=Paper=
{{Paper
|id=Vol-2588/paper49
|storemode=property
|title=Increasing the Accuracy of the Information Load Annual Growth Evaluation on the Internet of Things
|pdfUrl=https://ceur-ws.org/Vol-2588/paper49.pdf
|volume=Vol-2588
|authors=Igor Zhukov,Nickolay Pechurin,Lyudmila Kondratova,Maksim Iavich,Kalyy Yerzhanov
|dblpUrl=https://dblp.org/rec/conf/cmigin/ZhukovPKIY19
}}
==Increasing the Accuracy of the Information Load Annual Growth Evaluation on the Internet of Things==
https://ceur-ws.org/Vol-2588/paper49.pdf
Increasing the Accuracy of the Information Load Annual
Growth Evaluation on the Internet of Things
Igor Zhukov 1 [0000-0002-9785-0233], Nickolay Pechurin 1 [0000-000-1727-7455],
Lyudmila Kondratova 2 [0000-0002-9170-4198], Maksim Iavich 4 [0000-0002-3109-7971] and
Kalyy Yerzhanov 4 [0000-0003-3166-3715]
1
National Aviation University, Kyiv, Ukraine
2
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine
4
Scientific Cyber Security Association, Tbilisi, Georgia
4
Yessenov University, Aktau, Kazakhstan
zhuia@ukr.net, nkpech@i.ua, kaly7524@gmail.com
Abstract. The paper is devoted to the problem of assessing the primary
information load on the Internet of things, i.e. assessing the amount of
information that enters the network directly from things that are and are only
generators of information (issues of information consumption by “things” are
beyond the scope of this article). Having advanced the postulate that the amount
of information emanating from a thing, is proportional to the product of its mass
(thing) by its specific orderliness, it is concluded that it is possible to use a
model similar to the V.V. Leontyev model of interindustry balance. In the
proposed network (graph) model, balancing is performed not according to the
volumes of generated things, like Leontyev’s, but according to “volumes of the
generated order”. The proposed balance model will make it possible to more
accurately predict the development of the Internet of things than this can be
done, for example, using time series.
Keywords: Internet of Things, Information Load, Balance.
1 Introduction
The successful resolution of the vivid problem of computer engineering and sciences,
the creation of global systems of artificial intelligence, is impossible without
constructing a reliable picture of the state and prospects for the development of their
(systems) infrastructure, which is a global cyber-physical system (network), often
referred to as the Internet of Things [1]. One of the indicators characterizing the state
of the Internet of things is the total amount of primary information generated over a
fixed period of time and entering the network. That search for the source of relevant
data to evaluate the (primary) load on the Internet of Things, led the authors to the
following considerations about the possible ways of evaluation of this indicator. The
amount of information generated by an individual thing is determined by the degree
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0) CMiGIN-2019: International Workshop on Conflict Management
in Global Information Networks.
of its (dis) ordering (Boltzmann, Shannon et al), and its total volume is also
determined by the number of these things. But these two parameters form the basis for
assessing an indicator completely alien to the problems of artificial intelligence and
the Internet of Things - an indicator of the welfare (prosperity, efficiency, proximity
to the “first world” etc) of the state achieved during the year, which, as usual, is
macroeconomic indicators such as GDP, GDP for PPP, GNP, IPP etc). The concept of
gross domestic product, introduced in 1934 by Simon Kuznets (the annual market
value of consumed, exported and accumulated goods (and services)) has since been
successfully used to evaluate and compare the economic power of countries. This
indicator could be used directly to solve the problem of determining the total amount
of primary information generated over a fixed period of time (here - for a year) and
transmitted through the sensors to the network. However, by default, the modern
understanding of this term is that GDP is not only goods (carriers of the material
world, i.e. things that are the subject of our interest), but also services. Kuznets GDP
only indirectly represents the country's capacity for the production of things - goods
(and therefore, primary information for the Internet of things): it is rather not the
result of the production of things (ordering the world under the Moon - the
construction of buildings, bridges and ships), but the process of ordering it. GDP is
the mass of the annual water flow of the Dnieper (Volga), the result of streamlining is
silt in the floodplain, and the service disappears without a trace in the Black (Caspian)
Sea. First of all, a sensor (microprocessor) is put on a thing, not a service. In general,
if we consider only a thing on which you can establish the primary (or terminal —
which side to look at — the last mile / li / lee / verst / kilometer) Internet of Things
device, then Kuznets GDP hides the true picture [2].
