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Dynamic Power Management in Wireless SensorNetworks: An Application-driven Approach

Rodrigo M. Passos, Claudionor J. N. Coelho Jr,Antonio A. F. Loureiro, and Raquel A. F. Mini

Department of Computer ScienceFederal University of Minas Gerais, Brazil

{passos,coelho,loureiro,raquel }@dcc.ufmg.br

Abstract Energy is a limited resource in wireless sen-sor networks. In fact, the reduction of power consumptionis crucial to increase the lifetime of low power sensor net-works. Several approaches on dynamic power managementhave contributed to reduce the power consumption, but fewof them consider the application constraints to optimize it.In this paper, we propose a new application-driven powermanagement approach, where we model the sensor nodeoperation and the application constraints using the hybrid

automata framework. We also model a real sensor networkapplication for re detection and we show the performanceof our approach in terms of energy drop, comparing it to an Ideal Model and a Naive approach.

I. I NTRODUCTION

Wireless sensor networks represent a recent researcharea, due to their great capability of performing environ-ment monitoring and information collection. However, asensor node has limited resources such as processing andstorage capacity. Furthermore, a sensor node is typicallybattery operated, which means that it is also energy con-strained.

A sensor node can only operate as long as its bat-tery maintains power. Therefore aspects like architecture,communication protocols, algorithms, circuits and sens-ing must be energy efcient. Additionally, a DynamicPower Management (DPM) can reduce the power con-sumption and, consequently, improve the network life-time.

Different DPMs techniques have been proposed to re-duce the power consumption in sensor nodes and in

general battery-powered embedded systems [1][2][3][4].Most of these techniques exploit the sleep and idle states,where the power consumption is lower, following the phi-losophy of getting the work done as quickly as possibleand sleep. Furthermore, the communication task is themajor consumer of energy, and should be performed onlywhen it is really needed.

The DPM has to decide when a sensor node should goto a sleep or idle state and the amount of time to stay there,and even when a transmission task could be done, which isnot a trivial problem. We believe that, in order to devise amore efcient power management mechanism, the appli-cation constraints should be considered, mainly in sensornetworks, that strongly depend on an application.

The interaction between the application sensor node

and the environment may be represented by externalevents, which must be considered when reducing thepower consumption. It is also important to consider thestate of computation when the system turns componentson/off to reduce power. The state of the computation ineach period of time represents the state of the applicationand its restrictions in an instant of time, which can havea direct inuence on the decisions taken by a power man-ager.

Based on the these concepts, we propose a new dy-namic power management technique that considers the

applications constraints to exploit sleep and idle states.Our main goal is to represent the DPM of a sensor node asa hybrid automaton, responsible to handle the applicationcommunication, sensing and processing requirements andthe sensor node hardware, as a unique model.

We use the hybrid automata framework due to its abil-ity to handle both application control and application data,modeling the application behavior in a formal way. Hy-brid systems are usually employed in safety-critical ap-plications, like sensor networks, and it seems to be agood way to represent a DPM technique that considers the

minimization of power consumption, balanced against theneed for real-time responsiveness and the reliable achieve-ment of the application requirements.

There are few proposals that also consider applicationconstraints in their DPM models [4][3]. However, in thebest of our knowledge, there is no existing work on mak-ing an application-driven DPM model using the hybrid au-


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tomata framework in wireless sensor networks.In order to show the performance of our approach, in

terms of energy drop and power consumption, we modela real sensor network application for re detection, andcompare it to an Ideal Model , that describes the best en-ergy usage in a sensor node (not realistic). We also com-pare our model to a naive approach, where no DPM isused, to show our model behavior establishing both alower and an upper bound.

This paper is organized as follows. In Section II, wediscuss the related work. In Section III, the theory of ahybrid automata is briey described and our approach fordynamic power management is explained in details. Sec-tion IV describes an ideal DPM model used to establisha lower bound. In Section V, a case study is presented.We model a real sensor network application for re detec-tion and, based on this application constraints, we use ourdynamic power management approach to control the op-eration of the sensor nodes. In Section VI, we present ourresults and compare our approach to both the ideal andnaive approaches through NS2 [5] simulations. Finally,Section VII presents our concluding remarks and somefuture work.

II . R ELATED W OR K

The sensor network lifetime is highly dependent onthe power consumption performed at each sensor node.A more efcient power management results in a longernetwork lifetime. Several methodologies have been pro-posed, at hardware and system levels, to design energy ef-cient communication process [6], sensor node operatingsystem [7] and sensor node circuits.

