Patent Publication Number: US-8990127-B2

Title: Method and system for ontology-driven querying and programming of sensors

Description:
This application is a National Stage of International Application No. PCT/AU2009/000799 filed on Jun. 22, 2009, the entirety of which is hereby incorporated by reference. 
     TECHNICAL FIELD 
     The described embodiments relate to methods and system for ontology-driven querying and programming of sensors. In particular, the sensors may be part of a remote sensor network in communication with a node that is accessible using a public network. 
     BACKGROUND 
     Programming sensor nodes for data collection in sensor networks is notoriously difficult. A programmer has to think not only in terms of the network-wide result to be achieved, but also how to deal with message routing, data loss, energy conservation, radio behaviour, radio management and local event interactions, as well as heterogeneity in the underlying sensor architectures, sensor capabilities and programming languages supported by such sensors. 
     It is desired to address or ameliorate one or more shortcomings or disadvantages associated with existing techniques for data collection and/or programming of sensors, or to at least provide useful alternatives thereto. 
     SUMMARY 
     Certain embodiments relate to a method of ontology-driven querying or programming of at least one sensor, the method comprising: 
     generating at a query origin a query or command for execution in relation to the at least one sensor; 
     transmitting the query or command to an ontology transformer over a first network; 
     classifying the query or command according to an ontology and one or more predetermined capabilities of the at least one sensor; 
     generating a transformed query or program based on the classified query or command using one or more code fragments stored in a memory accessible to the ontology transformer; 
     transmitting the transformed query or program to at least one sensor node in communication with the at least one sensor for execution of the transformed query or program by the at least one sensor node in relation to the at least one sensor; 
     receiving from the at least one sensor node at least one result of the query or program; and 
     returning the at least one result. 
     Other embodiments relate to a system for ontology-driven querying or programming of at least one sensor, the system comprising: 
     means for generating at a query origin a query or command for execution in relation to the at least one sensor and for transmitting the query or command to an ontology transformer over a first network; 
     means for classifying the query or command according to an ontology and one or more predetermined capabilities of the at least one sensor; 
     means for generating a transformed query or program based on the classified query or command using one or more code fragments stored in a memory accessible to the ontology transformer, wherein the means for generating a transformed query or program comprises means for transmitting the transformed query or program to at least one sensor node in communication with the at least one sensor for execution of the transformed query or program by the at least one sensor node in relation to the at least one sensor and comprises means for receiving from the at least one sensor node at least one result of the query or program and returning the at least one result. 
     Other embodiments relate to a system for ontology-driven querying or programming of at least one sensor, the system comprising: 
     a query origin configured to generate a query or command for execution in relation to the at least one sensor; 
     an ontology reasoner configured to classify the query or command according to an ontology and one or more predetermined capabilities of the at least one sensor; 
     at least one sensor node in communication with the at least one sensor; and 
     an ontology transformer in communication with the query origin, the ontology reasoner and the at least one sensor, wherein the ontology transformer is configured: to receive the query or command and pass the query or command to the ontology reasoner for classification, to generate a transformed query or program based on the classified query or command using one or more code fragments stored in a memory accessible to the ontology transformer, to transmit the transformed query or program to the at least one sensor node for execution of the transformed query or program by the at least one sensor node in relation to the at least one sensor, to receive from the at least one sensor node at least one result of the query or program and to return the at least one result. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments are described in further detail below, by way of example, with reference to the accompanying drawings in which: 
         FIG. 1  is a block diagram of a system for ontology-driven querying and programming of sensors; 
         FIG. 2  is a block diagram showing some components of the system of  FIG. 1  in further detail; and 
         FIG. 3  is a flow chart of a method for ontology-driven querying and programming of sensors. 
         FIG. 4  is an illustration depicting a table of definition of sensors. 
         FIG. 5  is an illustration depicting a statistic class for weather station capability. 
         FIG. 6  is an illustration depicting units of time for the weather station. 
         FIG. 7  is an illustration depicting a table of a definition of hour and second. 
         FIG. 8  is an illustration depicting weather station capability concepts. 
         FIG. 9  is an illustration depicting a complete definition of capability classes. 
         FIG. 10  is an illustration depicting example queries. 
         FIG. 11  is an illustration depicting examples of queryPeriodData queries. 
         FIG. 12  is an illustration depicting a weather station device description. 
         FIG. 13  is an illustration depicting a class to find which devices can handle myCurData1 query. 
     
    
    
     DETAILED DESCRIPTION 
     The described embodiments relate generally to methods and systems for ontology-driven querying and programming of sensors. Such sensors may include, for example, single or plural sensors, possibly operating in isolation or as a part of a sensor network, in communication with a sensor node. The sensor node is responsible for direct querying or control of each of the sensors associated with that node. The embodiments described herein facilitate ontology-driven querying or control of the sensors via the relevant node in a manner that is independent of any technical requirements of the interface of each sensor, thereby tolerating the heterogeneity that is common among different sensors across various sensor networks. Embodiments are described herein in a generalised manner, as well as with reference to specific examples. 
