1. Analytics for the IoT: A Deep Dive into Algorithms
Class 5: Optimization
September 16, 2016 Louis W. Giokas

2. This Week’s Agenda
Monday Intro. to Analytics for the IoT Tuesday Languages and Environments Wednesday Classification Thursday Failure Analysis & Anomaly Detection Friday Optimization

3. Course Description
The Internet of Things (IoT) generates a vast amount of data. This data can be analyzed to provide new insights to improve product design, improve product operation, enhance safety and efficiency, etc.
The large amount of data requires that we use and develop algorithms that are both correct and efficient.
There are a number of considerations that drive these developments such as size, time constraints and communications requirements.
In this course we will look at some of the algorithms that are used in the IoT and their applications.

4. Today’s Agenda Optimization Overview Optimization for the IoT Examples
Conclusion

5. Optimization Overview
Optimization is a mathematical process that has grown out of the field of Operations Research.
There are two types we consider:
Linear Programming
Nonlinear Programming
The main difference is that in the former the function to be optimized is linear, and in the later they are nonlinear.

6. Optimization Overview
The general form of the optimization problem is to maximize some function given:
Constraints
Boundary conditions
Bounds for the variables (upper and lower)
There are many techniques that can be used to solve the resulting system of equations:
Newton-Raphson, Nelder-Meade, Levenberg-Marquardt, Conjugate Gradient, Trust Region

7. Optimization Overview
The implementation of these methods is done in software packages
SAS: PROC LP and PROC NLP
Big, complex procedures with options to implement the whole range of methods
R: NLopt, trustOptim, neldermead, maxNR, ucminf, etc.
Lots of targeted functions for specific techniques
May call another function listed (or not) to implement the method indicated

8. Optimization Overview
General form of the problem to be solved:
For Linear Programming the function f can contain only linear terms.
For Nonlinear Programming the function f can contain any power of the
variables (the x’s) as well as the constraints.
This results in a set of differential equations that we need to solve.

9. Optimization Overview
The general problem of optimization involves finding a convex feasible
region or space and then solving an objective function that intersects this
region at a maximum (or minimum) point. In the linear case the convex region
Is defined by lines or hyperplanes. In the nonlinear case these are more complex
curves and surfaces.

10. Optimization for the IoT
The main innovation that the IoT brings to optimization is the availablity of up to date information on an on-going basis
Sensors update the parameters in our equations as we make adjustments to the system.
Can be measured at a time interval or can be an event driven approach
We are both predicting what the best value will be and measuring the affect of those choices
Generates a feedback loop

11. Optimization for the IoT
is a part of
our IoT
portfolio

12. Examples
In the past, optimization was performed as an off-line process
Typically used during the design process for a product, process or facility
Today we are seeing optimization being used real-time
Increased processing power
Dynamic conditions
Sensors allow constant feedback

13. Examples Traffic in a Network
Objective Function
Constraints
Linear Problem: Find the maximum flow through the
network.

14. Examples Traffic in a Network
Nonlinear Problem: find the time through
The network give a flow.
Objective Function
Constraints
Time Calculations

15. Examples Chemical Mixing Problem

16. Examples Chemical Mixing Problem
Objective Function
Constraints

17. Examples Smart Buildings Monitoring and sensors
Optimize the cost function for energy inputs
Constant change with internal and external conditions
Respond to these events
Anticipate

18. Examples
Insurance claims processing: optimal allocation of resources (teams of agents)
Yes, this is an IoT situation
Automated sensors in automobiles send accident information to the insurance company
Based on this information and location information for the agents and teams an optimization model is run to determine an optimal allocation of cases to agents, taking into account time, travel, severity

19. Examples Supply Chain Optimize warehouse operations Optimize shipping
Placement
Optimize shipping
Route planning
Optimize flow
Predictive optimization based on sensor input
Minimize disruptions, choose alternative supplies and paths

20. Conclusion
We introduced the concepts of linear programming and nonlinear programming
We showed how these relate to the IoT
We looked at several examples of using optimization techniques for the IoT

21. Conclusion
For the week, we looked in more detail than before, at the algorithms that are/can be used in the IoT
This is just a primer
We also looked at some examples
My contact information:
Design News: naperlou
LinkedIn: Louis Giokas