In addition, Dynamic Power Management schemeshave proposed to reduce the power consumption by se-lectively shutting down idle components. Much work hasbeen done exploiting sleep state and active power man-agement [1][8], sentry-based power management [9], Dy-namic Voltage Scaling (DVS) [2] and Dynamic Voltageand Frequency Scaling [2], software and operating systempower management and battery state awareness powermanagement.

However, there are few proposals that use applicationconstraints in a DPM scheme. In fact, to our knowl-edge, there are only two studies that address this possi-

bility [4][3]. The work proposed in [4] inuenced our so-lution presented in this paper. In [4], it is proposed an Extended Power State Machine (EPSM) that includes thestate of an embedded program in the power state machineformulation. This EPSM model is used to adapt the Qual-ity of Service (QoS) in communication intensive devicesto ensure low power consumption in embedded systems.

In our approach, we extend these concepts to a wirelesssensor network context, using the hybrid automata frame-work. In [3], the DPM uses an adaptive learning treescheme, where the quality of the shutdown control algo-rithm depends on the knowledge of the user behavior.

In [10], a uniform dissipation model and a hotspot dis-sipation model are proposed. However, those models donot represent the communication among sensors and con-sider that when a event occurs, all nodes inside its area of inuence will immediately see this event, which may notbe an appropriate approach to deal with sensor networks.We believe our model can represent the energy dissipationmodel in a more acceptable way.

Hybrid automatons have been used to characterize theenergy consumption model in sensor networks [11].However, when the detection of a critical event is the maingoal, hybrid automatons can also be used to improve thereliability and responsiveness of the application, by ana-lyzing a critical event, even when the remaining energy iscritically low.

III. A N EW DYNAMIC P OWER M ANAGEMENTA PPROACH

Reducing the power consumption is one of the mainchallenges in wireless sensor networks. In fact, the net-work lifetime depends on how the energy is spent at eachsensor node. Therefore, all aspects in sensor networksmust be energy efcient, such as communication proto-cols and the sensor node architecture.

Several techniques to reduce power consumption canbe applied at the design time, known as static approaches.In contrast, during run time, dynamic techniques can im-prove the reduction of power consumption by selectivelyshutting down hardware components. These techniquesare known as Dynamic Power Management .

Most of the DPM schemes exploit the idle and sleepstates where the power consumption is lower. The ba-sic operation of these schemes consists in deciding whencomponents should be turned off or stand by and whenthey should be turned back on. A wrong decision in thisprocess reects directly in a waste of energy, if a com-ponent is working when it should be turned off, and fur-thermore, can be crucial for the application requirementsif a component is turned off when it should be working,

specially in real time applications.We believe that the decision of turning off/on compo-

nents in a DPM scheme should be highly inuenced bythe application needs. In other words, the computationstate at each instant of time should be considered as themain information to achieve the application requirementsand to reduce the power consumption.


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As sensor networks are highly dependent on an appli-cation, the application constraints represented by the ap-plication requirements and the environment can be usedto optimize the power consumption of a sensor node andto achieve the application goals in a acceptable way.

Embracing the philosophy of getting the work done asquickly as possible and going to sleep, we propose a newDPM model that considers the application constraints tokeep the sensor node, as much as possible, in a sleep oridle state, without losing the necessary real time respon-siveness of the most sensor networks applications. Ourmain idea is to exploit the sleep mode and different sam-pling and transmission rates, according to the environmentchanges and the application requirements. In other words,we exploit the foreseen situations when the environmentvariables change or remain the same, to execute a transi-tion between operation and sleep modes.

A. The Theory of Hybrid Automata

A hybrid system consists of a discrete program withinan analog environment [12]. The hybrid automata is theformal representation of a hybrid system, as a nite-statemachine, where the states are represented as a nite set of control locations. The continuous activities of the envi-ronment are represented by a set of differential equations,dened for each control location. The hybrid automatapresents a framework to represent both discrete and con-tinuous processes of systems embedded in continuouslychanging environments that need to react to changes inreal time, like sensor networks.

According to [12], a hybrid automaton consists of thefollowing components:

1) Variables: a nite set of real-numbered variables;2) Control graph: a nite directed multigraph (V, E ).

The vertices are called locations and the edges lo-cation switches;

3) Initial, invariant and ow conditions: determine thepossible values that can be assigned to the variables.The ow conditions are represented by differentialequations;

4) Jump conditions: determine when a jump operationshould be taken among the locations;

5) Events: a nite set of events that determine whena location switch should be triggered.

In the hybrid automata, the discrete states of the systemare modeled by the vertices of a graph, called locations,and the discrete dynamics modeled by the edges, calledlocation switches. The continuous dynamics of the sys-tem is represented by ow conditions through differentialequations. Each location determines a ow condition andeach location switch may cause a discrete change in the

Fig. 1. Graphical representation of a hybrid automaton

state of the system, determined by a jump condition. Eachlocation continuously observes an invariant condition of the system state. A violation of the invariant conditionwill cause a location switch.

