Internet of Things - Data Tables (IoT-DT) [1]
By Som Karamchetty [2]
Current Problem:
As the calendar
turned to 2020, the world got besieged by the Corona Virus or COVID-19. Nations
are worried about how they may be impacted by this virus. If not this virus,
some other health problem or some other problem worries every nation from time
to time.
In order to
assess the severity of the situation, healthcare officials would like to have
statistics about people and the symptoms they exhibit. By collecting
appropriate data, they can determine how many people have contracted a certain
virus and take measures to address the problem at hand. Thus, the first step is
to collect data. Advanced nations talk about Internet of Things (IoT) and other
advanced technologies to collect the data rapidly, address such problems, and develop
solutions.
Yes! Internet of
Things (IoT) is attracting significant interest as it allows a large number of
sensors to be networked and the data collected by the sensors is fed to a
processor via the Internet for analytics and appropriate decision making in a
rapid manner. There are two main drawbacks with traditional IoT. 1) Not all
types of data are amenable for collection by sensors (some data and information
are generated and input by people), and 2) Sensors and networks are expensive, especially
for developing countries.
With the high
degree of penetration of mobile phones in developing countries, it is easier
for people with mobile phones to provide data and information relatively easily
and cheaply. They can collect data by taking measurements using cheap analytic sensors.
Therefore, it is possible to query and
collect essential data and information by canvasing and promoting networks of
people with mobile phones into hierarchical tree structures.
A number of
people with their mobile phones living on a street in a town or city can form
the nodes of a tree with a hub at the street level. Several such street level
hubs can be networked and the whole town or city can be configured as a tree
network.
In a similar
manner, a number of villagers with their mobile phones can form the nodes of a
tree with a hub at the village level. Several such village level Hubs can be
networked at the district level and on up to the national level.
The operators at
the Hub level may pose simple questions and the lower level hubs pass them down
the line to the people at the nodes. The people may provide data or information
as simple entries in response to the questions. Such data is collected and
processed by the Hub operators and the compiled data in tabular or other form
are passed on to the higher level Hubs.
With such a
network of people with mobile phones, the status of a certain situation (the
status of the progress or regress of a virus) can be easily and quickly
obtained.
For example, by
asking the people about the presence or absence or the value of a set of
symptoms (e.g. Fever, Cough, and Shortness of Breath for Corona Virus), which
may point to certain health issues can be collected by each Hub. Ordinarily,
information gets generated leading to rumors about some situation. However,
with the organized networks of people with (pre-validated) mobile phones, the
data and information collected are not mere rumors but can be valid and
dependable data. Starting from the people living on a street or in a village,
data can be collected quickly up to the national level.
By analyzing the
data at the hubs at various hierarchical levels, regions with intensity of a
disease can be identified and a national picture can be charted really fast. Of
course, the accuracy and reliability depends on the community’s ability to get
frank, honest, and factual information from the people. The completeness of the
information may depend on the commitment of the people and the incentives they
get to become part of the mobile phone tree network and provide the data as
accurately as possible.
Please see the
full article below here, which describes the IoT-DT Method being suggested by
the author.
{Please note that detailed explanations in the article are presented with farming applications as examples. But, the healthcare case can be easily understood by the readers. If any readers are particularly interested in explanations specifically for the healthcare field, the author will do so. In the meantime, a general Health care explanation is presented at Figure 16 in the section on Various Applications.}
{Please note that detailed explanations in the article are presented with farming applications as examples. But, the healthcare case can be easily understood by the readers. If any readers are particularly interested in explanations specifically for the healthcare field, the author will do so. In the meantime, a general Health care explanation is presented at Figure 16 in the section on Various Applications.}
Abstract:
Internet of
Things (IoT) is attracting significant interest as it allows a large number of
sensors to be networked and data collected by sensors is fed to a processor via
the Internet for analytics and appropriate decision making in a rapid manner.
However, not all types of data are amenable for collection by sensors.
