Distributed Management in Crisis and Emergency Situations. Mathematical machines and systems
The purpose of this paper is to analyse and demonstrate possible application of the developed high-level Spatial Grasp Technology, SGT, and its Spatial Grasp Language, SGL, for dealing with different local and global crisis and emergency situations in dynamic distributed environments. Метою даної ст...
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| Опубліковано в: : | Математичні машини і системи |
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Інститут проблем математичних машин і систем НАН України
2019
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| Цитувати: | Distributed Management in Crisis and Emergency Situations. Mathematical machines and systems / P.S. Sapaty // Математичні машини і системи. — 2019. — № 1. — С. 3–15. — Бібліогр.: 17 назв. — англ. |
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Digital Library of Periodicals of National Academy of Sciences of Ukraine| _version_ | 1859656153904971776 |
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| author | Sapaty, P.S. |
| author_facet | Sapaty, P.S. |
| citation_txt | Distributed Management in Crisis and Emergency Situations. Mathematical machines and systems / P.S. Sapaty // Математичні машини і системи. — 2019. — № 1. — С. 3–15. — Бібліогр.: 17 назв. — англ. |
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| description | The purpose of this paper is to analyse and demonstrate possible application of the developed high-level Spatial Grasp Technology, SGT, and its Spatial Grasp Language, SGL, for dealing with different local and global crisis and emergency situations in dynamic distributed environments.
Метою даної статті є аналіз і демонстрація можливого застосування розробленої високорівневої Tехнології просторового захоплення (ТПЗ), а також її базової Мови просторового захоплення (МПЗ) для вирішення різних локальних і глобальних завдань, пов’язаних із кризовими та надзвичайними ситуаціями в динамічних розподілених середовищах.
Целью данной статьи являются анализ и демонстрация возможного применения разработанной высокоуровневой Tехнологии пространственного захвата (ТПЗ), а также ее базового Языка пространственного захвата (ЯПЗ) для решения различных локальных и глобальных задач, связанных с кризисными и чрезвычайными ситуациями в динамических распределенных средах.
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© Sapaty P.S., 2019 3
ISSN 1028-9763. Математичні машини і системи, 2019, № 1
ОБЧИСЛЮВАЛЬНІ СИСТЕМИ
UDC 623.764
P.S. SAPATY
*
DISTRIBUTED MANAGEMENT IN CRISIS AND EMERGENCY SITUATION
*
Institute of Mathematical Machines and Systems National Academy of Sciences of Ukraine, Kyiv, Ukraine
Анотація. Метою даної статті є аналіз і демонстрація можливого застосування розробленої
високорівневої Tехнології просторового захоплення (ТПЗ), а також її базової Мови просторового
захоплення (МПЗ) для вирішення різних локальних і глобальних завдань, пов’язаних із кризовими та
надзвичайними ситуаціями в динамічних розподілених середовищах. Коротко викладені основні
поняття ТПЗ, рекурсивної організації МПЗ і її мережевого інтерпретатора, безліч взаємодіючих
копій якого можуть встановлюватися по всьому світу і інтегруватися з іншими системами або
працювати автономно у критичних ситуаціях. На МПЗ описані основні операції щодо створення і
управління розподіленою мережею, які можуть працювати поверх існуючих систем зв’язку, таких,
як інтернет, або самостійно виконувати функції мережевих протоколів високого рівня у разі не-
локальних криз і катастроф. Досліджені і продемонстровані в МПЗ дві прикладні області з мож-
ливістю виникнення в них кризових ситуацій. Перша стосується повністю розподіленого аналізу і
відстежування множинних мобільних об’єктів у розподілених просторах зі складними і заплута-
ними маршрутами, що можуть бути пов'язані з крилатими ракетами, об’єктами оборони та
сміттям у космічному просторі, а також масовою міграцією людей через міжнародні кордони.
Інша область належить до розподілених соціальних мереж, в яких різні співтовариства з різними
культурами, традиціями або релігіями можуть бути просторово близькі одне до одного, співпа-
дати або перетинатися. На МПЗ показано, як моделювати просторову динаміку таких угрупувань,
регулярно знаходити топографічні центри різних співтовариств і оцінювати відстань між ними,
що може бути корисним для прогнозування та запобігання різних соціальних конфліктів. Ця тех-
нологія в її попередніх варіантах застосовувалась і апробовувалась у різних країнах, а її остання
версія може бути легко встановлена за угодою на будь-яких платформах.
