2011 International Conference on Selected Topics in Mobile and Wireless Networking (iCOST)

An Energy Aware MPR-based Broadcasting Algorithms for Wireless Sensor Networks

Tarek Moulahi, Herve Guyennet, Mohamed Lehsaini Department of Computer Sciences

U ni versity of Besanc;on

Besanc;on, France

{tmoulahi, herve.guyennet, mlehsaini} @Iifc.univ-fcomte.fr

Abstract- Broadcasting in wireless sensor networks (WSN) is to

disseminate packets of data from a node to all nodes in the

network. Since wireless communications consume great amounts

of sensor's energy, many algorithms and methods were

introduced to minimize the cost of broadcasting such as MPR

(Multipoint Relay) and DS-MPR (Dominated connecting Set with

MPR). In this paper, we introduce first, a slight modification of

MPR, by involving the remaining energy of sensors in the

selection of relay nodes. We call our algorithm MPR remaining

Energy (MPRE). Then, we focus on DS-MPR which also involves

the remaining energy of nodes in the selection of relay nodes, so

we modify it to become applicable in a realistic environment. We

call our second algorithm Realistic environment with DS-MPRf

(RDS-MPR). We illustrate that our algorithm increases the

lifetime of nodes, compared to MPR and pure flooding, due to

their cooperative way to choose the relay sensors and their

balancing of relaying nodes.

Keywords- Wireless Sensor Networks; Broadcasting; MultiPoint Relay; Connected Dominating Sets; Remaining Energy.

I. INTRODUCTION

Last few years, the research community was interested in

the field of WSN due to their low cost and their various fields

of applications: health care, surveillance, environment,

military ...

In addition, WSN can provide a good observation of nature

and a high capacity of information collecting. However, the most important disadvantage of WSN is their limit in energy,

so many methods, algorithms and protocols were introduced

and developed taking in consideration this constraint. Since

WSN are considered ad-hoc networks with other

characteristics, many algorithms and methods of ad-hoc networks could be reused and reconfigured according to the

specificity of WSN.

In this paper, we focus on broadcasting algorithms in WSN.

It consists on distributing a packet from a source sensor to all

other sensors in the network. Many researches were developed

discussing solutions to broadcast information in this kind of

network and taking into account the minimization of energy

consumption. Most existing broadcasting protocols for WSNs

use the unit disk model to represent the physical layer. In this

paper, we apply also the Log-Normal shadowing model to

978-1-4577-2004-8/11/$26.00 ©2011 IEEE 13

Salem Nasri Department of Electrical Engineering

ENIM, University of Monastir

Monastir, Tunisia salem.nasri@enim.rnu.tn

represent a realistic simulation environment and we focus our studies on the performance of MPR. Since the communication

process consumes more energy compared to the others

processes: sensing, receiving and processing, methods and

algorithms were focused on reducing the number of relay

nodes in order to minimize the number of communication

packets [1].

The remainder of the paper is organized as follows: Section

2 outlines the most known broadcasting algorithms. In section

3, we proposed a new algorithm called MPRE which implies

the remaining energy of sensors in the selection of relay nodes.

For this, we used the unit disc model which considers that a

node receives correctly information from a sender node if it is inside the disc area. Section 4 presented RDS-MPR which is a

new broadcasting algorithm based on remaining sensor energy

in realistic environment.

II. RELATED WORK

Most eXlstmg broadcasting protocols for wireless sensor networks aim to extend the network lifetime. Most of them are

focalized on minimizing the number of relay nodes in order to

reduce the energy consumption [1,2,3]. We assume the

communication process is the most factor of energy

consuming. Furthermore, some others algorithms aimed to

find an optimum number of relay nodes [4] and others have evaluated the broadcasting protocols for various WSN

topologies in terms of energy and performing time [5]. In [6],

the authors presented a secure broadcasting protocol based on

power preserving, and in [9], the authors proposed another

broadcasting protocol based on distance scheme. [7,8]

compared between several broadcasting algorithms. In [10,

11], the broadcasting process is based on connected

dominating set which contains the nodes that have the high

weight in the network. In [12], the authors improved the work

cited in [10,11] and proposed a heuristic method that reduce

the size of connected dominating set to decrease the energy

consumed. Wu et al. [13] presented an extension of DS-MPR

to find the connected dominating set in mobile ad-hoc

networks. The concern of algorithms that aim to reduce the

number of relay nodes is the unbalancing load. Indeed, these

algorithms would use the same nodes as relay nodes that might

have a negative effect on nodes lifetime. In this paper, we tried

to balance the load between the nodes and reduce the number

of relay nodes. Our algorithm may increase the number of

relay nodes but it extends the lifetime of some nodes. This contribution is advantageous if the nodes are placed in specific

positions so that they provide full coverage of the interest area.