     Referring now to  FIG. 1 , there is shown a system  100  for ontology-driven querying or programming of sensors. System  100  comprises one or more computer systems  105 , such as a personal computer or server, each having a query editor  110  executable thereon. System  100  also comprises an ontology server  120 , one or more ontology transformers  125 , one or more ontology reasoners  135 , one or more gateways  145 , one or more nodes  155  and one or more sensors  160  associated with each node  155 . 
     The query editor  110  comprises executable program code stored in a memory (not shown) of computer system  105  or otherwise accessible to computer system  105 . Query editor  110  functions to facilitate user query or command formation and to interpret and display (in a display not shown) results received in relation to such queries or commands. The executable program code of query editor  110  comprises, or otherwise has access to, program code for executing an ontology-aware interface  115  for facilitating the creation or generation of queries or commands that are compliant with a predetermined ontology in relation to the one or more sensors  160 . The query editor  110  is in communication with the ontology transformer  125  over a network, such as a public network like the Internet, via ontology server  120  so that the ontology transformer  125  receives the queries or commands generated by query editor  110  for processing as described herein. 
     Ontology transformer  125  and ontology reasoner  135  may be resident or executable on the same server system  140  or on separate servers. Server systems  140  are in communication with ontology server  120  over a network and with gateways  145  over a network. 
     When ontology transformer  125  receives a query or command from query editor  110 , it passes the query or command to ontology reasoner  135 , with which the ontology transformer  125  is in communication. Ontology transformer  125  may be comprised in a computer system, such as server system  140 , and has a memory  120  (shown in  FIG. 2 ) in which are stored code fragments  130 . The code fragments  130  can be assembled to form suitable commands based on a classification of the query or command by ontology reasoner  135 . The classified query or command generated by ontology reasoner  135  is processed by ontology transformer  125  to select one or more code fragments  130  that are suitable to execute the query or command. Ontology transformer  125  uses the selected code fragments  130  to generate and send a command to one or more nodes  155  via gateway  145  for execution in relation to one or more of the sensors  160 . Such a command may be for retrieving data or information in relation to measurements made by the sensors  160  or for reprogramming or reconfiguring the sensors  160 . 
     Ontology transformer  125  has access to a capability ontology that defines the particular functional capabilities of the sensors  160  in terms of the queries they can handle. The capability ontology should be designed so that the named capability classes that identify query types are disjoint. The ontology transformer  125  includes program code that searches the classification result for known, named, capability superclasses (immediate or transitive) of the query. When such a superclass is found (of which there is at most one), the ontology transformer  125  then invokes a particular module or method that is written to handle that query type. For example, when a query is classified to be of the type that reprograms the sensor node, the ontology transformer  125  executes program code that assembles and loads a new program for the sensor node  155 , using instance information in the user&#39;s query to specify what the new program should do. 
     Transmission of commands from ontology transformer  125  to one or more nodes  155  may be performed over one or more networks, such as a public network like the Internet. Where numerous nodes  155  are to be accessed, an intermediate server or gateway node, such as gateway  145 , may be employed to facilitate communication between such nodes  155  and ontology transformer  125 . 
     As shown in  FIG. 1 , each node  155  has associated therewith, and is in communication with, one or more sensors  160 . Such association of node  155  and sensors  160  may be physical and/or logical. The number of nodes  155  in system  100  may be as few as one and as many as the system architecture can support. The number of sensors  160  associated with each node  155  may similarly be as few as one and as many as the node and sensor network architecture can support. System  100  is specifically designed for scalability of the number of nodes  155  and sensors  160  because of the ontology-driven querying or command paradigm employed by system  100  that allows queries or commands to be processed independently of the underlying hardware or technical requirements of each of the sensors  160 . 
     Ontology server  120  provides a repository for an ontology to be employed by all ontology transformers  125  within system  100 . This ontology provides contextual information and a vocabulary for the phrasing of queries and commands over the sensor network, and may be considered to be comprised of two parts: a domain ontology that provides general background terms and a capability ontology that specifically models the functions of the sensors  160  and nodes  155  in the network. This capability ontology is also shared amongst the heterogeneous sensor networks  150  and is loaded into query editors  110 . 
     For a domain ontology, some embodiments use an ontology expressed in the W3C&#39;s ontology language OWL, extended from SWEET (from NASA, at http://sweet.jpl.nasa.gov). SWEET provides contextual classes phenomena: Wind, space: Direction, property: Speed, property: Temperature including and property: RelativeHumidity. Some embodiments may extend SWEET to define classes and properties that are generally useful to describe the capabilities of sensors in a particular sensor network, for example including those shown in,  FIGS. 4 ,  5 , and  6 . In the capability ontology, classes and properties may be defined that specifically model the capabilities of the sensors, for example such as those in  FIGS. 8 ,  9 ,  10 ,  11 , and  12 . 
     Each ontology transformer  125  comprises computer program instructions (described in further detail below, with reference to  FIG. 2 ) that allow the ontology transformer  125  to be configured to receive a query or command from query editor  110 . Ontology transformer  125  employs ontology reasoner  135  to classify the query or command and map the classified query or command onto a capability ontology that accounts for the specific capabilities of the sensors in the destination sensor network  150 . 
