Experimental Evaluation of Transmission PowerControl Strategies in Wireless Sensor Networks

Mohsin Raza , Ghufran Ahmed , Noor M Khan

ACME Center of Research in Wireless Communications (ARWiC)

Department of Electronics Engineering, Mohammad Ali Jinnah University

Islamabad 44000, Pakistan{ mohsinraza119, gahmad78}@gmail.com, noor@ieee.org

Abstract—Despite the overwhelming enhancement in the fieldof wireless sensor networks, the battery issues remain quitepersistent. even the battery life of the sensor nodes becomes moreand more critical with the increasing number of applications.In order to improve the life time of wireless sensor nodes,various transmission power control strategies have been proposedwhich offer improved life time of the wireless sensor networks.However, most of these simulation-based protocols fail to workin realistic fading environment and hence do not perform theassigned task properly. The purpose of this paper is to countercheck some of these transmission power control strategies usinga test bed comprising of Sun SPOT wireless sensor nodes forthe onward evaluation of their strengths and weaknesses. Onthe basis of obtained experimental results, the performance ofselected transmission power control strategies is evaluated interms of their efficiencies, energy savings and packet receptionrates.

Index Terms—Transmission Power Control (TPC) , WirelessSensor Networks (WSNs)

I. INTRODUCTION

A. Overview

Wireless sensor networks (WSNs) comprise of tiny

sensor devices called nodes which work collaboratively to

sense environmental features and deliver data to sink in a

hop-by-hop fashion. Sink collects data from all over the

network and then routes it to the final destination via internet

or satellite link. A typical WSN is shown in Figure. 1.

With all the benefits these sensor nodes provide, one can’t

overlook the constrained nature of these nodes. Sensor

nodes are resource constrained in terms of processing power,

transmission range and battery capacity. Despite all the limits

posed by WSN, researchers are working hard to design

and develop such protocols which can enhance the overall

performance of the sensor networks as well as the network

lifetime. Transmission power control (TPC) is one of the

many dimensions researchers are working on to particularly

improve the network life time as it efficiently deals with

unwanted power loss and also reduces the interference with

in the network.

In TPC strategy, transmission power is not always set to

maximum, rather an optimum level for transmission is sorted

out with the mutual coordination between the nodes by

establishing necessary feedbacks. The feedback sent by the

receiver lets a node to decide if the transmission power level

�

Fig. 1. A typical Wireless Sensor Network

is needed to be modified and up to what extent to attain

the optimum transmission power levels to fulfill both the

objectives i.e. life time enhancement of the network and

better packet reception rate (PRR). In figure 2, four nodes

are displayed i.e n1-n4 where n1 is acting as a transmitter.

If n1 needs to send a packet to n2, transmission power level

tp1 is suffice, however, tp1 is not sufficient to send packets

to n2 and n3, therefore, n1 defines three different power

levels for each of its neighboring nodes considering efficient

energy utilization and getting optimum PRR. So if a packet

is transmitted by n1 to n3: if tp1 is used then PRR would

be poor, if tp3 is used then unnecessary power is wasted.

Therefore only tp2 serves as optimum power level for this

particular case. In this way, maximum energy can be saved

without degrading the packet reception rate (PRR).

Once the optimum transmission power level is set, it

undergoes a continuous change depending on the channel

behavior and the receiver’s feedback. The feedback serves

as an ensuring factor to certify that the transmission power

does not deviate much from the optimum transmission power

level.

In this paper, experimental study of three well-known TPC

algorithms (ODTPC [1], ATPC [2] and MODTPC [3]) is

performed. The study helps to decide which one is better in

terms of energy saving and packet reception rate. Some of

978-1-4673-4451-7/12/$31.00 ©2012 IEEE

the changes have also been proposed in these algorithms to

improve over all performance of the discussed TPC strategies.

�

Fig. 2. Transmission Power Control in Wireless Sensor Network

The rest of the paper is organized as follows: Related work

is discussed in section II. Detailed discussion of the study

is discussed in section III. Section IV discusses about the

experimental setup and experimental results. Finally, section

V concludes the paper.

