opportunities to determine the effectiveness of …wireless sensor network. the design and...

International Journal “Information Theories and Applications”, Vol. 25, Number 3, © 2018

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OPPORTUNITIES TO DETERMINE THE EFFECTIVENESS OF SENSOR NETWORKS

Filip Tsvetanov, Ivanka Georgieva

Abstract: The actuality of WSN determined by the concept of intellectualization of various objects

including homes, offices, buildings, industrial processes, etc. Sensor networks are distributed networks

built of tiny sensor nodes exchanging information over wireless channels with the ability to record data

on all parameters that is been asked to monitor and transmit measurement data to a base station or

nodes to retransmit signals. The design and deployment of sensor networks require solving a variety of

complex problems that are specific to each sensor network. Therefore, sensor networks have

requirements for effectiveness in the implementation of the objectives in their design. Lead research

shows that currently there is no method for comprehensive evaluation of the efficiency of sensor

networks. In this paper is proposed an algorithm for determining the effectiveness of the sensor network

according to specific criteria

Keywords: sensor networks, effectiveness, algorithms, sensor notes.

ITHEA Keywords: C.2.Computer Communication networks, C.2.1.Network architecture and design.

Introduction

Sensor networks is distributed, self-organizing systems of miniature autonomous wireless sensing

nodes united by a radio channel and which can connect to global computer networks. The area of

sensor network coverage can range from several meters to several kilometers, thanks to the ability to

retransmit messages from one node to another. Sensors measure and transmit data on temperature,

pressure, humidity, light, vibration, and more. The choice of sensors determines the functionality of the

wireless sensor network. The design and deployment of sensor networks require solving a variety of

complex problems that are specific to each sensor network. For this reason, sensory networks are

subject to performance requirements in terms of achieving the objectives set when designing them.

The effectiveness of the sensor network is the ability to fulfill a particular purpose under certain

conditions and with a certain quality. Efficiency metrics characterize the adaptability of the sensor

network to perform the set tasks and are a summary indicator of the optimal functioning of the network.

The conducted literature study shows that the performance of sensor networks is an ongoing research

problem that has come to the attention of many researchers, both from the academic community and


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the industry. The current research focused on determining the performance of basic single parameters

of sensor networks. Comprehensive evaluation of the sensory network performance recommended by

the expert judgment method.

Summary of Contributions. In the present work, we propose one algorithm for determining the efficiency

of sensor networks by collective parameters. The algorithm involves developing a model of the sensed

sensor network, depending on the formulated collective performance parameters.

Characteristics of sensor networks, influencing efficiency:

1) Connected to the design features of the sensors

Sensor nodes have resource constraints: limited energy, limited communication and computational

capabilities, and limited memory. The choice of the sensor determines the functionality of WSN. The

wireless sensors (WS) used in the network are a set of the sensors or sensors for different quantities,

an interface module, a control unit (Microcontroller, Microcontroller Unit, Processor), Memory and T / R

Transceiver (Figure 1).

Figure 1. The architecture of wireless sensor

For successful connection in WSN, it is necessary for WS to have a small DC consumption, for which it

is first necessary to use appropriate integrated circuits in the blocks. In addition, WS will only be on a

regular basis for a short period to carry out the measurement and store the data in its memory as well

Sensor

Adjustment and configuration

Control Functionals

Signal conditioningand Driver

Analog Gain

Amplifier

Interface module

DSPADC

Procesor

Display Memory

Communications

Battery

Power Converter

Power unit

Algorithm

Operating system


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as broadcast it. The sensor is in the Sleep Mode for the rest of the time, where it consumes a minimum

electricity. It is the time of inclusion in a call (Immediancy) does not exceed a few ms.

2) Associated with the choice of communication range and frequency of measurement

When selecting a WSN range for a particular application, it should be borne in mind that lower

frequency operation is suitable for premises with large mobile and stationary objects (machines, tanks,

vehicles, etc.) and in the presence of dirt in the air with fine particles. At the same time, the use of higher

frequency bands means remoteness from industrial interference and therefore the ability to operate with

lower radiated power and lower DC consumption. Depending on the measurement frequency, WS

operates with Periodic Sampling or Even-Driven, which done only when the controlled value passes a

certain value.

