RTCP: Redundancy aware Topology Control

Protocol for Wireless Sensor Network

Bahia Zebbane, Manel Chenait and Nadjib Badache

LSI Laboratory, Computer Science Department

University of Sciences and Technology Houari Boumediene (USTHB)

BP 32 El Alia Bab Ezzouar Algers 16111, Algeria

Email: bzebbane@usthb.dz, mchenait@usthb.dz, badache@lsi-usthb.dz

Abstract—One of the main design challenges, in wireless sensornetworks, is to save energy of sensors and obtain a long systemlifetime without sacrificing the quality of network coverage andconnectivity. Topology control based sleep scheduling is theprimary technique of energy saving which consists to exploitnode redundancy and save energy by putting as many nodes aspossible in sleep mode, while maintaining a connected network.In this paper, we propose a Redundancy aware Topology ControlProtocol (RTCP) for wireless sensor network which exploits thesensor redundancy in the same region by dividing the networkinto groups so that a connected backbone can be maintainedby keeping a necessary set of working nodes and turning offthe redundant ones. RTCP identifies equivalent nodes in termsof communication based only on the connectivity information ofone-hop neighbors which leads to a low communication overheadand then schedule nodes based on this equivalence. RTCP allowsonly the election of one sensor node to be active in each group.The simulation results illustrate that RTCP outperforms someother existing algorithms in terms of energy conservation andnetwork lifetime.

Index Terms—Topology control, energy conservation, sleep-scheduling, connectivity, node redundancy.

I. INTRODUCTION

Wireless sensor networks (WSNs) have gained worldwide

attention in recent years due to their potential applications

for several domains such as military target tracking, traffic

surveillance, health care, environment monitoring, disaster

management. For example, in disaster management, one of

the biggest problems in time of disaster is to determine the

communication infrastructures, WSN can be one of the most

suitable infrastructures to be applied in disaster management

because of their wireless and self-organized characteristics.

Wireless sensor network is a self-organized network that

consists of a large number of sensor nodes deployed on the

target field. Most of these sensors are equipped with limited

energy supplies, and it is inconvenient to recharge the batteries

because nodes may be deployed in a hostile environment. In

order to sustain sensors to run for a long period of time with

limited energy capacity, it is crucial to save energy in sensor

operations and , then, enhance the network lifetime. Indeed,

radio communication consumes a large amount of energy

among all the node activities. Typically, a radio can operate in

four distinct modes of operation: Idle, Receive, Transmit, and

Sleep. While it is expected that the radio consumes most of

the energy in the Transmit and Receive modes, running in the

Idle mode is also costly. In most cases, operating in Idle mode

results in significantly high energy consumption, because the

radio electronics have been turned on and they continually

decode radio signals, even noise, to detect the presence of

incoming packets. Many measurements have shown that idle

listening consumes 50 ∼ 100% of the energy required for

receiving. Thus, it is desirable to completely shut down the

radio (enter into sleep mode) rather than transiting into the

Idle mode. Putting some sensor nodes into sleep mode and

allowing switches between sleep and active modes is referred

to as Duty-Cycling.

Duty-cycling called also sleep-scheduling technique define

coordinated sleep/wakeup schedule among nodes in the net-

work. This technique reduces significantly the energy con-

sumption of sensor nodes as, ideally, it keeps nodes active

only when there is network activity [1], [2]. In addition, the

duty cycling can be achieved by exploiting node redundancy

and selecting only a minimum subset of nodes to remain

active with the purpose of maintaining connectivity. Finding

the optimal subset of nodes, that guarantees connectivity, is

referred to as Topology Control (TC) [1].

Topology control is considered one of the most important

techniques used in wireless ad hoc and sensor networks to

reduce energy consumption and radio interference [3]. It aims

at exploiting the network density to conserve energy and

extend the network lifetime. In addition to the energy savings,

this technique causes the number of transmitted messages

to decline, which lowers signal interference and the failed

transmission attempts [4]. Indeed, using a topology control

approach with an energy efficient routing can increase data

transfer reliability which is an important property for several

applications such as disaster management. TC approach can

be classified into power control and sleep-scheduling [1],

[3]. The former reduce the transmission power of sensors in

order to extend the network lifetime (eg.[5]) while the second

define coordinated sleep/wakeup schedule among nodes in the

network (eg.[6]).