II. RELATED WORK

As stated earlier TPC is a well studied topic in the field

of WSN. The main goal of transmission power control

algorithms is to search for the optimal transmission power

level for each sensor node. Hence in such algorithms, power

level switches among different levels based upon channel

state, feedback from the receiver and TPC algorithms, as

each TPC algorithm defines its own mechanism to optimize

the transmission power level. A few of the TPC algorithms

are discussed briefly.

PCBL algorithm [4] starts with an initialization phase which

runs periodically. In the initialization phase, each sensor node

transmits a certain number of beacon packets to each of its

neighboring nodes at a single power level. This process is

repeated for all available power levels. The receiver node then

counts the packets received at each power level and calculates

the packet reception rate (PRR). This PRR is shared with

the sender by using a notification message. Based on this

notification, sender chooses the minimum power level that

has 100% PRR. ATPC [2] adjusts the transmission power

adaptively. The ATPC module does this job and inform

transmitter about the optimal power level. After receiving the

packet, receiver finds the link quality difference by taking

the difference of current RSSI with the RSSI threshold. Link

quality monitor then notifies the transmitter if there is a need

of power level adjustment or not.

In contrast, there is no initialization phase in ODTPC [1].

It starts with transmitting data packets using maximum

transmission power level. The power level adjustment is

according to the feedback from the receiver.

Although ODTPC [1] does not use any initialization phase,

it unnecessarily consumes high energy as it does not change

the power level when the transmission power is within the

threshold boundaries.

A recently proposed TPC strategy, Modified On Demand

Transmission Power Control (MODTPC) [3], provides an

extension of ODTPC [1] in which a node tries to adjust

the transmission power as low as possible. Whenever a

node receives a data packet with RSSI level violating the

defined thresholds, it sends a feedback containing the current

RSSI value. Based upon this value, transmitter adjusts its

transmission power level for next data packet. If feedback is

not sent, transmitter decreases the power level till it touches

the lower threshold level. This process continues for all the

future data packets.

Two other well known power control algorithms are

Multiplicative-increase Additive-Decrease power control

(MIAD PC) and Packet Error Rate Power Control (PER

PC) [5]. PER PC works on the basis of signal to inference

plus noise ratio (SINR), however, MIAD PC adjusts the

transmission power level on the basis of the packet reception

rate (PRR).

All the algorithms discussed have their own strengths and

weaknesses. Both PCBL [4] and ATPC [2] have an overhead

of initialization phase especially in case of the PCBL [4] in

which the initialization phase runs periodically, making the

overhead unbearable. In contrast, there is no initialization

phase in rest of the algorithms discussed. ODTPC [1]

and MODTPC [3] start with transmitting data packets

using maximum transmission power level. The power level

adjustment is according to the feedback from the receiver.

However, both the algorithms unnecessarily consume high

energy as change in the power level follows step by step

procedure which takes multiple feedbacks to adjust the power

level to the optimum settings.

III. TRANSMISSION POWER CONTROL STRATEGIES:

MODTPC, ODTPC AND ATPC

In this section, three well known power control strategies are

discussed with some proposed modifications in ATPC [2], and

MODTPC [3] approach. The unique features of MODTPC [3]

which distinguishes it from other TPC approaches is that it

tries to adjust the transmission power level as low as possible

but in order to accomplish this the MODTPC [3] compromises

on the PRR which highly degrades its performance. In order to

improve the performance of MODTPC [3], an initial margin of

approximately 15 dBm in the lower threshold is recommended.

Along with this the average PRR must be kept in observation.

If the packet loss is still high, this margin can be gradually

increased. On the same basis if the 100 percent PRR is

maintained, the margin window can be gradually decreased.

Lower threshold value of RSSI is adjusted to -75 dBm for

the ATPC [2] and ODTPC as they rarely cross the threshold

level. Sun SPOT [6] using CC2420 [7] radio is tested for

various RSSI level and assures 95% PRR up to -85 dBm

RSSI as shown in Fig. 3, therefore, very few packets are lost.

