metaheuristic Applications in IoT environment

1. Metaheuristic Applications in
Internet of Things
Ibrahim Fares

2. Outlines
• What is Metaheuristic.
• What is Internet of Things.
• The is Internet of Things Needs Swarm Intelligence.
• Swarm Intelligence and its Applications.
• Applications of Swarm Intelligence in IoT Processes.
• Conclusion.
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3. What is Metaheuristic
• Metaheuristic can be seen as a general algorithmic
framework which can be applied to different
optimization problems with relatively few modifications
to make them adapted to a specific problem.
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4. Metaheuristic Classification
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5. Swarm Intelligence(SI)
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6. Swarm Intelligence(SI)
• Swarm Intelligence (SI) is the
collective behavior of a self-organized
and decentralized system that may be
natural or artificial intelligence.
• SI can be found widely in Colonies of
ants, school of fishes, honey bees etc.
• Aim: the main aim of SI is to increase
the performance and robustness.
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7. Swarm Intelligence(SI)
• Features: Swarm algorithms are faster
and more robust solutions to solve
complex set of problems.
• Applications:
✓Swarm intelligence algorithms have
been widely applied to robotics,
DNA computing, vehicle routing
problem.
✓Recently, applied to data intensive
Internet of Things (IoT) operations
and services.
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8. Internet of Things(IoT)
• IoT is a system of interrelated computing devices,
mechanical and digital machines, objects, animals or
people that are provided with unique identifiers
(UIDs) and the ability to transfer data over a network
without requiring human-to-human or human-to-
computer interaction.
• The IoT is an umbrella of technologies.
• IoT services: like environmental monitoring, fitness
tracking, home appliance control, smart waste
management >> are already being deployed in Smart
Cities.
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9. Internet of Things(IoT)
• The smart device or Things
generate data in high volume,
velocity, variety and veracity >>>
Big Data.
• It is estimated that 20-25 Billion
Things will be connected to the
Internet by 2020, which would
generate around 1.2 zettabytes of
data.
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10. The IoT Needs Swarm Intelligence
• Optimization of data intensive
of IoT processes highly desirable.!
• Why using SI in IoT?
✓Because the utilization of SI algorithms in IoT can
make the IoT data processing much faster and
efficient.
✓This would give a boost to the emerging IoT
ecosystems, improve the consumer experience of
IoT applications and services.
✓SI with it’s optimization capabilities can ameliorate
IoT processes that are data intensive.
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11. Applications of Swarm Intelligence in IoT
Processes
• We have chosen three domains where SI could have a better
solution.
A. Vehicle routing problem (VRP) for connected cars with ACO [1].
B.Data routing from a wide spread sensor network with ACO[2].
C.Cloud Computing techniques for data optimization [3].
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12. SI in Connected Cars
• Vehicle Routing Problem (VRP) is
a well studied logistic problem in SI.
• The problem deals with the cost
minimization of running ”n” vehicles
that has to serve ”m” customers.
• Vehicle routing is an important service
for connected cars offered by IoT
platforms.
• Using the data the current platforms are
offering services like shortest path to a
fuel station, predictive analysis of car
health and more.
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13. SI in Connected Cars…Cont.
• They would generate Gigabytes of data
per second to:
✓Create Locally Dynamic Maps (LDM),
logistics
delivery, transporting people, vehicle
health
analytics, usage based insurance and
more.
• Using ACO, novel IoT services can be
developed to provide route construction,
trail update and route improvement
strategies.
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14. SI in Data Routing
• SI finds wide application in communication for its
application of routing in telecommunication
networks.
• It is estimated that 20-30 Billion sensors will be
connected to the Internet by 2020.
• So, intelligent routing, storage of the sensor data will
be required for the communication backbone.
• Use of SI would ensure systematic data routing and
storage which in turn prevent data loss.
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15. SI in Cloud computing for Data Optimization
• The value of IoT lies in processing, storage and
visualization of data.
• 1.2 zettabytes of IoT data are estimated to be generated by IoT devices which
would undoubtedly require Cloud for storage.
• With such a large volume of data, data processing algorithms might potentially
have a performance loss thereby increase in processing time.
• Utilizing ACO for data optimization to reduce the overall data processing time is
what suggest.
• ACO offers large scale optimization for fast data mining on large scale dataset.
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16. SI in Cloud computing for Data
Optimization…Cont.
• ACO can handle high dimensional data so that the performance of the
algorithm does not decay with large datasets, capacity of handling
dynamical data which provides almost real-time data processing, and
multi-objective optimization in ACO can manage data coming from
different sources.
• This is an efficient and robust solution for data optimization in IoT.
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17. Conclusion
• Metaheuristic algorithms have been widely studied and applied to many
well known problems.
• In this presentation we examine the intersection of SI with the IoT.
• IoT is an umbrella of several verticals which required data intensive IoT
processes to provide consumer services.
• We advocate for utilizing such ACO algorithm and other algorithms to
optimize the IoT processes and make them more efficient.
• As a current and future goal, we plan to implement the three scenarios
described in this presentation and evaluate their performances by recently
metaheuristic developed algorithms.
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18. References
• I. Kassabalidis, M.A. El-Sharkawi, R. J. Marks, P. Arabshahi, andA. A. Gray, “Swarm
intelligence for routing in communication networks,” in GlobalTelecommunications
Conference, 2001. GLOBECOM ’01. IEEE, vol. 6, pp. 3613–3617 vol.6, 2001.
• J. E. Bell and P. R. McMullen, “Ant colony optimization techniques for the vehicle routing
problem,” Advanced Engineering Informatics, vol. 18, no. 1, pp. 41 – 48, 2004.
• I. Kassabalidis, M.A. El-Sharkawi, R. J. Marks, P. Arabshahi, andA. A. Gray, “Swarm
intelligence for routing in communication networks,” in GlobalTelecommunications
Conference, 2001.GLOBECOM ’01. IEEE, vol. 6, pp. 3613–3617 vol.6, 2001.
• S. Cheng,Y. Shi, Q. Qin, and R. Bai, Swarm Intelligence in Big Data Analytics, pp. 417–426.
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