Several topology control algorithms using the scheduling

technique have been proposed for Ad Hoc and sensor net-

works. The final goal of these protocols consists in managing

the topology in order to reduce energy consumption as well as

maintaining a connected network [6], [7], [8], [9], [10], [11],

[12], [13]. They determine how many and which nodes should

be allowed to sleep with the purpose to ensure connectivity.

However, the number of the active nodes is not always the

minimum and sometimes the formed topology is not con-

nected. It is difficult to ensure connectivity without sacrificing

the optimality (activate the minimum set of nodes). Where

optimality is achieved, the topology formed by the active nodes

may be disconnected. Furthermore, a large communication

overhead will be produced during the search of the redundant

nodes which leads to large energy consumption. For all these

reasons, we propose a Redundancy aware Topology Control

Protocol (RTCP) which identifies node redundancy in terms of

communication, groups redundant nodes together, and finally

schedule nodes in the groups for active or sleep modes.

The group forming approach used by RTCP organizes nodes

into groups with a low communication overhead and time

complexity.

The rest of the paper is organized as follows : in Section

II, we give some related work in this field. The detailed

description of the algorithm is given in Section III. In Section

IV, we evaluate our proposed approach by comparing it with

a group-based protocol proposed in [9]. Finally, a conclusion

is given in section V .

II. RELATED WORK

In this section, we present some representative sleep-

scheduling algorithms proposed for sensor networks with the

purpose to save energy as well as ensure connectivity. We

can classify the protocols into four categories: Flat protocols

([7], [8]), Grid-based protocols ([6], [12], [13]), Cluster-based

protocols ([10], [11]) and Group-based protocols ([9]).

In SPAN [7], nodes exchange their routing table, if a

node discovers that two of its neighbors cannot communicate

with each other directly or through an existing coordinator,

it decides to be volunteer a coordinator, joins the routing

infrastructure and stays awake. SPAN does not guarantee

a connected topology; nodes decide to sleep or join the

network based on connectivity information supplied by a

routing protocol. Similar to SPAN, ASCENT [8] adaptively

elects active nodes from all nodes in the network in order

to reduce the energy consumption and ensure the network

connectivity. If the connectivity is poor, the sink node starts

sending help messages to signal neighbors that are passive to

join the network. On the other hand, if a node experiences

high loss rates due to collisions, then it may decide to go

to sleep mode. However, Maintaining the loss rate and node

density may incur high communication overhead and adversely

affects the integrity of a nodes decision of going to sleep or

active. Based on the physical location, GAF [6] and GTC

[12], [13], could identify node redundancy and group nodes

into virtual grids in such a way any two nodes from adjacent

grids can communicate with each other and so only one

active node in each grid is enough to maintain a connected

network. The attractive feature of GAF and GTC is that

they do not incur a high overhead as grids can be built

statically once the communication range is known, but the

assumption that the communication range is a perfect disc is

far from reality. ECTC [10] and TECA [11] are hierarchical

distributed topology control algorithms, which use a clustering

approach. They are built on the notion that when a region of

a wireless sensor network has a sufficient density of nodes,

significant energy saving is obtained by allowing redundant

nodes to sleep. In order to ensure connectivity, some nodes are

selected as cluster heads and others act as bridges. Thus, the

entire network gets connected. However, the communication

overhead is much higher during the cluster forming process

which leads to large energy consumption. CPA [9] partitions

sensors into groups so that a connected backbone network

can be maintained by keeping only one arbitrary node from

each group in active status while putting others to sleep. CPA

starts from the initial partition where each node forms a unique

group and continuously merges two groups into a large one.

With CPA, the collection of total energy of all nodes in the

network, to calculate the utility value, increases the complexity

of the proposed solution in terms of communication and run

time. In addition, a deadlock problem can occur during the

merging process. A solution to this problem is given in [14]

based on a timeout mechanism and adapting CPA to ensure

coverage.

Motivated by the above limitations, a new distributed

Redundancy aware Topology Control Protocol (RTCP) is

proposed in this paper. RTCP shares a common character

among above sleep-scheduling protocols which is the energy

conserving while ensuring connectivity, by selecting a subset

of active nodes. It uses the neighbor set as the communication

redundancy metric, this allows RTCP to avoid any assump-

tions about communication regularity. In addition, it identifies

redundant nodes and organizes them into groups with a low

communication overhead and time complexity. On the other

hand, RTCP try to reduce the number of active nodes in the

scheduling phase by selecting only a single node in each

group.

