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12MODELS AND METHODS OF GROWTH OF PRODUCTIVITY OF WIRELESS NETWORKS IN COMPONENTS OF COMPUTERIZED SYSTEMS OF MEASUREMENT
OF MECHANICAL QUANTITIES
Dudnik A.
Ph.D., Associate Professor Associate Professor of the Department of Network and Internet Technologies
Kyiv National Taras Shevchenko University Bondarenko Yu.
post-graduate student of the department of computer multimedia technologies
National Aviation University, Kyiv
Abstract
The article is devoted to models and methods of improving the productivity of wireless sensor networks, which are part of computerized systems for measuring mechanical quantities, based on the decomposition of the lower levels of the reference model OSI. The method of increasing the productivity of networks is proposed, which functionally combines physical and network levels, which improves its efficiency in zones of uncertain reception almost twice. The model of the structural scheme of the device for increasing the quality of data transmission in zones of uncertain reception or with insufficient noise immunity, based on the so-called method of monitoring the quality of communication, is developed.
Keywords: wireless network, mechanical values, sensor, performance, signal, computerized measurement system, information and measuring system.
Introduction. Among the various classes of computer information systems and networks, a special place is occupied by systems and networks whose transport service is based on the use of radio air as a medium for transmitting computerized systems for measuring mechanical quantities (wireless sensor networks). Therefore, when creating the scientific basis for the construction of computerized systems for measuring mechanical magnitude, the performance of wireless sensor networks is important on the basis of the modification of the existing classical reference model of interaction of open systems (OS OS / ISO), according to which the majority of data transfer tools are designed, created and operated information-measuring systems, as well as theoretical analysis and the search for optimal methods for modeling, interaction and management of computerized systems for measuring mechanical quantities N. Issues of the study of information-measuring systems, including the study of technologies for modeling, control and interaction of computerized systems for measuring mechanical quantities, are devoted to the work of modern scientists Klasnikova VP, Ornadsky DP, Os-molovsky AI, as well as works by Heyer D., Irwin J., Lyrier J., Roshan P., Stollings V., Harley D. et al.
Formulation of the problem. The purpose of this study is to analyze and substantiate theoretical foundations, as well as to develop models and methods related to increasing the productivity of wireless networks that are part of computerized systems for measuring mechanical quantities.
Presenting main material. The comparative analysis of the basic existing technologies of transmission of indicators of measurement is carried out. Based on experimental studies, deficiencies and degrees of freedom have been identified to improve control systems, interoperability and increase productivity in existing technologies for wireless data transmission of information and measurement systems. Models of wireless computerized systems for measuring mechanical quantities and peculiarities of modeling transmission of
measurement parameters in a wireless environment are considered [1-5].
The multi-beam nature of the propagation of radio waves, when each point of the space is characterized by the fact that it receives signals from different directions and with different time delays, multiple signals interference and their significant distortion emerge. Such distortions have a significant effect on system characteristics if the delay time exceeds the transmission time of the measurement data packet. That is, in computer systems of wireless communication, as well as for wired systems, the main rule is the main rule according to which the package must reach the endpoint before the last its symbol is transmitted. But in the case of multi-directional wireless signal propagation, this condition is much more difficult [1-5].
The conceptual model of the simple variant of the network of scannet (distributed network with many branches based on Bluetooth technology), which is part of the computerized system of the measuring system, is represented by an open (non-closed) multiphase mass service system. From the point of view of the classification of the reference model, two lower levels were described. Transaction, which is an indivisible object in the system of simulation of general purpose, was generated by a bit that moves to the scatteret from the source of information to the consumer. Each phase was modeled by G/M/n with failures and discipline of FIFO. The universal imitative modeling system provides collection and statistical processing of data transacted, detained at each point of the model, as well as the intensity of the failures. The time delay of bits in the transmission channels (Wqueues) of the given network is determined according to the formula for calculating the delay time in the queue of the multichannel device with expectation:
W
1
N n+1
IP, P
queues N queues
1
I
i=i
(1)
i * c
L
i=1
queues
where X is the intensity of receiving bits transmitted to the i-th state; Lqueues - the average number of bits transmitted and determined by the following formula:
N
n ■ n!(1 p. /n)
1=1
where n is the number of distributed sub-channels of wireless transmission of the Bluetooth network, which in this case is 23 sub-channels; Po - the probability that the sub-channel is currently busy is determined by the formula:
Pc = (1 +
N N
I P. I Pi
i=1
■ +
i=1
2!
+
N
IN Pin ...+j=1—+
N
\ ' „n+1
P,
i=1
n!