3) Associated with the topology of the sensor network

The simplest type of networks is Star Network. Their name followed by spatial location (Fig. 2a) of the

end nodes (End Point, Edge Node, Node, Device) D about the control node (Gateway, Base Station,

Bridge, Controller, PAN Coordinator) C. The latter is a specialized data exchange node, but it can also

be a personal or pocket PC (PDA). By managing the operation of D, exchanging sensor data and

relaying the collected data to other networks. For star networks, the Single-Hop System - Sensor data

transferred to the receiver with a single "jump", the structure of the Point-to-Multipoint type. The main

advantage of these networks is that they have the smallest DC consumption of all WSNs.

The most common are the WSN Mesh Network. Their structure is of the Multipoint-to-Multipoint type

(Figure 2.b). They also have a control node C, and the remaining nodes in the network are end-R, in

addition to working as sensors, can be and routers (Router, Mesh Node, Coordinator), by exchanging

data through C and to each other. This means that the connection between two R-type nodes can be

immediate, but much more often goes through other nodes. Thus, the data requires several "jump" from

node to node until they reach the coordinator. This indicates that this type of network is a Multi-Hop

System. In addition, several routes between two nodes are possible, and the network programming

selects the shortest of them. In the case of a damaged R or major interference on the nearest router,

another router is automatically been chosen, which is why they are self-healing networks (Self-Repairing

Network). When switching WS on and off, and in the presence of moving WS in it (as in GSM networks),

it also changes routes, which makes it a self-configuring network. Self-organization allows the network

to automatically recognize and activate each new node.


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D

D D

D

DD

C

a)

R

R

R R

R

R

C

b)

R

R

RR

R

R

C

D

D

D

D

D

D

c)

Figure 2.Topology of wireless sensor networks


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The third type of network structures of WSN is Point-to-Point-to-Point or Peer-to-Peer. Unlike the Star

WSN, where the entire control is concentrated in the C-node, the control distributed between all nodes

and therefore called distributed (Distributed Control). The P2P networks cover a significantly larger area

(theoretically unlimited) than the star type. Each sensor network, regardless of which type has only one

control device (coordinator). The main task of the coordinator is to set parameters and create a network,

to select the basic radio channel according to the unique network identifier. The coordinator is the most

complex device in the network, has the most memory and great power consumption compared to other

network devices. Routers used to extend network coverage because they are capable of performing

repeater functions between devices located away from each other. Another feature of sensor networks

in studying their effectiveness is the number of main routers (the main hop note or routers). By this

criterion, they can divided into two groups: a single hop node - a powerful sensor with a transmitter for

transmitting the signal a base station and multiple hop nodes, but also to collect data from other nodes.

Related work:

[Tuzhilkin et al, 2012] proposes an approach for complex multi-criterion assessment of the sensory

network efficiency through the expert judgment method. In this case, determining the performance of an

entire sensor network is a complex multi-criterion task, which is very important to prioritize the indicators

(sort by importance). These studies used in cases where the requested information cannot obtained

experimentally. The effectiveness of individual parameters such as loss tolerance investigated by a

stimulation study method with the Cooja simulator packages from [Vasco, 2016].

In Diwa et al., 2017, an effective approach is proposed through an individual parameter to increase the

energy efficiency of WSM, resulting in research suggesting sensor clustering algorithms that prolong

network life by avoiding node communication with a base station over long distances.

[Tsvetanov et.al. 2014] proposed model allowed assessing the effectiveness of the communication

between nodes in a WSN based on Dijkstra's algorithm and the algorithm of efficiency. Simulation

studies conducted in various network parameters. Based on the interpretation of the results we make

the following summary:

[Chiasserini et al., 2004] has developed a Mark Sensor Network Model to check the system's

performance in terms of power consumption, network capacity and data delivery delay.