Another circumstance associated with classical GDP should be mentioned. Having
made the natural assumption that the price of a thing (one of the arguments of GDP
etc), as an object of the material world and goods, is rigidly related to the degree of its
order (things), we conclude that the significance of a thing as a generator of primary
information of a network of things, in GDP taken into account by its market price, is
an increasing function of the degree of ordering of things. It is ordering that must give
rise to the true price of a thing, and, therefore, indirectly the amount of information
associated with it. However, in practice, a ton of coal (a thing that has a certain fixed
value) has different prices, if you count in Donetsk or Rotterdam. The article is
devoted to the problem of estimating, on the basis of GDP in value (money) terms, the
primary information load on the Internet of things, i.e. estimating the amount of
information received, through terminal software and hardware devices, to the network
directly from things that are and only are information generators. The issues of
information consumption by “things” are beyond the scope of this opus. The selected
indicator is an indicator carefully monitored by international research organizations, it
contains indirect data on the intensity of primary information flows [3,4].
The first step in formalizing a task is to accept the principle of its decomposition. If
the development of a global (world) computer network of things is predicted, then it is
proposed to decompose it into more than two hundred interconnected subscribers, the
economies of states that produce the corresponding volumes of things. If the
development of a regional network is forecasted, for example, at the state level, then
one of the possible ways of decomposition is to decompose it into interconnected
sectors of the economy, acting as subscribers-sources of information flows to the
computer network of things, as is done in the classical V.V. Leontyev model of inter-
branch balance.
The usual period for issuing statistical information (on GDP, PPP GDP, GNP, IPP
and the like) by global and regional monitoring organizations is one year, so in this
article the unit of measurement of the intensity of information flows [10-12] is
[information units] / year.
Assumption. The source of primary information for / in the computer network of
things are things themselves, i.e. material objects ordered to a certain extent. In other
words, the postulate is advanced: the amount of information coming from a thing,
taken into account by the GDP indicator, is proportional to the product of its (thing)
mass by the degree of ordering of a unit of mass, i.e. specific ordering.
Then the intensity of the general information flow is composed of the flow from
existing (operated) things and from things that are added during the year and the latter
is represented by GDP. GDP in value terms contains only aggregated, indirect
information about the intensity of the real physical flow of produced things.
The task is to find a method for estimating, on the basis of a macroeconomic
indicator of GDP, the intensities of the physical flows of manufactured things,
respectively ordered for the subsequent calculation of the intensities of the primary
(input for the network, the “first mile”) information flows of the computer network of
things – the Internet of Things [13-16].
The use of GDP of PPP, GNP and other indicators that additional distort the
essence of things is not considered in this opus.
2. Approaches and issues using GDP intensities to estimate the
increment of the primary data flows
The initial prerequisite when using the GDP indicator to assess the intensity of
information flows is this: the (sought) intensity of the primary information flow
appears to be an increasing (non-decreasing) function of GDP.
There are a number of approaches to using GDP to solve the problem of estimating
the annual increase in the information load on the Internet of Things.
The first, obvious approach to solving the problem is to directly use the values of
GDP in the formation of the time series for forecasting.
Along with the known shortcomings of universal forecasting algorithms based on
time series, we have a relatively short time series due to the novelty of such an object
of study as the Internet of Things [5].
Another mistake that arises when solving our problem of estimating by the values
of GDP arises due to the convolution, in classical GDP in value terms, of two
components: the annual volume of goods produced, i.e. things for sale, and the annual
volume of services (in the context of this article is noise).