     Ontology transformer  125  builds a transformed query based on the classified query and capability ontology by sourcing code fragments  130 . In this way, ontology transformer  125  may be considered to apply a dynamically determined ontology transformer to each received query or command thereby allowing the query or command to be executed in relation to the sensors, regardless of any heterogeneity among the sensors. 
     Code fragments  130  comprise data in the form of language templates or program fragments in the language of the destination sensor nodes  155 . Such code fragments  130  are used by the ontology transformer  125  to generate executable programs or queries in the language of the sensor nodes  155 , as described herein. 
     The transformed query or command is sent by ontology transformer  125  to gateway  145  (which may comprise a node  155  within sensor network  150 ) over a network or via a dedicated communication medium, whether wired or wireless. Ontology transformer  125  may also use a result filtering module  245  ( FIG. 2 ) to filter responses returned from sensor network  150  via gateway  145 , where the returned data from gateway  145  is more than what is required for a complete response to the original query. For example, using a weather station as an example of a sensor node  155  that provides a native command to request the current data from all sensors simultaneously, the ontology and query editor  110  may permit the user to query the current measurement from any particular sensor (see QueryCurrentData in  FIG. 9 : one or more sensors must be named in the query). The ontology transformer  125  issues the same command to the weather station (which acts as a sensor node  155 ) in each case, because each such query is a subclass of QueryCurrentData, but the ontology transformer filtering code within result filtering module  245  discards the part or parts of the response that do not correspond to the named sensor or sensors. 
     Ontology reasoner  135  comprises computer executable program code for performing logical reasoning using the established axioms of an ontology. In particular, ontology reasoner  135  performs the function of classifying the classes and properties of the ontology into subsumption hierarchy. Thus, ontology reasoner  135  is employed by ontology transformer  125  to classify a query or command according to the established ontology, which is referred to herein as the domain ontology. Where ontology reasoner  135  is unable to classify the query or command according to the domain ontology, a message to this effect is provided to ontology transformer  125 , which in turn notifies query editor  110  so that a suitable message can be displayed to the originating user or other query originator, if necessary. 
     As a validation step prior to sending the query to ontology transformer  125 , query editor  110  may initially provide the query or command to ontology reasoner  135  for classification. If the query or command can be classified as a subconcept of a capability concept for at least one sensor node  155 , then this, in effect, validates the query or command for further processing within system  100 . If classification cannot be performed, then query editor  110  can be used to reframe the query or command. 
     Gateway  145  comprises a node within the sensor network  150  that may or may not have its own sensors  160  associated therewith and may comprise a fully functional sensor node  155 . Gateway  145  is used as the network access point for sensor network  150 , for example through a public or private network, such as the Internet or a public switched telephone network (PSTN). Gateway  145  should have a reliable power supply in order that it can reliably perform its gateway function. Gateway  145  is used as a point of control and single access to and from sensor network  150 . 
     Each sensor node  155  generally comprises a programmable system for activating and controlling the individual sensors  160 . Each sensor node  155  may store measurements and communicate with other nodes  155  by radio or fixed network to share sensor measurements or other messages, including messages received via gateway  145 . Non-limiting examples of sensors  160  may comprise chemical, biological, electrical, physical or other sensors for measuring environmental, medical, industrial or other conditions, for example. 
     Sensor network  150  comprises a collection or grouping, whether virtual or physically associated, of sensor nodes  155  and sensors  160 , where nodes  155  and sensors  160  within network  150  have at least one common theme, function, role or association that makes them desirably grouped together within a network. 
     The capability ontology is designed such that the classification of a query by the ontology reasoner  135  places that query into a class of like queries (a capability class) for which common program code in the ontology transformer  125  may be used to execute the query. The ontology transformer  125  includes program code (such as may be comprised in query management module  240  described below in relation to  FIG. 2 ) that searches the classification result for known, named, capability classes that are superclasses (immediate or transitively) of the query. When such a superclass is found, the program code then invokes a particular module or method that is written to handle that capability class. Some capability classes require parameters, such as a particular date or temperature range, so the individual part of the query (comprising datatype and object property instances) can be passed as a parameter to query management module  240  and can be used to particularise the query according to the user request. 
     For example, sensor network  150  may use the Environdata WeatherMaster 1600 weather station, which has an inbuilt processor and memory with a predefined control language, with the weather station acting as a sensor node  155  with several sensors  160 . One of the recognised commands, the STORAGE command, can be used to reprogram the weather station to take selected sensor readings at selected intervals and to store the raw readings or aggregate readings in a selected memory location. The STORAGE command itself begins with the keyword “STORAGE” which is followed, on the same line of text, by parameters to reflect those selections. For effective use of the command, it must be preceded by the one-line command “MEM ON” and followed by the one-line command “MEM OFF”. All three commands must be transmitted to the weather station to reprogram the weather station. 
