Charith Perera, Prem Prakash Jayaraman, Arkady Zaslavsky, Peter Christen, and Dimitrios Georgakopoulos, MOSDEN: An Internet of Things Middleware for Resource Constrained Mobile Devices, Proceedings of the 47th Hawaii International Conference on System Sciences (HICSS), Kona, Hawaii, USA, January, 2014

1. MOSDEN: An Internet of Things Middleware
for Resource Constrained Mobile Devices
Charith Perera, Prem Prakash Jayaraman, Arkady Zaslavsky, Peter Christen, Dimitrios Georgakopoulos
47TH HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS), KONA, HAWAII, USA,
JANUARY, 2014

2. Agenda
• Background and The Problem
• Functional Requirements
• Objectives and Assumptions
• MOSDEN: Architectural Design
• Implementation
• Experimentation, Evaluation and Results
• Future Work and Research Directions
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3. Background and The Problem
Large number of sensors
Real-time Decision
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Heterogeneity
Resource limitations

4. Functional Requirements
Main: Establish Communication between
Sensors and Data Analytic Device
Processing-ability Extendibility
Middle-man
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Usability
Heterogeneity
Multi-Protocol
Configurability

5. Real World Scenario
The Australian Plant Phenomics Facility

6. Australian Agriculture
• Agricultural research obtains $AUS1.2 billion per annum
• Fourth largest wheat and barley exporter after US, Canada
and EU
• BUT has to deal with scarcity of resources:
 Water quality and quantity
 Low soil fertility
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7. • Grains Research and Development Corporation (GRDC)
trials plant varieties in very many 10m x 10m plots across
Australia.
• Every year, Australian grain breeders plant up to 1 million
plots across the country to find the best high yielding
• Information sources about plant variety performance:
• Site visits
• Australian Bureau of Meteorology
• Issues in current practices:
• Site visits are expensive and time-consuming (e.g., 400km away)
• Lack of accurate information limits the quality of results
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8. Why Configuration matters?
• Monitoring/Sensing strategies (data collection frequency, realtime event detection, data archiving for pattern recognition, etc.) need
to be changed depending on the time of the day, time of the
year, phase of the growing plant, type of the crop, energy
efficiency and availability, sensor data accuracy, etc…
Need to be considered in developing a solution:
• Agricultural/biological scientists and engineers do not know
much about computer science.
• Users focus on what they want
• Learning curve, usability, processing time, dynamicity of
sensors…
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9. Phenonet:
A Distributed Sensor Network for Phenomics
• Aim is to Improve yield by improving crop selection process. How?
• Sensor-based monitoring and Sophisticated data analysis
• Combined research effort from CSIRO’s ICT Centre and High
Resolution Plant Phenomics Centre
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10. Objectives and Assumptions
Categorization of IoT devices based on their computational capabilities
High Price
High Capability
Wall-mounted
Devices with a
screen powered by
Android, capability
equals to a modern
mobile phone
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Low Price
Low Capability
Low-cost
computational device
without screen
powered by Android,
capabilities equals to a
Raspberry Pi

11. Mobile Sensor Data Engine (MOSDEN)
• Can be installed on Android powered devices*
• Can collect data from both internal and
external sensors
• Can perform preliminary data filtering and
fusing tasks (e.g. AVG, comparison <>==)
• Heterogeneity addressed through plugins
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12. MOSDEN and Cloud Communication
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13. Distribution and Installation of MOSDEN Plugins
Extendible and scalable plugin architecture to support easy sensor data
collection. We utilize the Android ecosystem to distribute the plugins.
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14. Implementation
Four Screens are provided
SENSORS: List all sensors
supported and basic descriptions
about the sensors
VERTUAL SENSORS: List all
active virtual sensors. Sensors type
and real-time data values are listed
MAPS: Show sensors’ locations
on a map
HOME: Settings and application
control options are provided
Screenshot of the MOSDEN
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15. Implementation
Nexus 4 1
Nexus 7 2
Galaxy S 3
Screenshot of the GSN middleware where 3 devices has been connected
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16. Experimentation and Evaluation
1 Device 1 (D1): Google Nexus 4 mobile
phone, Qualcomm Snapdragon S4 Pro CPU,
2 GB RAM, 16GB storage, Android 4.2.2
(Jelly Bean)
2 Device 2 (D2): Google Nexus 7 tablet,
NVIDIA Tegra 3 quad-core processor, 1 GB
RAM, 16GB storage, Android 4.2.2 (Jelly
Bean)
3 Device 3 (D3): Samsung I9000 Galaxy S, 1
GHz Cortex-A8 CPU, 512 MB RAM, 16GB
storage, Android 2.3.6 (Gingerbread)
Sensors used: 52 different types of sensors
manufactured by Libelium
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17. Results and Lessons Learned
• Device 3 1 GHz Cortex-A8 CPU, 512 MB RAM failed to
process more than 20 parallel queries
• Other devices handle well
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18. Results and Lessons Learned
• Resource rich devices consumes more energy
• Resource consumption slightly increases when workload
increases
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19. Results and Lessons Learned
• Storage requirement is very low which allows to accommodate
more sensors and queries
• Latency increases significantly when processing more than 20
data streams
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20. Results and Lessons Learned
• Scalable: MOSDEN performed well even when large number of
sensors data streams are connected
• Extendable: Plugin architecture allows to add support to any
type of sensors
• Usability: Simple, easy to use, and support non-technical
personal
• Saving: Communication bandwidth by eliminating redundant
values, combining data values, and discarding data
• Distribution: MOSDEN utilizes the existing Android ecosystem
where it can potentially make use of the well
established application distribution channels
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21. Potential Applications
Waste Management
Smart Infrastructure
Supply chain Management
Environment Monitoring
Smart Home

22. Conclusion and Future Work
• Extend MOSDEN with plugin architecture to support additional
reasoning and data fusing mechanisms
• Support dynamic and autonomous discovery of InternetConnected Objects (ICO)
• Develop software to support easy plugin development
• Develop server-side models, algorithms, techniques to support
optimized sensing strategies
• Evaluate the pros and cons of processing data by computational
devices that are belongs to different categories
• Support comprehensive event detection and real-time actuation
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23. Thank You!
CSIRO Computational Informatics
Charith Perera
t +61 2 6216 7135
e Charith.Perera@csiro.au
w www.charithperera.net
SEMANTIC DATA MANAGEMENT / INFORMATION ENGINEERING LAB