Figure 1 depicts a hybrid automaton. The example rep-resents a system with two locations, l1 and l2 , and just onedata variable x . The system always starts at l 1 and, ini-tially, the value of x is 20. Each location has its own owconditions, invariant and initial values. At the location l 1 ,the value of x increases according to the ow conditionx = 0 .2 + x . A transition from l1 to l2 may be taken atany time after the value of x is higher than 30, accordingto the l1 invariant condition x 30. However, the tran-sition must be taken as soon as the value of x reaches the jump condition x > 31. At location l2 , the rate of x de-creases according to the ow condition x = x 0.5. Atransition from l2 to l1 is taken according to the invariantcondition x < 25 and the jump condition x = 25 .

To make it easier to understand, Figure 1 automaton canbe used to represent a thermostat, that keeps the temper-ature between 25 and 31 degrees Celsius. For a formaldescription of hybrid automata, refer to [12].

B. The Hybrid Automata for Dynamic Power Manage-

ment The hybrid automata represents a framework to handle

both discrete and continuous variables. On the other hand,a wireless sensor network is composed of sensor nodescapable of performing discrete processing and able to re-act to continuous environment changes using samplings.Thus, a hybrid automata seems to be a good way to rep-resent the Dynamic Power Management of a sensor node,according to the application needs and the external eventsrepresented by the environment change rates.

By representing the application behavior in a single hy-

brid automaton, energy can be saved by turning off unusedhardware components of the sensor node. Furthermore, itcan improve the expected responsiveness of the system.

In order to monitor and control the sensor node opera-tion in an acceptable way, it is necessary to know and todescribe the application in the following terms [13]:

1) sensor node hardware;


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2) application sensing;3) application processing; and4) application communication.In fact, the DPM of a sensor node is a hybrid automata

that represents these aspects and the application environ-ment, to optimize the power consumption by keeping thesensor node in a low power mode, as much as possible,without losing the necessary responsiveness of the appli-cation.

In this context, we use the hybrid automata framework

to model different control modes, where only the neces-sary hardware is turned on and different sampling andtransmission rates are performed, according to changesin the environment represented as events. These controlmodes are unique for each application and represented bythe locations of the hybrid automaton.

As the communication operation of a sensor node is themajor power consumer, this operation must be performedcarefully or when extremely necessary, according to theapplication deadlines or due to unexpected environmentchanges. We exploit this concept by keeping the hybridautomaton in a location where lower communication ratesare performed, or none operation is performed, if the en-vironment behaves as expected. Otherwise, if the behav-ior is unexpected, a higher transmission rate can be per-formed by a transition to a specic control mode (loca-tion).

A DPM Hybrid Automata H = ( L,E,Inv,Flow,l 0 )can be described as follows:

1) L is a nite set of discrete states or locations. Eachlocation represents a possible operation mode of thesensor node, where different hardware congura-tions are mapped and different communication andsampling rates are performed. The locations arerepresented by the vertices of the hybrid automaton.

2) E is the nite set of edges. The edges represent thetransitions or events. The events are represented ina hybrid automaton as the jump conditions, where alocation takes a transition to another location. Forthe sensor nodes, the events are a set of externalevents, represented by changes occurred in the en-vironment. The events can also be internal, such asan event related to the battery.

3) Inv denes the invariant conditions assigned to

each location l. The invariant conditions representthe exception set. Each exception indicates whenthe system control (DPM) must leave a location l.Typical exceptions are timeouts and sensor readingsthat trigger a discrete location change.

4) Flow denes the ow conditions represented bydifferential equations. A ow condition in a loca-

tion l indicates the rate of changing of a specicvariable in the system. For our DPM scheme, theow conditions are the key information, due the factthat if a changing rate in the environment is previ-ously known, and the environment behaves accord-ing to this rate, so a transition to a sleep mode lo-cation can be taken, improving the power consump-tion.

5) l0 denes the initial location of the system and ini-tial values for the system variables.

In the DPM Hybrid Automata the locations representa control mode of the sensor node, where different hard-ware congurations are loaded and unused componentsare turned off. Thus, the conguration of the sensor nodeis completely determined by the location in which the con-trol resides.

The transitions among the locations are determinedmainly by external events, represented by the environmentchanges. Thus, the DPM will react differently in each sit-uation, according to the environment changing rate.