III. RTCP APPROACH

The topology control problem is divided into two major

steps, identification of equivalent nodes and scheduling nodes

for Sleep and Active modes based on this equivalence. An

efficient topology control protocol should maximize the num-

ber of sleeping nodes as long as the network is connected.

Like other topology control protocol, RTCP is divided into

two phases, the group forming phase and the leaders election

phase. In the first phase, the network is divided into groups

so that nodes within the same group are equivalent for routing

and no matter which node is selected from each group. In the

second one, only one node in each group is required to be

active while others turn off their radio.

A. Group forming phase

In this phase, the energy dissipated during the group forming

must be negligible as to not affect the scheduling phase and

the network lifetime. This can be achieved by minimizing the

number of exchanged messages, i.e. communication overhead.

That is the reason why we use the Neighbor List as the basis

13

4

5

2

810

7

11

9

6

1 3

4

5

2

8

10

7

11

9

6

(a) (b)

Fig. 1. (a) The initial network topology represented by a communication graph. (b) The network topology after the group forming phase.

in defining and measuring communication redundancy and

equivalence, which is the key concept in our solution. The

process of group forming takes place as follows:

• As the first step, each node broadcasts a discovery mes-

sage, containing its NodeID and its Neighbor List.

• Upon receipt of this message, each neighbor node locally

computes the Shared Neighbor List and Union Neighbor

List with each of its neighbors.

• Thereafter, each node calculates locally the Redundancy

Degree with each one of its neighbors, after that each

node independently calculates its Equivalent Neighbor

List or its group members. If the Redundancy Degree

is higher than a Threshold of connectivity level, nodes

are equivalent and could be in the same group. Formally,

a node j ∈ EquivalentNeighborListi iff

RedundancyDegreei,j =|SharedNeighborListi,j ||UnionNeighborListi,j |

∗ 100

>= Tcl% (1)

where

SharedNeighborListi,j =Neighborlisti

⋂NeighborListj (2)

UnionNeighborListi,j = Neighborlisti − {j}⋃NeighborListj − {i} (3)

and Tcl is the Threshold of connectivity level.

1) Illustration: To further illustrate the steps presented

above, we present an example scenario before moving into

the node scheduling phase (leader election phase). Figure 1(a)

shows a part of topology consisting of 11 nodes connected via

wireless links, from which we can find the node neighbor lists,

the Redundancy degrees, the list of equivalent neighbors as

shown in TABLE I. Figure 1(b) shows the resulting topology

after the group forming phase. Taking the example of node 6,

its neighbor list consists of 7 nodes. If we suppose that the

Tcl value is 80%, nodes 3, 6 and 10 are equivalent and form a

group. Nodes have the same information without exchanging

it, hence the advantage of our solution.

B. Leaders election phase

Once the group forming phase is completed, every group

starts the stage of sleep/wake scheduling. In order to save

energy, only one node in each group is required to be active

TABLE I6’S NEIGHBORING TABLE AND ITS EQUIVALENTNEIGHBORLIST.

Node Neighbor List Redundancy Degree

2 {1, 3, 4, 5, 6, 8, 10} 1

3= 33.33%

3 {2, 5, 6, 9, 10, 11} 5

6= 83.33%

5 {1, 2, 3, 4, 6, 8, 10} 1

3= 33.33%

7 {6, 9, 10,11} 1

2= 50%

9 {3, 6, 7, 10, 11} 2

3= 66.66%

10 {2, 3, 5, 6, 7, 9, 11} 1 = 100%

11 {3, 6, 7, 9, 10} 2

3= 66.66%

EquivalentNeighborList(6) {3, 10}

while others turn off their radio (ie. go to sleep mode). RTCP

uses a load balancing strategy for selecting active nodes,

called Round Robin Scheduling. This strategy allows nodes to

periodically represent their group. In other words, the active

nodes must be changed from time to time to let other nodes

become active and represent the group. The objective behind

this technique is to keep all nodes alive as long as possible.

At the beginning of the scheduling phase, all nodes are

active to configure the network, ie. the active nodes election.

The scheduling technique ”Round Robin” uses the energy

level of nodes as an election criterion. Priority is given to

the node with a higher energy level to represent the group

and become the leader. In case where multiple nodes have

the same energy level, the node identity is used as a second

criterion. A leader node remains active for a period of activity

Ta, calculated according to its estimated lifetime TTL (Time

To Live). The TTL value is an estimate of the node lifetime if it

works using a maximum of resources (computing, transmitting

and receiving). The other group members go in energy con-

servation mode (sleep mode) during a sleep period, noted Ts.