     Query editor  110  supports a user to construct a query as an expression in the ontology language. For example, let us consider the reprogramming query of  FIG. 10 . By the definition of the class given in  FIG. 10 , the query is a subclass of a CreatedFunctions class. The user creates this query class and requests the reasoner ontology  135  to classify it. If it is not a subclass of a capability class, then it is not a valid query and can be rejected. In this case, it is a subclass of the setStorageFunction class of  FIG. 9 . The user is then prompted to enter datatype values for (inherited) instance properties of the class. Because the capability ontology is designed with appropriate datatype properties for classes used in the query, the query editor  110  immediately prompts for the property values: in this case integers for hasStrgTabNo, time1 and time2ofDay, as properties of CreatedFunctions. Suppose the user enters the values of 3, 0900 and 0, respectively. Further, suppose the ontology has been previously initialised with a sensorNo datatype property of HumiditySensor set to a value of 5, which corresponds to the weather station&#39;s native sensor numbering system, the Day class to have a property timetypeCmd with value “HOUR” (which, in the native language means to measure daily at a fixed time of day) and the Average class to have a datatype property statCmd set to a value of “AVERAGE”. When the ontology transformer  125  receives the query and instances from the query editor  110 , it classifies the query as a subclass of the setStorageFunction capability class and uses internal code to translate the query to the form required by the weather station as follows. The ontology transformer  125  constructs the “STORAGE” command string, appending the parameters appropriately as retrieved from the query instances. It also internally generates a commandNo through an internal sequence number allocation method and inserts fixed character strings where required. In this case, in the language of the WeatherMaster, the requested command becomes “STORAGE 6 AVERAGE 3 5 1 0 0 HOUR 0900 0”, where the parameters correspond respectively to commandNo, statCmd, hasStrgTabNo, sensorNo, 1, 0, 0, timetypeCmd, time1, and time2ofDay. Further, the ontology transformer  125  brackets this line of text by the two MEM commands discussed above. 
     Referring now to  FIG. 2 , ontology transformer  125  is shown in greater detail with reference to at least one node  155  and at least one sensor  160 . Ontology transformer  125  comprises a processor  210  and a memory  220 . Various software components  225 ,  230 ,  240  and  245 , in the form of executable program instructions, are stored in memory  220  and are executable by processor  210 . Processor  210  may comprise one or more processors, either physical or virtual and either co-located or distributed. Memory  220  is accessible to processor  210  and may comprise one or more storage media, whether distributed or localised. 
     When executing, the ontology transformer  125  loads a domain ontology  225  into memory  220  from ontology server  120 . Ontology transformer  125  also loads a predetermined local ontology of sensor capabilities  235  from ontology server  120  or retained in the ontology transformer  125 , and combines the capability ontology with the domain ontology  225  to form an ontology that the ontology transformer  125  uses to map classified queries or commands onto suitable code fragments  130  that can be executed at nodes  155 . The capability and domain ontologies may be combined by file concatenation, after removing any duplication of header text or other redundant text in the second file. Alternatively one ontology may be imported into the other using features of the ontology language, or, similarly, a third skeleton ontology may import each of them. 
     Memory  220  also comprises a query management module  240  that, when executed by processor  210 , is configured to receive a query from query editor  110 , combine that with the domain ontology and sensor capability ontology, and then invoke ontology reasoner  135  to classify the query into a query class already described in the capability ontology. If the query class is not subsumed by a query class in the capability ontology, then the query is rejected as invalid because the query does not request a function that the sensor network is able to perform. In order to combine the query with an ontology, the query may be asserted as a named class description in the ontology by, for example, appending it to the ontology file prior to submitting the ontology to the ontology reasoner  155  for classification. 
     Ontology transformer  125  uses the query class to access a library or file system of code fragments  130  or code templates that embody the structures to execute the query class in the language of the sensor network. Ontology transformer  125  then completes the transformation by completing the templates or program fragments according to parameters specified in the query, and submits the transformed query or command (as a program) to the gateway  145  for execution on the sensor network  150 . The template is filled in by inserting data retrieved from the instance of the query class and its properties specified by the query editor or as asserted as instances in the ontology beforehand. 
     Queries submitted through query editor  110  may be composed of two parts. The first part is a class definition (and optionally associated property axioms) that is classified to determine the query type. The second part may be a group of instances that are used to further refine the query with grounded values for datatype properties or other object properties of the class. Query editor  110  supports the entering of both types of information, but not all query types require instance data. As an example, the setStorageFunction query type of  FIG. 9  inherits a datatype property called “time 1” from the Function class (via the CreatedFunctions class), and the query editor prompts the user to enter an integer into this field. Only the class definition part of the query is used for classification. The ontology transformer  125  may use this integer value as part of the command to the weather station—in particular it is a multiple for a time unit to separate sensing measurements. Similarly, ontology instances may be used to store some fixed values used in query processing (for example, the respective digit which corresponds to the weather station&#39;s identifier for each sensor in commands). These instance values become parameters to be inserted into the strings or program code being assembled for a sensor node command under construction. 
     Ontology transformer  125  receives responses back from the sensor network nodes  155  via the gateway  145 . In the case that the response received contains more data than is required, to satisfy the original query which can happen if the sensor network is not capable of being as selective as required, ontology transformer  125  will apply a result filter using a result filtering module  245  to reduce the results to only those specified in the original query. The ontology transformer  125  then returns the filtered or unfiltered results to the query editor  110 . 