Furthermore, sensors are not cheap for developing countries where people can
provide data relatively cheaply.
The Internet of
Things – Data Tables (IoT-DT) is similar to IoT in that it networks simple
tables. People can enter data into those simple tables which data is
transmitted to the processors via the Internet. With the high prevalence of
mobile phones in developing countries, it will be easier to implement IoT-DT
networks. Rural farmers, healthcare workers, educational institutions, small
businesses, river monitors, and many other people can provide simple data into
Internetworked tables. It is also possible to develop hybrid systems where some
sensors can also be connected alongside the Data Tables.
In this article,
the IoT-DT method and some applications are described.
Overview:
Internet of Things
(IoT) is one of the advanced technology developments receiving significant
attention with a promise of good economic benefits. But, by itself, IoT cannot
address many situations and developing countries cannot afford this technology
in areas of their economies which are already struggling with high costs, low
productivity, and poor economics. This drawback for IoT is because it depends
on numerous high technology sensors.
Therefore, we have
to devise a method and system that allows people to input data instead of
relying on sensors to provide the data. In fact, such a system will have a
unique role in situations where certain types of data are not necessarily or
easily obtainable with sensors alone.
In this article, I
will explain a number of cases with types of data that fit the type of system
suggested.
The system can
easily be upgraded to incorporate the traditional IoT arrangement where data
are provided by sensor networks. In such cases, the system deals with data
coming from people as well as from sensors. The architecture shows the benefits
that the method and system will provide the users.
Problem:
Modern economies
depend on data analytics, which require ready access to large amounts of data
in various fields. Online Surveys, Contact Forms, and IoT networks address this
issue. However, these are disparate methods and require substantial human intervention
to merge the data and make it useful for data analytics and for decision making.
Even of greater impediment to widespread adoption of IoT systems is the fact
that they require a vast array of digital sensors that many current economies
cannot afford and sensors cannot collect and provide data and information that
only people have or can gain access to.
Solution:
In order to
address these problems, a method called IoT-DT is being invented as described
here. This method uses Internet connections to query information from selected
sets of Internet-connected (networked) users and make the collected data and
information available for processing at the hubs. Another enhanced variation is
a hybrid system where some nodes contain conventional IoT, i.e. sensors are
connected while some other nodes are configured as IoT-DT so that they obtain
data and information that humans can provide. Thus, the hybrid system will have
the merits of both methods.
Benefits:
IoT-DT will allow
countries with plenty of labor to use the Internet to collect data from a very
wide range of sources and be ready to gradually transition to move to hybrid
systems. Even, developed countries will find the progression affordable to a
wider cross section of users.
IoT-DT will provide
enormous utility for billions of people globally to connect to the Internet
using their smartphones (or mobile phones) and supply data and information to
enable a variety of data analytic systems.
IoT-DT will allow
operators (people) to provide (input) graphs, pictures, videos, and sound
tracks so that data analytics can be more comprehensive than mere IOT. It is
also possible to provide the results of software objects via the IoT-DT
networks to the hub processors.
Introduction:
IoT
Explained:
In IoT, a number
of sensors are connected by means of the Internet. As the Sensors provide data
that they collect, Processors analyze the data, and output the results in the
form of conclusions,
and recommendations for action. Figure 1 shows a simple illustration of IoT.
Figure
1: A Simple Illustration of IoT
IoT is
also called variously as Internet of Everything (IoE or IoV).
The
definition of IoT from Wikipedia is reproduced below here.
The Internet of things (IoT) is the inter-networking
of physical devices, vehicles (also referred to as "connected
devices" and "smart devices"),
buildings, and other items embedded
with electronics,
software,
sensors,
actuators,
and network
connectivity which enable these objects to collect
and exchange data. [1]
According to Forrest Stroud, “The Internet of Things refers to the
ever-growing network of physical objects that feature an IP address for internet connectivity, and the communication that occurs between
these objects and other Internet-enabled devices and systems.” [2] “Examples of
objects that can fall into the scope of Internet of Things include connected
security systems, thermostats, cars, electronic appliances, lights in household
and commercial environments, alarm clocks, speaker systems, vending machines
and more.” [2]
Figure 2 depicts a typical illustration to show that IoT will become a
vast network of devices some of which are sensors while others are processors.
