M 3 iot

IoT Domain Analyst
Dr. Arvind Kumar | School of Electronics Engineering, VIT Vellore |
https://sites.google.com/view/arvindk

M.3: Simulation Scenarios
Models to simulate real-world scenarios, Application of the models,
stages of data lifecycle, reuse existing IoT solutions, reusability plan.

Model
• It is an abstraction from reality used to help understand the object or system being modeled.
• People use modeling all the time to make decisions in their everyday lives although they usually
don’t do so in a formal way.
Here some common things are models:
1. Maps are models of a portion of the earth’s surface.
2. Most computer games are models of real or imaginary worlds programmed in a computer.
3. Many toys are models of real objects, scaled down or changed in their operation so that
they are not dangerous or messy like toy trucks, guns, swords, dolls, dishes, stoves.

Model
• People naturally use their experiences to create mental models of things they encounter in
ways to help themselves learn and survive.
• Models that are run on a computer require the translation of a mental model into a set of
rules and structures that can be represented in mathematical terms using a programming or
modeling language.

Types of Models
1. Physical Models
• Physical models are scale representations of the same physical entities they represent.
• They are used primarily in engineering of large-scale projects to examine a limited set of
behavioral characteristics in the system.
2. Mathematical Models
• Mathematical models use mathematical equations to represent the key relationships among
system components.
• The equations can be derived in a number of ways. Many of them come from extensive
scientific studies that have formulated a mathematical relationship and then tested it against
real data.
• Mathematical models of large-scale systems often use a combination of approaches --
inserting tested equations where the relationships are well known and inserting statistical
relationships where there is less certainty.

Types of Models
• Such models can also use probabilistic relationships for events that are random or
exhibit some type of variable pattern.
• For example,
• Models of weather analyze the long-term weather records for the area under
consideration and calculate the frequency of different weather incidents.
• These are represented as their probability of occurrence, assuming that the past is
a strong indication of future events.

Types of Models
3. Simulation Models
• Simulation models are a special subset of mathematical or physical models that
allow the user to ask "what if" questions about the system.
• Changes are made in the physical conditions or their mathematical representation
and the model is run many times to "simulate" the impacts of the changes in the
conditions.
• The model results are then compared to gain insight into the behavior of the
system.

Modeling Terminology
• Accuracy –
• The closeness of a measured or modeled/computed value to its “true” value. The “true”
value is the value it would have if we had perfect information.
• Algorithm –
• A set of rules for solving some problem. On a computer, an algorithm is a set of rules in
computer code that solve a problem.
• Calibration –
• The process of adjusting model parameters within physically defensible ranges until the
resulting predictions give the best possible fit to the observed data.

• Conceptual Model –
• A hypothesis regarding the important factors that govern the behavior of an
object or process of interest.
• This can be an interpretation or working description of the characteristics and
dynamics of a physical system.
• Deterministic Model –
• A model that provides a single solution for the variables being modeled.
• Because this type of model does not explicitly simulate the effects of data uncertainty
or variability, changes in model outputs are solely due to changes in model
components.

• Empirical Model –
• It is one where the structure is determined by the observed statistical relationship
among experimental data.
• These models can be used to develop relationships that are useful for forecasting and
describing trends in behavior but they are not necessarily mechanistically relevant that is
they don’t explain the real causes and mechanisms for the relationships.
• Parameters –
• Terms in the model that are fixed during a model run or simulation but can be changed
in different runs as a method for conducting sensitivity analysis or to achieve calibration
goals.

• Sensitivity –
• The degree to which the model outputs are affected by changes in a selected input parameters.
• Statistical Models –
• Models obtained by fitting observational data to a mathematical function.
• Stochastic Model –
• A model that includes variability in model parameters.
• This variability is a function of:
1. changing environmental conditions,
2. spatial and temporal aggregation within the model framework,
3. random variability.
• The solutions obtained by the model or output is therefore a function of model components and
random variability.

• Variable –
• A measured or estimated quantity which describes an object or can be observed in a system and
which is subject to change.
• Validation –
• Answers the questions
• Is the science valid and does the model use current methods and techniques?
• Is the numerical model adequate to convey the science principles at the level of the question
being asked?
• Is the model arriving at an acceptably accurate representation of the phenomenon being
modeled?"
• Verification –
• Does the code for the model run correctly and provide a mathematically correct
answer?
• Do the algorithms being used accurately represent the mathematical function on the computer?