Another approach is to use only a part of the GDP indicator, namely, the intensity
of production of goods: the additive nature of the convolution allows us to identify the
component of GDP associated with the production of things (GDP in the physical
dimension).
The carriers of information coming into the network of things from end users'
terminal devices per unit of time are the "things" themselves, i.e. the material objects
provided by the I / O devices, which can be played by various software and hardware,
in particular, the executable devices. and (intelligent) sensors [6].
Therefore, the next approach to assessing the initial information load is to evaluate
the intensities of the flows of precisely “things”, on the basis of which data are
obtained on the intensity of information flows coming from each of the sources
(network subscribers). In addition, as in the approaches discussed above, it is also
necessary to establish a (functional) relationship between the intensities of the flows
of each type of thing and the corresponding information flows.
This method is difficult to apply directly because of the complexity of the relevant
multi-product model and the lack of a monitoring system.
The circumstance that generates another approach to the use of the GDP indicator
for assessing the increment in the intensities of primary information flows is that the
values of GDP for a particular subject (GDP i) are not independent, their values are
affected by the nature of the relationship of the subscribers. In particular, these
interconnections can be determined by the technological aspects of the production of
things, if the subscriber is a (macro) economic subject, then these interconnections
can be formalized on the basis of the inter-industry balance model of V. Leontyev [7].
In fairness, it should be noted that GDP is not the only traceable indicator on the
basis of which the intensity of primary information flows of the Internet of Things can
be estimated.
An alternative may be the level of "produced" greenhouse gases, but scientists here
have to rush in order to forestall the young northern researcher, who, if she has
recently achieved her goal, will reduce this figure to nothing.
3. Statement of the problem for building a model for estimating the
annual increase in the information load on the Internet of Things
with increased accuracy
Assuming that the amount of generated information is a non-decreasing function of
GDP, we come to the corollary that the increment in information flows generated by
the state is greater, the higher the level of its well-being. The generally accepted
notion of the contribution of states to national and global welfare (progress) is
illustrated either by the number of things that appeared in the current year (in recent
years), measured by the volume of GDP produced in the same year (in recent years),
or per capita GDP or derivatives of these indicators such as GDP PPP, GNP, IPP and
the like, where GDP refers to the amount of gross value added created by all resident
producers in the economy of a given country, plus any taxes on products and minus
any subsidies, not included in the cost of production.
By default, such a design describes the real balance of the achievements of
countries in the (material) development of the world.
Definition. Country OZ = state OZ = world territorial community OZ (hereinafter -
OZ).
Definition. Up to the provisions of existing international law, OZ’s own material
resource is understood to mean the material world observed in a cone with a center in
the center of the Earth and generating rays passing through an administrative border
or a border including exclusive economic zones, OZ.
Assumption. During the astronomical year (the period with which the GDP
indicator used in the article and the like is associated) the mass and ordering of one’s
own material OZ resource in a natural way, i.e. without the direct participation of a
person, it does not change: a ton of oil recovered by an Azerbaijani is not replaced by
a corresponding underground natural influx from Iran; a ton of graphite in a cloud
from the vicinity of the Uzh river does not fall on the cranberries of Belarusian
marshes; the ozone layer is fixed all year and lies within the above-described cone; a
mass-order resource received from the Sun is located over and in Sahara for a whole
year, not being mixed by dust storms in the EU states.