Ключові слова: кризи і управління надзвичайними ситуаціями, мережеві технології високого рівня,
Мова просторового захоплення, розподілене відстежування мобільних об'єктів, соціальна ди-
наміка і конфлікти.
Аннотация. Целью данной статьи являются анализ и демонстрация возможного применения
разработанной высокоуровневой Tехнологии пространственного захвата (ТПЗ), а также ее базо-
вого Языка пространственного захвата (ЯПЗ) для решения различных локальных и глобальных за-
дач, связанных с кризисными и чрезвычайными ситуациями в динамических распределенных средах.
Кратко изложены основные понятия ТПЗ, рекурсивной организации ЯПЗ и его сетевого интер-
претатора, множество взаимодействующих копий которого может устанавливаться по всему
миру и интегрироваться с другими системами или же работать автономно в критических ситу-
ациях. В ЯПЗ описаны базовые операции по созданию и управлению распределенной сетью, кото-
рые могут работать поверх существующих систем связи, таких, как интернет, или же самосто-
ятельно выполнять функции сетевых протоколов высокого уровня в случае нелокальных кризисов и
катастроф. Исследованы и продемонстрированы в ЯПЗ две прикладные области с возможностью
возникновения в них кризисных ситуаций. Первая касается полностью распределенного анализа и
отслеживания множественных мобильных объектов в распределенных пространствах со слож-
ными и запутанными маршрутами, которые могут быть связаны с крылатыми ракетами, объ-
ектами обороны и мусором в космическом пространстве, а также массовой миграцией людей че-
рез международные границы. Другая область относится к распределенным социальным сетям, в
которых разные сообщества с разными культурами, традициями или религиями могут быть про-
странственно близки друг к другу, совпадать или пересекаться. В ЯПЗ показано, как моделиро-
вать пространственную динамику таких группировок, регулярно находить топографические цен-
тры различных сообществ и оценивать расстояние между ними, что может быть полезным для
4 ISSN 1028-9763. Математичні машини і системи, 2019, № 1
прогнозирования или предотвращения различных социальных конфликтов. Данная технология в ее
предыдущих вариантах применялась и апробировалась в разных странах, а ее последняя версия
может быть легко установлена по соглашению на любых платформах.
Ключевые слова: кризисы и управление чрезвычайными ситуациями, сетевые технологии высокого
уровня, Язык пространственного захвата, распределенное отслеживание мобильных объектов,
социальная динамика и конфликты.
Abstract. The purpose of this paper is to analyse and demonstrate possible application of the developed
high-level Spatial Grasp Technology, SGT, and its Spatial Grasp Language, SGL, for dealing with differ-
ent local and global crisis and emergency situations in dynamic distributed environments. Main concepts
of SGT, recursive organization of SGL, and its networked interpreter are briefed, where numerous com-
municating interpreter copies can be installed worldwide and integrated with other systems or operate
autonomously in critical situations. Basic network creation and management operations are described in
SGL which may operate on top of existing communication systems like internet or serve as high level net-
work protocols on their own in case of nonlocal crises and disasters. Two crisis-prone application areas
are investigated and demonstrated in SGL. The first one is dealing with fully distributed analysis and trac-
ing of multiple mobile objects in distributed spaces with complex and tricky routes, which may relate to
cruise missiles, defence objects and debris in outer space, or massively migrating individuals through in-
ternational borders. The other considered area represents distributed social networks in which different
communities, with different cultures, traditions or religions may spatially coincide. It is shown in SGL how
simulate spatial dynamics of such societies, regularly find topographical centres of different communities
and evaluate distances between them, which may be useful for prediction or prevention of different social
conflicts. The proposed technology had trial implementations and applications in different countries, and
its latest version can be readily installed by agreement on any platforms needed.
Keywords: crises and emergency management, high-level networking technology, Spatial Grasp Lan-
guage, distributed tracing of mobile objects, social dynamics and conflicts.
