Application of Fuzzy Inference Systems in the Transmission of

Wireless Sensor Networks

Pedro Henrique Gouvea Coelho, J. F. M. do Amaral, N. N. de Almeida, A. C. X. S. Fonseca

and K. P. Guimarães

State Univ. of Rio de Janeiro, FEN/DETEL, R. S. Francisco Xavier, 524/Sala 5001E, Maracanã, RJ, 20550-900,Brazil

Keywords: Fuzzy Logic Systems, Artificial Intelligence Applications, Node Routing, Wireless Sensor Networks.

Abstract: The purpose of this paper is to apply fuzzy logic techniques for determining the data transmission path in

wireless sensor networks. Wireless sensor networks are used in many applications and require efficient

operation with speedy transmission of information and long lifespan. For suitable sensor transmission, a

fuzzy system is defined considering as the goal to minimize the information transmission distance and

maximize the battery lifespan of the network routers. Case studies simulations were carried out and the

results indicate satisfactory performance of the method.

1 INTRODUCTION

Industrial wireless sensor networks are an emerging

class of wireless sensor networks that faces speciﬁc

constraints linked to the peculiarities of the

industrial production. (Akyildiz et al., 2002).

The main advantages of wireless networks are

reduction of devices installation time, no need for

cabling structure, savings in the cost of projects and

infrastructure, flexibility of devices configuration,

savings in assembly costs, flexibility in changing

architectures and the possibility of installing sensors

in places that are difficult to reach.

However, industrial wireless sensor networks face

several challenges such as the reliability and

robustness in harsh environments, as well as the

ability to properly execute and achieve the goal in

parallel with all the other industrial processes.

Besides, industrial wireless sensor networks

solutions should be versatile, simple to use and

install, long lifespan and low-cost devices – as a

matter of fact, the combination of requirements hard

to meet (Matin, 2012).

The deployment and the setup of wireless sensor are

overwhelmingly challenging tasks that become even

more challenging in industrial applications. The

environment where industrial wireless sensor

networks are deployed is extremely dynamic, it can

depend on the speciﬁc product, the phase of life of

the product and the kind of service provision

considered. Indeed, each kind of product or phase of

life has different requirements and imposes on the

monitoring system different constraints. One of the

challenges to face is the impact of the propagation

environment. When the industrial wireless sensor

network is deployed inside a factory to assess the

production process quality, one has to tackle the

interference and the radio environment produced by

the production machines. For that matter, such

network has to be deployed and calibrated not only

to guarantee the correct assessment of the production

process, but above all not to interfere with the

production process. The same logic holds for

networks used to monitor electricity, water and gas

consumption (Kumari and Prachi, 2015).

The environment in which sensors will be deployed

needs to be considered in order to find the optimal

locations for sensors. Operation lifetime, due the

power management policy, is one of the key issues

in all the wireless sensor network applications,

including industrial applications. Many industrial

wireless sensor networks applications, particularly in

the ﬁeld of environmental monitoring, require the

autonomous power supply from alternative power

sources, such as wind or solar power. Even though it

is possible to have a constant power supply in some

industrial environments, sensors tend to be battery

powered in order to keep the monitoring non-

intrusive. But, in most cases, batteries are not

expected to be reloaded or changed. So, energy

618

Coelho, P., Amaral, J., Almeida, N., Fonseca, A. and Guimarães, K.

Application of Fuzzy Inference Systems in the Transmission of Wireless Sensor Networks.

DOI: 10.5220/0006338406180624

In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 1, pages 618-624

ISBN: 978-989-758-247-9

should be saved. There are many ways to obtain that,

both in software and hardware.

The hardware solution requires to carefully choosing

the components. These latter should be low energy

consuming while providing the needed capacity. In

some particular applications, energy harvesting

modules can be envisioned, like solar cells or

kinematic sensors, etc., but their usage is still

marginal.

The software solution focuses energy preservation

by controlling the number of messages to be sent

and the range. As a matter of fact, radio activities,

such as sending and receiving data are the activities

that consume more energy in wireless sensor

networks compared with processing and sensing

activities. Therefore, it is of paramount importance

to monitor carefully the amount of data to send and

the frequency at which it is sent, i.e. the number and

size of messages, while preserving the quality of

service expected by the application. Similarly, the

further the messages are sent the more the energy

needed and the more the interferences generated.

Thus, it is important to monitor the transmission

range based on the target to reach (Lonare and

Wahane, 2013).

Usually, mesh or tree topologies are used to

minimize these interference effects on the

transmission system. Mesh or tree topologies

transmit the message from one node to another using

other nodes (sensor and repeaters), which act as

intermediate routers, directing messages to the

others until it reaches its destination (gateway or

manager node), as shown in figure 1.