In an attempt to break the silence for many years, the authors dare, in the spirit of
Karl Marx, as well as Friedrich Engels (“question everything!”), To doubt that the
balance of achievements of the countries (and, accordingly, each citizen of the
country) in the material development of the world (and therefore the loading of the
Internet of Things), measured using the above indicators (the degree of ordering of
the latter) really reflects the changes in the material world in the direction of its
ordering that are carried out by a specific country (citizen of the country). Doubts, for
example, are the assertion (see estimates of GDP and similar indicators in [3]) that in
2018 the Swiss produced products (GDP - face value per capita = $ 80,690 USD)
twice as much as a German (GDP - face value per capita population = 44 549 US
dollars), and that, in turn, is four times as many Russians (10 608 US dollars). Taking
into account the circumstances of inclusion of the services provided in the GDP
(through the use of IPP etc) does not reduce the intrusive contrast of the picture of the
achievements of world territorial communities in the development of the material
world (guidance of the world material order of things). The technology is
incomprehensible (we will leave the considerations of current, in the sense of the
present, socioeconomists outside our vision), according to which the Swiss, having
received a cubic meter (weakly, but not to the zero degree) of ordered propane-
butane, will disorder part of it in order to order the remainder so that “amount of order
”in this remaining part of the initial number of carbon, hydrogen, etc. atoms - see the
table of Dmitriy Mendeleev, so (strikingly, according to the estimation by GDP)
differed from the received“ amount of order ”. It is possible to restore order on a fixed
subset B of the set (atoms) A only by disordering the set I - the complement of B to
A. In the example of the Swiss "hard workers" (producers of GDP), the observed
striking differences were explained (and are explained) by the striking difference in
the efficiency of the (Swiss) technology of the redistribution of order between the
atoms of the gas cubic meter mentioned above. Introducing, for simplification, the
concept of specific ordering as the strength (power) of bonds assigned to one atom (or
unit mass of matter), we have a possible estimate of order strength (quantity) on the
set A as the product of the number (mass) of atoms of the set A and the force (power)
of bonds assigned to one atom (or unit of mass), i.e. specific ordering on the set B.
Similarly, a possible estimate of the order strength (quantity) on the set I can be
estimated as the product D of the number (mass) of atoms of the set I by the specific
ordering on the set I. The T / D ratio can serve as an indicator of (technological)
development - in 2018, for the Swiss, this indicator is 8 times higher than it is for the
hard worker - Russian. Puzzling is the fact that over time the Swiss manages to
produce twice as much "order" than the German (of the order in the Tissot more than
in Siemens?).
The ultimate goal of constructing this model is to provide an a posteriori
opportunity to verify the adequacy of the existing model of interstate balancing
(orderliness of the material world), built on macroeconomic indicators, where things
are considered as goods with value, and ordering is the addition of (added) value - see
works already mentioned, not in vain, the unforgettable and eternal Karl Marx.
The task is to synthesize a model of balance of (state) achievements based on the
usual, at least for the domestic population, criteria: the degree of ordering of the
things of the material world in which they live (and for some time they hope, with the
permission of the Almighty, to continue) the authors of this article. At the same time,
it is supposed to evaluate its applicability for solving the problem of estimating the
increment of the primary information load on the Internet of things with increased
accuracy.
4. The problem solution in building a model for assessing the
annual increase in the information load on the Internet of Things
with increased accuracy
Definition. A thing is called a physical object, in most cases equipped with a
(intelligent) sensor [6] and connected, with the help of the latter, to a computer
network of things. A thing generates a primary information stream (a stream of the
first verst) of the corresponding intensity, measured by the amount of information
generated per unit of time (a kind of bitrate). The C2H5OH molecule is a thing at the
micro level; here her model is an oxygen atom bound in a single whole, two of carbon
and six of hydrogen.
Assumption. The minimum (degenerate, atomic) thing here will be considered the
atom, as represented by Democritus, and somewhat later, two thousand years, used by
Dmitry Mendeleev (electron, positron, neutron and others like them are not
considered in this article, although it is intuitively felt that there will be no
fundamental differences in this detail). Those who wish can use newer models such as
Higgs particles instead of the democritic term.
Statement. In the material (Earth) world of countries, the law of conservation of
“mass of order” is valid (similar to the law of conservation of matter and energy),
where by mass of order we mean the product: [mass of one thing] • [degree of order
associated with one thing] • [number of things], i.e. at the atomic-molecular level -
something similar to the entropy indicators of Ludwig Boltzmann's companions (if he
really had the latter).
In other words, [mass of order] = [degree of order associated with one thing, i.e.
specific degree of order, specific ordering] • [mass of one thing] • [number of things]
= [specific ordering of things] • [mass of one thing] • [number of things].