The value of Ts is calculated according to the leader’s activity

period so that Ts = Ta. After the expiry of Ts, nodes wake

up and trigger the election process of a new leader. In order

to give nodes the possibility to work periodically and ensure

routing data for several periods, the value of Ta must be less

than or equal to half of the TTL. Formally, Ta = TTL/pso that p > 1 and p represents the number of periods of a

node activity. It should be noted that the value of p must not

be greater, so as not to increase the election frequency and

consequently energy consumption.

C. RTCP complexity analysis

We analyze the complexity of our protocol, in terms of com-

munication overhead. The analysis concerns only the group

forming phase because it significantly affects the effectiveness

of the protocol. Let’s assume that n sensors are deployed into

an area of interest.

In the first phase, each node broadcasts a discovery message

to its neighbors. The communication overhead is O(n).

IV. PERFORMANCE EVALUATION

In order to evaluate the performance of RTCP, we imple-

mented it in order to perform simulations using Ns2 simulator.

Our protocol is simulated using random topologies with dif-

ferent densities. TABLE II summarizes simulation parameters.

The performance evaluation of our solution consists of two

parts: Group forming phase and Sleep/wake scheduling phase.

TABLE IISIMULATION PARAMETERS.

Topography 20*20 mNumber of nodes 20, 50, 100, 200 nodesInitial Energy 5 joulePropagation model Two-Ray-GroundRadio transmission range 5mTransmit power 0.6WReceive power 0.3 WIdle power 0.3 WSleep power 0.1W

A. Group forming phase

1) Number of groups formed: In order to compare the per-

formance of our algorithm with that of CPA, we implemented

the distributed version of CPA and simulated it in the same

environment as our protocol.

During the simulation of CPA, using different topologies,

we noticed that with the 20 nodes topology, only the first

iteration (merge nodes in pairs) of the group forming phase

ended because the energy consumed, by each node, reaches

82%. Consequently, the nodes do not have enough energy

to end the second iteration. For other topologies, nodes die

during the utility values calculation for each pair of neighbors.

This is due to the high number of messages exchanged for

the collection of total energy of all nodes and messages

exchanged to merge nodes. Thus, the energy consumed during

the merging phase in CPA is not at all negligible as the authors

of the CPA mention it and justify it by the fact that this phase

runs once after network deployment.

For this reason and in order to compare the performance

of our grouping nodes mechanism and that of CPA, in terms

of number of groups, the messages exchanged for the total

energy collection were neglected and only merging messages

were considered to complete the nodes merging process in

CPA. TABLE III summarizes the results.

From the results, we note that the number of groups formed

for both protocols increases with the connectivity degree

mindeg for CPA and with the Threshold of connectivity level

Tcl for RTCP. This is due to the need for a large number

of active nodes, at a given time, to ensure high connectivity.

In addition, with CPA, the number of groups increases with

increasing nodes density because nodes lose their energy

during the merging process which leads to stop it. By cons,

with RTCP, the number of groups is fewer than that of CPA

when the value of Tcl is 80%, but when the Tcl value is 90%

only the topology D4 gives good results compared to those of

CPA. This indicate that RTCP can identify redundant nodes

more efficiently than CPA when the density is high.

TABLE IIINUMBER OF GROUPS FORMED IN RTCP AND CPA UNDER DIFFERENT

NODE DENSITIES.

Node Density D1=20 D2=50 D3=100 D4=200

RTCP(Tcl = 80%) 9 32 51 82

RTCP(Tcl = 90%) 13 39 78 145

CPA(mindeg=1) 13 26 55 170

CPA(mindeg=2) 16 33 62 171

CPA(mindeg=3) 20 39 63 171

CPA(mindeg=4) 20 45 76 171

2) Energy consumed: Fig. 2 shows the energy consumed,

during the group forming phase, by both RTCP and CPA. It

should be noted that we neglected the energy consumed to

collect the total energy, which is an important parameter in

the calculation of CPA utility values. So, we considered only

the energy consumed, after the exchange of merging messages

between groups. The results show that energy consumption in-

creases by increasing the number of nodes for both protocols.

However, for RTCP, the consumed energy is really negligible.

Therefore, we can say that with RTCP, in the worst case,

even though the number of groups is equal to the number of

nodes, the energy consumed in the group forming phase does

not affect the network lifetime. Unlike CPA, we see that the

increase of energy consumption rate is significant, especially

with the D3 and D4 density; where all nodes lose their energy

without ended the merging process. So, RTCP is scalable with

the network size but CPA is not.