Figure 1: Mesh network characteristics.

The importance of data transmission in wireless

networks motivates research works for improvement

in routing algorithms. Some studies have already

been carried out, such as in (Alshawi et al., 2012) in

which it is proposed to include fuzzy logic

techniques to the gossip algorithm with the purpose

of reducing energy waste in data transmission. Other

researchers (Shah et al., 2015) propose a fuzzy logic

algorithm in which the main goal is to achieve fast

real-time communication. An algorithm that

performs routing through clusters and search for the

best cluster-head to maximize the number of

transmitted information is presented in (Amri et al.,

2014). The present work uses a fuzzy system to find

an efficient route for transmission between a sensor

and the gateway in order to obtain the shortest

possible time, avoid transmission failures in case

there is an inactive router, and save the battery

consumption of the routers and thus increase their

lifespan. The article is organized in six sections.

Section 2 presents a brief description of wireless

sensor networks. Section 3 does a brief discussion to

routers energy management. Section 4 describes the

proposed method. In section 5 a case study is

described and section 6 presents conclusions and

future developments.

2 WIRELESS SENSOR NETS

A Wireless Sensor Network (WSN) is an

autonomous network of smart sensors with a high

degree of cooperation among them. It is responsible

for monitoring a process or an environment,

processing the collected information, classifying the

degree of importance of the information, and

spreading it to the other sensors or routers closest to

the gateway. The sensor network consists of the

elements: sensor, observer, phenomenon, router and

gateway, as shown in figure 2.

Figure 2: Elements of a wireless sensor networks.

The sensors measure the physical quantities and

generate measurement reports by means of signal

diffusion. For each type of phenomenon, there is a

different type of sensor, which has a specific

physical characteristic of the process. The observer

is a user, or multiple users, who receive and request,

when necessary, the information of the phenomena

collected by the sensors, and broadcast by the

wireless sensor net. The phenomenon is the process

that will be monitored by the sensor element, and

Application of Fuzzy Inference Systems in the Transmission of Wireless Sensor Networks

619

diffused by the wireless sensor network for the final

evaluation of the observer.

The sensors measure the physical quantities and

generate measurement reports by means of signal

diffusion. For each type of phenomenon, there is a

different type of sensor, which presents a specific

physical characteristic of the process. The observer

is a user, or multiple users, who receive and request,

when necessary, the information of the phenomena

collected by the sensors and broadcast by the

wireless sensor network. The phenomenon is the

process that will be monitored by the sensor

element, and diffused by the wireless sensor network

for the final evaluation of the observer element. The

router directs the information generated by the

sensors to another element of the network within its

transmission radius, so that the information can

reach the gateway. The gateway is responsible for

receiving all the electromagnetic information sent by

the sensor nodes, decoding them into physical

quantities of the monitored phenomenon so that the

observer is able to understand them and trigger, for

example, an alarm if the physical magnitude is

outside of the acceptable value. WSNs are classic

examples of the so-called Low-Power and Lossy

Networks (LLNs). LLNs are made up of many

embedded devices with limited power, memory, and

processing resources (Akyildiz et al, 2002). Thus,

WSNs are classified as a type of a LLN network

with a specific purpose for remote monitoring of

physical quantities (e.g. temperature, pollution level,

pressure, interference, etc.) and can also work as

actuators on certain control elements. There is also

another important question regarding the nature of

the data used in the routing decision-making

process. Many of them are dynamic, that is, they

change their value over time. There are two

traditionally used routing algorithms: flooding and

gossiping algorithms (Chandna and Singla, 2015).

The flooding algorithm has the strategy of sending

each packet of data from one node to all nodes that

are within its reach, and so it proceeds until it

reaches the gateway. This algorithm has the

disadvantage of transmitting unnecessary data,

increasing the power consumption. The gossiping

algorithm is based on a random choice between

nodes that are in range, thus causing a delay in data

propagation.

3 ENERGY MANAGEMENT

In recent years, the number of WSNs deployments

for real-life applications has rapidly increased.

However, the energy problem remains one of the

main barriers limiting the full exploitation of this

technology. Sensor nodes are usually powered by

battery with limited capacity and even when there is

possibility of obtaining additional energy from

external environment, for example solar or piezo-

electric, it remains a limited resource to be

consumed judiciously. Among the characteristics of

the WSNs, regardless of their application, the energy

issue is the one that imposes the most restriction on

the lifespan of the network. This causes several

solutions to be presented as a way to maximize

network durability without sacrificing the system

reliability. Most of the sensor nodes have the

characteristic of being disposable, because in certain

applications of the network its maintenance is

impracticable. The physical resources of the sensor

nodes can be represented by an energy model, in the

consumption and in the interaction level with a

model of functions. Such energy model can be seen

as an energy provider for consumer elements,

through a battery with finite stored energy capacity.

