Real-time Power Configuration for EnergyConservation in Wireless Sensor Networks

Hoang Duc Chinh and Sanjib Kumar Panda*Department of Electrical and Computer Engineering

National University of SingaporeSingapore

∗Email: eleskp@nus.edu.sg

Abstract—Energy conservation is one of the most importantgoal when designing a wireless sensor network (WSN). Sincethe communication task of the sensor node usually requiresa large amount of energy for its execution, it is essentialto configure the sensor node in such a way that the energyefficient communication can be achieved. In this paper, first thepower consumed by the WSN to perform data transmission isinvestigated. Then it is applied to configure the operation ofthe sensor nodes as well as the cluster-based WSNs with theincorporation of time scheduling for receiving data in practice.The energy consumption of the sensor nodes and therefore theWSN can be reduced, thereby increasing the network life time.Experimental results are provided to illustrate the efficacy ofthe network using the proposed configuration strategy. From theexperimental test results obtained, the WSN lifetime until the 1stsensor node dies can be extended by around 32% as comparedto conventional approach.

I. INTRODUCTION

WSNs are envisioned to be employed in a wide rangeof applications. The WSNs consists of a number of sensornodes. Each sensor node is equipped with a sensing device, alow computational capacity processor, a short-range wirelesstransmitter-receiver and a power supply [1]. The sensor nodesmonitor some surrounding environmental phenomenon such astemperature, humidity, vibration, etc., process and forward thedata towards the sinks or base stations (BSs). In many cases,the sensor nodes are powered by a finite energy source likebatteries that can not be replaced due to location in inacces-sible deployment field or un-economical. Therefore, in orderto enable long lifetime of the WSNs, it is essential to developenergy efficient strategies for the WSNs operation. Wirelesscommunication often dominates the energy consumption in aWSN, thus the communication task needs to be well managedto conserve energy [2].

The radio component of the sensor node operates in differ-ent modes such as: transmission, reception, idle and sleep. Inorder to achieve energy savings for the WSNs, the reduction ofenergy consumption needs to be carried out in all states. Thisrequires a WSN to effectively apply all of the aforementionedapproaches. As such, the transmission power (TX POWER)of the sensor nodes needs to be selected properly to be ableto transfer data successfully with minimum energy required.Listening for data also requires a large amount of energy.In order to reduce energy consumption for listening task,transmission schedule with time synchronization can be madefor the sensor nodes to avoid idle listening, and thus lessenergy can be used for receiving data [3].

Several approaches have been proposed to achieve energyefficient communication in WSNs and extend the network life-time. In multi-hop communication protocols such as MinimumTransmission Energy (MTE) routing protocol [4] or EnergyAware Routing (EAR) [5], the power consumption of thenode is reduced by searching and sending data via the nearneighbor nodes. Collection tree protocol is one of the multihopcommunication protocols that has been realized as reported inthe available literature so far [6]. However, since the protocolsonly focus on finding the best routes to relay the data from thesource to the base station without preprocessing, the issue ofredundancy may become serious in a densely deployed sensornetworks in which the neighboring sensor nodes perceive andtransfer the same information. Thereby, high efficiency ofthese protocols may not be achieved. Cluster-based protocolssuch as Low Energy Adaptive Clustering Hierarchy (LEACH)[7] groups the sensor nodes into clusters, transmission poweris reduced since the member of clusters only need to transmitdata to the cluster heads and then subsequently to the BS.Cluster formation of the WSN also allows to schedule thelistening time for the cluster head to reduce the idle listening.

In this paper, we study the power consumption of thewireless sensor nodes for performing communication task inpractice. The measurements are later used to configure theoperation of the sensor nodes’ radio components to achieveenergy conservation for the networks. TX POWER adjustmentis first applied to a linear network to investigate the energysavings. The power configuration is then performed for bothtransmitting and receiving data in cluster based WSNs. Practi-cal implementation of the proposed power configuration strate-gies are carried out and real time experiments are conductedon a hardware test-bed of the WSN, called IRIS platform.