At the molecular level of consideration, an example of one thing is the C 2H5OH
molecule: [mass of one thing] - the total mass of atoms forming this molecule [degree
of order associated with one thing, i.e. specific degree of order, specific ordering] can
be measured by the degree of “approximation” of the binary relation existing in the
molecule (binary valence bonds between atoms, see the structure of the molecule) R
to the linear order relation L or to the lattice. This proximity can be very
approximately estimated by the ratio of the total possible number of distinguishable
bonds to the number of bonds forming a linear order, i.e. transitive fault power R; I
must say that this measure is a function of only the number of atoms that make up the
molecule, and does not characterize the degree of ordering of the molecule itself,
however, using this (naive) measure greatly simplifies the task of assessing the degree
of specific ordering of a thing, similar to how the use of the logarithmic function
greatly simplifies the procedures estimation of entropy, allowing the effective
application of the famous constant of 1,3806488(13)· 10 Joule /К.
23
At the macroeconomic level, the order mass index is currently measured as the
product of the mass of matter and its value (cost), i.e. represents the material
component of GDP: [mass of order] = [degree of order associated with one thing] •
[mass of one thing] • [number of things] or, to simplify macroeconomic monitoring
and calculations, [mass of order] = [degree of order associated with a unit of mass of
things] • [mass of things].
Let us dwell in more detail on the introduced mass order index (see also the use of
the Boltzmann constant in calculating the entropy of the thermodynamic state S = k *
W, where W is the number of corresponding microstates), associating it with
indicators of the theory of relations.
Let there be a set M of 5 things {a, b, c, d, e}, in which an arbitrary element a can
be (or not) in relation to element b, and the combination of these elementary relations
forms a (binary) relation on M. The total possible elementary relationship is 52 = 25.
Putting order on M includes the following steps.
1) Partitioning M into a subset of things-products of ordering TP and a subset of
things-the results of disordering DE, i.e. things - sources (energy) for ordering.
2) Checking the sufficiency of the resource (the presence of elementary
relations) to establish order, ideally linear, on TP. In fact, this is the presence of one
and only one route, which includes all the TP elements once, where the existence of a
direct path from a to b is equivalent to the fact that a is in elementary relation with b.
In this case, elementary connections between DE elements can be used, and their use
is the resource (energy) consumption from DE. A negative test result indicates that
the available resource of mass of the order is insufficient for the complete ordering of
TP (i.e., it is impossible to convert TP into a final product (product)).
3) Streamlining TP. Here you can use different ways of ordering, for example. -
algorithms, presented in due time by Donald Ervinovich Knut, of arithmetic or logical
multiplication of matrixes of elementary routes. If an elementary route from DE
entered the route, then this order element is excluded from D, thereby decreasing the
ordering index D. If the route does not include elements from DE at all, then this is
evidence that “the subject of the transformation of nature” (Swiss hard worker in our
example) actually does not produce anything, but only reproduces the resulting order
(not production-ordering, but a transaction).
The described allusions of ordering with the constructions of the theory of
matrices and relations are (unnecessarily) cumbersome, complicate the understanding
of the idea of constructing a model of balance in the generation of the material world
(things), and therefore, for simplicity, the quantity of order is not associated with a
system of atoms (things), but with each individual atom (thing) by attributing a thing
to the degree of its (thing) inclusion in an ordered system, which, at a certain level of
abstraction, is called the final product. The concept of the valency of an atom in
chemistry, the valency (degree) of a vertex in graph theory - this is an indicator of the
degree of “inclusion” of a thing (atom or vertex of a graph) in an ordered system.
Assumption. The increment in the intensity of the primary information flow is
determined solely by the increment in the number of newly created (produced over a
selected period of time) things; the set of distinguishable entities creating (producing)
things is given.
The mentioned subjects, with the number n, are the subscribers of the created
network of things (intelligent sensors of the first verst).