     The Gateway  145  transmits and installs the classified and transformed program or query received from the ontology transformer  125  onto one or more nodes  155  and loads the program or query into a memory  255  of the relevant sensor network nodes  155  of the sensor network for which the gateway  145  is responsible. The classified and transformed query or program is then executed by a processor  250  of each node  155  to which the query or program was directed. According to the program or query, some sensors  160  of the network may become active and take measurements, which are recorded in the memory  255  of the node  155  and possibly further analysed by the processor  250  according to the query or program instructions. The measurements are then transmitted to the gateway  145  in one or more messages. The gateway  145  may aggregate responses from more than one sensor node  155  within sensor network  150  and will then transmit one or more responses to the ontology transformer  125 . 
     Referring now to  FIG. 3 , there is shown a method  300  of ontology-driven querying and programming of sensors. Method  300  begins at step  305 , in which the ontology-aware interface  115  accepts a query or command. The query or command may be represented as a fragment of web ontology language (OWL) code, for example, containing one or more class descriptions and property axioms and may also include instances of classes, object properties and datatype properties. The query or command may be received from a query origin that relies on user input or from an automated querying system. Alternatively, the query or command may be represented as description logic axioms or by graphical means in a graphical ontology editor, such as Protégé or a Protégé plug-in. 
     As an optional validation sub-process, the query or command, together with an ontology (comprising a domain ontology and one or more capability ontologies) may be sent by query editor  110  to an ontology reasoner  135  at step  310  for classification of step  315  and the classified ontology returned to the ontology-aware interface  115  for presentation to the user and for error reporting. In order for the query to be valid it must be classified as a sub-class of a query class in the ontology capability class, and the sensor nodes in the ontology that are capable of responding to that query class are the nodes to which the transformed query may be sent. 
     At step  320 , the query or command is sent to the ontology transformer  125 . The ontology transformer  125  repeats steps  310  and  315  (in steps  325  and  330 ) by sending the query or command to ontology reasoner  135  to classify the query or command, to validate it, and to discover which sensor nodes are capable of responding to the query or command. At step  335 , ontology reasoner  135  returns the classified query or command to ontology transformer  135 . This is generally a representation of the combined ontology and query or command that places the query or command within a subsumption hierarchy and from which the query or command&#39;s subclasses and superclasses in the ontology may be readily extracted. 
     At step  340 , the ontology transformer  125  retrieves code fragments  130  or command templates that correspond to the query class in which the query was classified, and then assembles the fragments and fills in remaining parameters arising from the query or command, so producing an executable query or program for the sensor network  150 . At step  345  the executable query or program is transmitted to the sensor network  150  by the appropriate communication medium and the relevant nodes  155  sensor network  150  are instructed to execute it. 
     At step  350 , which may be repeated many times if results are requested at timed intervals or according to other recurring criteria, the results from the nodes  155  in sensor network  150  are returned to the ontology transformer  125  via gateway  145 . 
     At step  355 , if the results contain more data than required for the original query, the excess data is filtered out and the remaining results are returned to the query editor or possibly some other destination (as specified in the original query or command) at step  360 . For example, if the user query requests temperature data in a query of the queryCurrentData type ( FIG. 9 ), the weather station can only return data for all sensors at once, so then the query to the weather station just asks for all sensors and the ontology transformer  125  parses the result and discards the unwanted data. In more detail, the sensor number is stored as an instance for each sensor in the ontology, and passed with the concept part of the query to the ontology transformer  125 . The ontology transformer  125  constructs the query without the sensor number, but then just passes back lines of the response that have the right sensor number in the appropriate position on the line. 
     Specific examples of components of system  100  and/or steps of method  300  are described hereinafter. All such examples are merely illustrative and are non-limiting, being intended for contextualising the described embodiments. 
     According to the described embodiments, a user, which may be a human or software agent and may be unfamiliar with sensor availability or programming style, can use the language of an ontology to phrase a query that is a request for some sensor observations to be made or a command for some specific reconfiguration of those sensors. This query or command is sent, in a modified form, to one or more sensor nodes  155 , which act as sensor control devices. A sensor node  155  control may comprise, for example, a programmable weather station, where the sensors  160  are sensors that accomplish a weather-related measurement function, such as temperature, relative humidity, wind speed or wind direction measurement, for example. In this example, the weather station (as one example of a sensor node  155 ) receives the transformed query or command and executes that query or command in relation to the environmental sensors with which it is associated and in communication (whether or not physically connected thereto). 
     For illustration purposes, the weather station example is continued. In this example, the weather station may be a Weather Master 1600, available from Environdata Australia Pty Ltd of Queensland, Australia, as one sensor node  155 . Query editor  110 , ontology transformer  125  and ontology reasoner  135  may use a web ontology language such as OWL, and in particular the sub language OWL-DL, as the ontology language. Pellet version 1.5.1 by Clark &amp; Parsia LLC may be used as the ontology reasoner  135  for OWL-DL. Protégé OWL editor 3.3.1 from Stanford University may be used as the query editor  110 , optionally in combination with a Java (Version 6) plug-in to Protégé. The Java plug-in implements the ontology-aware interface  115  to provide a graphical user interface (GUI) and for interfacing with a Java implementation of ontology transformer  125 . 
