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A scalable semantic data fusion framework for heterogeneous sensors data

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Abstract

Data fusion is a fundamental research topic especially in the Internet of Things (IoT). A massive quantity of data is increasingly being generated by heterogeneous sensors which make data integration more difficult. A noticeable body of research has attempted to mitigate the incompatibility between the collected data to facilitate meaningful data integration between machines by using the semantic web technologies. However, there are still some critical issues including scalability and measurement unit conflicts. Therefore, this paper proposes a scalable semantic data fusion framework that aims at improving the scalability of data fusion and detecting and reconciling measurement unit conflicts. This framework is fully implemented to demonstrate its scalability during the process of data fusion, and its ability to handle measurement unit conflicts. Two experiments were conducted to evaluate the scalability and effectiveness of the proposed framework using real dataset that was collected from different sensors. To evaluate the scalability of the proposed framework, a set of queries was adapted and the average response time was calculated from the execution of every query. Whereas, the total number of the conflicts detected and resolved by the proposed framework were used to evaluate the effectiveness. Experimental results show that the proposed framework improves the scalability of data fusion among heterogeneous sensors’ data, and effective in detecting and resolving data unit conflicts.

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Author information

Authors and Affiliations

  1. Department of Information Technology, Faculty of Computer Science and Information Technology, Sana’a University, Sana’a, Yemen

    Ibrahim Ahmed Al-Baltah

  2. Department of Software Engineering and Information Systems, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Seri Kembangan, Malaysia

    Abdul Azim Abd Ghani

  3. Department of Computer Science and Information Technology, Yemen Academy for Graduate Studies, Sana’a, Yemen

    Ghilan Mohammed Al-Gomaei

  4. Department of Information Technology, Dhamar University, Dhamar, Yemen

    Fua’ad Hassan Abdulrazzak

  5. Oman Research and Education Network, Muscat, Oman

    Abdulmonem Ali Al Kharusi

Authors
  1. Ibrahim Ahmed Al-Baltah
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  2. Abdul Azim Abd Ghani
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  3. Ghilan Mohammed Al-Gomaei
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  4. Fua’ad Hassan Abdulrazzak
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  5. Abdulmonem Ali Al Kharusi
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Correspondence to Ibrahim Ahmed Al-Baltah.

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Appendix

Appendix

@prefix map: <#> .

@prefix db: <http://www.localhost:2020/resource/> .

@prefix : <http://www.localhost:2020/resource/> .

@prefix vocab: <vocab/> .

@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .

@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .

@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

@prefix d2rq: <http://www.wiwiss.fu-berlin.de/suhl/bizer/D2RQ/0.1# >.

@prefix jdbc: <http://www.d2rq.org/terms/jdbc/> .

@prefix ssn: <http://www.w3.org/2005/Incubator/ssn/ssnx/ssn#>.

@prefix geo: <http://www.w3.org/2003/01/geo/wgs84_pos#>.

@prefix iot-lite: <http://www.iot.ee.surrey.ac.uk/fiware/ontologies/iot-lite#>.

@prefix qu: <http://www.purl.org/NET/ssnx/qu/qu#>.

@prefix unit: <http://www.purl.oclc.org/NET/ssnx/qu/unit#>.

@prefix time: <http://www.w3.org/2006/time#>.

@prefix m3-lite: <http://www.purl.org/iot/vocab/m3-lite#>.

map:database a d2rq:Database;

d2rq:jdbcDriver “com.mysql.jdbc.Driver”;

d2rq:jdbcDSN “jdbc:mysql://localhost/heterogeneous_sensors”;

d2rq:username “admin”;

d2rq:password “admin”;

jdbc:autoReconnect “true”;

jdbc:zeroDateTimeBehavior “convertToNull”;

.

# Table node_location.

map:node_location a d2rq:ClassMap;

d2rq:dataStorage map:database;

d2rq:uriPattern “node_location/@@node_location.location_name|urlify@@”;

d2rq:class geo:location;

d2rq:classDefinitionLabel “node_location”;

.

map:node_location_latitude a d2rq:PropertyBridge;

d2rq:belongsToClassMap map:node_location;

d2rq:property geo:lat;

d2rq:propertyDefinitionLabel “node location latitude”;

d2rq:column “node_location.latitude”;

d2rq:datatype xsd:double;

.

map:node_location_longitude a d2rq:PropertyBridge;

d2rq:belongsToClassMap map:node_location;

d2rq:property geo:long;

d2rq:propertyDefinitionLabel “node_location longitude”;

d2rq:column “node_location.longitude”;

d2rq:datatype xsd:double;

.