Each sensor informs the level of energy available to

its provider, called an observer or sink. The energy

model consists of the following elements: battery,

radio (communication interface), processing and

sensing unit. From the power model it is possible to

obtain individual information from each sensor node

of the network and perform a survey of the power

map of the network, which in turn can be used to

make decisions about what can and cannot be done

to improve performance the network. It is important

to highlight that in this energy model, the element

that consumes the most energy is communication,

mainly in the function of transmission and routing of

the data collected by the sensing units. A sensor

node is internally divided into four main units:

i) processing unit, consisting of a

microprocessor or microcontroller;

ii) transmission unit (communication)

consisting of a short-range radio for -

wireless communication;

iii) sensing unit that connects the node to

the physical world, and consists of a

group of sensors and actuators;

iv) power unit.

It should be stressed that the path loss of the

transmitted signal between two sensor nodes may be

as high as the fourth order exponent of the distance

between them, because the antennas of the sensor

nodes are near to the ground (Akyildiz et al, 2002).

Figure 3 shows the main components of a sensor

structure.

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620

Figure 3: Sensor node structure of a WSN (Matin, 2012).

Due to the difficulty in having access to a dense

WSN in full operation to carry out simulations

regarding the energy consumption among the

sensors, some proposals of models of energy

dissipation for the sensor nodes are found in the

literature. The main objective of these models is to

characterize the energy consumption in each sensor

node in a WSN. The common models used are:

uniform dissipation model, hotspot based dissipation

model and model based on four modes of operation.

Among other authors, (Kashani and Ziafat, 2011), in

addition to being concerned with Dynamic Power

Management (DPM), present a new approach to

energy management based on a centralized adaptive

clustering protocol for self-organization of the

sensor map through neural networks, which can

group the sensor nodes based on several parameters:

available energy level and coordinates of sensor

nodes, in order to distribute the energy consumption

between the sensor nodes and extend the lifetime of

the network.

4 METHODOLOGY

For the sensors collected information be transmitted

to the gateway, it is often necessary for it to pass

through other sensors or routers until it reaches the

sink or observer, which will take an action. If a

sensor or router is not functioning properly, the

information must find another path to go to the

gateway. In addition, these sensors and routers are

usually battery operated so they have a limited

lifespan and it becomes impractical to frequently

change their power source. To extend the period of

use of the sensors it is interesting to optimize the

data transfer in order to save the unnecessary use of

routers and avoid transmission failures due to lack of

power. One of the main goals in the transmission of

information is to ensure that the data arrives at the

gateway in the shortest possible time, since each

additional router uses extra time. One way to

minimize the delay time is to use a shorter, more

efficient path in which a minimum number of

routers are used to transmit information. In order to

achieve that, a fuzzy inference system is

implemented that will determine for each sensor or

router what the next element to receive the

information should be. For this, the system uses

fuzzy inference rules that take into account the

distance that the information will travel until it

reaches the gateway and the state of conservation of

the router, in other words, if it is in operation and

has a good battery charge. The details of that

modeling are found in the next section where a case

study is elaborated to illustrate better the method.

5 CASE STUDY

In this section the simulation of a wireless routing

system is elaborated, in which the sensors measure

the physical quantities and transmit the collected

data to the gateway through a network of routers and

sensors. We used the MATLAB software and the

Fuzzy Logic toolbox as a tool to illustrate the

method for a case study. Initially an algorithm was

developed capable of generating a configuration

simulating the location of sensors / routers in an

industrial area. The sensors monitor various physical

quantities such as temperature, pressure and flow.

We consider that all sensors are also routers, so all

points can be used for routing. One hundred nodes

were distributed in a square area with 1km² and the

gateway was placed in the center of the environment

in order to avoid great distances between it and some

routers. The transmission range of each router was

defined as 200m. At first we calculate which nodes

can be reached by each of the mesh sensors / routers.

This information will be used to define which of

them will be evaluated by the fuzzy system. The

choice of nodes to be used in the transmission of the

information collected by the sensors is performed by

the fuzzy system and is done based on the battery

level and the distance between the nodes that are

within its range and the gateway. We set the node

closest to the gateway and with the highest battery

level as the priority. Thus, we will be preventing

transmission failure due to lack of energy, while at

the same time, a minimum number of components in

the process will be used in the transmission of

information. The fuzzy system has as inputs the

distance between the sensors / routers and the

gateway, only those that can receive the information,

i. e. the routers that are within reach of the

Application of Fuzzy Inference Systems in the Transmission of Wireless Sensor Networks

621

transmitter sensor, and the battery level of these. The

output of the system is a classification of the quality

of the node to receive the information. After

executing the fuzzy system, the node with the

highest grade is chosen to carry out the transmission.