1. Introduction
With world dynamics growing rapidly, such words as disaster, crisis, and emergency are fre-
quently used in everyday life and in different points throughout the Globe, with detailed clarifica-
tion and comparison of such terminology found in existing publications [1]. Emergency response
and crisis management are already vital activities and essential part of infrastructures in different
organizations [2]. Crisis and security management are also considered in a global scale like deal-
ing with disasters that could break global communications and the internet [3], associated with
missile defence [4], and even moving to outer space [5]. Global terrorism [6], cyber attacks [7],
natural disasters with their social and political impact [8, 9], religious conflicts [10], as well as
many others with relation to international security [11, 12], are the areas were disasters, crises
and emergency are common, with urgent need of their effective prevention, alleviation, and man-
agement. In this paper, we are briefing the developed high-level networking control, processing,
and management technology [13–17] which is suitable for runtime dealing with different crisis
and emergency situations, also showing practical examples of its application in the area men-
tioned.
The rest of this paper is organized as follows. Section 2 describes the high-level Spatial
Grasp Technology, SGT, together with its basic Spatial Grasp Language, SGL, also organization
and main features of the networked SGL Interpreter. Section 3 shows expression and solution in
SGL of the basic and most vital network communication and management mechanisms, which
can fully operate on their own even if traditional communications, internet including, not operat-
ing. These include network creation from scratch, finding and collecting any path between nodes,
forming any spanning tree as well as shortest path tree from a node to all other nodes with accu-
mulating shortest paths to all nodes in the starting node, also setting routing tables in particular
nodes for this tree, and finally, creating routing tables in all nodes allowing shortest paths com-
munications between any nodes of the network. Section 4 demonstrates how to organize in SGL
ISSN 1028-9763. Математичні машини і системи, 2019, № 1 5
Figure 1 –Spatial pattern growth & coverage & matching
Figure 2 – SGL recursive syntax
the discovery and tracing of complexly moving targets, which may be cruise missiles or any other
objects in terrestrial or celestial environments, to be accomplished in a fully distributed and paral-
lel mode. Section 5 shows how to outline different communities in a distributed social network,
find their topographical centres and evaluate physical distance between them, which may be use-
ful for preventing possible social conflicts, while doing this repeatedly with simulation of spatial
mobility of individuals in time. Section 6 concludes the paper.
2. Spatial Grasp Technology (SGT)
2.1. General Features
Within SGT, a high-level scenario for any task to be performed in a distributed world is repre-
sented as an active self-evolving pat-
tern rather than traditional program,
sequential or parallel. This pattern,
written in a high-level Spatial Grasp
Language, SGL, and expressing top
semantics of the problem to be solved,
can start from any world point, being
as the source of pattern’s activity. It
then spatially propagates, replicates,
modifies, covers and matches the dis-
tributed world in a parallel wavelike
mode, as shown in Fig. 1.
The self-spreading & matching patterns can create knowledge infrastructures arbitrarily
distributed between system components (like humans, robots, sensors, etc.). These infrastructures,
which may be left active, can effectively express distributed databases, command and control,
situation awareness, autonomous decisions, as well as any other existing or hypothetical compu-
tational and control models.
2.2. Spatial Grasp Language (SGL)
SGL allows us to directly move through, observe, and provide any actions and decisions in fully
distributed environments (whether
physical, virtual, executive, or com-
bined). It has universal recursive struc-
ture, shown in Fig. 2, capable of repre-
senting any parallel and distributed al-
gorithms operating on, over, or in spa-
tially scattered data or other distributed
systems.
An SGL scenario develops as
parallel transition between sets of pro-
gress points (or props), with self-
modified and self-replicating scenario
code freely moving in distributed spac-
es. Starting from a prop, an action may
result in new props (which may be
multiple). Each prop has a resulting value, which may be arbitrarily complex, and resulting state
(one of: thru, done, fail, and abort). Different actions may evolve independently or interde-
pendently from the same prop, splitting and parallelizing in space. Actions may also spatially
6 ISSN 1028-9763. Математичні машини і системи, 2019, № 1
Figure 3 – SGL interpreter organization and main components
succeed each other, with new ones applied sequentially or in parallel from the props reached by
previous actions.