Thus, the essence of the problem is to find a way to more accurately assess the
intensity (bitrate) of flows generated by things produced by the entire population of
subscribers, a group of subscribers (for example, countries - manufacturers of things),
as well as individual devices.
The unit for measuring the intensity of production - a source of information load
on the network, adopted a conventional unit - US dollars. Thus, there are n producers
of gross product, each of which (manufacturers) generates streams of primary
information entering the network (determines the information load on the network).
As already mentioned, a method for treating chronic illness of forecasting based
on time series is proposed, based on the construction of a dynamic algebraic (single-
product stream) model that takes into account the interconnection of parameters on
the basis of which the time series is built. At the same time, we expect greater
accuracy in estimating the bitrate than when taking into account relationships by
using various “autocorrelation” methods (AR, MA, ARMA etc) and, possibly,
methods of the MGUA type (for the latter, at least, in terms of the “accuracy of the
estimated bitrate” / labor input ”) [8].
By the way, considerations of what exactly is semi-voluntaristic, for example,
economists and exchange experts from Rotterdam will forgive us (politicizing),
setting the price of a thing when measuring the volume of manufactured goods in
terms of GDP (PPP GDP) can significantly reduce the accuracy of estimating bitrate
(for now, within the scope of this article), out of brackets.
For each i-th subscriber, we introduce Si, Ti, Di - values proportional to the
intensity of the flows of things actually generated, accumulated during the
observation period, used by the i-th subscriber in the production process, respectively.
By the way, if we assume that the system obeys the balance condition according to
V.V. Leontyev E X E A X E R 0 [9],
T T T
where the technological matrix A takes into account the scattered part of the flows of
things consumed during the production process within the i-th industry (i-th
subscriber in our context), then Ti is associated with RT, Di - with elements of the
main diagonal A.
5. A model for assessing the annual increase in the information load
on the Internet of Things with increased accuracy
Balance condition α. In a balanced system, the law of conservation of matter is
fulfilled; the authors could not resist the temptation to once again enjoy, hopefully,
together with the reader, his presentation in the style of Mikhail Lomonosov in his
letter to Leonard Paul Euler: “... all changes in nature occurring, the essence of the
state is that how much what one body will be taken away, so much will be added to
another, so if where some matter disappears, it will multiply in another place ... ”
n n
In our case, this is a system of X iS ,i X ji X i ,iD X i ,iT X ij , i 1, n .
j 1, j i j 1, j i
Here XnS,n is a value proportional to the intensity of the flow of things generated
by the subscriber itself n. If the country's economy is the subscriber, then X nS, n is a
value proportional to the intensity of the flow of natural resources (mineral and
biological) extracted (per year), labor resources can also act as a carrier (generator) of
information (sorry for the association with the word “thing” but you won’t throw a
word out of a song!). Xn, nD is a value proportional to the intensity of the flow of things
used by subscriber n in the production process. If the economy of the country is the
subscriber, then Xn, nD is a value proportional to the intensity of the flow of consumed
resources — own and borrowed; this may include, for example, a hundred tons of the
mass of (chemical) SO3 radicals and H2SO4 molecules scattered in the ambient air
from the Chemical Technology Plant No. 512 on the left bank of Kiev in 1991 (the
information source [4] does not provide statistics on this facility today because of the
untimely the demise of the latter). As a rule, this is the value of the “destroyed”
(dissipated) resource for energy production (in turn, spent on the production of
things); and this part of “things” ceases to be associated with a particular subscriber.
Nevertheless, further, within the framework of the article, we will assume that the
mass represented by the variable Xn, nD does not leave the limits of the economy that
generated it (see the assumption above). Xn, nТ is a value proportional to the intensity
of the flow of things accumulated by subscriber n during the observation period. If the
country's economy is the subscriber, then Xn, nТ is a value proportional to the intensity
of the flow of things produced during the year (actually, national wealth).