However, MODTPC [3] algorithm forces it to stay on the lower

threshold to save energy, therefore it frequently crosses the

threshold which results in poor PRR. In order to improve the

PRR, the lower threshold must be set to -70dBm in case of

MODTPC [3] which is also subject to change, depending on

PRR.

In case of ATPC [2], the parameters are defined a little bit

�

-95 -90 -85 -80 -75 -70 -65 -600

10

20

30

40

50

60

70

80

90

100

110

RSSI (dBm)

PRR (%

)

Fig. 3. PRR Vs. RSSI

differently to properly adjust the transmission power levels to

receive the optimum outcome. In ATPC [2], the progressive

levels of the transmission power are obtained by establishing

a line equation (y = ax + b; where x=transmission power

level, y=rssi, a=slope and b=intercept) through the curve fitting

technique on raw data collected during the initialization phase.

ATPC [2] claims that the slope remains the same which is quite

valid as the path loss is independent of the transmission power

and experimental study supports this claim up to a certain

extent. However, in ATPC [2] the relations are mismanaged

and fail to effectively adjust the transmission power level. So

to improve the performance it is necessary to redefine the

procedure followed by the ATPC. After the setup phase aiand bi are evaluated as defined by equation (1) and (2):

ai =N

∑Nj=1 tpj .r

ji −

∑Nj=1 tpj

∑Nj=1 r

ji

N∑N

j=1 tp2j − (

∑Nj=1 tpj)

2(1)

bi =

∑Nj=1 r

ji

∑Nj=1(tpj)

2 −∑Nj=1 tpj

∑Nj=1 tpj .r

ji

N∑N

j=1 tp2j − (

∑Nj=1 tpj)

2(2)

After the initialization of ai and bi, the first transmission

power level is adjusted as defined in equation 3, derived from

the line equation and established by the setup phase to give

first transmission power level adjustment. The transmission

power level undergoes a change with every feedback received,

thus in order to evaluate the optimum transmission power

level the RSSI value received through feedback is taken as

bi, which when applied to equation 3 gives the change in

transmission power necessary to achieve admissible RSSI

levels at the receiver end. The optimum transmission level can

then be evaluated through equation 4, where tpopt is previously

adjusted optimum power level.

tpj =RSSILQ − bi

ai

(3)

tpopt = tp′opt − tpj (4)

IV. EXPERIMENTS AND RESULTS

A. Experimental Setup

In this section the performance analysis of three well

known transmission power control algorithms in WSN is

presented using Sun SPOT(Small Programmable Object

Technology) [6] test bed. These nodes are developed by Sun

Microsystems Inc. [8] . Various plots presented in this section

are obtained through multiple runs of these algorithms on the

test bed. Here, NPC stands for No Power Control and uses

maximum transmission power level. For the test purposes

the RSSI lower threshold level is set to -75 dBm in case

of ODTPC and ATPC and -70 dBm for MODTPC. The

experimental parameters are listed in table I.

TABLE IEXPERIMENTAL PARAMETERS

Experimental Parameters ValuesTransmission Power levels -31 to -3 (0 to -25 dBm)RSSI 0 to -100 dBmRSSILower -84 dBmPath loss Exponent 2.5Carrier Frequency 2.4 GHz

�

0 50 100 150 200 250 300 350 400 450 500-100

-90

-80

-70

-60

-50

-40

-30

-20

Data Packet

RS

SI (

dBm

)

MODTPCATPCODTPCNPC

Fig. 4. RSSI Behavior using NPC, ATPC, MODTPC and ODTPC

The RSSI behavior using NPC (No Power Control),

ODTPC, ATPC and MODTPC is shown in figure 4. The

relative transmission power control of these TPC strategies is

shown in figure 5 which shows whether the change in power

level by the topology is swift or gradual. ATPC has the ability

to change transmission power swiftly which makes it less

vulnerable to packet loss, while the ODTPC and MODTPC

changes the power levels at a rate of one step at a time.