Figure 3 shows an IoT network with Technical Details.
Figure 2: A General illustration of an IoT network. (Source [3])
Figure 3: Technical Details in an IoT. (Source [4])
It is estimated
that billions of devices can be connected to form IoT networks, monitored, and
controlled. The sensors collect and feed data to processors via the Internet.
Operators or application software can take appropriate action using such
processed data. Currently, many companies are actively pursuing technology and
application developments and installing IoT systems.
In the highly
industrialized countries, the added cost of IoT networks and systems is
expected to be economically viable essentially because of the high labor costs
in these countries. On the other hand, in developing countries, there is a
surplus of cheap labor and paucity of investment capital to install such
sophisticated digital systems. Moreover, there are a number of example applications
where sensors cannot get the data that humans can easily acquire or generate,
and input into Internet connected systems.
A technical detail
about the operation of IoT is explained in Figure 4. The IoT device makes a
measurement upon command (in case the device is a sensor) and the result of the
measurement is passed on to the IoT Agent. IoT Agent is a software application
that interprets the measurement received from the Device, converts it to Data,
and hands it over (communicates) to the Context Broker. Context Broker is where
the measured Data is recorded.
Figure 4: IoT Detail (Source: [5])
That the IoT Agent
is actually handling Data from the IoT Device inspires us to be creative following
the realization that the Iot Agent can actually handle and deal with Data
whether such Data is provided by a suitably designed measurement device
(sensor) or by a human that actually inputs the Data. This is shown in Figure 5
and explained as follows. Here, we see that Instead of a Device, a human
operator enters data after reading an instrument (it is not unimportant how he
got the Data value at this point) and the Data is passed on to the IoT Agent. The
IoT Agent is (appropriately coded) software application that transmits the Data
to the Context Broker. The Context Broker has a location reserved for the data
vale to be returned by the Device. The same reservation of a data location is
applicable when the data is actually entered by a human Operator. Deploying
such an arrangement, we now create the IoT-DT method and system as shown in
Figure 5.
Figure 5: IoT-DT Detail
In order to
explain the similarity of the operation of IoT and IoT-DT networks, an exploded
view of an IoT sensor is shown in Figure 6. It has a location for data and a connection
to the sensor part in the device. As shown in Figure 7, the sensor part is
removed and a human Operator can provide the value of an attributed requested
by the Processor. Unlike specific sensors, human Operators deal with multiple
tasks and so in order to assist them, the Variable name is supplied for which
the Value is sought from the Operator.
Figure 8 shows the exploded detail
of the IoT-DT node in an IoT-DT network.
Figure 6: Illustrating the key Point to go from IoT to IoT-DT
Figure 7: Illustrating the key advancement from IoT to IoT-DT
Thus, a Table
object can be supplied to the operator with a request to fill the data or
information relevant to the situation. However, in order to assist the
Operator, the header of the Table and the Name of the Attribute are prefilled
by the system and all that the Operator has to do is to fill the Value element
of the Table. Thus the Data Table (DT) becomes a node of the Internet where the
Operator is expected to enter an appropriate Value. This arrangement is shown
in Figure 8.
Figure 8: Details of A Data Table Presented to an Operator.
This is also the
reason why the method is being called IoT-DT. The Data Table resides on the
Operator's mobile phone, which has an IP address linked to the IoT-DT.
In this example, a
simple Table with only one Value to be entered by the Operator is shown for
simplicity in the explanation of the principle of the method. However, in
reality, the Table can have several rows and columns with many elements (e.g.
see Tables 1 and 2). Some
elements will be prefilled with the names of the Attributes. Some other
elements may be used to guide the Operator so that there is strong consistency
in Values received from different operators. Once the operator inputs the data
and submits the resulting Table, it is automatically communicated to the
Processor and the Values are deposited into the right locations in the Table at
the Processor or Hub location.