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Sensor Network Performance Metrics:

Despite the high requirements in terms of operation, each WSM must possess a number of functional

metrics that must followed in order to solve the task. We believe that an important condition for obtaining

a reasonably efficient operation of the sensor networks is correctly and accurately define the parameters

for the respective network. The performance metric is a quantitative feature of the network considered in

relation to certain conditions of its operation. Each wireless network has an individual set of parameters

defined by the location of the devices and their technical characteristics, network-operating conditions

(terrain, meteorological condition, etc.). In order to achieve this purpose, we consider the classification

of metrics as Individual and Collective [Vasco, 2016] as closest to our understanding.

Individual metrics refer only to one node and do not give any idea of the behavior of the network as a

whole. Individual metrics are essential when removing errors in a specific sensor or evaluating its

specific results. In addition, constant tracking of individual metrics, including computation and

transmission, leads to depletion the energy of the wireless nodes and reduced their life.

When calculating metrics based on collective parameters, a pre-defined part of the network or even the

entire network is included. For example, a data collection request for collective packet loss will be the

sum of the losses of individual bundles from all sensors that send data associated with a specific event.

Collective delay of the difference between the moment of receiving the data and the time when the last

event packet from all target nodes arrived at the coordinator. Collective parameters for network

performance research were discussed in [Vasco, 2016] and [Sohraby, 2007].

We believe that since WSN are different from traditional communication networks, and therefore

different performance measures may require evaluate them. Therefore, the collective parameters

evaluating the performance of the network as a whole must include all the necessary requirements that

affect the life of the network, the speed, quality and efficiency of packets traveling thru the network.

These are parameters such as:

― Delay Tolerance - defines the time limits for the delay of packet delivery in a network;

― Loss Tolerance - defines limitations on data transmission losses on the network;

― Capacity - measures the total network capacity for data transmission;

― Reliability - the ratio of successfully received packets over the total number of packets

transmitted;

― Energy Efficiency – the number of packets that can be transmitted successfully using a unit of

energy;

― Criticality - determines how the network deals with traffic priorities;


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― Fault Tolerance of the error - determines the tolerance of the network to permanent or

temporary node failure.

― System lifetime- can defined in several ways: (a) the duration of time until some node depletes

all its energy, or (b) the duration of time until the QoS of applications cannot guaranteed; or (c)

the duration of time until the network has disjoined.

― Coverage- defined as the ratio of the monitored space to the entire space.

Modeling of sensor networks

The performance of a sensor network should considered at the design stage of the network. An

important stage in the design of sensor networks is the development of models, depending on the

objectives set. Formulated above metrics to determine the parameters for collective evaluation of the

effectiveness can applied successfully in the development of WSN. Depending on the objective and the

expected effectiveness, it recommended developing the following models [Sohraby, 2007], [Tsvetanov,

2013]:

― Traffic model - greatly influences protocol design and affects performance. The four models and

the related performance traffic patterns in WSN, event-based delivery, continuous delivery,

query-based delivery, and hybrid delivery.

― Energy models, radiocommunication function of the sensors is the most energy-intensive

function in the node. Including Model for Sensing, Model for Communication, Model for

Computation.

― Node Model to save energy, a common approach is to allow the nodes to sleep when they do

not need transmission or receipt. This behavior accepted to model in two states of the sensors:

active (A) and sleep (S).

― Network Models - The access to the data transmission channel is controlled and distributed by

MAC protocols, as in a decentralized environment a packet collision may occur in the channel,

which is why it is important that the data be successfully transmitted over time transmitted

successfully in a time slot (routing Model). The network model used to model the routing policy

and determine the average bit rate between nodes. This routing policy determines when to

transmit data to the next hop, for which purpose the sensor node selects the one with a single

node that will lead to the lowest power consumption.

― Interference model, to calculate a successful transmission probability in the time slot, for

example in a CSMA / CA mechanism with handshaking.


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― Closed loop - system model including the sensor node model, the MAC protocol model, and the

routing model. This model provides a comprehensive assessment of the effectiveness of WSN.