     The general ontology described herein may be a purpose-built ontology, as described herein, that imports the ontology SWEET 1.0 published by NASA. This ontology may be thought of as a combination of a domain ontology and a sensor capability ontology, as described herein. In the present context, the domain ontology may be considered to be that part of the ontology that may persist unchanged as a base ontology, while the capability ontology is changed and extended to account for each new sensor network  150  added to the system  100 . 
     The examples described below show extracts the domain ontology displayed in Protégé, but it should be understood that other manifestations of the OWL ontology language may be used, including but not limited to logical expressions, the Manchester Syntax, the OWL 2 Functional-Style-Syntax or RDF/XML, which is an XML (extensible mark-up language) syntax for RDF (Resource Description Framework). 
     The weather station sensor capabilities may be modelled in the ontology as described below. The weather station has four sensors: a temperature sensor, a relative humidity sensor, a wind speed sensor and a wind direction sensor. Each sensor of the weather station is modelled as a class with the SWEET superclass “material_thing: Sensor restricted to using the measures” property with a sensor-specific filler, as illustrated in  FIG. 4 . 
     The weather station, and many other sensing devices, can take a measurement and compute the average, maximum and minimum of the current value and past (stored) values. This capability is modelled as subclasses of a created class “Statistic” with no special restrictions but disjoint to each other, as shown in  FIG. 5 . Measurements can be taken periodically, once every x units, for some integer x and where a unit is either day, hour, minute or second. A class is defined for each of these units as follows and illustrated in  FIGS. 6 and 7 . Each of these units is defined to be a subclass of the corresponding class of the SWEET ontology. For example, the capability class “Second’ is a subclass of “time: Second’ of the domain ontology, which has the superclass “time: Duration”. Further, the property “hasSubPeriod” is used to model how each unit relates to other time units. For example, the class “Hour” has restrictions corresponding to having each of “Minute” and “Second’ as sub-periods but (with an additional closure axiom) no other sub-periods. The smallest unit, the “Second’, is defined to not have any sub-periods, as shown in  FIG. 7 . In  FIG. 7 , definitions above the horizontal line are complete (necessary and sufficient); the extra information below the line represents conditions inherited from SWEET and displayed by the Protege tool, but not important for this discussion. 
     To request data from the weather station, it is desirable to be able to use specific dates as time specifications, thus we model the class “Date” as a subclass of SWEET&#39;s “time: Instant”. 
     The basic elements of the sensor device capability have been described, and now modeling of the structure of the language for interacting with the weather station is described. The functions of the weather station language are organised so that any user query can be mapped to the weather station language by a simple classification made by the ontology reasoner  135 . For example, the weather station supports three kinds of queries as native capabilities:
         Query the current data of all sensors[queryCurrentData]   Query a memory for data from date A until date B [queryPeriodData]   Reprogram by adding or deleting program lines in the weather station, which describe exactly what, when and how we want to measure [setStorageFunction]       

     A corresponding ontology structure is created as illustrated in  FIG. 8 . The class “Function” is created, which is a direct subclass of “owl: Thing”. Below that, two classes are introduced, which act like a container for other classes and help to keep the hierarchy well ordered. All queries are placed under the “CreatedFunctions” class (see following section), and there is a “Capabilities” class, such as “WM1600Capabilities” to describe the capability classes, which are a focus of the ontology. There is no need for different sensor devices to have different “Capabilities” subclasses—alternatively the capabilities may be grouped into classes by another convenient class. However, each capability class must be defined to be disjoint from other capability classes. 
     Each of the capability classes are defined in more detail by restrictions which correspond to the parameters required by the weather station to enact a selected function, as shown in  FIG. 9 . Complete (necessary and sufficient) definitions are used for these capability classes and their definitions ensure that they are disjoint. The disjointness of the definitions can be confirmed by the ontology reasoner  135 , if desired when developing the appropriate capability description. 
     The classes defined so far provide a framework for expressing user queries. A valid query for any device is exactly a class definition that is a (semantic) subclass of a predefined device capability. By ‘semantic subclass’ is meant that the query may be interpreted to be a subclass by a sound OWL reasoner; it is not required that the query be explicitly asserted as a subclass. For example, to reprogram the weather station, it is necessary to use the three properties “usesPeriod”, “usesSensor” and “usesStatistic”. To ask the weather station about the current temperature, it is necessary to use the “usesSensor” property, and also express that the other properties are not used, by negating these restrictions. 
       FIG. 10  shows the definitions of two example queries, one to reprogram the weather station, and one to request the current humidity. As is evident, these queries are similar to the parent capability classes. In the context of a more complex ontology, the queries could appear to be syntactically quite different to the capability classes, because the ontology reasoner  135  is capable of interpreting the ontology correctly to identify the proper semantic relationships. To keep the ontology well-ordered (with no significant semantic effect), both queries are defined as subclasses of the “CreatedFunctions” class. 
     The example query to request the current humidity is quite simple, as it is just specified to use only the “HumiditySensor”, while the other properties of the corresponding capability definition are negated. 