First Modeling Example
➢Model the time it takes to go through traffic from your house to a destination like
work.
• Let's say you need to decide the best route to take to work.
• To make this decision you will need to formulate at least one objective for your
trip.
• Here are some of the possible objectives.
• Minimize the amount of time it takes to get there
• Avoid traffic congestion.
• Find a route that excludes freeways.
• Plot a path between your house and work to make sure you travel by the
same spot every day.

• Assuming that we focus just on the first objective: Minimize the amount of time it takes
to get there
• We need to decide what will affect that objective.
• To do that, we must create a conceptual model of the system.
• List all of the variables that impact our travel time from home to work and what we believe
are the cause and effect relationships across all of those variables.
• For any model we are creating or studying, our ideas on the variables and cause and effect
relationships come from published information, analysis of data from a real system, and our
own knowledge of the system.
• The phenomena we are modeling may also be constrained by physical laws or prevailing
theories of their operation so our conceptual model should reflect those limitations.

• For our traffic example, we know that we need to traverse the street system to get from
one place to another and that we need to observe traffic laws governing speed, one-way
streets, and traffic control devices.
• The first part of the class exercise is to define as many of the conditions as possible that
will impact the travel time to work along with the cause and effect relationships among the
variables.
• We need to estimate the direction of the relationship, the form of the relationship, and,
if possible, a quantitative representation of that relationship.

• For example,
• we know that bad weather will slow traffic down.
• The worse the weather, the slower the traffic.
• We can hypothesize that the impact on traffic is non-linear.
• We may not have the data to exactly quantify the relationship but we could start with a
simple classification of weather events and an estimate of their impacts on the flow of
traffic.
• Here are some examples of weather conditions - can we fill in an estimate of the
impacts on traffic flow?

• As we simplify the model, we need to decide which phenomena will be represented as
• variables (items whose values will change based on the relationships represented
in the model)
• parameters (items that are assigned a reasonable constant value to represent a finite set of
conditions).
• For example, every stop sign you stop at may take a different amount of time depending upon
how many other cars are approaching the same intersection.
• However, you may choose to create a parameter that uses an average amount of stopping time
to represent the range of conditions rather than have to gather data or find some other way of
estimating the number of cars approaching each intersection during your trip.

Integrity in the Data LifeCycle
02-03-2022 https://sites.google.com/view/arvindk 19

The 5 Stages of
Data LifeCycle
Management
➢Data LifeCycle Management is a process that
helps organisations to manage the flow of
data throughout its lifecycle – from initial
creation through to destruction.
➢While there are many interpretations as to
the various phases of a typical data lifecycle,
they can be summarised as follows:
https://sites.google.com/view/arvindk
02-03-2022 20

• Manual Data Entry
• External Acquisition
• Capture from Devices
Creation
• Security
• Backup & Recovery
Storage
• Data viewing, processing, modification and saving
• Data Sharing
Usage
• Data Archived and Protected
• Available for use
Archival
• Purging
Destruction

1. Data Creation
The first phase of the data lifecycle is the creation/capture of data. This
data can be in many forms e.g. PDF, image, Word document, SQL
database data. Data is typically created by an organisation in one of 3
ways:
▪ Data Acquisition: acquiring already existing data which has been
produced outside the organization.
▪ Data Entry: manual entry of new data by personnel within the
organization.
▪ Data Capture: capture of data generated by devices used in various
processes in the organization.

2. Storage
➢Once data has been created within the organisation, it needs to be
stored and protected, with the appropriate level of security applied.
➢A robust backup and recovery process should also be implemented to
ensure retention of data during the lifecycle.

3. Usage
➢During the usage phase of the data lifecycle, data is used to support
activities in the organisation.
➢Data can be viewed, processed, modified and saved.
➢An audit trail should be maintained for all critical data to ensure that
all modifications to data are fully traceable.
➢Data may also be made available to share with others outside the
organization.

4. Archival
➢Data Archival is the copying of data to an environment where it is
stored in case it is needed again in an active production environment,
and the removal of this data from all active production environments.
➢A data archive is simply a place where data is stored, but where no
maintenance or general usage occurs.
➢If necessary, the data can be restored to an environment where it can
be used.

5. Destruction
➢The volume of archived data inevitably grows, and while you may want to save all
your data forever, that’s not feasible.
➢Storage cost and compliance issues exert pressure to destroy data you no longer
need.
➢Data destruction or purging is the removal of every copy of a data item from an
organisation.
➢It is typically done from an archive storage location.
➢The challenge of this phase of the lifecycle is to ensure that the data has been
properly destroyed.
➢It is important to ensure before destroying data that the data items have
exceeded their required regulatory retention period.
➢Having a clearly defined and documented data lifecycle management process is
key to ensuring Data Governance can be carried out effectively within your
organisation.