The structure of this paper is organized as follows: thenetwork operation assumptions and sensor node hardwareplatform are presented in Section II. Section III investigatesthe transmission power consumption and configuration of thesensor node. Section IV proposed a clustering protocol thatuses transmission power adjustment incorporating with thetime scheduling for data reception in the cluster-based WSNs.Section V provides the experimental results and discussions.Section VI gives the conclusions.

978-1-4673-2054-2/12/$31.00 ©2012 IEEE 152

II. PRELIMINARY

A. Assumptions

A scenario in which the sensor nodes are deployed at fixedpositions to continuously monitor a pre-defined parameter inthe environment is considered. The assumptions of networkoperation are made as follows:

- All the nodes and BS are stationary after being deployedin the field.

- Every node can be either a sensing source that perceivephysical parameters and send data or relay node that is ableto receive and forward the data packet to the another node orthe BS.

- All the nodes measure the environmental parameters at afixed sampling rate, Tsample and send it periodically to thereceiver nodes.

- The BS node is within the maximum communication rangeof every node, i.e. the furthest node from the BS is possible tosend data to the BS by using its maximum radio TX POWER,Pmaxtx at the success rate of greater than 90 %.- Every node is considered to be alive if its battery voltage

is above a threshold voltage, Vth, representing the remainingenergy.

B. Hardware platform

The IRIS hardware platform is used to develop and demon-strate our work. The operation of the IRIS node is supportedby TinyOS, a lightweight operating system (OS) specificallydesigned for low-power wireless sensors [12]. It is an opensource OS that enables developers to freely design and im-plement communication protocols and applications for WSNs.The IRIS mote can be powered by any combination of batterieswith a dc output range of 2.7-3.3V [10]. The energy capacityof this power source can be monitored by the node. Brousselyet. al. discuss various battery life prediction methods based ondifferent parameters such as voltage, current and temperature[8]. The most simplistic technique to estimate the battery’sremaining charge is by monitoring its voltage while the systemis in operation. The measurement of the battery voltage is alsoused to evaluate the lifetime of the sensor nodes.

The MEMSIC IRIS sensor node’s radio operation consumesthe maximum energy, the radio component’s current consump-tion is 16.5 − 17.5 mA compared to 8 mA and 8 µA ofthe microcontroller with the active mode and sleep moderespectively [10]. The power consumption of the node, Pnode

for communication task is presented as follow:

Pcomm = PTx + PRx + Pidle (1)

where PTx is the TX POWER of the transceiver that can bevaried, PRx is the power consumed to receive data that isfixed for a particular data packet length and Pidle is the powerconsumption in the idle mode, i.e. listening for the incomingdata. In order to reduce Pcomm, the PTx needs to be adjustedproperly and the Pidle needs to be minimized.

III. TRANSMISSION POWER CONFIGURATION FOR SENSORNODES

A. Transmission Power Measurement

The IRIS hardware platform is equipped with AT86RF230transceiver. This transceiver allows users to adjust theTX POWER among 16 levels from the lowest TX-Level 15 tothe highest TX-Level 0 with the corresponding output powervarying from -17.2 dBm to 3 dBm [11]. The test-bed for themeasurements is shown in Fig. 1. A 3V power supply is usedto sustain the IRIS sensor node.

Fig. 1. Diagram of the experiment setup for measuring current consumptionof data transmission

At each level of the TX POWER, the value of currentconsumption is obtained by measuring the voltage across theseries resistor R. Fig. 2 shows the current consumed to transmitdata with different TX POWER. When the TX POWER isset to the highest level, the current consumption of the nodeis 20.5mA. At the lowest TX POWER level, the current con-sumption is around 13.6mA. By selecting proper TX POWERinstead of transmitting at the highest level, the energy con-sumption of the sensor node can be optimized.