An algorithm for determining the performance of sensor networks by collective parameters:

Based on the acquired own experience of research on the effectiveness of individual parameters of

sensor networks [Tsvetanov, Radev 2013], [Tsvetanov, 2013], [Tsvetanov, 2014], research into literary

sources and the understanding that WSN modeling is a useful tool for evaluating WSN performance.

The authors offer a scientifically grounded approach to determining sensor network performance

through collective parameters including the following steps:

First Step. Analysis of the assigned task and purpose of the sensor network and determining the

type of efficiency.

The efficiency of a wireless network is critical to its operation and assess the quality of

communication between nodes in the network. It is known that the energy for communication between

nodes is large, which is why the method of data transmission between nodes is essential to network

performance. Let's look at wireless network with N nodes, which is modeled as a graph G = (V = {1, ...

N}), with N peak of Count, where in the 2D space each peak i is identified by a set of coordinates pi =

(xi, yi ). It is assumed that Euclidean length di,j between two peaks i, j ∈ V is the physical distance

between nodes, which is represented as a matrix E. It assumed that the global efficiency and local

wireless network could quantified. It assumed that global and local efficiency indicators normalized on a

scale from zero to one. Zero has the lowest efficiency, and with one the highest efficiency of the sensor

network. [Tsvetanov. F.]. To achieve the objectives of the task, we assume that the global network

performance needs to be determined. The global or overall efficiency of a WSN from an assessment of

the Eglob communication can be determined according to the dependency:

Е = 1− 1 1 , (1)

Where, dij is the Euclidean length between the pairs of nodes i и j, located within radio

communication range.

Step Two. Choosing a model for sensor networks

Depending on the purpose of the sensor network, it is necessary to determine the type of model,

under section "Modeling of sensor network." For example, if the claim is a requirement for the


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effectiveness of the communication between the sensors nodes can be designed energy model that

takes into account all of the specific parameters for the conduct of the process, the hardware

characteristics by power consumption per node:

Е , = Е + + + + , (2)

Where Еrx it is the energy of receiving data, Etx is the energy to send data, Elisten is the energy to

listen to the channel, Eedle It is energy in standby mode for data transmission, Esleep is the energy of

sleep on a device.

To measure energy consumption and generate residual energy in a sensor, determine the

duration Δt between two time intervals t1 and t2 between which the energy consumption of each sensor

is measured

∆ = − (3)

It assumed that the remaining battery power of each node defined as Е , = , − ∆ − Е , ∆ , (4)

Where ∆ = ∆ . (5)

Step Three. Determine the type of performance metrics for each model. Metrics must be

measurable. Analytical determination of the selected metrics and determination of their limit values and

loading in the survey model. All simulation parameters of the sensor network are included as: network

area in meters in the two-dimensional space on the axes x and y; total number of nodes; position control

of the control node, communication range of the devices; initialization power values for reception and

data transmission, sleep, idling of network devices, topology modeling, etc.


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Step Four. A simulation program is developed and simulations conducted in different network

exploitation scenarios. In these studies can get a visual idea of working capacity of the network (Figure

3) energy resources of each of the sensors in the network (Figure 4.) to determine the global efficiency.

Figure 3. Simulation scenario for the Study of sensor network

Figure 4. Sensors energy resources depending on their communication


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The global efficiency increased with increasing the range of the radio node, due to a reduction in the

number of intermediate transfers of data, the number of jumps and reduced length of the route;

Figure 5. The efficiency of communication at N = 50, depending on the radio communication

range and the number of connections

Global efficiency is higher in networks having more connections between the devices, which may be

due to allowing a higher number of alternate routes to provide an energy efficient communication

between any two units in the network.

Step Five. Analysis of Research Results. If the effectiveness of the research parameter or

collective parameters is satisfactory, proceed to practical implementation and network construction.

If it is not satisfactory, proceed to the selection of step two to determine additional parameters.


