Sec­ tion V provides a modular software architecture for AmI­ based control systems. It allows generic administration, choice, and supervision of all parts of such complex control systems. Therefore novel software concepts like ontologies were inte­ grated. The presented event-driven architecture allows further­ more to change modules at runtime, for example in case of adding new sensors or other elements.

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The architecture is currently implemented for the use case of a illumination control and later on for thermal comfort control. We think that the platform provides an agile control system that is able to adjust to the dynamics of AmI systems. Com­ bining those approaches as an add-on to existing controllers and BAS will lead to improved control performance. ACKNOW LEDGEMENT

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