distributed computing in iot
TRANSCRIPT
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Distributed Computing in IoT System-on-a-Chip for Smart Cameras as an Example
By: Kishan Patel 26th October 2016
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Contents • Introduction
• Challenges
• Solution
• Conclusion
• References
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Internet of Things
• Sensors
• Communication
• Computation
• Service
• IoT is a collection of connected devices and services that work together to do useful stuff.
Components of IoT
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Introduction
• In IoT there is billions of smart objects, such as
Sensors, Actuators, Smart phones and Smart
Vehicles, etc. are connected with each other to
sensing physical signals and giving better service
in real time human need.
• In this paper presentation, my main focus is on
sensors, communication & computation.
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Challenges
• Ultra-Big data
• Infeasible and inefficient to
handle with cloud services
because of computing and
communication recourses.
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Challenges
• Using centralized approached
where all the data analysis works
are executed on cloud servers.
• Cloud server have to do job of
Computing and Communicating
that processed data itself.
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Solution • To handle Ultra-big data, Distributed computing is a
efficient way rather then centralize.
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How Distributed Computing solve the problem?
Distributed smart camera
as a node of an IoT
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Example: Distributed Computing In Video Sensor Network
Video Surveillance
• In this case target is to store video sequence when critical event occurs.
• Approaches: 1) Centralized approach: All data are
streamed back to the server. 2) Distributed approach: Intra processing
stage and Inter Processing stage
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Example: Distributed Computing In Video Sensor Network
Video Surveillance
• As showing in figure, with Distributed Approach, 91.3% of the transmission bandwidth can be saved.
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Example: Distributed Computing In Video Sensor Network
Vehicle Localization
• The Second case is a Vehicle neighboring map generation system with video cameras in an intelligent transportation system.
• Aggregator: • RSU (Road site unit): • OBU (On-Board unit):
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Example: Distributed Computing In Video Sensor Network
Vehicle Localization
Here six different cases taken to test case:
• Low-end sensor is the video sensor with simple video capturing, coding and transmission ability.
• Middle-level sensor is the video sensor with moving object detection ability. It can transmit video data to aggregator only when it detects moving object.
• High-end sensor is the video sensor with a vehicle detection subsystem. It can transmit vehicle information instead of sending video data to aggregator.
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Example: Distributed Computing In Video Sensor Network
Vehicle Localization
Result is shown in figure,
Sensors Corresponding Aggregators
All aggregators Cloud severs
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Conclusion
• Distributed Computing is an essential technique for
internet of things (IoT) to off-load the computation
from the cloud servers as well as reduce the
transmission bandwidth requirements.
• Experimental results show the proposed design can
achieve high area and power efficiency.
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References • K.-W. Chen, H.-M. Tsai, C.-H. Hsieh, S.-D. Lin, C.-C. Wang, S.-W.Yang, S.-Y. Chien, C.-H. Lee, Y.-C. Su,
C.-T. Chou, Y.-J. Lee, H.-K. Pao,R.-S. Guo, C.-J. Chen, M.-H. Yang, B.-Y. Chen, and Y.-P. Hung,
“Connected vehicle safety science, system, and framework,” in Proc. 2014 IEEE World Forum on
Internet of Things (WF-IoT), Mar. 2014, pp. 235–240.
• Shao-Yi Chien, Wei-Kai Chan, Yu-Hsiang Tseng, Chia-Han Lee, V. Srinivasa Somayazulu, Yen-Kuang
Chen, “Distributed Computing in IoT: System-on-a-Chip for Smart Cameras as an Example ”
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