III. MPR -BASED PROTOCOL OF BROADCASTING IN IDEAL

ENVIRONMENT

In an ideal environment based on the assumption that a

sensor receives correctly a packet if it exists inside the radius of transmission area of a sender sensor and it can't receive the

packet if not as shown in Fig. 1. Node A receives correctly the

packet because it is inside the disk but node B can't receive the

packet because it is located outside the disk.

Node B

Figure I. Transmission in unit disk model.

In table I, we give a mathematical notation description. We define the WSN as a graph G (V,E), where the sensors

constitute the set of vertex V and E the set of edges. An edge

exists between two nodes A and B in this graph if the distance

between these nodes is less or equal than the transmission

range.

TABLE I. SYMBOLS AND NOTATIONS Symbol Signification

C(V,E) Connected graph where V: the set of vertex and E: the set of edges.

II Initial sender node Nl(u) The set of neighbors of node u N2(1l) The set of neighbors of u's neighbors EO Energy consumed by a node during a

communication Er Remaining energy of node u v Element of NI(u) w Element of N2(u) or Nl(v) S2(u) Set of unreachable nodes S3(u) Set of exhausted nodes where Er(u)<EO A. MPRE heuristic

In this subsection, we define our heuristic method. We try

to determinate the relay nodes, extract the exhausted nodes

and also find the set of unreachable nodes.

I. {Initialization :}

MPRE(u) =0 {The relay nodes searched}

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Sl(u) = N2(u) {Put in SI(u) the set of two-hop

neighbors of u}

S2(u)=0

S3(u)= 0 2. {Finding nodes in SI which has a single parent in NI(u)}

while 3v: wES1(u) /\3/ vEN1(u): WE Nl(v) do

if Er(v )�EO then MPRE(u)=MPRE( u)u{v j Sl(u)=Sl(u)\{wj

Else S2(u)=S2(u)u{wj

S3(u)=S3(u)u{vj

Sl(u)=Sl(u)\{wj

end if end while

3. {Completing the set of MPRE}

while Sl(u) *- 0do for all v in Nl(u) do {Choose the node which has the maximum degree}

Sv={v, viE Nl(u);IN1(v) nSl(u)1 = Max(IN1(vi) nSl(u)l)j {If there is more than one, choose the node} {which has maximum remaining energy}

if ISvl>l then v=Max(Er(vi); viESv)

end if {Verify that the node chosen can}

{ communicate and add it to the set of}

{searched nodes }

if Er(v )�EO then MPRE(u)=MPRE(u)u{vj Sl (u)=Sl (u)\Nl (v) {If the node can't communicate add it to}

{The set of exhausted nodes}

else S3(u)=S3(u)u{vj end if

end for all if Sl(u) *- 0then {Finding the set of unreachable nodes}

while 3v, \71;: wEN1(v) /\ vES3(u) do

S2(u)=S2(u)u{wj Sl(u)=Sl(u)\{wj

end while end if

end while

MPRE heuristic provides three sets: a MPRE( u) : the set of relay nodes • S2(u): the set of unreachable nodes • S3(u): the set of exhausted nodes

In addition, this method selects periodically relay nodes

based on their number of neighbors and their remaining energy.

Figure 2. Relay nodes by MPRE

TABLE II. RELAY NODES WITH MPRE

Relay nodes

MPR MPRE

1 st broadcasting {b,d} {b,d}

2nd broadcasting {b,d} {c,d} 3rd broadcasting {b,d} {b,d} 4th broadcasting {b,d} {c,d}

The Fig. 2 illustrates relay nodes provided by MPRE. We note

that the set of relay nodes is not always the same because of

the change in the number of neighboring nodes and their

remaining energy as presented in table II.