Fig. 2. Current consumption for data transmission with different TX POWERlevels

B. Transmission Efficiency

In order to investigate the selection of the TX POWER, anexperiment is carried out to observe the maximum distance,the sensor node can successfully transmit its data packet tothe receiver at each particular TX POWER level. The radiocomponent of a transmitter node is initially set a TX POWERlevel. The node continuously transmits a total of 100 datapackets to a BS. Each data packet includes the number of

Proceedings of the 2012 IEEE ICCS

153

packets that have been transmitted so far. Number of datapacket received at the BS is recorded and compared to thenumber of transmitted data packets extracted from the packetcontent. The distance was gradually varied and the amountof packets received is observed at each point of distance.Fig. 3 shows the success rate of direct data transmissionbetween two nodes when varying TX POWER at differentdistances from the sender to the receiver in the line of sight.Since the application scenarios are later carried out in indoorenvironment, the investigation is carried out for the 7 low levelof the transceiver TX POWER with the maximum distance is25 m.

Fig. 3. Experiment setup for measuring current consumption of datatransmission

C. Case study on the TX POWER configuration

This section presents a case study on the application ofthe TX POWER configuration for a linear network in whicheach node needs to send data via the nearest neighboringnode towards a sink node, i.e. BS. A four node network isconsidered and the deployment of the nodes are illustrated inFig. 4.

Fig. 4. The deployment of a linear network

At first, each sensor node sends an advertisement mes-sage to the BS that contains the node ID at the maximumTX POWER. The BS identifies the relative distance from thesender to it based on receiver signal strength indication (RSSI).After that, assignment messages are sent by the BS to eachsensor node to inform the node its nearest neighbor. Table.I shows the value of RSSI corresponding to the transmittingdistance in three ranges that is obtained by experiment. Withconsidering the values in Table I, the different TX POWERlevel was assigned. If the distance between the nodes wereless than 1.5meters, the TX POWER level setting would be15; likewise, if it is between 1.5 - 12 meters, the TX POWERlevel setting would be 10 and any distance more than 12meters,the TX POWER level would be 9. Fig. 5 shows the display

TABLE IRSSI VALUE CORRESPONDING TO TRANSMITTING DISTANCE

Distance [m] RSSI value (HEX)0-1.5 > C1.5-12 5-812-16 2-5

of the node arrangement that has been assigned by the BS.It can be observed from Fig. 5 that for the first arrangement,the sink receives the data in the order reflected in the boxstarting from the furthest to the nearest node. The nodes wererandomly rearranged for the second arrangement. As reflectedin Fig. 5, the arrangement of the nodes has been updated afterchanging location of the nodes. This results in a new routefor the data to traverse through, using a multi-hop function,to the sink. With the assignment of TX POWER level to the

Fig. 5. Display of sensor nodes arrangement

nodes, it has reduced the power consumption from the 3Vbattery. The improvements for one sensor node are as shownin the Table. II. As it can be observed that the savings of power

TABLE IIIMPROVEMENT OF THE SENSOR NODE POWER CONSUMPTION

Distance [m] Power Power Percentageconsumption consumption of powerwith max with adjusted savings [%]TX POWER [mW] TX POWER [mW]

0-1.5 61.5 40.8 33.71.5-12 49.2 2012-16 50.4 18

consumption with the three settings are tremendous. When thesensor nodes are very close to each other, i.e. in the range 0-1.5meters, the power savings with proper adjustment of theTX POWER is up to 33%. Meanwhile, in the range of 12-15meters, the power savings achieved is up to 18%.

IV. POWER CONFIGURATION FOR CLUSTER BASEDWIRELESS SENSOR NETWORKS

A. Network Operation

In the communication task, the power consumption for datalistening is also significant that includes the PRx and Pidle.The cluster based protocol is the one that enables energyefficient communication of the WSNs. By grouping the nodesinto clusters with the assistance of data aggregation and fusiontechniques, efficient usage of energy resource is obtained

Proceedings of the 2012 IEEE ICCS

154

because the overall amount of data transmitted to the BS issignificantly decreased, intra-cluster communication enables toreduce the transmission distance of cluster member nodes andthen reduce energy consumption. Furthermore, duty cyclingof the cluster members can be carried out by the cluster head(CH) within the cluster, therefore, member nodes are allowedto enter sleep mode for a longer time. The CH also needs toturn on its radio for a sufficient period of time to receive data,and idle listening can be avoided.