Henceforth, we
will show Data Tables at the nodes of networks to depict the IoT-DT networks in
our discussion.
IoT-DT:
With the abundance
of both cheap labor and mobile phones, it will be easier for people to collect
and input data to IoT systems created (invented!) to accomplish such networked
data collection. This is the current invention called, Internet of Things –
Data Tables (IoT-DT). In other words, Tables of Data on machines are networked.
Human operators enter data into those networked tables, which are given
Internet (or subnet) addresses in IoT-DT systems, just as the devices were
given Internet (or subnet) addresses in IoT systems. The concept (invention) of
IoT-DT is being introduced by this inventor.
The method depends
on several steps. The architecture consists of a processor with an Internet
address and a number of simple tables created and assigned sub-net addresses.
Thus, the Processor and the Tables form an Internet of Things - Data Tables
(IoT-DT). The Processor has a software application. The tables are transmitted
over the Internet to operators over the latter’s mobile phones or other
available networked connections. The operators fill the tables on their mobile
phones with data and information they have or they generate (create). The
processor receives the tables over the Internet. The processor at the Hub processes the
data in all the tables received. The compiled and analyzed data and conclusions
are available with the processor for use by other systems or human decision
makers.
With a view to explaining
the operation of IoT–DT, a simple example is shown in Figure 9. Four tables for four Farmers are
shown. The Processor fills the Variable (Investment) and the Value is left
blank. Each table is transmitted via the Internet to four
different operators, say, farmers, who now receive a table each on their mobile
phones. Each of them is asked to enter into the Table the Value of investment
they made. They enter the amounts into the tables (the tables in Figure 9 show
the Values filled in by the Farmers in this example case) on their mobile
phones and submit. Each table has already been given an Internet Sub-Net
address and the tables are connected to a Processor, which has the Internet
address. Thus, the Processor and the Tables on the farmers’ mobile phones form
an Internet of Things - Data Tables (IoT-DT). The Processor receives (gets) the
tables with the data entered by the farmers. In a simple case, we may say that
the software application merely adds the values it received from each of the
tables. Now the processor has the total investment made by all the farmers on
the network. Obviously, this is a simple operation used merely to illustrate
the method. The operator (Farmer, in this example) enters the data in simple
table supplied to him on his mobile phone. The processor takes care of the
Internet addresses and communication protocols. The attractiveness of the
method is the simplicity of data entry as far as the operator (farmer) is
concerned and no burden is placed on the farmer to be knowledgeable of any of
the network formalities and details.
Although in this
simple example, we stated that the farmer enters numerical data for the value,
in more sophisticated cases, the data entered may be graphs, pictures, videos,
and sound tracks. With the availability of such technology, Farmers may like to
shoot pictures of plants infested by viruses or videos of variations in plant
growth on a farm, record sounds of animals destroying crops and Processors may
observe them to get diagnosticians opinions.
Figure 9:
Data Tables and Operator (Hub) connected to Internet.
The simplicity of
the method may be easy to understand when people remember how mobile phones are
used to exchange text and multi-media messages. Using her mobile phone, User-1
sends a text message (SMS) to another, User-2, with another mobile phone. The
cellphone network takes care of all the protocols and the message arrives on
the mobile phone of the User-2. User-2 can simply type a reply and submit. The reply
goes only to the User-1, again the system and network taking care of all the
protocols and addresses. The mobile phones have distinct IP addresses and the
message is at a lower (let us call it subnet level) on the mobile phone.
In yet another extension
of this simple example, let us say that User-1 sends a message to ten different
users. When the ten different users respond by sending replies (messages) back
to User-1, they all arrive on the Mobile phone of User-1 and she will know who
sent what reply. This clearly shows that the system and network take care of
the addresses and protocols while the User-2 and others need only provide a
reply. Figure 10 is a depiction to explain briefly the mobile phone connections
to facilitate Text (SMS) messages.