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Conclusion

The presented approach for determining the performance of sensor networks is the result of several

years of authors' work. The paper analyzes the constructional and technological characteristics of the

sensor networks that influence their efficiency, as well as the indicators that are important for obtaining

and evaluating their effectiveness. A summary algorithm for assessing the performance of sensor

networks proposed based on the authors' experience in studying the individual sensor network

parameters and understanding that the collective parameters give a complete indication of the

effectiveness of the designed sensor network. The algorithm for determining network efficiency includes

an in-depth analysis of the objective and possible approaches to solving it. An important challenge for

getting a science-based response is the right choice of model and chosen modeling technology.

Bibliography

[Diwa et al, 2017], Diwa Кumari Sinha, Er. Varsha Mishra, Er. Harish Patidar, Node Probability based

Clustering Approach for Energy Efficient Routing in Wireless Sensor Network, International Journal of

Computer Trends and Technology (IJCTT) – Volume 48 Number 2 June 2017, ISSN: 2231-2803

http://www.ijcttjournal.org, р.р.71-77

[Tsvetanov et al. 2014], Tsvetanov F., Tsvetanov K., Ivanova E., Georgieva I., (2014), Modelling and

Simulation of Communication Efficiency in Low-Speed Networks, Proceedings of the International

conference, TELFOR’2014 р.р. 178-182, ISBN 978-1-4799-6190-0, DOI:

10.1109/TELFOR.2014.7034384

[Tuzhilkin et al, 2012], O.V. Tuzhilkin, N.S. Ulianin, Methods of evaluating the performance of wireless

sensor network, “Izvestiya SFEDU, Engineering sciences”, 5/2012, p.p 28-32

[Vasco, 2016], Vasco Nuno Sousa Simões Pereira, Performance Measurement in Wireless Sensor

Networks, University of Coimbra, Doctoral thesis, 2016

[Мочалов, 2009], В. А. Мочалов, Алгоритмы оценки надежности структуры сенсорной сети,

Информационно управляющие системы, № 5, 2009, р.р. 61-66

[Chiasserini et al, 2004], C.F. Chiasserini and M. Garetto, Modeling the Performance of Wireless Sensor

Networks, IEEE INFOCOM 2004, http://infocom2004.ieee-infocom.org/Papers/06_1.PDF

[Sohraby, 2007], Kazem Sohraby, Daniel Minoli, Taieb Znati, Wireless sensor networks Technology,

Protocols, and Applications, John Wiley & Sons, Inc., Hoboken, New Jersey, ISBN 978-0-471-74300-2


267

[Цветанов, Радев 2013], Цветанов Ф, Радев Д, (2013), Алгоритъм за времето на достъп до канала

за връзка на нискоскоростни комуникационни мрежи, Научна конференция-РУ&СУ'13, секция

Комуникационна техника и технологии, Русе, р.р. 67-72. Google Scholar

[Цветанов, 2013], Цветанов Ф., Кипрова Л., Георгиева И., (2013), Алгоритми за подобряване

достъпа до каналите в нискоскоростни безжични мрежи, (2013), Списание, Известие на съюза на

учените – Русе, Серия "Технически науки" ISSN 1311-106X. Google Scholar

Tsvetanov F., Georgieva I., Radev D. (2013), „Modeling of Low-Rate Sensor Network Access“,

Telecommunications forum TELFOR. 21st, 2013, Belgrade, p.p.74-78

[Tsvetanov, 2014 tesis], Tsvetanov F., 2014, Investigation of the low-speed communication networks,

Phd tesis, Ruse University "Angel Kanchev".

Authors' Information

Filip Tsvetanov – South-West University, Blagoevgrad, Bulgaria, Assoc. Prof. of

Communication and Computer Engineering: e-mail: [email protected]

Major Fields of Scientific Research: Sensors networks, networks security,

efficiency low speed networks.

Ivanka Georgieva – South –West University, Blagoevgrad, Bulgaria, Assoc. Prof.

of Department of electrotechnic, electronic and automation, e-mail:

[email protected]

Major Fields of Scientific Research: Sensors networks, industrial networks, the

efficiency of networks, industrial application of Internet of Thinks.

opportunities to determine the effectiveness of …wireless sensor network. the design and...

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