Membership functions with triangular and

trapezoidal format were used, and the linguistic

variables are:

i) Distance: near / reasonable / far;

ii) Battery: low / medium / high;

iii) Output: poor / fair / good.

The membership function for an input variable is

shown in figure 4 and the membership function for

an output variable is shown in figure 5.

Figure 4: Membership function for input variable.

The rule matrix of the implemented fuzzy system is

presented in Table 1.

Table 1: Fuzzy matrix.

Battery

Distance

Low Medium High

Near Fair Good Good

Reasonable Poor Fair Fair

Far Poor Poor Fair

In order to verify the effectiveness of the proposed

fuzzy system, some simulations were performed.

Initially the sensor node where the information

would be sent from is defined. Then the fuzzy

system was executed to choose the node to which

the information would be directed. The node with

the best classification was chosen and the

information transmitted to it. After this, the fuzzy

system is executed again, using now the information

regarding this new router. This process is repeated

successively until the gateway is reached. In a first

Figure 5: Membership function for output variable.

simulation, a sensor was chosen to the left of the

gateway. The path traveled and the amount of

battery existing in the used routers can be seen in

figure 6.

Figure 6: Path followed by information collected from a

sensor on the left of the gateway.

In a second simulation a sensor was chosen in the

lower right corner. The path obtained for

transmission is shown in figure 7. The amount of

battery in the nearby routers is also indicated.

In this simulation it was observed that the

information passed through four nodes of the

environment until reaching the gateway. It is also

observed that the third chosen node was the one with

the highest level of battery among the nearby nodes.

A new simulation was performed in which the same

source sensor was maintained, but the battery of the

third node traveled in the previous path was

modified. Initially it had the value of 53% and in this

simulation a very low value of 5% was used. The

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new transmission path obtained can be seen in figure

Figure 7: Path covered by information collected from a

sensor in the lower right corner of the gateway.

Figure 8: New path covered by information in the

simulation shown in figure 7 after changes carried out.

Because the previously used battery node is now

at a low level, it is preferable to save this router for

cases where it needs to be used. It is observed that

the new path used went through the node that had

the highest level of battery still acceptable, that is,

30%, which was also close to the gateway. A new

simulation was performed by changing the battery of

this node, which was decreased to 10%. The result

obtained is shown in figure 9.

Figure 9: New path covered by information in the

simulation shown in figure 8 after additional changes.

In this case, the nearest node with a higher battery

level, 24%, was on the left and was chosen instead

of the routes previously found. In a real system, this

process would run simultaneously on several nodes

according to the need of the plant. Therefore, the

system was run again simulating five sensors

transmitting information at the same time. The found

trajectories can be seen in figure 10.

Figure 10: Path covered by information from five sensors

transmitting concurrently.

The simulation was not performed with more

sensors to allow a better visualization of the

behavior at each node. Through the implemented

simulations it is possible to observe the proper

behavior of the proposed fuzzy system, which

focused the choice of a shorter path to the gateway,

but considered the amount of residual energy in each

router in order to extend the useful life of the

network.

Application of Fuzzy Inference Systems in the Transmission of Wireless Sensor Networks

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6 CONCLUSIONS

This paper proposed the use of fuzzy system

techniques for use in wireless sensor networks

focusing on shortest path and energy saving for the

sensors in routing applications. Case studies of a

wireless routing system was carried out, in which

sensors measuring physical quantities were

simulated, transmitting the data collected by them to

the gateway through a network of routers and

sensors. Five simulations were presented in the

present paper in which favorable results were

achieved. In the simulations, we performed the fuzzy

inference system that determined the best route for

routing taking into account the node's reach, the

battery level of the possible nodes that could

propagate the information and the distances among

them and the gateway. The decision on which way

to go was established based on the matrix of rules

and functions of pertinence of the fuzzy inference

system presented in this work. Through these

simulations it was possible to illustrate the trajectory

of a chosen path in the transmission of the

information collected by a sensor until its reception,

in the gateway. The results show that the fuzzy

systems represent a suitable method for this

application, presenting satisfactory results, since

they chose a shorter path to the gateway considering

the amount of energy in each router, extending the

lifespan of the network. More complex networks

involving new case studies are under way as well as

a comprehensive comparison taking into account

traditional techniques and algorithms. The huge

amount of information and time needed to perform

that task did not allow the authors to finish that task.

There is sure a long way to go.

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