We consider the application scenario in which N sensornodes are deployed randomly in an indoor environmental fieldwith an area 20 × 20m2 to monitor the ambient temperatureas illustrated in Fig. 6. The sensor nodes are organized into kclusters by using a clustering protocol. The proposed clusteringprotocol includes two phases: setup phase and data trans-mission phase. The setup phase performs two tasks: clusterformation and cluster head selection. Meanwhile, during thedata transmission phase, sensor nodes within each clustertransfer data to the CHs; the CHs perform data aggregationand fusion, then send the compressed data to the BS.

Fig. 6. Deployment of the sensor nodes in the deployment field

In this paper, the K-Means algorithm [9] is adopted to formthe clusters. In the clustering setup phase, each sensor nodesends a HELLO message to the BS with the information oftheir geographical location and energy level. The BS computesand allocates sensor nodes to clusters according to theirlocation by using the K-Means algorithm. The average energylevel of all alive nodes are also computed at the BS. Only thenodes which have residual energy higher than the average levelare eligible to be a cluster head candidate for the current round.After forming the clusters, the BS chooses the node amongstthe candidates with highest residual energy within each clusterto be the CH. Then an assignment message is sent to everysensor node in the network containing the information of thecluster it belongs to as well as the time schedule to transferdata. Once the assignment message reaches a sensor node, thenode extracts the network information from this message suchas the CH identification and transmission time schedule, andstores this information in its memory to forward data duringthe data transmission phase. The process of cluster formationand cluster head selection is repeated periodically during thenetwork operation with the time cycle defined by users.

Once all the nodes receive the assignment message, and

the transmission schedule is initialized, the sensor nodesstart to perform the sensing task and transmit data to theCHs. Transmission power level of cluster member nodes isoptimized because of the minimum spatial distance to theCHs achieved by the K-Means algorithm. Furthermore, byusing time scheduling for data transmission, cluster membernodes only need to turn on their respective radio componentsduring the transmission, and turn off after finish transmittingthe data as shown in Fig. 7. Data aggregation and fusion arecarried out at the CHs level, and only the compressed datapacket is sent to the BS. Therefore, the amount of informationtransmission is reduced and thus results in the reduction ofenergy consumption. The radio component of the CH needsto be turned on for a period of time, ton(CH) = TRx +TAGG,where TRx is the period of time needed to receive data fromall the cluster members and TAGG is the period of time theCH spend to aggregate and send data to the BS.

Fig. 7. The sensing and radio operations intervals for both CH an clustermember nodes

V. EXPERIMENTAL RESULTS

The effects of power configuration for the cluster basedWSN on its network lifetime are evaluated and compared ina real life environmental monitoring application. The aboveclustering protocol is developed on the TinyOS operatingsystem for the sensor nodes. The K-Means clustering algo-rithm is implemented in Java and run on a Linux basedcomputer that connects to the BS node. The network hasbeen experimented with a small scale WSN that contains 12MEMSIC IRIS nodes, each one is equipped with an MDA100sensor board [10] that has a built-in thermistor. The sensornodes measure the ambient temperature and send to their CHs.When the CH receives the sensing values from all the member,it computes the average, maximum and minimum temperatureof the cluster. Then these computed values are included inan aggregation packet and sent to the BS. Each sensor nodeis powered by two rechargeable batteries that are initiallycharged to full capacity. Nodes are consider to be dead if theirbatteries voltage drops an amount of ∆Vth. The setting valuesfor experiments are summarized in Table III.

Proceedings of the 2012 IEEE ICCS

155

TABLE IIISETTING VALUES FOR WSN EXPERIMENT

Parameter Value UnitN 12 nodesk 3 clustersTRound 30,000 msTSensing 500 msTDATA 5,000 msTRx 300 msTAgg 50 mstmaxon(CH)

400 mstmaxon(CM)

30 ms∆Vth 100 mV

Figs. 8 shows the current consumption of the sensor nodesduring the data transmission phase of a 4 node cluster. Duringthe data transmission phase, the cluster member tries it best tosend data before dropping the packet during the transmissiontime interval, tmax

on(CM). Meanwhile, the maximum time intervala CH turns on its radio is tmax

on(CH) that includes time forreceiving data packets from all the members, processing dataand transmit the compressed packet to the BS. As shown inFig. 8, the cluster member nodes 1, 2 and 3 turn their radio onfor around 8.5 ms, 5.5 ms and 10 ms respectively, meanwhilethe time interval of turning on radio component of the CH isaround 83 ms for data receiving, processing and transferringto the BS.