Balance Condition of β. The assumption is made that there is a direct linear
dependence of the amount of information generated by the i-th subscriber and
entering the network per unit of time on the intensity of the flow of things; of course,
the thing itself is only a carrier of a terminal device (an intelligent sensor [6]) -
actually the generator of primary information of the first verst):
Cij X ij Bij , i, j 1, n, j i
For Si , Ti , Di i 1, n CiS ,i X iS ,i S iS ,i ; C i ,iT X i ,iT Ti ,iT ;
Ci ,iD X i ,iD Di ,iD , i 1, n .
Here the coefficient coefficient of Сij is the specific ordering of the flow of things,
estimated value of Хij: the more streamlined the unit of flow of things, the greater the
magnitude Сij. If the subscriber is the country's economy, then Сij is the price
attributed to (unit) of Хij.
Balance condition of γ. This type of balance is the (macro) economic expression
of the law of conservation of energy. There is performed the balance of information
n
flows values: Ci ,iT X i ,iT Cij X ij Gi , i 1, n
j 1, j i
or in matrix form: СХ = G.
Balance condition of δ. There are satisfied the natural conditions of non-
negativity for the variables:
X ij 0, i, j 1, n, j i ,
X iS ,i 0 , X i ,iT 0 , X i ,iD 0 , i 1, n
Cij 0, i, j 1, n, j i ,
CiS ,i 0 , Ci , iT 0 , Ci ,iD 0 , SiS ,i 0 , Ti ,iT 0 , Di ,iD 0 , i 1, n ,
Bij 0, Gi 0, i, j 1, n, j i
Features of model constructions. The constructions of γ – the essence of a
system of nonlinear equations with the additional restriction of non-negativity of δ.
Here (n(n-1) + 3n) = n(n+2)) variables of Х; as many it is С and В; the variables of S,
T, D, G are on n. Total equations it is (n (block a) + n(n+2) (block b) + n (block c) =
n(n+4)).
The generated model describes the interconnections of the Internet of things
subscribers, however, it has no pragmatic meaning (estimating of X) and therefore
requires an external supplement.
The first step here is to eliminate the component of GDP, which is to a small
degree related to X, which is the “volume of services” component. The adoption of
assumptions that GDP is an additive function of the volume of goods and services
produced, as well as the ratio of the volume of goods produced to the volume of
services rendered, is a constant for the planning period, allows you to use and trust, at
least within the scope of this article, the values presented, for example ., in [3,4].
As follows from the foregoing, by S, T, D, G we understand the “material” parts of
GDP as truly generating information flows of the first verst. The obvious, possibly
compromising highly intelligent manufacturers, naming the remaining part of the
"imaginary" part of the GDP - by chance. Manufacturers of high-value “ideas” [1],
that is, high-tech and even more so-high-value intellectual property products,
probably do not require protection of ways of setting prices for their products,
although …
Suppose that the efficiency of production technologies of things (processing of
resources, streamlining of matter coming from outside and inside, adding value to
matter) are constants: Сij = const.
Then we have a homogeneous SLAE with a matrix of structure coefficients as
shown above.
If the subscribers of the Internet of things are territorial associations (state
economies), it is natural to assume that the “mass of the state” (literally!) Does not
change over time or changes slightly (of course, in the context of the picture of
changes in this indicator in many economies of other states , and not in relation to the
"mass" of the state itself), or the mass of the substance forming (possibly only
potentially) its (state) "thing value" changes insignificantly, i.e. under the assumption
n n
Сij = 1 ( i, j 1, n ) X ji X ij , i 1, n .
j 1, j i j 1, j i
Having made the (natural) assumption that the material component of GDP is the
resources ordered by various technologies from the sources that have these resources,
we come to the identification of the possibility of using multi-product flow models in
the network, the nodes of which represent economic entities that have the
technologies for organizing resources.
Let us introduce the assumption that subject i has the only type of source in the
technological chain (-s), of resource; he builds his strategy for managing them in such
a way as to ensure a cycle in the graph (network) starting at i such that the sum of the
weights of the arcs included in this cycle is maximal (here the weight of the arc i, j
represents the amount of produced things related to the supply of the source resource
from subject i to subject j).