Figure 2 depicts a DPM Hybrid Automaton , where thelocations, invariant conditions, events and ow conditionsrepresent a real sensor network application for re detec-tion in forests. The hybrid automaton is dened accord-ing to the application requirements and the environmentexpected behavior.

Fig. 2. Graphical representation of a hybrid automaton for re detec-tion

As showed in Figure 2, the automaton contains threebasic locations. Location L1 represents an inactivity state(sleep) (sensing off, radio off), location L2 and L3 repre-

sent a operating state where the sampling and communi-cation operations are performed in different rates. In L2 Low Sensing , the sensing and the radio are turned on ina lower rate than L3 High Sensing . In fact, the location L2 should be reached when expected changes occur in theenvironment. Otherwise, the location L3 should be used.We also consider that even in low battery situations the


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location L3 should be reached, to achieve the applicationrequirements, even if it results in the sensor node death.

Using the hybrid automata framework, our main ideais based on the fact that, if an unimportant or unexpectedevent happens in the environment, the sensor node shouldbe in a sleep mode. However, this concept must be bal-anced against others related to the application needs (e.g.multihop and re-transmission) according to the sensornode function in the entire network context. As example,if all nodes decide to take a transition to a sleep mode lo-cation, the network will be inactive for a moment, whichcan be crucial to the application requirements.

IV. I DEAL DYNAMIC P OWER M ANAGEMENT M ODEL

The Ideal Dynamic Power Management Model repre-sents unrealistic model, where all sensor nodes have aglobal knowledge about the network and the environment.In this context, the sensor node DPM scheme knows theexact time that re events occur or the exact moment toact as a router for another sensor node. Therefore, thesensor node knows the exact moment to sleep and to wake

up, achieving the application deadlines and improving thesensor node energy consumption.In order to evaluate the DPM technique proposed in this

work, we compare it with an Ideal Model that representsthe lower bound for our technique, in terms of energyconsumption. The main difference between the HybridAutomata Model and the Ideal Model is the fact that inthe Ideal Model sensor nodes have a complete knowledgeabout the network and the environment, resulting in themost efcient DPM scheme for a re detection applica-tion. In the next section, both models are simulated andcompared.

V. C AS E S TUDY : F IR E D ETECTION A PPLICATION

The DPM technique proposed in this work depends onthe application. In fact, the more we know about the ap-plication (behavior, requirements, deadlines), the more re-alistic the DPM hybrid automaton will be, resulting in amore efcient power management in a sensor node. Inorder to show the performance of our technique we havemodeled a sensor network application for re detection inforests, as very few real sensor network applications arecurrently available.

A. Fire Detection

According to INPE [14], the Brazilian Institute for Space Research , there are an average of 243, 000 burn-ing focus during the dryness season in Brazil, from Mayto September. These burnings, most of them man-made,

are the major threat to forests, parks and environmentalprotection areas.

Fire detection systems can help to reduce the damagecaused by burnings. In fact, the ability of quickly and ef-fectively detect and locate a re is at the heart of almostall re detection systems. Most of the re detection sys-tems are based on digital image processing, obtained fromspecic orbital satellites, by nding pixels with a bright-ness temperature above a threshold. On the other hand,re detection can also be performed by other methods of activation, mainly temperature or smoke. In case of tem-perature, the re detection system can be set to trigger analarm at a given temperature or to notify temperature rises.

In this context, wireless sensor networks can be used asa re detection system, due to their ability to collect infor-mation from the environment using sensors. The sensornetwork can be programmed to report temperature rises,air humidity and even wind direction. These data are use-ful to determine the re probability, its direction and itsintensity, helping in a more efcient re combat.

In order to model a real application for re detection, allavailable information about the monitoring area should beconsidered. Our motivating area is the region of Belo Hor-izonte, Brazil , surrounded by ecological parks and protec-tion areas. The available temperature data in degrees Cel-sius, obtained from [14] about this region from May toSeptember, can be described as follows:

1) Maximum absolute temperature: 29;2) Minimum absolute temperature: 8;3) Maximum average temperature: 25.5;4) Minimum average temperature: 15.5.Additionally, we assumed the following temperature in-

formation about re detection:1) The minimum temperature to be considered as re

is 35 degrees;2) Temperature variations bellow 0.5 degrees are con-

sidered normal and do not need to be reported;3) Variations above 5 degrees among samplings in a

short period of time are considered abnormal andshould be analyzed as possible re, even if the tem-perature remains bellow 35 degrees.

B. Hybrid Automaton for Fire Detection

According to the available information about the appli-cation behavior obtained from [14] and according to theapplication deadlines, which determine that in case of rethe data should be sent every second, otherwise it shouldbe sent at least every sixty seconds, we have modeled ahybrid automaton for re detection, to represent the dy-namic power management of the sensor nodes.