Fig. 8. Current consumption during the data transmission phase

The energy conservation is further improved by applyingthe TX Power configuration as presented in Section III. TheTX Power level for sending data is set at each node accordingto the distance from the node to the CH if the node is a clustermember or the distance from the node to the BS if the nodeis a CH. Fig. 9 shows the network lifetime achieved by usingK-Means clustering protocol in two cases. The lifetime of theWSN with the TX Power adjustment applied is extended whencompared with the WSN in which sensor nodes transmit dataat maximum transmission power. As shown in Fig. 9, the firstnode and the last node in the adjusted TX Power networkruns out of energy after around 12 hours and 16.6 hoursrespectively. Meanwhile in the case of maximum TX Powernetwork, the first node dies after 9.06 hours and the last nodecan operate up to 16.3 hours.

Fig. 9. The lifetime of the 12 node WSN

VI. CONCLUSION

In this paper, the power consumption of communicationtask in WSN has been investigated. The transmission powerconfiguration is studied with the MEMSIC IRIS sensor nodes.The results are applied to configure the transmission power ofa linear network and enables 18% to 33.7% power savings. Forfurther improvement, the configuration is performed with theincorporation of time scheduling for receiving data in cluster-based WSN. Experimental results show that the proposedprotocol successfully assists the operation of the network andis able to extend the network lifetime. In addition, the pro-posed power configuration strategy can be applied in variousprotocols to improve the performance of the WSNs.

REFERENCES

[1] I. F. Akyildiz and W. Su and Y. Sankarasubramaniam and E. Cayirci.,’Wireless sensor networks: a survey, Computer Networks, 38, pp. 393-422, 2002.

[2] G. Xing, C. Lu, Y. Zhang, Q. Huang, and R. Pless, Minimum powerconfiguration for wireless communication in sensor networks, ACMTransactions on Sensor Networks Vol. 3, 2, Article 11, June 2007.

[3] M. Moh, E. J. Kim, and T. S. Moh, Design and analysis of distributedpower scheduling for data aggregation in wireless sensor networks,International Journal of Sensor Networks Vol 1, 3/4, pp. 143-155, January2006.

[4] M. Ettus, System capacity, latency, and power consumption in multihop-routed SS-CDMA wireless networks, Radio and Wireless Conference(RAWCON), pp. 55-58, August 1998.

[5] R. Shah, J. Rabaey, Energy aware routing for low energy ad hoc sensornetworks, Proceedings of the IEEE Wireless Communications andNetworking Conference (WCNC), Orlando, FL, March 2002.

[6] O. Gnawali, R. Fonseca, K. Jamieson, D. Moss and P. Levis, CollectionTree Protocol, ACM Conference on Embedded Networked SensorSystems (Sensys), 2009.

[7] Wendi B. Heinzelman and Anantha P. Ch and IEEE and Anantha P. Chan-drakasan and Member and Hari Balakrishnan and and Hari Balakrishnan.,An Application-Specific Protocol Architecture for Wireless MicrosensorNetworks, IEEE Transactions on Wireless Communications, Vol. 1, pp.660-670, 2002.

[8] M. Broussely and G. Pistoia, Eds., Industrial Applications of BatteriesFrom Cars to Aerospace and Energy Storage, Elsevier, ch. 13, 2007.

[9] Lloyd, S. P. Least squares quantization in PCM. IEEE Transactions onInformation Theory 28 (2), pp 129137, 1982.

[10] IRIS Datasheet., http://www.memsic.com/products/wireless-sensor-networks/wireless-modules.html

[11] Atmel, AT86RF230 Datasheet revision E,http://www.atmel.com/devices/at86rf230.aspx, February 2009.

[12] Philip Levis and David Gay., TinyOS Programming. CambridgeUniversity Press, 1st edition, April 2009.

Proceedings of the 2012 IEEE ICCS

156