The predicted values of the components of GDP obtained by analyzing this model
are the ones required to determine the correct resource allocation strategy for creating
the appropriate Internet of Things infrastructure. The model parameters are built on
the basis of data received from monitoring systems, and the essence of the
determinate values obtained, as we see it, as the expected value of the corresponding
estimated values. Thus for the estimation of the expected value the selection of values
of constituents of GDP is used, length of that is related to the depth of
prognostication.
In case of the use middle on the values given in the system of monitoring, with
subsequent application of algorithms of type of algorithm of Floyd, there is the known
risk of "error middle".
The case of the use of supposition is possible, that weight of arcs are casual sizes,
conformable to the different laws of probability distribution.
If dates are stochastic, we come to the conclusion, that here distortion of results is
also possible for the parameters, related to the subjects, possessing "eventual", in
economic sense, technologies (by subjects at the end of technological chainless):
similar to foregoing, "error of middle", arising up at calculations on networks.
The point is that external addition of this model can be limit on the level of future
GDP for some subjects of economy, caused, for example, by their unwillingness to
exceed some set level of height of GDP (for example – in a 3% а year).
This circumstance is expressed, in particular, by the considerable increase of stake
of high-frequency constituent in the spectrum of corresponding function of
distribution, and consequently, using of mean value for forming of elements offered
to using as a model, can result into the bias error in the estimation of actual value
GDP (or making GDP).
On other words, a macroeconomic situation is such presently, that the known
assumptions accepted for simplification of model for multiphase service system of
type of supposition about independence of L. Kleinrok [8], accepting is impossible
from a nascent systematic error.
A model was approved on the example of balancing, in the sense accepted in this
article, five state associations which have greatest resources.
An analysis of this special model showed that such is not, generating the
informative stream of the first mile, stream of things on the network of the subscribers
incorporated in the computer network of things, that the condition of material (thing)
balance (type of inter-branch V. Leontiev in natural expression) was executed.
The model offered in the article was also applied for balancing taking into account
mass ordering (resource) acting Sun.
As a result of her research (with the use of accessible to the authors of data) drawn
conclusion that the energy (mass ordering) got from sunny batteries can not be
examined as "got from proceeded in energy sources", not only that is why, the power
resource of a Sun limits.
Technological matrix in equalization of balance such, that balancing roots do not
exist: disordering at creation of the technologies, presented by this matrix , the arrived
at organization exceeds, presently.
6. Conclusion
Money mode GDP may be used to estimate year information flow growing to Internet
of things with carefulness. More exact, then money mode GDP scale of year
information flow growing to Internet of things, will be year mass order growing: GDP
produces systematic mistake under year information flow growing to Internet of
things estimation. Account, in the offered (by a stream) model, connections at the
"production of order" increases exactness of evaluation of annual increment of the
informative loading on the internet of things. The faze by faze changes of efficiency
of primary resource, estimated by classic Karl Marcs addition of cost, for the aims (at
least) of evaluation of annual increment of the informative loading on the internet of
things, it is necessary to replace the increase of degree of efficiency. And exactly the
degree of efficiency of thing determines her price in the conditions of the balanced
system. The simple network model gives acceptable results at the evaluation of annual
increment of the informative loading on the internet of things only on condition of
absence of subjects, a production of things volume is related that to the functions of
distribution with a wide spectrum. At presence of the indicated productive things
subjects, it is necessary to use (more difficult) the stochastic network models of
multifood streams and corresponding methods of their analysis.
An increase of the informative loading on the internet of things from a concrete
subject also is the certificate of successes of the last in strengthening own welfare:
than anymore he generates to information on the first mile, the anymore prophetic a
subject put (certainly, due to the disordering subsets of other things) in order. An
existent estimation of successes of subjects is on business of strengthening of own
material welfare measureable the degree of efficiency of accessible great number of
things, on the basis of GDP in a money term, is not objective: making GDP generate
equalizations of balance, not having non-trivial decisions (and we have no nontrivial
appropriate flow task solving).
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