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Figure 2 represents a hybrid automaton for re detec-tion, that considers the application behavior and require-ments, and the sensor node behavior (hardware, sensing,communication and processing) as a DPM scheme.

The data variable x represents the temperature and it isthe only external variable considered in this model. Thevariable x represents the environment changes and it isused in almost all transitions among locations as the maininformation. The variable z is used as a timer and it isuseful in locations where the sensing is off, like L1.

According to the application deadlines, we havemapped three basic locations. Each location maps a differ-ent hardware conguration and works with different ratesof sampling and communication, according to the temper-ature changes. Each location behavior is determined bythe ow, guard, jump and invariant conditions.

The location L1 Inactivity represents the sensor nodesleep mode, where the radio and the sensing are turnedoff. The control remains at location L1 for 60 seconds,according to the invariant condition z 60. As soon asthe jump condition z > 60 is reached, a transition to thelocation L2 occurs. In location L1, the sensor node is notable to react to any environment changes and cannot work as router, in a multihop communication.

The location L2 Low Sensing represents a sensor nodecontrol mode, where the sensing and communication op-erations are performed in a lower rate. In fact, while inthis location, the sensor node turns on the radio and thesensing at every 10 seconds and transmits the sensed tem-perature. After transmitting, the radio and the sensing areturned off until the next 10 seconds time-out is reached.

The location L2 should be reached when the environ-ment temperature changes at a known rate, according tothe invariant condition x 29. Temperature changes un-til this value are not considered as re indication. Other-wise, a transition to location L3 may be taken at any timethe temperature is higher then 29 degrees. According tothe jump condition x > 35, the possibility of re is immi-nent, and the control mode L3 should be used.

The location L3 High Sensing represents a sensornode control mode, where the sensing and communica-tion operations are performed in a higher rate than L2. Thesensing and the radio are turned on and the sensed temper-ature is sent at every second. The hardware components

are never turned off in this location until a transition back to L2 is taken. The location L3 is used when the changingrate of the environment temperature is unknown, or thetemperature reaches a risky value, imminent re.

The ow conditions, represented by differential equa-tions, are represented in all locations only for the variabley that represents the energy drop and is not used in the

transition conditions. The ow condition for the tempera-ture x is not represented by the model due to the fact thatwe do not know a differential equation to represent thetemperature behavior in the environment. Instead, we usestatistical data about the monitoring area [14].

C. Basic Operation

In the beginning, all nodes start at location L2. Accord-ing to the location L2 invariant and jump conditions, if the temperature remains the same or the sensed tempera-

ture compared to the last sensed temperature, representedby x old , does not represent a signicant change (the dif-ference is not higher then 0.5 degrees), a transition to thelocation L1 is taken.

The control remains at the location L1 in sleep modefor 60 seconds, where no operation can be performed. Inthis situation, if a re event occurs, the model will have adetection delay of at most 60 seconds, which is not rele-vant for the application. In a normal situation, where notemperature changes are observed or the changes are lim-ited to the normal condition, 0.5 degrees among sensings,

the model will transit between location L1 and L2. In fact,the temperature will be transmitted at every 60 seconds.In the intervals among transmissions, the sensor node willbe in sleep mode, reducing the energy consumption.

In cases that the difference between x old and x arehigher than 0.5 degrees, the control remains at location L2 during 10 samplings, to determine if the temperaturechanging is normal or not. If the temperature keeps get-ting higher, the control remains at location L2 until theinvariant condition x 29, where a transition to location L3 may be taken. These situations represent the temper-ature changes that usually happen in a normal day. Theyare analyzed to avoid a transition to L3, the major energyconsumer location.

Otherwise, if the difference between x old and x ishigher than 5 degrees, a transition to location L3 mustbe taken, even if the temperature remains bellow the L2invariant condition x 29. These situations are not ex-pected and they can represent the start of re. Once inlocation L3, this abnormal change is checked by at least10 samplings, evaluating the temperature at every second.If the temperature does not get higher than the L3 invari-ant condition x 35, a false alarm has been detected anda transition back to location L2 is taken.

Unexpected temperature changes and risky tempera-tures may lead a transition to location L3, where the tem-perature will be sensed and transmitted at every second. Inthese situations, the possibility of re is imminent. How-ever, unexpected hot days at the monitoring season (thetemperature is higher than the L2 invariant condition and


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lower than L3 invariant condition) may lead to a false tran-sition to location L3. To avoid these situations, every timethe location L3 is reached, a 10 sampling operation is per-formed. If the temperature does not reach the invariantcondition x 35, a transition back to location L2 is taken.