While analyzing the performance of the power control

�

0 50 100 150 200 250 300 350 400 450 500-25

-15

-10

-7

-5

-3

-10

Data Packet

Pow

er L

evel

(dB

m) MODTPC

ATPCODTPCNPC

Fig. 5. Transmission Power Adjustment using ODTPC, MODTPC, ATPCand NPC

algorithms the over all energy saving acts as one of the

most important parameters. A pre defined set of packets are

transmitted using ODTPC, MODTPC and ATPC and NPC

mechanism under similar circumstances and their power

consumption is evaluated. Figure 6 shows the the battery

consumption of ODTPC, MODTPC, ATPC and NPC. NPC

no doubt consumes much more energy as compared to

the three power control strategies. MODTPC outperforms

ATPC and ODTPC in terms of energy saving which makes

MODTPC the optimum choice for the applications where the

node energy is the most critical constraint. But the energy

saving achieved in the MODTPC has a trade off. The PRR

of MODTPC is not very good and offers much higher packet

loss rate as compared to other two strategies. A plot of

PRR is shown in figure 7 in terms of PRR. During the

experiments it was observed that ATPC out performs both

ODTPC and MODTPC in terms of PRR but on the contrary

it also consumes more power than ODTPC and MODTPC.

�

��� ���� ���� ���� ���� ����

���

���

��

��

����

����

����

����

�������� ����������� ��!�"

Fig. 6. Overall Battery Consumption of a Node

�

�� �� �� �� �� ���

����

���

��

���

����

����

����

���

�������

Fig. 7. PRR for NPC, ATPC, ODTPC and MODTPC

V. CONCLUSION

Transmission power control (TPC) is a way to reduce

the energy consumption in WSNs. A lot of work has been

presented in the literature. In this paper, three well known

transmission power control strategies are evaluated and

their performance analysis is presented based upon the

experimental study. From the experimental results, we can

conclude that MODTPC [3] dramatically reduces the energy

consumption as compare to ODTPC [1] and ATPC [2]. It

is also cleared from the experimental results that ATPC [2]

performs better in terms of PRR than the other two strategies:

ODTPC [1] and MODTPC [3].

ACKNOWLEDGMENT

Authors would like to acknowledge the help and support

of all the wireless communication research group members

at ACME Research Center for Wireless Communications

(ARWiC), M. A. Jinnah University, Islamabad.

REFERENCES

[1] M. Kim, S. Chang, and Y. Kwon, “ODTPC - On-Demand TransmissionPower Control for Wireless Sensor Networks,” in Proc. IEEE Interna-tional Conference on Information Networking, 2008.

[2] S. Lin, J. Zhang, G. Zhou, L. Gu, J. A. Stankovic, and T. He, “ATPC:Adaptive Transmission Power Control for Wireless Sensor Networks,”in Proc. ACM Conference on Embedded Networked Sensor Systems(SenSys), 2006.

[3] M. M. Y. Masood, G. Ahmed, and N. M. Khan, “Modified On DemandTransmission Power Control Strategy for Wireless Sensor Networks,” inProc. 4th IEEE International Conference on Information and Communi-cation Technologies (ICICT), 2011.

[4] D. Son, B. Krishnamachari, and J. Heidanmann, “Experimental Study onthe Effects of Transmission Power Control and Blacklisting for WirelessSensor Networks,” in Proc. IEEE Communications Society Conferenceon Sensor, Mesh and Ad Hoc Communications and Networks (SECON),October 2004.

[5] B. Z. Ares, P. G. Park, C. Fischione, A. Speranson, and K. Johansson, “OnPower Control for Wireless Sensor Networks: System Model, MiddlewareComponent and Experimental Evaluation,” in Proc. European ControlConference, 2007.

[6] http://www.sunspotworld.com, “Sun SPOT (Small Programmable ObjectTechnology).”

[7] CC2420 2.4 GHz IEEE 802.15.4 / ZigBee-ready RF Transceiver,http://www.chipcon.com.

[8] http://www.oracle.com/us/sun/index.htm, “Sun microsystems inc.”