B. Sleep/wake scheduling phase

To see the effect of scheduling on the network lifetime,

we first simulated the network without using any scheduling

mechanism. We used the flooding as the routing protocol. In

fact, we used several source nodes that broadcast messages

every second.

Fig. 3, Fig. 4, Fig. 5, and Fig. 6 show the fraction of

survived nodes versus time for the densities D1, D2, D3, and

D3 respectively.

As shown in Fig. 3, the network lifetime with the flood-

ing reaches 222 seconds but most nodes (85%) die at 151

seconds. The same result is noted with CPA(mindeg=3) and

CPA(mindeg=4) as the number of groups is equal to the

number of nodes in the network. Therefore, all nodes are

active. By cons, we find that using a scheduling scheme

extends the network lifetime by 3 times for CPA(mindeg=2),

Fig. 2. Consumed energy VS node density.

Fig. 3. Fraction of survived nodes VS time for Density D1.

3.8 times for CPA(mindeg=1), 4 times for RTCP(Tcl=90%),

and 5 times for RTCP(Tcl=80%). The results of RTCP are

better than those of CPA(mindeg=1) and CPA(mindeg=2) in

terms of the number of survived nodes at each time, knowing

that we have neglected the energy consumed for the CPA total

energy collection.

Fig. 4 shows that the use of scheduling extends the life

of the system by 4.5 times for CPA(mindeg=1), 4.7 times

for CPA(mindeg=2), 6.8 times for CPA(mindeg=3), 7 times

for RTCP(Tcl=90%), 8.2 times for RTCP(Tcl=80%), and

8.4 times CPA(mindeg=4). In addition, the RTCP(Tcl=80%)

results are better than those of CPA due to the fact that (1)

the energy consumed by RTCP, during the nodes grouping, is

negligible compared to that of CPA, (2) the number of groups

formed is fewer than that of CPA. We can observe also that

CPA(mindeg=4) gives a better lifetime than the others, despite

the large number of groups formed. This is justified by the fact

that there are fewer groups that trigger the election. Therefore,

fewer exchanged messages during the election process that

results in less energy consumed. This leads us to conclude that

the technique of scheduling affects the network life. However,

with CPA(mindeg=4), the fraction of survived nodes reaches

18% at 30 seconds while all nodes are alive with the others.

Fig. 4. Fraction of survived nodes VS time for Density D2.

Fig. 5. Fraction of survived nodes VS time for Density D3.

Fig. 5 and Fig. 6 show that RTCP extends the network

lifetime by 7 times when Tcl=80% and 5 times when Tcl=90%

for the topology of 100 nodes. For the topology of 200 nodes,

we observe that RTCP extends the network lifetime by 4.5

times. This is due to the neglected energy consumed during

the group forming phase and the reduced number of groups

which allows an important number of nodes to enter a sleep

mode and conserve their energy.

From all results, we can say that the use of a topology

control protocol, based sleep-scheduling, conjointly with a

routing protocol extends the sensor network lifetime. The

effectiveness of the protocol in terms of network lifetime is

influenced by three parameters: (1) the technique used to group

equivalent nodes, (2) scheduling technique used for the active

nodes election (3) the routing protocol used.

Fig. 6. Fraction of survived nodes VS time for Density D4.

V. CONCLUSION

In this paper, we proposed a protocol that guarantees energy

saving while providing network connectivity. Our protocol

called RTCP exploits the sensor redundancy in the same region

by dividing the network into groups so that a connected

backbone can be maintained by keeping a necessary set of

working nodes and turning off the redundant ones. The group

forming approach used by RTCP organizes nodes into groups

with a low communication overhead which is of O(n) so that

n is the number of sensors and time complexity. Moreover, it

uses a load balancing technique that ensures fairness among

nodes of the same group. The simulation results show that : (1)

the use of topology control protocol based sleep-scheduling,

with a routing protocol, extends the network lifetime, (2) our

solution outperforms existing group-based solutions in terms

of energy saving. Finally, we can say that the effectiveness

of any topology protocol in terms of network lifetime is

influenced by the technique used to nodes grouping, the

scheduling technique used for the active nodes election, and

the routing protocol. As future work, we plan to (1) improve

the protocol in terms of energy saving by using an other

scheduling policy and routing protocols and (2) compare it

with more topology control protocols.

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