The application-driven DPM ( App-DPM ) basic oper-ation is illustrated in Figure 3. Figure 3(a) shows thetemperature variation in 1000 seconds, and Figure 3(b)shows the transitions among the hybrid automaton loca-tions modeled for the re detection application. The rateof the temperature variation is the main information to de-termine the transitions among locations. Therefore, due tothe strong environment changes, the locations L2 and L3are reached more often, even when no re really exists.

Figure 3(c) shows the basic operation of the Ideal DPM model. Due to the global knowledge of this model, a tran-sition to the location L3 occurs only when the temperaturevariation indicates re, between 700 and 800 seconds of the simulation. The rest of the simulation, the sensor noderemains at location L1, in sleep mode. At every 60 sec-onds, the L1 invariant condition is reached, and a transi-tion to location L2 occurs.

As the App-DPM is a more realistic model, the loca-tion L1 is kept as long as expected environment changesoccur. When the temperature increases in an unexpectedway (represented in many situations in Figure 3(a)), moretransmissions occurs. The temperatures variations lowerthan 35 degrees, indicated in Figure 3(a), represent the App-DPM model worst case, causing several unnecessarytransitions to L2 and L3. However, the temperature vari-ations are just illustrative to show the model basic opera-tion and it cannot be considered as a real behavior of themonitoring eld.

In fact, the Ideal DPM will have a better performance,against the App-DPM model, in almost all situations.However, the power consumption in both models are verysimilar, as indicated in Figure 3(d) that indicates the sen-sor node energy drop when the App-DPM and the Ideal DPM models are used. When expected environmentchanges happen, the sensor node is kept in location L1(sleep mode) for both models, and the energy drop is al-most linear. Otherwise, unexpected changes require moresampling and communication operations, and much morepower is consumed. However, more transitions lead to

more responsiveness of the system, and must be balancedagainst the power consumption, which is fully determinedby the applications needs.

Using this application-driven DPM technique, in a nor-mal temperature behavior day, each sensor node will be insleep mode 97% of the day, assuming that the sensor nodeis able to transmit directly to the sink node, where no mul-

tihop communication is needed. In a single hop commu-nication, the shutdown process is easier, once the sensornode does not need to worry about the neighborhood sen-sors, because they are able to transmit to the monitoringnode by their own.

VI . P ERFORMANCE E VALUATION

In order to evaluate the performance of the application-driven DPM technique proposed in this work, we com-pare it with an Ideal DPM Model , using the re detectionapplication as the motivating example. We use the idealmodel as a lower bound, in terms of energy consumption,to show our technique performance.

We also compare our DPM technique with a naive ap-proach, where no DPM technique is used. In the naiveapproach, the temperature data is sent, from the sensornodes to the sink node, at every 5 seconds. We use thenaive model as an upper bound, in terms of energy con-sumption, to illustrate the performance of a sensor nodeusing a DPM technique against the performance of a sen-sor node without a DPM scheme.

We have implemented the three models using the ns2simulator [5]. In the following, the simulation setup andthe performance evaluation analysis are discussed.

A. Performance Metrics and Simulation Setup

The simulation scenario, used in all simulations, con-sists of a wireless sensor network composed of 100 sen-sor nodes distributed in a 50 50m 2 eld. As we do notconsider the multihop operation in this work, we assumethat sensor nodes are capable of transmitting directly tothe sink node, in a single hop transmission. Due to thisfact, the monitoring node is positioned in the middle of the eld, at position (25, 25). We also assume that allnodes are xed, positioned at a random ( px , p y ) positionin the monitoring eld.

Each sensor node has an initial energy of 100J (joules).The energy consumption in an idle or sleep mode and theenergy spent in a transmission or sensing operation arebased on the Mica2 node power consumption [15].

The environment is represented as a temperature grid,where each ( px , p y ) position of the monitoring eld hasa temperature value, according to the possible tempera-

ture values for the monitoring region. Thus, every sensingoperation is made by getting the given temperature at thesensor node xed position ( px , p y ).

Environment temperature changes are simulated asevents. Each event represents a new temperature value fora specic region in the monitoring eld. The events occurin a random ( px , p y ) position. If two or more events affect


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Fig. 3. Comparison between the App-DPM model and Ideal DPMmodel basic operation

the same region, the new temperature value is obtained byan average among the events temperature values.

The events are static (no movement) and have a xedsize. The event size represents the inuence region, de-termined by the event inuence radius. According to theinuence radius, a position ( px , p y ) in the eld may beaffected by a new temperature value that represents an en-vironment change for the sensor nodes positioned in thesame region. In all simulations, the event inuence radiusis uniformly distributed between 5 and 50m .