N
n!(n -I A,,)
)-1
/ , j i=1
where p is the load on this wireless data transmission network, determined by the formula:
N
I4
P
i=1
ß
(2)
where X is the intensity of the receipt of bits to the data transmission network at that state; ^ is the intensity of service of bits in the data transmission network. Substituting all these values into formula (1), we obtain the following resultant formula:
II4 S 4
II4
^ i=1_^ n+1 ^ i=1 , i=1
ß
+ -
1! 2!
S 4 14
+ ■ ■ ■ + (-^-) n + (—
n!
ß
) n+1)-
S 4
n!(n - -)
W = — ■
nepen
ß
n
Master unit "Master" transmits bits at odd moments of time, and the subordinate device "Slave" - in a guy. This model has a hierarchical structure because the device, which for a particular section of the network is the main, for another site can be subordinate. An experimental study of this model was carried out using the GPSS instrument tool, with 106 runs. From the obtained results it was found that the delay time correlation corresponds to the actual available ratio of network parameters [6].
The model of infrared traffic is considered. The purpose of this study is to construct a one-channel model of wireless transmission of parameters of measurement of the infrared sensor network, the results of which would reflect the time and quantity parameters of the data transmission measurement of mechanical quantities. An imitation model of infrared communication is provided by an open one-channel mass service system. Transaction, which is an indivisible object in the system of simulation of general purpose, is generated by a bit that moves in the infrared network from the source of information to the consumer. The model, according to the classification, is a model, G/M/1 with discipline FIFO. The bait delay time in the transmission channels (Wqueues) of the given system will be determined according to the formula to the formula for
Z 4,
«1(1 - —)2 /±
n
calculating the delay time in the queue of the singlechannel device with expectation [7-10]:
L„
W
queues
queues N
(3)
I K( 1 - Pn)
i=1
where Lqueues - The average number of bits to be transmitted is determined by the following formula:
N
Lqueues 1\ nPn i=1
where Pn is the probability that the infrared data channel is n bits and is determined by the formula:
N
Pn = Pc 1 Pn
i=1
Po - the probability that the sub-channel is currently busy is determined by the formula:
N
1 -I A
i=1
N (n+1)
1 -I p"
i=1
where p is the load on the wireless data network, determined by the formula (2). Substituting these values into formula (3), we obtain the following resultant formula:
N
I4
i=1
N
I
n=0
n
M
N
14
N
14
-(—)n
M
W =
uepeu
1 _ (j=i_)(N+i)
M
N
I4 (1 _ Pn )
i=1
As a result of the simulation, the parameters of the network operation were received, in particular the delay in the queues of the transmission channels of both devices, which corresponds to the bandwidth of the transmission channel in the case when the load on this channel is maximal [6]. The standards of a network of type IEEE 802.11 are investigated, using one or another discipline of queues, which is given by using one of the queue management algorithms. The main task of this research is the simulation of a multichannel wireless sensor network measuring data transmission, using different queuing algorithms. Definition in practice of an optimal data transfer control algorithm, by conducting comparative characteristics. The algorithms used for modeling are taken arbitrarily [7-10].
The queue management algorithms are required to operate in times of temporary overloads with the maximum loading of transmission channels of a wireless network, which is part of a computerized system for measuring mechanical quantities, if the network device can not cope with the transmission of bits to the source interface at the rate at which they enter . If the cause of the overload is the inadequate performance of the processor unit of the wireless network device, then the raw bits temporarily accumulate in the input queue of the corresponding input interface. There may be several queues up to the input interface if different service requests differ in several classes. In the same case, when
the reason for the overload is the limited throughput of the source interface, bits are temporarily stored in the initial queue (or queues) of this interface. Conceptual models of networks using different queuing algorithms in WI-FI networks are represented by an open multiphase mass service system. Each phase was modeled by SMO G\M\n constructed according to the conditions of one or another queuing algorithm. The resulting formula for determining the time delay of bits in the transmission channels (Wqueues) of this network is formula 1 because the model of this network is also presented as a multichannel QoS with expectations. The model consists of measuring sensors and a wireless access point. Let's describe the operation of this system: the bits coming from the sensor transmitting the measurement data become in the queue for maintenance at the access point. Next at the access point, they are in turn redistributed to the workstations that are their destination. They then turn in the queue for processing to the desired destination [7-10]. 3 models of the corresponding structure were considered in which 3 different traffic control algorithms were used in various IEEE 802.11 standards, namely "FIFO", "Priority service", "Weighted queues". A study was conducted to compare the characteristics between these algorithms according to the bandwidth criteria of transmission channels, the maximum rate of transmission of indicators, the transmission rate of indicators, and the number of subchannels. After a series of comparative studies, the Analystc hierarchy process (AHP) decision-making procedure was followed by the following aggregate coefficients: the FIFO algorithm 0.228, the "priority service" algorithm 0.222, the algorithm " Weighted queues "- 0.55. As a result of these comparisons, it was investigated that the "weighted queue" algorithm is twice as good as 2 others. Studies have been conducted that showed that the results obtained using the G/M/n model for modeling transmission of sensor measurements by sensor networks are the most accurate among other models.