Elementary operations can directly use states and values of props reached by other actions
whatever complex and remote they might be. Any prop can associate with a position in physical,
virtual, executive or combined world. Staying with world points, it is possible to directly access
and impact local world parameters in them. Overall organization and control of the breadth and
depth space navigation and coverage is provided by SGL rules, which may be nested. These
rules, for example, can be: elementary arithmetic, string, or logic operation; hop in a physical,
virtual, execution, or combined space; hierarchical fusion and return of both local and remote da-
ta; distributed control, both sequential and parallel; a variety of special contexts for navigation in
space, influencing embraced operations and decisions; type or sense of a value or its chosen us-
age guiding automatic interpretation; creation or removal of nodes and links in distributed
knowledge networks. A rule can also be a compound one integrating other rules or defined as a
result of operations of arbitrary complexity.
Working in fully distributed physical, virtual or executive environments, SGL has differ-
ent types of variables, called spatial, effectively serving multiple cooperative processes: Heritable
Variables – starting in a prop and serving all subsequent props which can share them in both read
& write operations; Frontal Variables – transferred on wavefronts between consecutive props and
replicated if multiple new props emerge; Environmental Variables accessing different elements of
physical and virtual words when navigating them, also certain parameters of SGL interpreter; and
Nodal Variables as a temporary property of world nodes, accessed and shared by all activities as-
sociated with and reaching these nodes. These types of variables, especially when used together,
allow us to create flexible and robust spatial algorithms working in between components of dis-
tributed systems rather than in them. Such algorithms can replicate, spread and migrate in distrib-
uted environments (partially or as a whole), always preserving global integrity and control.
2.3. SGL Networked Interpreter
An SGL interpreter consists of a number of specialized modules handling and sharing specific
data structures, as in Fig. 3.
The interpreters can
communicate with each other,
and their distributed network can
be mobile and open, changing the
number of nodes and communi-
cation structure at runtime. The
backbone and nerve system of the
distributed interpreter is its dy-
namic spatial track system with
its parts kept in the Track Forest
memory of local interpreters. It is
logically interlinked with similar
parts in other interpreter copies
thus providing altogether global
control coverage. The distributed
track structure enables for both hierarchical and horizontal control, also remote data and code ac-
cess, with high integrity of emerging parallel and distributed solutions achieved without any cen-
tralized resources. Dynamically created track forests, spanning the systems in which SGL scenar-
ios evolve, are also used for supporting spatial variables and echoing & merging control states
and remote data, while self-optimizing in parallel echo processes. They also route further grasps
ISSN 1028-9763. Математичні машини і системи, 2019, № 1 7
to the positions in physical, virtual, executive or combined spaces reached by the previous grasps,
uniting them with frontal variables left there by preceding grasps.
The distributed SGL interpreter may have any number of communicating nodes, up to
thousands to millions to billions, effectively converting the whole world into a universal spatial
machine operating under spreading intelligent scenarios. Any number of such scenarios can oper-
ate simultaneously (cooperatively or competitively) while starting at any time and from same or
different world points. The SGL interpreter copies may be integrated with (or implanted into) any
existing systems, popular media and email including. They can also be concealed if to operate in
hostile environments, allowing the latter to be analyzed and impacted in a stealth manner.
3. Distributed Network Management Basics in SGT
3.1. Exemplary Network Creation
We are starting here from scratch, having only elementary communications between different
nodes limited by some threshold physical distance Th, and without any communication infra-
structure between them. Only copies of SGL interpreter are installed in each node. Different stag-
es of the possible network infrastructure creation are shown in Fig. 4, with nodes named a, b, c, d,
e, f, g, and h originally distributed in space as shown in Fig. 4, a.
Figure 4 – Distributed network creation
Let us start in some node, let it be a as in Fig. 4, a, after writing in SGL:
hop(a)
Starting in node a and stepwise reaching all other nodes by hopping to neighboring nodes
within the given maximum allowed distance Th between them, as shown in Fig 4, b (to avoid
looping, the nodes are allowed to be entered first time only):
hop(a);
repeat(hop_first(nodes(all, within(Th))))
Starting in node a and reaching all other nodes as before, but together with creation of
links-channels of type Ln between all neighboring nodes within Th distance, as shown in Fig. 4, c,
can be done by:
hop(a);
repeat(hop_first(nodes(all, within(Th)));
stay(hop(nodes(all, within(Th))); BACK > NAME;
create(link(Ln), node(BACK))))
To avoid competing attempts of establishing same link between two nodes when starting
from them both, we allow doing this only after comparing their names, allowing the node with
higher value to dominate (could be organized vice versa too).