The events behavior also include a duration parameter.Each event has a established moment to start and to end.In all simulations, we assume that the event duration isuniformly distributed between 25 and 200 seconds.

However, the most important information about anevent is the re probability. This parameter determineswhen each event represents a re temperature. There temperature represents the main inuence in theapplication-driven DPM scheme since the sensor network application represents a re detection system.

Finally, the event arrival model follows a Poisson dis-tribution. This process is appropriate to model eventsthat happen randomly and independently from each other.We use the Poisson process to distribute 250 temperaturechange events in simulations of 5000 seconds.

B. Simulation Results and Analysis

According to the simulation setup, described above, weperformed simulations to evaluate the performance of theapplication-driven DPM approach, addressed in this sec-tion as App-DPM , against an Ideal DPM model and a Naive approach. Basically, the models are compared interms of power consumption and energy savings. Themost relevant information is related to the environmentchanges, represented as re probabilities.

Figure 4 shows the result of a comparison among the App-DPM , the Ideal DPM and the Naive models. The re-sult indicates the behavior of the three models that repre-sent the energy spent (or the energy drop) in a sensor nodepositioned in the center of the monitoring eld, after 5000seconds of simulation, for different re probabilities.

As expected, in the Naive approach, where no DPMscheme is implemented, the total energy spent is constantto whatever re probability. The indication of re does not

modies the model behavior. We use this result to showthe gains of a DPM approach that considers the applica-tion behavior into the power management scheme.

As indicated, the higher the re probability, the higherthe power consumption will be for both App-DPM and Ideal DPM models. It happens because re indicationsdemand more communication operations by the sensor


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node. As modeled by the DPM hybrid automaton (Fig-ure 2), the node spends more time in the Location 3 , dueto the fact that an unexpected environment behavior mayhappen, as the re probability increases. For lower reprobabilities, the node spends more time in the Location1, in a sleep mode, and more energy is saved.

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Fig. 4. Energy consumption of models according to the re prob

The Ideal DPM model represents an unrealistic behav-

ior. The better performance of this model is due to thefact that, unexpected environment changes that do not rep-resent re, are not analyzed by the Ideal Model due to itsglobal knowledge about the network and the environment.

The Naive and the Ideal DPM models behave as a lowerbound and an upper bound in terms of energy consump-tion, until the re probability reaches 70%. After thispoint, the re indication is presented at almost all the sim-ulation time, and the node performs much more transmis-sion operations in the App-DPM and Ideal DPM modelsthan in the Naive model. In fact, a transmission occursat every 5 seconds in the Naive model, for whatever reprobability, and at every second for the other models inhigher re probabilities. Although much more energy isconsumed at higher re probabilities, the sensor presentsa better responsiviness, as required by the application.

The Ideal DPM model spends more energy than the App-DPM in higher re probabilities, due to the betterresponsiveness of the Ideal DPM model. In this model,there is no detection delay, due to the global knowledge of the model. Thus, more transmissions occurs in the Ideal DPM . On the other hand, if an unexpected environmentchange occurs while the App-DPM is in a sleep mode (Lo-

cation 1), there will be a detection delay, which is muchmore realistic. However, the behavior of the Ideal DPM and the App-DPM models are very similar, which showsthe good performance of the App-DPM approach.

In fact, in the App-DPM , the detection delay will be atmost 60 seconds, which is not relevant for the re detec-tion process. Therefore, the greater the sleep time, the

more efcient the energy consumption will be, and theworse the application responsiveness will be.

The results presented in Figure 4 can be reinforced bythe information presented in Table I. In situations that theenvironment temperature changes at a known rate, and nore really happens, the App-DPM can result in a gain of 266.92% in energy saving, over a Naive approach. Unex-pected situations, when the re probability is higher, indi-cate that the App-DPM model spends more energy. How-ever, the application responsiviness is increased. We con-sider that the sensor node should react to an unexpectedenvironment change to perform its duty, even it causes thesensor node death. In fact, Table I indicates that the App-DPM performance is very similar to the Ideal DPM ,showing that the App-DPM model is able to achieve theapplication responsiveness requirements, improving thepower consumption.