The model of sensory network at the conceptual level is explored. Based on the simulation results of this network at the standard band of the Bluetooth (2400 -2483.5 MHz ISM band), the formula (1) obtained the characteristic in Fig. 1 [7-10].
0,00002 0,00003 0,00004 0,00005
Fig. 1. The graph of the dependence of the bandwidth of the transmission channel on the processing delay time
The standard frequency band was expanded by 80 MHz, and by the formula (1), the characteristic that is shown in Fig. 2
0,00002 0,00003 0,00004 0,00005
Fig. 2. Graph of dependence of the bandwidth of the canal of transmission from the delay time to processing
with the bandwidth expansion
As a result, it is concluded that the bandwidth expansion, albeit at the same time, aggravated the rapid transmission of metering indicators by the Bluetooth touch-sensitive network, but significantly improves other parameters, including bandwidth. The infrared sensor network is investigated. The transmission of
measurement indicators, in this case, will take place from one measuring device to another. The transmission channel bandwidth dependence (C) is investigated from the delay time for processing in the feeder channel, shown in Fig. 3, was obtained by the formula (3) [7-10].
0,0002 0,0003 0,0004 0,0005
Fig. 3. Flow Bandwidth Schedule Depending on the Processing Delay Time on the Channel Servitor.
On this graph there is a linear increase in the load. That testifies to the stable behavior of servicing devices in the modeling of stationary processes using models of
type G/M/1. The algorithm of the functional association of the lower levels of the reference OSI model is
proposed.
Fig. 4 Block diagram of the signal quality status analysis algorithm
This technology was applied to research networks IEEE 802.11 using the three above-mentioned algorithms. This made it possible to obtain the following results, using formula 5, shown in Fig. 6, 7
The algorithm is based on controlling the bits of the physical level frame. It is designed for the purpose of obtaining the required information from frame fields that contain information about the data medium. In this
method, the network level sends queries for the physical state of the signal at the given time at certain intervals. Information about the status of the signal is contained in the field in which the first bits contain information about the speed of transmission, and others about the state of the signal [7-10]. Typically, each of the above states of the signal corresponds to a number from 0 to 6. Based on the data obtained, the network level generates an idea of changing the states of communication, constantly comparing the current and the previous state (n <n0 or n > n0). In case when the state change corresponds to n <n0, the network level sends a re-request. This cycle will take place until the situation becomes the opposite (n>n0) (the branch is "yes").
Only then will the network level send to the channel package and will order an order for its transfer. Then the channel level, in the presence of a free channel, will order the generation of bits to the physical layer, according to the specific package [7-10].
The features of this algorithm were taken into account in the structural scheme of a wireless data transmission device line that would serve as a communication function between applications of the physical and network levels of the reference OSI model. That is, it would work on the basis of this algorithm. It is for this reason that the signal analyzer quality of 5 rice is entered into the existing structural scheme of the wireless communication device. 5
Fig. 5. Wireless network device with system for improving the quality of transmission of metering indicators in zones of uncertain reception or with insufficient immunity
When constructing the device, the blocks are divided into modules according to their belonging to one or another level of the reference model. The device includes a control unit 1 that is part of the NMS, a network layer level 2 module, a sublayer of the level 3 of the model level 3, a host interface of 3.1, a built-in microcontroller 3.2, an application unit of the receiver /
C 0.000078 0.000077 0.000077 0.000076 0.000076 0.000075 0.00007S 0.000074 0.000074
12100
transmitter 3.3, a bus interface unit 3.4, MAC sub-level MAC channel level 4, frequency band controller 4.1, radio frequency receiver/transmitter 4.2, signal analyzer 5, physical level 6 module, physical level interface 6.1, antenna 6.2, automatic frequency setting unit 7.
Xo I
12200
12300
12400
12SOO
K6iT.c 12600
Fig. 6. Chart of the delay time dependence on the load of the transmission channel
The saturation point X0 is determined by the formula:
X 0 =
1
VdRd
where Xo - the largest load for this network;
Vd - visit factor for node d;
Rd is the time the bits are hosted for the node d.
The transfer rate of measurement metrics used in simulation was consistent with Peak Cell Rate (PCR) -the maximum data transfer rate is 54 Mbps.
In the wireless transmission channels of this network, the redistribution of flows will occur with the use of the queuing algorithm "Weighted queues". That is, the distribution of bits of information in the transmission channels will be in accordance with the percentage
of bandwidth given to this class of traffic. The percentage of bandwidth allocated to one or another class of traffic according to its priority.