8 ISSN 1028-9763. Математичні машини і системи, 2019, № 1
Figure 5 – Reaching a node from
another node
Figure 6 – Creating any Spanning
Tree from a node to all other nodes
We will finally be having the full network shown in Fig. 4, d with all established links-
channels of the type Ln, as was required.
3.2. Finding and Collecting Any Path between Nodes
Having created the network infrastructure, as above, we
may, starting in some node like a, reach any other needed
node like f, by the following SGL scenario navigating the
network in a wavelike mode, i.e. stepwise and in parallel
(see Fig. 5).
hop(a); repeat(hop_first(links(all));
or((NAME == f; done), stay))
We may additionally decide to collect the passed
path and organize its output at the destination node, as fol-
lows.
hop(a); frontal(Path = NAME);
repeat(hop_first(link(any)); Path &&= NAME;
or((NAME == f; output(Path); done), stay))
Arbitrary path found between these two nodes (may not be optimal like the one in Fig. 5)
is printed in node f like: (a, d, h, f). Such path can also be issued in the starting node a by the
modified scenario:
hop(a); frontal(Path = Name);
output_repeat(hop_first(link(any)); Path &&= NAME;
or(blind(NAME == f; Path), stay))
3.3. A Spanning Tree from a Node to All Other Nodes
In the previous example, we have found any path from a
node to some particular node, which was then issued out-
side the network within the network asynchronous wave-
like navigation. In similar network navigation, we may
create a Spanning Tree (ST) starting in the same node and
covering the whole network, thus explicitly showing pos-
sible paths to all other nodes. This can be easily done with
registering this ST directly in the distributed network
structure, by remembering node predecessor names in a
special variable Up associated with each node, as follows,
see also Fig. 6:
nodal(Up); hop(a);
repeat(hop_first(links(any)); Up = BACK)
Such a tree can directly guide movement from any node, like e, to the starting node a:
hop(e); repeat(hop(Up))
We may also, if needed, collect this passed path in both ways and issue it in node a.
a) If to register from node e to node a, will be having Path (e, c, b, d, a):
hop(e); Path = NAME;
ISSN 1028-9763. Математичні машини і системи, 2019, № 1 9
Figure 7 – Creating Shortest Path
Tree from a node to all other nodes
Figure 8 – Registering in
the SPT root node the
shortest paths to all
other nodes
repeat(hop(Up); Path &&= NAME); output(Path)
b) If to register from node a to node e, will be having Path(a, d, b, c, e):
hop(e); Path = NAME;
repeat(hop(Up); Path = NAME && Path); output(Path)
3.4. Shortest Path Tree (SPT) from a node to all other
nodes
With some modification, we may create and register in-
stead of any Spanning Tree, a Shortest Path Tree (SPT)
from a node to all other nodes, as shown in Fig. 7 (which,
in general, may be one of a number of such SPTs).
The following scenario can accomplish this with
registering the resultant SPT in the network structure
similarly to the ST scenario before. The main difference
will be with re-registering the SPT predecessor nodes in
variables Up in nodes if better (shorter) solutions for
shortest paths to these nodes appear available.
nodal(Dist, Up); hop(a); Dist = 0; frontal(Far);
repeat(hop(links(all)); Far += 1;
or(Dist == nil, Dist > Far); Dist = Far; Up = BACK)
Registering in the starting node of the shortest paths to all
other nodes on the basis of SPT found can be done by the synchro-
nous two-vector structure at the starting node, as shown in Fig. 8
(keeping names of all other nodes in Dest, and corresponding paths
to them in Route):
hop(a); nodal(Dest, Route); hop(all_other);
frontal(Path);
repeat(Path = NAME && Path; hop(Up));
seize(Dest &&= Path[last]; Route &&= unit(Path))
Using collected shortest paths in the SPT root node, any other
node can be conveniently reached, like, for example, node e:
hop(a); frontal(Path) = Route[order(Dest, e)];
repeat(hop(link(any), node(withdraw(Path, 1))))
The path followed from node a to node e will be as:
adce.