TABLE IPERCENTAGE OF A PP -DPM ENERGY SAVINGS WHEN COMPARED

WITH THE IDEAL DPM MODEL AND THE NAIVE MODEL

Fire App-DPM App-DPM Probability Ideal DPM Naive

0% 62.72% +266 .92%10% 55.15% +226 .21%20% 32.46% +176 .87%30% 34.51% +151 .19%40% 17.93% +40 .03%50% 14.18% +22 .10%60% 6.59% +10 .29%70% +4 .69% +3 .33%80% +5 .94% 5.87%90% +3 .81% 20.52%100% +0 .99% 26.87%

Average 18.69% +76 .69%

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Fig. 5. Remaining energy of each sensor after 5000 seconds of simu-lation and re probability of 0%


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Fig. 6. Remaining energy of each sensor after 5000 seconds of simu-lation and re probability of 50%

The Ideal DPM and the App-DPM similarity, and thebetter performance of the App-DPM against the Naive ap-proach are illustrated again in Figure 5. This gure rep-

resents the nal energy map of the sensor network in there detection monitoring eld. It shows the remaining en-ergy at each sensor node, after 5000 seconds of simula-tion, with re probability of 0%. As no re events occurs,the node operation and energy dissipation are exactly thesame for all models.

Figure 6 represents the result for a re probability of 50%. We can see that the App-DPM model is similar tothe Ideal DPM model. The energy dissipation is very ir-regular in this situation, because events occur at randompositions and have different durations. Even at a higherre probability, the DPM models present a more efcientpower consumption.

VII . C ONCLUSION AND F UTURE W OR K

In this work we proposed a new dynamic power man-agement technique that considers the application require-ments and the sensor node operation as a unique model, toachieve low power consumption balanced against the re-quired application responsiveness. We achieve low powerconsumption by exploiting sleep states, when the environ-ment changes as expected. We also improve the applica-

tion responsiveness by increasing the sampling and com-munication rates, when the environment does not behaveas expected.

A case study was presented for a re detection applica-tion that considers the temperature behavior to determinethe application responsiveness and to achieve lower powerconsumption. We showed, by simulation, a signicant

power reduction when this technique was applied, com-pared to a model with no DPM scheme. We also showedthat our technique is very similar to an ideal DPM scheme.

This work also presents two more contributions. First,the DPM technique represented by the hybrid automataframework. Second, our DPM technique seems to repre-sent a more realistic energy dissipation model, which isparticularly useful for the construction of energy maps,based on prediction techniques [16].

However, our DPM technique has some limitations.The main limitation of our technique is that we do notconsider multihop operations into the power managementscheme. It is proposed as future work, as we concentratedour efforts to show the power savings of an application-driven DPM approach. We also intend to compare ourDPM technique to a real DPM scheme and to model theenergy spent when turning on/off hardware components.

R EFERENCES[1] A. Sinha and A. Chandrakasan, Dynamic power management

in wireless sensor networks, IEEE Design & Test of Computers ,vol. 18, pp. 6274, 2001.

[2] IBM and MontaVista Software, Dynamic Power Management for Embedded Systems , 2002.

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[4] A. Zuquim et al, Efcient power management in real-time em-bedded systems, IEEE International Conference on EmergingTechnologies and Factory Automation - ETFA03 , 2003.

[5] K.Fall and K.Varadhan, The ns manual, http://www.isi.edu/nsman/ns/index.html.

[6] M.Perillo and W.Heinzelman, Sensor management and routingprotocols for prolonging network lifetime, Tech. Rep., Univer-sity of Rochester, 2003.

[7] J. Hill, R. Szewczyk, A. Woo, S. Hollar, D.E.Culler, and K.S.J.Pister, System architecture directions for networked sensors, Architectural Support for Programming Languages and Operat-ing Systems , pp. 93104, 2000, http://webs.cs.berkeley.edu.

[8] B. Brock and K. Rajamani, Dynamic power management forembedded systems, IEEE SOC Conference , 2003.

[9] J.Hui, Z.Ren, and B.H.Krogh, Sentry-based power managementin wireless sensor networks, Second International Workshop onInformation Processing in Sensor Networks, 2003.

[10] Y. J. Zhao, R. Govindan, and D. Estrin, Residual energy scansfor monitoring wireless sensor networks, IEEE WCNC02 .

[11] S. Coleri et al, Lifetime analysis of a sensor network withhybridautomata modelling, WSNA, 2002.

[12] T. A. Henzinger, The theory of hybrid automata, Proc. of LICS ,pp. 278292, 1996.

[13] K. Sohrabi et al, Protocols for self-organization of a wirelesssensor network, IEEE Personal Communications , vol. 7, pp.1627, 2000.

[14] INPE, Brazilian insti tute for space research,http://www.inpe.br.

[15] V. Shnayder et al, Simulating the power consumption of large-scale sensor network applications, SenSys, November 2004.

[16] R. A. F. Mini, M. V. Machado, A. A. F. Loureiro, and Badri Nath,Prediction-based energy map for wireless sensor networks, Ad Hoc Networks Journal , 2004.

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