B. Illustration of MPRE

To illustrate the performance of MPRE, we used NS2

simulator [14] on a WSN with 50 nodes. Each node has a

lithium battery with 1.5 volts and 2.9 Ah. We assumed that the

size of broadcasting message is 200 Bytes and a

communication consumes 20mW. So, to evaluate our heuristic

method we compared its performance to MPR and pure

flooding protocols. Fig. 3 shows that the number of relay

nodes in MPRE is near to MPR and better than pure flooding.

Moreover, MPRE does not imply the same nodes as relay

nodes. This effect may increase the nodes lifetime when

compared to MPR that implies the same nodes to relay

packets. In Fig. 4, we show that the number of exhausted

nodes is higher than that in MPR. Thus, our algorithm

contributes greatly to prolong the nodes lifetime in the

network.

15

." QJ

"1:1 0 c >-.!!! � '0 .... QJ

.g E :::>

z

60 50 40 30 20

� 10 0

10

� 60 o

� 50 QJ

; 40 '"

� 30 QJ

'0 20 .... � 10 E

20 30 40 50

Number of nodes

Figure 3. Number of relay nodes

:::> 0 +l1-M".�Tlh-....,.--,-,..., z

10 20 30 40 50 61 70 80 90

Number of transmission (*10000)

-+-Pure flooding

_MPR --+-MPRE

-+-Pure flooding

......... MPR --+-MPRE

Figure 4. Number of exhausted nodes.

IV. DS-MPR-BASED PROTOCOL OF BROADCASTING IN

REALISTIC ENVIRONMENT.

Dominated connecting set with multipoint relay is a

broadcasting algorithm. In this algorithm, we compute the

weight of each node in the network. This weight is calculated based on the remaining energy and the degree of each node.

After that, we choose a set of nodes to relay packets. This set

consists of nodes that have the greatest weight and should

provide a good reachability. However, this method is

conceived in an ideal environment. In what follows, we tried to improve it so that it is still powerful for a realistic

environment.

A. Lognormal model

With a realistic physical layer a reception can't be done as in a

unit disk model due to many factors like signal attenuation,

obstacle presence, distance between the sender and the receiver, communication slot: day or night. So, we propose

using lognormal model [2, 3]. In this model, we consider that

the probability of reception without any error can be

calculated according to the distance separating two nodes as

presented in equation (1).

P(x) =

(�)2a 1-� 2

(2Rc-x 2a --) Rc

2

0

if O<x:5;Rc

(I) if Rc<x:5;2Rc

otherwise

In this function, ex represents the power attenuation factor

which depends on the environment and x is the considered

distance separating the transmitter from the receiver.

Fig. 5 shows the evolution of this function when a =2, and illustrates the probability of successful reception with

lognormal model and unit disk model for Rc=l and Rc=2.

1.2

c -I 0.8 --+-- Rc=l (lognormal)

�

-��_ 0.6 � _ Rc=2 (lognormal)

:is 2i !l: 0.4 ........... Rc=l (unit disk)

0.2 """*""" Rc=2 (unit disk)

Distance between sender and receiver

Figure 5. Probability of successful reception according to the distance.

B. RDS-MPR heuristic

In this subsection, we use the same notation as shown in

table I. RDS-MPR is based on node's weight. This weight (2)

f41 is function of node remaining energy, probability of reception without errors and node degree.

W( )

deg( v) f3 Er(v) (

) u =ux + x +yxP u.v

Max{deg( Vi);ViEN,(U)} Max { Er(vi);viEN,(U)}

Where a +P+y= 1

(2)

In our contribution, we choose a =1/3; P=1/3; y=1/3. Thus, the equation (2) becomes (3):

W(u)=..!:.x deg(v) +..!:.x Er(v) +..!:.xP(u v } (3) 3 Max{deg(vi);ViEN,(u)} 3 Max{Er(v,};viEN,(U)} 3 '

1. {Initialization: }

RDSMPR(u) =0 {The relay nodes searched} Sl (u) = N2( u) {Put in S l(u) the set of two-hop neighbors of u}