For this SPT we can also create routing tables (RT) in all nodes having descendants (i.e. a,
d and c) rather than collecting full shortest paths in a to all other nodes, as before, by the follow-
ing scenario.
hop(a); nodal(Dest, Next); hop(all_other); frontal(Fin) = NAME;
repeat(hop(Up); seize(Dest &&= Fin; Next &&= BACK))
The result is depicted in Fig. 9 where vector Dest keeps names of all destination nodes in
relation to the current node (similar to Fig. 8), and the synchronous to it vector Next provides just
10 ISSN 1028-9763. Математичні машини і системи, 2019, № 1
Figure 9 – Routing tables in nodes
for SPT in Fig. 7
names of next hop nodes towards reaching the needed destinations (rather than full paths to these
destinations, as before).
Movement from the SPT root a to any other node (let it be e again) via the obtained set of
routing tables in nodes can be done by the following scenario:
hop(a); repeat(hop(link(any), node(Next[order(Dest, e)])))
Will be having the same result as before when re-
membered full paths to other nodes in the starting node,
i.e.: adce.
3.5. Creating Routing Tables from All Nodes to All
Other Nodes
The previous routing tables solution related only to the
single SPT providing shortest paths from the root node a to
all other nodes. We can also easily organize in SGL the
finding of routing tables placed in each node and providing
altogether shortest paths from any node to any other nodes,
as follows.
nodal(Dest, Next);
hop(nodes(all)); color(nodal(Dist, Up));
sequence(
(frontal(Far); Dist = 0;
repeat(hop(links(all)); Far += 1;
or(Dist == nil, Dist > Far); Dist = Far; Up = BACK)),
(frontal(Fin = NAME);
repeat(hop(links(all); Up == BACK;
seize(Dest &&= Fin; Next &&= BACK))))
This scenario is based on parallel creation of SPTs from all nodes to all other nodes, with
such SPTs shown in Fig. 10.
Figure 10 – SPTs from all nodes of the network to all other nodes
The obtained routing tables in all network nodes are shown in Fig. 11, with same mean-
ings as in Fig. 9 of synchronized Dest and Next vectors, now present in all nodes.
An example of following SP from any node, say g, to any other node, like e, via the RTs
of Fig. 11, will be by using the same scenario as shown before for a single SPT from node a:
hop(g); repeat(hop(link(any), node(Next[order(Dest, e)])))
ISSN 1028-9763. Математичні машини і системи, 2019, № 1 11
This scenario will follow the path gdc e with using RTs of Fig. 11 in nodes g, d
and c.
Figure 11 – Routing tables providing shortest paths from all nodes to all other nodes of the network
4. Spatial Objects Discovery and Tracking
Distributed sensor networks operating under SGT can catch and follow any moving objects
throughout the whole region despite limitations of individual sensors, as in Fig. 12. The latter
shows some area covered with a network of communicating radar stations, each having SGL in-
terpreter installed, with presumably hostile objects like, say, cruise missiles moving through the
area.
Figure 12 – Distributed objects tracking & destruction
The sensor-radar first seeing a new object (i.e. which is within the given visibility thresh-
old) is becoming the start of distributed operation, after which the object is supposed to be seen
by this radar for some time or may move and be visible by other sensors. Object’s moving & be-
havior history can be collected & updated at each passed radar sensor by the SGT-produced mo-
bile spatial intelligence individually assigned to this particular object and following its electroni-
cally via the radar network, just following its physical move in the real world.
Depending on the collected history, such object may be tried to be destroyed, it may also
happen to be ultimately lost after safely passing through the radar-controlled area. The SGL sce-
nario shown in Fig. 13 will be following the moving object wherever it may go, despite its possi-
ble tricky route, like that of a cruise missile. The scenario can operate with multiple moving ob-
12 ISSN 1028-9763. Математичні машини і системи, 2019, № 1
jects appearing at any time, where each sensor regularly searches for new targets, and each new
target is assigned individual tracking intelligence which can propagates in distributed virtual
space in parallel with other intelligences, following the physical movement of targets.
Figure 13 – SGL scenario for distributed tracing of mobile objects
Some additional explanation of different scenario stages numbered in Fig. 13 may be as
follows.