Figure 10: How SMS works (Source: [6])
It may be pointed
out that while Short Message Service (SMS)
can be used only to send text messages Enhanced Messaging Service (EMS) allows formatted text, sound effects, small
pictures and icons to be sent. Multimedia Messaging Service (MMS) allows users to send animations,
audio and video files in addition to text. [6]
We also see cases
where we connect our mobile phones to a cordless base station. A message (voice
or text) received by the mobile phone is delivered via Bluetooth to the base
station of the cordless phone system, which transmits the message to the
various cordless phones on that network. People can receive the message from
any of the cordless phones on that network. If they return a call from any of
those individual cordless phones, the message is transmitted to the original caller
on his mobile phone.
VOIP or Voice over
Internet Protocol is another example of connecting computing devices (e.g. desktops,
laptops, tablets, mobile phones) to the Internet and speech (voice) converted
into digital information packets and transferred over the Internet. [7]
A word of
explanation about URL (Uniform Resource Locator) and IP (Internet Protocol)
address is needed here. A computer or a mobile phone is recognized uniquely on
the Internet by the IP address assigned to it. A file on the computer or a
mobile phone is recognized uniquely by the URL given to it. URL
“represents an address of a certain file on the TCP/IP network and leads a user
to a file on any computer connected to the Internet anywhere in the world.” [8] Thus, in the above descriptions of IoT and
IoT-DT, The device (sensor) or the Table presented to an operator is actually
identified by its URL. However, there is no need for the operator (e.g. a Farmer or a health care worker at the Node) to know the URL
details as the system takes care of the communication and the operator is
expected to type the appropriate data and submit it.
Contact Forms and Survey Forms are isolated uses of question and
response sessions. [9]
In subsequent
sections of this paper, simple tables with more than one data item to be
entered are shown to explain the general operation. In many of the suggested
applications, as the tables are meant to be used by unsophisticated people
using mobile phones, they will not be made highly complex. Although in theory,
IoT-DT can accommodate complex tables, only simple tables that the operators (such
as Farmers in the above example) can enter using their mobile phones. Thus, the
use of complex relational databases at the data entering end is not implied in
the current discussion.
The processor at
the hub will take care of sectioning tables into smaller tables and transmit
such simpler tables to operators. When the data and information are entered and
submitted by the operators and received at the hub end, the processor will
again compile the data into bigger tables, if so needed at the higher levels.
The similarity and
the distinction in the features of IoT and IoT-DT are explained with reference
to Figure 11. In IoT, a number of sensors are given Internet
addresses and they are networked using the Internet connections. As the sensors
read and pass that data onto the hubs, the processors at the hubs process the
data received according to the application programs built into the processors.
On the other hand,
with IoT-DT, people at the nodes input the data into tables assigned/communicated to them.
Such tables are given Internet (or subnet addresses or URL’s) and are connected
to the processors using the Internet. The data input into the tables are
processed by the processors that are also on the Internet.
Figure
11: Comparison of IoT and IoT-DT
It is possible to combine the traditional IoT and IoT-DT concepts and develop a hybrid system where some sensors are deployed to collect data and some people enter data into data tables and both sorts of data are processed by processors using appropriate software applications.
Illustrative
Example Application:
Next, I will
explain the IoT-DT concept with a simple example of some practical utility.
Take the case of
a farmer who has some land. He grows onions on it. Consumers, governments,
markets, investors, and a host of other people/organizations have an interest
in the data that various farmers similar to this farmer can generate and
provide. For example, these various people and organizations want to know when
and how much of that produce would be harvested. If there is a sensor connected
to the land, if it can read all the data needed, and if it is on the Internet,
then such data would be readily available to whoever wants it, provided they are
connected and authorized to get it.