     The example query to reprogram the weather station takes advantage of the ontology to enable the system to recognize when a query can be answered by another query that has also been requested. For this query ( FIG. 10 ), a period restriction (usesPeriod some (time: duration and (hasSubPeriod some Day))) is given that is necessary and sufficient, in addition to an only necessary restriction (usesPeriod some Day). This means that a reprogramming query that describes a request for a measurement which can be answered by filtering (selecting) the measured data from another reprogramming query will be classified by the ontology reasoner  135  as a subclass of the latter. For example, a reprogramming query which measures the data from the humidity sensor every minute would be classified as a subclass of the reprogramming query measuring data from the humidity sensor each five seconds. Relying on this, after classification, the ontology transformer  135  can simply transmit the highest-classified reprogramming queries to the weather station, as others which are subsumed by at least one highest-classified query are redundant. 
     When the ontology reasoner  135  is invoked, either by ontology transformer  125  or query editor  110 , each query that is valid, in the sense that it corresponds to a defined capability of a sensing device, will be immediately classified as a subclass of the respective capability. The reasoner&#39;s classification may be inspected visually using the query editor  110  or programmatically with other software such as the ontology transformer  125 . Furthermore, the ontology reasoner  135  may be invoked to ensure that the query class is itself satisfiable in the context of the ontology, which will enable the early rejection of some queries that can never have an answer (for example, a query with a restriction that usesPeriod some Day and another restriction that not usesPeriod owl: Thing)). Because of the original design of these capability classes, this means that the corresponding structure of the device command language is immediately identified. 
     Further examples of queries are shown in  FIG. 11 . These example queries will both be classified as subclasses of the capability class “queryPeriodData”, although appearing syntactically quite different. 
     So far, queries have been described as classes that act as a template, or schema, that is itself a subclass of a more general device capability. The ontology is also used to represent OWL datatype properties of queries, so that it is possible to distinguish queries by the values of query parameters. At this level, queries are represented as instances of a particular query class and its properties, having the query parameters instantiated as the values of OWL datatype properties. Similarly, the current state of the weather station is modelled as a collection of instances of the ontology.
         The capability ontology captures the definition of multiple sensor devices by defining a class for each device as a subclass of a “Device” class, and the “hasFunction” property as a property having domain Device and range Function, as shown in  FIG. 12 .       

     As illustrated in  FIG. 10 , queries that reprogram the device or request measured data may be phrased by a user in the terms of the capability ontology. This can be implemented by creating a definition of a class corresponding to the query, by verifying the validity of the request (using ontology reasoner  135 ), by passing the request onto relevant device-specific handlers (sensor nodes  155 ), and then onto the native device interface (sensors  160 ), and then receiving the response and passing it to a viewing application, such as query editor  110  or another destination. 
     For example, the Pellet OWL-DL reasoner can be used, which runs on a remote host  140  and can be used by the ontology transformer  125 , as well as by the user&#39;s Protégé client. The user can query and program the weather station through the Protégé OWL Editor 3.3.1 as the user interface, or more conveniently through a Protégé plug-in graphical user interface (GUI) that is able to simplify the ontology display and interact with the ontology transformer  125 . It is assumed that the Protégé and plug-in client are installed locally on computer system  105  for the user, and that the ontology transformer  125  is deployed on another computer systems  140 , which is in communication with the weather station according to its own device-specific interface requirements. The communication between the query editor  110  and the ontology transformer  125 , and between each of those and the ontology reasoner  135 , may be via a TCP/IP socket, for example. 
     As an alternative to direct use of Protégé as a user client tool, the definition of the capability classes in the ontology enables the run-time generation of a user interface which can provide, in this case, three options of selecting classes from a pre-filtered class tree, where only allowed capability classes are listed. Through this interface, a user can select permitted classes to phrase the query without direct Protégé interaction. This client tool can be implemented as a Protégé plug-in. After a selection, the client tool may automatically negate the restrictions which the user left unselected on the interface to form a query. 
     Embodiments have been described as they apply in particular to the programming of a weather station in its proprietary control language. The capability ontology is structured into storage functions, sensors, periods, etc according to the documented characteristics of that language. However, other suitable programming languages will have function groupings which may be similar to this in principle (and for which a functional classification within the ontology can be developed) but will differ by the terms used and by their classification structure. 
     A deployment method is described herein according to an implementation that is specific to the control interface available for the weather station. Those skilled in the art can adapt this method for an alternative deployment technique, such as via “Deluge” that can be used for over-the air programming of wireless sensor networks. 
     Embodiments have been described in terms of programming and querying features of the environment that may be measured by a weather station, but the embodiments are not restricted to this. Embodiments can apply to any measurements taken by in-situ sensors with a remote control possibility, such as still or video images; sound; human health indicators such as blood pressure, heartbeat, or blood sugar; water flow, pressure and quality parameters such as dissolved nutrients and turbidity; ecological parameters such as sunshine, wildlife location, density and motion; security monitoring parameters such as infrared beams and motion detectors; geophysical parameters such as seismic perturbations and wave motions, built environment or transport parameters such as energy consumption, heating, ventilation or traffic flow; or any other remote sensing measurement or application. 