1. Starting in all sensor nodes in parallel.
2. Describing types and initial values of the spatial variables used, like for the visibility
threshold.
3. Organizing continuous, endless, looping of the remaining stages 4 to 8 whatever their
success or failure.
4. Trying to find a new areal object with sufficient visibility, continuing the remaining
stages 5 to 8 if such an object discovered, otherwise terminating this branch.
5. Making the following sections 6 to 8 mobile and autonomous (which will also be re-
peated as much as needed) from the starting, main scenario body, thus allowing the stage 4 con-
centrate on the search of new objects again.
6. Looping in the current sensor-radar node if the object still has sufficient visibility, col-
lecting its behavior history, and trying to destroy it depending on the history collected, if a deci-
sion is taken.
7. After losing object’s visibility in the current node (thus appearing lower than the
threshold given), contacting all neighboring sensor-nodes in parallel and moving to the neighbor-
ing sensor in which this object’s visibility is highest.
8. Checking the visibility of the same object in the new node, and in case it is lower than
the given threshold, declaring the failure of the object’s observation along with its history collect-
ed, also terminating this scenario branch). Otherwise continue observation of the object from
stage 6 with its sufficient visibility.
The offered organization of tracing and impact of multiple moving objects in distributed
environments by networked sensors with embedded SGL interpreters and virus-like mobile spa-
tial intelligence in SGL, which can collectively operate without any, often vulnerable, central re-
sources, can also be effectively used in many other areas. These may include advanced operations
in outer space related to missile defense or dealing with space debris, tracing international crimi-
ISSN 1028-9763. Математичні машини і системи, 2019, № 1 13
Figure 14 – Finding centres of different communities
Figure 15 – Communities may become too close
to each other
nals, flow of different goods, materials and finances throughout the whole world, as well as con-
trol and management of human migration.
5. Finding Centres of Different
Communities and Analyzing Distances
between Them
Of practical interest may be finding
topographical centres of different social
communities with assessment of physical
distances between them, say, for preventing
possible conflicts as these groupings may
be pursuing quite different even hostile to
each other cultures, religions, traditions,
and principles. In Fig. 14, different
communities are expressed by different types of links between their members (like c1 and c2).
The following scenario outlines such communities and finds their topographical centres
with evaluating and outputting physical distance, or Closeness, between them.
nodal(Center1, Center2, Closeness);
Center1 = average(hop_nodes(all); yes(hop_links(c1)); WHERE);
Center2 = average(hop_nodes(all); yes(hop_links(c2)); WHERE);
Closeness = distance(Center1, Center2);
output(Closeness)
The found value in Closeness
may indicate existence of some unwanted
trends in a multicultural society, say, if
below a certain threshold (another
example, with different Closeness value
shown in Fig. 15), which may need
introduction and application of certain
educational, organizational, or even
security measures.
We can also model possible
spatial mobility of members belonging to
different groupings in time (which may
differ for different communities), with regular finding heir topographical centres and measuring
physical closeness between them, also providing warnings if this distance becomes suspicious.
This can be implemented by extended SGL scenario shown in Fig. 16.
Additional explanation of different, numbered, scenario stages in Fig. 16 may be as fol-
lows.
1. Description of different types of used spatial variables with initial values of some of
them.
2. Organizing three independent parallel branches consisting of: stage 3, stage 4, and stag-
es 5 to 8.
3. Simulating continuous randomized physical movement of individuals belonging to the
first community, with proper delay between iterations.
4. Simulating continuous randomized physical movement of individuals belonging to the
second community, with another delay between repetitions.
5. Organizing endless repetitive operation of the third branch consisting of stages 6 to 8.
14 ISSN 1028-9763. Математичні машини і системи, 2019, № 1
6. Finding topographical centers of all individuals belonging to the first and second com-
munities at different, current moments of time.
7. Calculating and assessing physical distance between the centers found with issuing
warning when their closeness becomes dangerous.
8. Setting certain time delay between repetitive invocations of stages 6 and 7.
Many other tendencies and problems in distributed social systems can be effectively simu-
lated and analyzed with the help of SGT [16].
