The implementation of all the components of MGSN can be the rst priority for future work. A scenario of saving energy of sensors using annotated energy usage data could be very interesting to implement. Moreover, MGSN enables data stream to be processed at the rst stage after generation and thus, reduce the amount of load in network trac and storage. It could be an interesting point to simulate and check how much trac MGSN can reduce through classication, clustering and ltering of sensor data.
Also, policy could be enabled to ensure which data to process locally, which data to send on the MGSN cloud or which data to send to the OpenIoT in the internet.
At the same time, making MGSN available in mobile devices through ecient but specic apps, OpenIoT will be more distributed and thus will be more compatible for large scale data collection. Research on the direction of Big Stream and the compatibility of MGSN in that domain can be an interesting area.
Another very exciting scope of research could be recommending sustainable practices or behavior for the end-users. Both external and on-board sensor data will be necessary for such requirement. In such case, it may also be analyzed if future behabior of end-users could be predicted.
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MGSN has been developed on Android Studio (1.5.1), Windows 64 Bit. The entire
source code is available on GitHub at https://github.com/m6461/XGSN_MobileAnalytics.
Figure A1.1. OpenIoT Installation
Figure A1.2. OpenIoT Wrapper File Conguration of X-GSN
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Figure A1.3. OpenIoT Execution Command
Figure A1.4. OpenIoT Query to Show Linked Data from Sensors
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Figure A1.5. Wrapper Code to Push Sensor Data in OpenIoT from JSON API - 1
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Figure A1.6. Wrapper Code to Push Sensor Data in OpenIoT from JSON API - 2
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Figure A1.7. Wrapper Code to Push Sensor Data in OpenIoT from JSON API - 3
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Figure A1.8. Wrapper Code to Push Sensor Data in OpenIoT from JSON API - 4
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Figure A1.9. Metadata Information for the JSON API
Figure A2.1. MGSN Code to Mitigate the Internal Resource Problem