Now, let us take
it to the next step. Let us connect all the sensors on the onion growing
farmers’ land to the Internet. A processor at a hub on the Internet can collect
the data from all those sensors, process it and make it available to various
parties. That would be a typical IoT application.
But, certain
types of data and information are not measurable by sensors. For example, the
number of acres planted, the name of the crop, expected yield, time of yield,
and certain other information are not measurable by sensors. These are part of
a farmer’s expectations, plans, and actions. Hence, we create a table and the
farmer would enter data into it as and when he generates or gets the data. By
giving an Internet/subnet address to that table, a Processor on the Internet
gets access to that data and processes it. If we have several farmers and each
of them is given an Internet connected table to input their data, the Processor
at the Hub on the network can process such data.
During the planning
stage, a farmer may enter data into the table provided to him as in Table 1.
Serial
Number
|
Item or
Attribute
|
Data
|
1
|
Farmer Id
|
|
2
|
Crop
|
|
3
|
Seeds required
|
|
4
|
Quality of seeds &
seedlings
|
|
5
|
Fertilizer required
|
|
6
|
Water required
|
|
7
|
Labor required
|
|
8
|
Machinery required
|
|
9
|
Expected crop
|
|
10
|
Anticipated harvest
date or period
|
|
11
|
Farmer invested hours
|
|
12
|
Incremental and
cumulative expenses
|
|
13
|
Electricity (&
fuels) used
|
|
14
|
Loans taken
|
|
15
|
Other
|
Table 1: Example Items (or Attributes) and data in a table made available to farmer to fill in data.
Figure 12: A Number of Farms Connected to a Processor at
a Hub (Group) on the Internet.
The Data from each farm are connected to the Internet to form a Group as previously explained in reference to Figure 9. It may be noted that in Figure 13, we used generic names as Attributes and not named them specifically. There are 1 to m Attributes in each table and 1 to n Members ( e.g. Farmers). (Instead of Tables, the Attribute values are shown one below the other in the Figure 13 only for simplicity in the illustration.) For the mathematical notation, we use i for each table given to a member (farmer) and j for each attribute in the tables. Thus, Attribute_I, j represents the jth attribute in the ith table. In this example, in order to get the Group value of the Attribute_j (Group.Attribute_j), the jth Attributes in tables from 1 to n are added. Such mathematical manipulations are carried out by the Processor at the Hub.
Figure 13: Data Tables are connected into a Group (Hub) on the Internet
As the tables
from various farmers are networked into a Group on the Internet, a Hub Table
that looks similar to the Farmer Table is created by the Processor at the hub
(Group) as shown in Table 2. The Values or Data are not shown in the Table for
the present.
Serial
Number
|
Item or
Attribute
|
Value or Data
|
1
|
Hub Id
|
|
2
|
Crop
|
|
3
|
Seeds required
|
|
4
|
Quality of seeds &
seedlings
|
|
5
|
Fertilizer required
|
|
6
|
Water required
|
|
7
|
Labor required
|
|
8
|
Machinery required
|
|
9
|
Expected crop
|
|
10
|
Anticipated harvest
date or period
|
|
11
|
Farmer invested hours
|
|
12
|
Incremental and
cumulative expenses
|
|
13
|
Electricity (&
fuels) used
|
|
14
|
Loans taken
|
|
15
|
Other
|
Table 2: Example Items (or Attributes) and Cells for Data in a Hub-Table
Figure 14: Various
Types of Data that may be requested from Farmers by the Processors at the Hubs.
During the growing
season, stakeholders (village and higher level officials and organizations)
will be interested to monitor Health of the crop, Pesticide required/applied, Water
requirement by day or week, Weed removal by day or week, Weather effects, Revised
expectation of the quantity of crop, Revised anticipated Harvest date or period,
Farmer invested hours, Anticipated Harvest date or period, Incremental and
cumulative expenses, Quality of plants, Electricity (& fuels) used, Loans
taken, and so on.