Figure 16 – SGL scenario for modelling dynamics and evaluating closeness of communities
6. Conclusions
We have briefed the invented and patented high-level networking technology which allows us to
solve complex problems in large distributed environments in parallel and fully distributed mode,
without vulnerable central resources, effectively using unlimited spatial mobility of recursive
control code dynamically matching any systems. Expression in SGL of tracing and investigating
of complexly moving objects by intelligent sensor networks, and simulating and analysing dy-
namics and closeness of different communities in social networks were demonstrated too. The
future plans include investigating more areas for SGT solutions of emergency, crisis, and security
problems, also further development of ideas and implementations expressed in [11, 12], with new
book related to international security based on SGT currently in preparation. SGL scenarios for
solving different networking problems appear to be extremely compact (can even be shorter if
abbreviations for different rules and special constants used, as in previous versions of SGL, see
[14, 15]), which allows us to create and modify solutions for crisis and emergency problems at
runtime, on the fly. The latest technology version can be readily installed by agreement on any
platforms needed.
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|
| id | nasplib_isofts_kiev_ua-123456789-151925 |
| institution | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| issn | 1028-9763 |
| language | English |
| last_indexed | 2025-12-07T13:40:07Z |
| publishDate | 2019 |
| publisher | Інститут проблем математичних машин і систем НАН України |
| record_format | dspace |
| spelling | Sapaty, P.S. 2019-06-01T15:59:19Z 2019-06-01T15:59:19Z 2019 Distributed Management in Crisis and Emergency Situations. Mathematical machines and systems / P.S. Sapaty // Математичні машини і системи. — 2019. — № 1. — С. 3–15. — Бібліогр.: 17 назв. — англ. 1028-9763 https://nasplib.isofts.kiev.ua/handle/123456789/151925 623.764 The purpose of this paper is to analyse and demonstrate possible application of the developed high-level Spatial Grasp Technology, SGT, and its Spatial Grasp Language, SGL, for dealing with different local and global crisis and emergency situations in dynamic distributed environments. Метою даної статті є аналіз і демонстрація можливого застосування розробленої високорівневої Tехнології просторового захоплення (ТПЗ), а також її базової Мови просторового захоплення (МПЗ) для вирішення різних локальних і глобальних завдань, пов’язаних із кризовими та надзвичайними ситуаціями в динамічних розподілених середовищах. Целью данной статьи являются анализ и демонстрация возможного применения разработанной высокоуровневой Tехнологии пространственного захвата (ТПЗ), а также ее базового Языка пространственного захвата (ЯПЗ) для решения различных локальных и глобальных задач, связанных с кризисными и чрезвычайными ситуациями в динамических распределенных средах. en Інститут проблем математичних машин і систем НАН України Математичні машини і системи Обчислювальні системи Distributed Management in Crisis and Emergency Situations. Mathematical machines and systems Распределенное управление в кризисных и чрезвычайных ситуациях Розрахункове управління у кризових і надзвичайних ситуаціях Article published earlier |
| spellingShingle | Distributed Management in Crisis and Emergency Situations. Mathematical machines and systems Sapaty, P.S. Обчислювальні системи |
| title | Distributed Management in Crisis and Emergency Situations. Mathematical machines and systems |
| title_alt | Распределенное управление в кризисных и чрезвычайных ситуациях Розрахункове управління у кризових і надзвичайних ситуаціях |
| title_full | Distributed Management in Crisis and Emergency Situations. Mathematical machines and systems |
| title_fullStr | Distributed Management in Crisis and Emergency Situations. Mathematical machines and systems |
| title_full_unstemmed | Distributed Management in Crisis and Emergency Situations. Mathematical machines and systems |
| title_short | Distributed Management in Crisis and Emergency Situations. Mathematical machines and systems |
| title_sort | distributed management in crisis and emergency situations. mathematical machines and systems |
| topic | Обчислювальні системи |
| topic_facet | Обчислювальні системи |
| url | https://nasplib.isofts.kiev.ua/handle/123456789/151925 |
| work_keys_str_mv | AT sapatyps distributedmanagementincrisisandemergencysituationsmathematicalmachinesandsystems AT sapatyps raspredelennoeupravlenievkrizisnyhičrezvyčainyhsituaciâh AT sapatyps rozrahunkoveupravlínnâukrizovihínadzvičainihsituacíâh |