     Embodiments are described in terms of a specific design and implementation that has been put into practice, but the embodiments permit many possible architectures and tool components. A plug-in to the third-party Protégé ontology editing software is described, but other suitable editors or methods for developing ontologies can be used, and other mechanisms may be employed to allow a user to express a sensor data requirement in the terms of an ontology, such as a specialist GUI or a textual format such as an OWL document. Such alternatives must accept that query, combine it with the domain ontology, invoke a reasoner for classification, retrieve code fragments according to the classification result, and assemble those code fragments in a suitable way. 
     A variation that would provide another way to assemble the code fragments would be via retrieval of compile-time flags through the classification, and then providing the flags to a compiler to conditionally compile a code library or to set compile-time parameters in the usual way. This might be done when, for example, the target language for the sensor network is NesC. Another variation would be to apply the technique to generate commands in the SQL-like language of TinyDB, a database system for TinyOS-based sensor networks, in place of the weather station native command language as detailed here. 
     Embodiments have been described in terms of the software components interacting via TCP/IP sockets, but other distributed computing or basic communication protocols could be used. 
     In the described embodiments the ontology comprises data that may be updated and extended as required to represent the addition of new sensor capabilities into the sensor network querying environment. These new sensor capabilities may represent both new domain-level capabilities (new phenomona may be measured) or new control and interface capabilities. However, because this information is represented in the ontology, and the ontology is read dynamically into the query interface and actioned dynamically through the reasoning tools, providing the new capability to users requires no change to user query tools and shields the user from needing to learn anything about the programming interface for the sensor nodes  155  or gateway  145 . 
     Improved features relate to the use of the domain and capability ontologies, supported by an ontology reasoner  135  that can make sound inferences over the ontology language, to classify user-specified queries into classes that identify the sensor-specific code that can be assembled and executed to realise the query. This is supported by the (domain and capability) ontology awareness of the query editor  110  and provides a navigable, hierarchical structure to describe sensors available for querying and also shields the user from awareness of the heterogeneity of sensor programming languages, while permitting sound and expressive user queries to be written. 
     Advantageous features of the described architecture and methodology reside in the use of the described formal reasoning (or logical inference) over the ontology as a component of the query/command processing. This formal reasoning is generally described as “classification” but may be more broadly described as “logical inference”. Such features allow the system  100  to perform query validation and allocation of queries to sensor nodes  155  that can handle such queries without requiring special code (i.e. purely on the basis of the structure of the ontology that is a data input to system  100 ). This is described and illustrated further in the Example provided below. 
     The described system architecture and methodology allows the reprogramming instructions to be checked for redundancy based on the ontology alone and therefore reduces the need for frequent reprogramming, which is important in an environment of a shared/multi-user sensor network resource. The described system architecture and methodology also allows for the organisation of node-specific program code used for translation into the query or command language of the sensor node  155  according to the structure of the node command language, rather than according to the structure of the network-wide query language that is embedded in the ontology. This isolates the syntaxes of the two languages and permits varying and, if desirable, multiple syntaxes in the ontology language for the same node-specific query or command without the cost of additional program code. For example, the classification process performed by the ontology reasoner  135  can place a query into a relevant class, and then the ontology transformer  125  will have the program code to handle that class. This allows the ontology transformer  125  to not be overly prescriptive about acceptable syntaxes for queries, and instead address the query semantics. The described system architecture and methodology also allows heterogeneity in sensor nodes and gateways to be hidden from the user, thereby making use of the sensor network easier for the user. 
     The following Example shows how the system  100  can classify a query so that it is immediately obvious which devices (e.g. sensor nodes  155 ) can handle the query. This enables query editor  110  to direct the query to the ontology transformer that is responsible for managing the relevant devices. 
     This Example uses the definition of the WM1600 class as given in  FIG. 12  and its capabilities as given in  FIG. 9 . Now a class can be constructed that corresponds to a query, say the “Request current humidity query” of  FIG. 10 . This class can be called, for example “myCurData1”. 
     Now a new class can be defined that describes all the devices (e.g. sensor nodes  155 ) that can accept the query “myCurData1” as shown in  FIG. 13 . 
     When this new class ( FIG. 13 ) is classified by the ontology reasoner  135 , every device which is capable of handling the query “myCurData1”, such as class “WM1600”, will become a superclass of this class. By maintaining a mapping for sensor nodes  155  to the ontology transformers  125  responsible for them, the query editor  110  can direct the query to the indicated ontology transformer  125 . 
     When this new class (Table 10) is classified by the ontology reasoner  135 , every device which is capable of handling the query “myCurData1”, such as class “WM1600”, will become a superclass of this class. By maintaining a mapping for sensor nodes  155  to the ontology transformers  125  responsible for them, the query editor  110  can direct the query to the indicated ontology transformer  125 . 
     In this description the examples of ontology expressions are given as screen copies of the expression in the ontology editor Protégé 3.3. In that style, expressions above a horizontal line are “necessary and sufficient” (also called “complete” or “equivalent classes”) and those below the horizontal line are “necessary” (also called “superclasses”). Expressions separated by large dots are combined conjunctively. Where the horizontal line is not displayed, the expressions are necessary and sufficient. 
     Throughout this specification and the claims which follow, unless the context requires otherwise, the word “comprise”, and variations such as “comprises” and “comprising”, will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps. 
     The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgment or admission or any form of suggestion that that prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.