Reduce, Reuse, Recycle – IoT Solutions
➢With new consumer electronics emerging on the market – millions of tons of
electronic waste is produced worldwide, each year .
➢Everybody enjoys new technology but how many of us act environmentally
responsible when we buy our newest mobile or smart device?
➢There are steps we can all take to be more responsible towards the environment
when we design our IoT projects, such as using the Three R's principle.
➢Reduce, Reuse, Recycle (RRR) is a concept that applies to many modern-day
areas, such as building & architecture, food production, and technology, in the
struggle to be more socially responsible and to address
➢the huge amount of waste we can see growing around us.

Reduce, Reuse, Recycle – IoT Solutions
➢So how should we rethink out IoT projects to comply with the Three R's principle?
➢Reduce: When designing the prototype of new projects, we should lower the
number of new items to buy .
For example,
➢ if you need a temperature sensor for your project, before ordering it online, ask
your techie friends if they have a spare one.
➢Reduce the energy our devices consume,
For example,
➢Reducing the clock frequency in the processor or lowering the sample rate of
sensors.
➢Put your Arduino in sleep mode for its idle periods and it can run for years on
battery .

Reduce, Reuse, Recycle - an environmental
approach to your IoT
Reuse:
➢The concept is that we should reuse existing technology as much as
possible before buying a new product or gadget.
➢Some of the GSM modules found in out-of-date mobile phones offer the
same functionality you will find in a new GSM board.
➢Most of these modules work with AT commands via a common serial
interface and so do many old phones.
➢You could also consider using the camera from a refurbished smartphone.

Reuse:
➢In addition, what about the flow sensors from a damaged coffee
machine or the water level sensor from a damaged washing machine?
➢If you need a new piece of hardware, just look around - you will find
it and you can boost your creativity.

Recycle:
➢We are most familiar with this principle in our consumerist life.
➢However, recycling is not just selecting plastic from paper; it can also
be a design principle and a source of creativity .
➢You can donate the technology you do not use anymore to be reused
or refurbished, or you could hack into and reuse it yourself.

➢https://www.dataworks.ie/5-stages-in-the-data-management-
lifecycle-process/
➢https://www.todaysoftmag.com/article/2582/reduce-reuse-recycle-
an-environmental-approach-to-your-iot-projects
➢https://cs.ccsu.edu/~stan/classes/CS530/Notes18/15-
SoftwareReuse.html

Reuse: Solutions
➢In most engineering disciplines, systems are designed by composing
existing components that have been used in other systems.
➢Reusing of existing components establish development of new
technologies in less time and effort.
➢We need to adopt a design process that is based on systematic reuse
(a plan)

Reuse types
System reuse
➢Complete systems, which may include several application programs.
Application system reuse
➢The whole of an application system may be reused either by
➢incorporating it without change into other systems (Commercial Off The-
Shelf - COTS reuse) or by developing application families.
Component reuse
➢Components of an application, like, sub-systems or single objects, may be
reused.
Object and function reuse
➢Software components that implement a single well defined object or
function may be reused.

Reuse Benefits
➢Accelerated development
➢Effective use of specialists
➢Increased dependability
➢Lower development costs
➢Reduced process risk
➢Standards compliance

Reuse Approaches

Reuse Landscape

SDLC - Overview
➢ Software Development Life Cycle (SDLC) is a process used by the
software industry to design, develop and test high quality softwares.
➢ The SDLC aims to produce a high-quality software that meets or
exceeds customer expectations, reaches completion within times and
cost estimates.
▪ It is also called as Software Development Process.
▪ SDLC is a framework defining tasks performed at each step in the
software development process.
▪ ISO/IEC 12207 is an international standard for software life-cycle
processes.
▪ It aims to be the standard that defines all the tasks required for
developing and maintaining software

What is SDLC?
➢SDLC is a process followed for a software project, within a software
organization.
➢It consists of a detailed plan describing how to develop, maintain,
replace and alter or enhance specific software.
➢The life cycle defines a methodology for improving the quality of
software and the overall development process.

• Manual Data Entry
• External Acquisition
• Capture from Devices
Creation
• Security
• Backup & Recovery
Storage
• Data viewing, processing, modification and saving
• Data Sharing
Usage
• Data Archived and Protected
• Available for use
Archival
• Purging
Destruction

Graphical representation of the various stages of a typical SDLC.
SDLC
Planning
Defining
Designing
Building
Testing
Deploym
ent

THANK YOU !
ARVINDKR.NITT@GMAIL.COM
HTTPS://SITES.GOOGLE.COM/VIEW/ARVINDK

M 3 iot

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