As the Harvest season arrives, the
stakeholders will be interested in the Labor required, Machinery required, Amount
of Crop Harvested, Storage required, Transportation required, Costs, Revenue, Farmer
invested hours, Anticipated Harvest date or period, Incremental and cumulative
expenses, Quality of crop, Loans taken, Electricity (& fuels) used, Loans
paid back and so on.
It may be
emphasized that this type of data collection and analyses can be extended to
millions of farms and hundreds of crops by this method as it scales.
A Variety of
Applications:
Now that the
above section described the method and the utility of the IoT-DT system with
the Farming sector as an example, it can actually be applied to numerous other
sectors. Typical example areas are: Education, Healthcare, Business, Security, Crime
prevention and Police, River pollution monitoring, and so on. Figures 15
through 18 show the types of data that would be sought from various Operators
at the subnet level by the Processors at the Hubs. Again, the software
applications incorporated at the Hubs will do the analytics and pass the
results to whoever requires them.
Figure 15: IoT-DT Data Type from Colleges for the Academic Field
Figure 16:
IoT-DT Data Type from Healthcare Facilities
Figure 17:
IoT-DT Data Type from Businesses.
Figure 18:
IoT-DT Data Type from River Monitoring Sites
This method can be extended to yet other
sectors. Examples are as follows: Storage houses, Shops, Machine shops, Factories,
Restaurants, Hotels, Schools, Colleges, Banks, Transporters, Mines and Minerals
processors, Power plants and Utilities, Water reservoirs and streams, and many
other economic and social activities.
Higher Levels:
In the above
sections, collection and processing of data by the Processor at the first level
Hub is shown. Actually, it is possible to develop a hierarchy of Hubs. Going
back to the Farmers’ example, it means that Villages, Panchayats, Mandals,
Districts, States, and finally the Country can be set up as higher level Hubs.
With such a hierarchy, compatible results of analytics can be aggregated to
higher levels. Thus in the specific example discussed before, the quantity of
Onions planted, expected, produced, and harvested can be known at the village
level and all the way up to the national level. Such aggregation can be made in a
very short time.
Aggregation to
higher levels is possible by setting up IoT-DT as shown in Figure 19.
It may be noted that the lower level hubs serve as the feeders of data to
successively higher level Hubs. The lowest level operator (e.g. the Farmer) is
requested for data only by the one Hub under which the farmer is situated and
there are no multi-level requests for data. Such aggregation of data allows for
a variety of analytics to be performed across villages, districts, and states.
Comparisons of productivity indexes, efficiency measures, cost-benefit factors,
and other calculated parameters would assist planners and decision makers to
advise lower level operators in improving production operations.
Multiple Hubs:
Figure 20:
Different Hubs at Higher Levels may Seek Data and Aggregate it.
Ease of Deployment
and Utilization:
The method is
very easy and fast to develop, deploy, and utilize by people who are familiar
with the art of programming, communication, computing, and analytics. At the
lowest level, the operators (data providers) will find it easy to fill the data
using their mobile phones. Even in cases, where several sets of data are
required, the requests for data can be broken down at or by the first Hub
processors and present simple one data input blank (templates) tables to the
data providers. The method is very easy for the first level data providers to
learn and implement.
The Hub level
Processor can send a mobile message asking for the data with separate
description of what the data is about, if it is needed. As people get to know
their end of the method, they become very conversant about it and they can
respond to requests from authorized and permitted people.
Compilation of results can be simple
arithmetic or statistical analysis that can be performed at the Hubs. Communication of data can be made automatic
and fast as it is Internet based. Latency is minimal subject only to the
Internet speeds available and the promptness of the operators.
Safety, Privacy, and
Security:
Obviously when a
method is simple, there can be scammers asking for data from the naive users.
It is not my intention to address the general security and privacy issues here.
The IT industry and the government have to address issues with spammers and
cyber security in general. The First level Hub Processor establishes the link
with data providers by enrolling them as part of the Sub-Network. It is these
processors that initiate the dialogue and give them an identity. They may
initially assign a password to each data provider to get access to the blank
data table. Such access protection will avoid spammers getting access to blank
data tables and providing spurious data to corrupt the system.