# Table sensor_node.

map:sensor_node a d2rq:ClassMap;

d2rq:dataStorage map:database;

d2rq:uriPattern “SensorNodes/@@sensor_node.Sens_Nod_ID|urlify@@”;

d2rq:class ssn:Device;

d2rq:classDefinitionLabel “sensor_node”;

.

map:sensor_node_location_name__ref a d2rq:PropertyBridge;

d2rq:belongsToClassMap map:sensor_node;

d2rq:property ssn:onPlatform;

d2rq:refersToClassMap map:node_location;

d2rq:join “sensor_node.location_name = > node_location.location_name”;

.

# Table sensors.

map:sensors a d2rq:ClassMap;

d2rq:dataStorage map:database;

d2rq:uriPattern “http://localhost:2020/resource/Sensors/urn:@@sensors.Sensor_Name|urlify@@:uuid:@@sensors.Sensor_uuid|urlify@@”;

d2rq:class ssn:Sensor;

d2rq:class ssn:SensingDevice;

.

map:sensors_Sens_Nod_ID__ref a d2rq:PropertyBridge;

d2rq:belongsToClassMap map:sensors;

d2rq:property iot-lite:isSubSystemof;

d2rq:refersToClassMap map:sensor_node;

d2rq:join “sensors.Sens_Nod_ID = > sensor_node.Sens_Nod_ID”;

.

map:sensors_property_name__ref a d2rq:PropertyBridge;

d2rq:belongsToClassMap map:sensors;

d2rq:property iot-lite:hasQuantityKind;

d2rq:property ssn:observedProperty;

d2rq:refersToClassMap map:property;

d2rq:join “sensors.property_name = > property.property_name”;

.

map:sensors_unit_name__ref a d2rq:PropertyBridge;

d2rq:belongsToClassMap map:sensors;

d2rq:property iot-lite:hasUnit;

d2rq:refersToClassMap map:property_unit;

d2rq:join “sensors.unit_name = > property_unit.unit_name”;

.

# Table measurements_data.

map:measurement_data a d2rq:ClassMap;

d2rq:dataStorage map:database;

d2rq:uriPattern “Measurments/Measurment@@measurement_data.Measu_ID@@”;

d2rq:class ssn:Observation;

.

map:measurement_data_TimeStamp a d2rq:PropertyBridge;

d2rq:belongsToClassMap map:measurement_data;

d2rq:property ssn:observationSamplingTime;

d2rq:column “measurement_data.TimeStamp”;

d2rq:datatype xsd:dateTime;

.

map:measurement_data_Data_Value a d2rq:PropertyBridge;

d2rq:belongsToClassMap map:measurement_data;

d2rq:property ssn:hasValue;

d2rq:column “measurement_data.Data_Value”;

d2rq:datatype xsd:double;

.

map:measurement_data_Sens_ID__ref a d2rq:PropertyBridge;

d2rq:belongsToClassMap map:measurement_data;

d2rq:property ssn:observedBy;

d2rq:refersToClassMap map:sensors;

d2rq:join “measurement_data.Sens_ID = > sensors.Sens_ID”;

.

map:measurement_data_property a d2rq:PropertyBridge;

d2rq:belongsToClassMap map:measurement_data;

d2rq:property ssn:observedProperty;

d2rq:uriPattern “http://purl.org/iot/vocab/m3-lite#@@property.property|urlencode@@”;

d2rq:join “measurement_data.Sens_ID = > sensors.Sens_ID”;

d2rq:join “sensors.property_name = > property.property_name”;

.

map:measurement_data_unit a d2rq:PropertyBridge;

d2rq:belongsToClassMap map:measurement_data;

d2rq:property iot-lite:hasUnit;

d2rq:uriPattern “http://purl.org/iot/vocab/m3-lite#@@property.property|urlencode@@”;

d2rq:join “measurement_data.Sens_ID = > sensors.Sens_ID”;

d2rq:join “sensors.unit_name = > property_unit.unit_name”;

.

# Table property.

map:property a d2rq:ClassMap;

d2rq:dataStorage map:database;

d2rq:uriPattern “http://purl.org/iot/vocab/m3-lite#@@property.property|urlencode@@”;

d2rq:class qu:QuantityKind;

.

# Table property_unit.

map:property_unit a d2rq:ClassMap;

d2rq:dataStorage map:database;

d2rq:uriPattern “http://purl.org/iot/vocab/m3-lite#@@property.property|urlencode@@”;

d2rq:class qu:Unit;

d2rq:classDefinitionLabel “property_unit”;

.

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Al-Baltah, I.A., Ghani, A.A.A., Al-Gomaei, G.M. et al. A scalable semantic data fusion framework for heterogeneous sensors data. J Ambient Intell Human Comput 14, 5047–5066 (2023). https://doi.org/10.1007/s12652-020-02527-5

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  • DOI: https://doi.org/10.1007/s12652-020-02527-5

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