C 0.000078 0.000077 0.000077 0.000076 0.000076 0.000075 0.000075 0.000074 0.000074
30400 30600
The experiment was carried out in accordance with the above-mentioned algorithm. On the basis of research data, by the formula (1), the characteristic
given in Fig. 6
_______
____ -Xo
_
V
/
1 zd
30800
31000
31200 31400
K6iTC 31600
Fig. 7. Chart of the delay time dependence on the load of the transmission channel
As shown in the graph, this algorithm introduces quite a few positive changes to the wireless sensor network. This is evident from the fact that with a significant increase in the load in the transmission channels, the delay time for bits in the queue for processing almost did not change. That is, these algorithms more than triple the performance of the network. It is likely that such an improvement is due to a reduction in the percentage of false packets and requests for their retransmission, which also require a certain amount of time to process, as well as the transition to a standard with greater power when the signal level falls, which definitely improves the stability of the touch network, significantly reducing the probability lack of signal in areas of uncertain reception.
Conclusions
The main feature of the methods for controlling the transmission of wireless data-measuring systems data is the limited capacity of the system and the unrestricted impact of interference on the environment of data transmission from third-party sources emitting ultrahigh frequencies. The network bandwidth may be reduced to 90% compared to a similar case in cable networks.
Simulation of algorithms for managing queues of wireless ips has revealed that using the "weighted queues" algorithm, the overall bandwidth of 9 mbps is better than using other algorithms.
The following solutions have been found to improve the quantitative and qualitative indices of wireless transmission of metrics for measuring mechanical quantities: expanding the frequency range of a short-circuit network allows to improve network throughput by almost 50%; the method of increasing productivity for wireless sensor networks is proposed, which functionally combines the physical and network levels, which can improve the bandwidth of the network in areas of uncertain reception almost twice; the algorithm of the balanced maintenance of applications for traffic of wireless computerized systems of measurement of
mechanical quantities is the most efficient technology of data transmission in wireless sensory networks, provided the level of inaccuracy of the initial data is 0.9 and on its basis the method "redistribution of bandwidth of the transmission channel of the sensor network" which improves service discipline in the queue, which will be described in subsequent work.
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РАСПОЗНАВАНИЕ СИГНАЛОВ ПРИ ПРОВЕДЕНИИ РАДИОМОНИТОРИНГА В КОГНИТИВНЫХ РАДИОСЕТЯХ
Иваненко С.А.
Харьковский национальный университет радиоэлектроники, ассистент
Безрук В.М.
Харьковский национальный университет радиоэлектроники, д.т.н. профессор
г. Харьков, Украина
SIGNAL RECOGNITION WHEN CONDUCTING RADIO MONITORING IN COGNITIVE RADIO NETWORKS
Ivanenko S.A.
Kharkiv National University of Radio Electronics, Assistant of Professor
Bezruk V.M.
Kharkiv National University of Radio Electronics, Professor
Kharkiv, Ukraine
АННОТАЦИЯ
В работе исследуется задача распознавания заданных сигналов при наличии класса неизвестных сигналов, которая возникает в процессе проведения радиомониторинга в когнитивных радиосетях. Решение данной задачи может быть необходимым при определении принадлежности обнаруживаемого излучения к классу вторичных или первичных пользователей или при определении новых, ранее неизвестных излучений. Следует отметить, что данная процедура обработки может быть совмещена с радиоконтролем в частотном диапазоне, который выполняется органами частотного регулирования. При этом следует учесть, что в процессе обработки могут присутствовать неизвестные сигналы, которых отсутствуют в базе данных когнитивной радиосети. Такие сигналы могут поступать на распознавание и приводить к ошибочному отнесению их к классу заданных известных сигналов. В настоящее время вопросы распознавания сигналов в когнитивных радиосетях еще не получило достаточного развития.
ABSTRACT
This article considers the problem of signal recognition during radio monitoring in cognitive radio networks. The solution of this problem may be necessary in determining the belonging of the detected signal to the class of secondary or primary users or in determining the appearance of new sources of radiation, which was unknown previously. It should be noted that this procedure can be combined with radio frequency control, which is currently performed by local frequency control authorities. It should be taken into account the fact that the air may be signals that are not in the database of cognitive radio network management. These signals can get to recognize what may cause the error of assigning this signal to a class of known signals.
Note that at this time, recognition in cognitive radio networks has not received sufficient development yet.
Ключевые слова: обнаружение сигналов, распознавание сигналов, радиомониторинг, когнитивное радио, неизвестные сигналы.
Keywords: signal detection, signal recognition, radio monitoring, cognitive radio, unknown signals.