Merits:
In the foregoing sections, a simple
generic framework is described. The method is easy to implement, use, and
process. With the availability of mobile phones with billions of people in the
world, it makes it very easy to set up networks and launch Internet of Things - Data Tables (IoT-DT). In developing countries, it offers people the means to
get measurements using simple devices available to them and enter the data into network(s) nodes assigned to them. It avoids
the need for expensive and sophisticated sensors that conventional IoT
requires. Even more important is the fact that sensors cannot get certain types
of data and people can get and input such data into IoT-DT networks. Eventually
when the developing countries can afford the sensors, the IoT-DT networks can
accommodate sensors also into the networks as described briefly as Hybrid
systems.
Benefits:
IoT-DT Networks
offer very affordable means to establish networks and collect significant
quantities of highly relevant data in countries with large populations and
manpower. As the data is aggregated and moved to higher levels of the networks,
data analytics can be carried out at selected levels and relevant information
provided to decision makers. Since messaging (SMS or EMS) is very cheap,
the cost is miniscule. Speed and Efficiency
are high. The system offers High Value to User(s), Suppliers, Organizations,
and Government(s).
Summary:
Key difference
from IoT:
IoT deals with
devices to control/set them if they are actuators and to get measured data from
them, if they are sensors.
IoT-DT deals with
data and information (instructions) either sent to a human operator or received from a human
operator. In case of human operators in IoT-DT, a mobile phone or a similar
device is connected for operation by the human operator.
In hybrid systems,
IoT-DT has both devices (sensors and actuators) and mobile phones that human
operators can operate.
In IoT-DT, human
operators receive data and information from a Processor at the hub and the
inputs they provide are communicated to the Processor for subsequent use in
data and information analytics.
Devices cannot
provide certain data and information by themselves as humans imagine such data
and information or generate it by their thinking.
There are numerous
cases where human imagined or generated data and information is highly
necessary and valuable in performing data analytics and reaching conclusions
for decision making.
IoT-DT makes it
easy to deal with such cases especially in developing countries where equipping
sensors is not cost effective as these countries have a surfeit of cheap human
labor.
Application Areas:
There are numerous business and production areas where IoT-DT
is applicable beneficially and profitably. Here are some typical example
applications.
• During the
planning stage, entrepreneurs (farmers, shoppers, distributors, professionals)
would know what the optimal choices are. It avoids growing too much or too
little of a commodity in the farming sector.
• During the
growing (manufacturing) phase, status can be ascertained, and corrective steps
can be taken.
• The data
allows enterprises to do better planning and re-planning through feedback and
feed-forward.
• Producers
and service providers would get reasonable and better returns.
• Consumers
get good supplies and good prices.
• Consumers
can plan their consumption choices to suit their budgets.
• It all adds
up to smart society and intelligent living.
• National
economy will be on a smart trajectory without Central Planning dictating what
individual producers and consumers should do.
• Smartness
is due to Data Analytics and information sharing on a continual basis.
Conclusion and Recommendation:
IoT-DT and its hybrid option (combination of IoT and IoT-DT) can have
billions of applications as there are billions of people globally having mobile
phones with global communication capability. It can become a multi-billion
dollar enterprise.
IoT-DT should be prototyped, and concept demonstrations should be
performed. Application software should be developed and major businesses
launched.
References:
[3] Figure Source: http://blog.legalsolutions.thomsonreuters.com/wp-content/uploads/2015/07/Internet-of-Things.jpg
[5] Source: https://fiware-tutorials.readthedocs.io/en/latest/iot-agent/index.html#what-is-an-iot-agent
[1]
Intellectual Property (IP) in this invention belongs to Dr. Som Karamchetty and
he can be contacted by people interested in collaborating with him to develop
the invention, for licensing the IP, and to develop applications.
[2]
Potomac, MD, USA, som.karamchetty@gmail.com.
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