16

S2(u) = 0 S3(u)= 0

2. {Finding nodes in Sl which has a single parent in N1(u)}

while 3v: wES1(u) '" 3!vEN1(u): WE N1(v) do if Er(v)�EO then

RDSMPR(u)=RDSMPR(u)u{vj Sl(u)=Sl(u)\{wj

else S2(u)=S2(u)u{wj

S3(u)=S3(u)u{vj

Sl(u)=Sl(u)\{wj

end if end while

3. { Calculation of the weight of all nodes in S 1 (u) }

for all v in N1(u) do

W(v)=a Er(v) +/3

IN,(v)(lSl(u)1 Max{Er(v,); Vi E Nl(u)} Max{IN, (v,) (I Sl(u); Vi E N, (u)I)

+ yP(u, v) end for all

4. {Completing the set of RDS-MPR }

while Sl(u) i- 0 do forall v in N1(u) do { Choose the node which has the maximum weight}

Sv={v, viE N1(u) '" vi� RDSMPR(u) ;W(v) = Max(W(vi))j

{If there is more than one, choose the node}

{ which has maximum remaining energy}

if ISvl>l then v=Max(Er(vi); viESv)

end if

{Verify that the node chosen can}

{communicate and add it to the set of}

{searched nodes }

if Er(v)�EO then

else

RDSMPR(u)=RDSMPR(u)u{vj Sl (u)=Sl (u)\N1 (v)

{If the node can't communicate add it to}

{ the set of exhausted nodes }

S3(u)=S3(u)u{vj end if

end for all

if Sl(u) i- 0then

{Finding the set of unreachable nodes}

while3v, \1'v: wES1(u) '" vES3(u) do

S2( u)=S2( u)u{w} S1 (u)=S1 (u)\{w)

end while end if

end while

RDS-MPR heuristic provides also three sets as MPRE: • RDSMPR(u) : the set of relay nodes • S2(u): the set of unreachable nodes • S3(u): the set of exhausted nodes

RDS-MPR could be applied in a realistic environment. It

did not choose the same nodes as relay nodes. It chose relay

nodes according to their remaining energy, the number of

neighbors and the probability of reception without errors from the source node.

k

Figure 6. Relay nodes by RDS-MPR.

TABLE III. THE PROBABILITY OF SUCCESSFUL RECEPTION

TABLE IV. RELAY NODES WITH RDS-MPR.

Relay nodes

MPR RDS-MPR

1st broadcasting {b,d} {d,b} 2nd broadcasting {b,d} {d,c,g} 3rd broadcasting {b,d} {d,b} 4th broadcasting {b,d} {d,c,g}

The Fig. 6 illustrates relay nodes provided by RDS-MPR. We

note that the set of relay nodes is not always the same because

of the change in the number of neighboring nodes, the

remaining energy and the probability of reception without

errors as presented in table IV. Table III presents the

probability of successful reception between the source node (a) and its neighbors.

C. RDS-MPR Illustration

Fig. 6 shows that the number of exhausted nodes in RDS­

MPR is smaller than in MPR and more smaller than in pure

flooding. Therefore, our method can increase the node

17

lifetime. As in MPRE, RDS-MPR does not use the same nodes

to relay packets and it balances the load between nodes by

choosing eventually distinct nodes to relay packets after every broadcasting. The simulation was performed using the same

context for the evaluation of MPRE.

--+-- Pure flooding

--tI---- MPR

---.- RDS-MPR

10 20 30 40 SO 61 70 80 90 Number of transmission (*10000)

Figure 6. Number of exhausted nodes after n relays.

V. CONCLUSION

Although WSN provide a lot of services with low cost but they have energy constraints. Therefore many methods and

algorithms are conceived to reduce energy consuming particularly in broadcasting process. Many existing methods

aimed to reduce the number of retransmission but they used

for that some nodes most than others. In this paper, we proposed two new algorithms to minimize energy consuming.

These algorithms did not choose the same nodes as relay

nodes. Thus, they enabled to load balancing between distinct

nodes. The relay nodes are chosen according to their

remaining energy and their degree. That do not select the same

nodes as relay nodes can extend nodes lifetime. The results

obtained illustrate that our contributions enable to increase

nodes lifetime and thus network lifetime. A future work can

focus on evaluation of DS-MPR and RDS-MPR in a mobile

environment.

[1]

[2]

[3]

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