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A Fuzzy Approach to Data Aggregation to ReducePower Consumption in Wireless Sensor Networks

Beatrice Lazzerini+, Francesco Marcelloni+, Massimo Vecchio, Silvio Croce+, Emmanuele Monaldi++Dipartimento di Ingegneria dell 'Informazione IMT- Institutions, Markets, Technologies

University ofPisa Lucca Institute for Advanced StudiesVia Diotisalvi 2, 56122 Pisa - ITALY Via San Micheletto 3, 55100 Lucca - ITALY

{b.lazzerini, fmarcelloni}jing.unipi.it massimo.vecchio@imtlucca.it

Abstract - In this paper, we propose a novel distributed help reduce energy consumption because aggregation allowsapproach based on fuzzy numbers and weighted average limiting transmission/reception of messages and thereforeoperators to reduce power consumption in wireless sensor reducing the time in which the radio is on.networks when we are interested in the estimation of an To the best of our knowledge, four types of dataaggregated value such as maximum or minimum temperaturemeasured in the network. The basic point of our approach is that aroeaion thechiquest for senra network have beeneach node maintains an estimate of the aggregated value. Based proposed in the literature: hierarchical data aggregation [15-on this estimate, the node decides whether a new value measured 17], gossip-based aggregation [6] [8], flooding-basedby the sensor on board the node or received through a message aggregation [8][10] and a mixture of gossip and flooding oneshas to be propagated along the network. We present the [9]. In particular, hierarchical data aggregation requires toapplication of our approach to the monitoring of the maximum maintain a hierarchical structure of the network. Aggregationtemperature in a two-hundred square meters flat and discuss is performed at each intermediate node along the path fromhow the lifetime of the network can be determined through the the leaves to the root. Thus, network traffic and workload ofdatasheet of the used mote and the number of received and sent the high-level nodes are reduced. Gossip-based aggregationmessages. does not require a specific topology of the network: when an

I. INTRODUCTION aggregation has to be computed, each node exchanges datawith its neighbors. The node sends data to a randomly chosen

The recent advances in micro-sensor technology, neighbor and waits for a time interval. At the end of theembedded computers and low-power analog/digital electronics interval, with high probability the data have been received andhave enabled the development of wireless ad-hoc sensor the aggregation can be performed. Instead of choosingnetworks (WASNs) as a new class of systems used in diverse randomly a neighbor, in flooding-based aggregation a nodeand useful applications [1] [2] [3]. WASNs of the future are sends a broadcast message to all neighboring nodes and waitsenvisioned to consist of hundreds of inexpensive nodes, called for data. Flooding is more efficient than gossip, but needs amotes, that can be readily deployed in physical environments fine tune in order to avoid implosion, overlapping and sourceto collect useful information (e.g., seismic, acoustic, medical blindness.and surveillance data) in a robust and autonomous manner. In this paper, we propose a novel distributed approachNowadays, there are still a lot of problems to be solved in based on fuzzy numbers and weighted average operators toorder to achieve this perspective [4]. Sensor nodes are usually perform an energy efficient flooding-based aggregation indeployed in hostile environments: as a result, nodes and wireless sensor networks. We assume no specific topology ofcommunication links are prone to failure. This makes the use the network. A distributed approach is desirable because thereof distributed architectures desirable [5-10]. Further, since is no need to create and maintain a hierarchical structure of thenodes are battery-powered, energy consumption is one of the network. In contrast to tree-based approaches that obtain thecritical aspects in the design of wireless sensor networks. aggregates at a single (or a few) sink node, the algorithm weDatasheets of commercial motes show that the radio on board propose maintains the aggregate at each node and thereforenodes is the main cause of energy consumption. Thus, in order each node can interpret the role of sink. Further, our algorithmto save energy, communication among nodes should be is more efficient than gossip-based algorithms because wereduced as much as possible without affecting the accuracy of take advantage of the broadcast nature of wirelessthe network. This is the reason why most of the research in transmission: all nodes within the radio range can hear anddistributed architectures has focused on power-aware routing receive the signal of a wireless transmission.protocols [11] [12] [13] [14]. In addition, the information of The basic point of our approach is that each nodeinterest is not generally the data at a single node, but an maintains an estimate of the aggregated value. For instance, ifaggregate of these data such as the maximum humidity or the the objective of the application is to monitor that theminimum temperature in the network. This characteristic can temperature in the network does not exceed an alarm

This work is partially supported by MIUR in the framework of project #2005090483_005 "Wireless sensor networks for monitoring natural phenomena".

1-4244-0363-4/06/$20.OO ©C2006 IEEE

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threshold, each node maintains the estimate of the maximum vk measured by the sensor on board the mote. The output oftemperature. To take the measurement error into account, we the module is the fuzzification of vk as the symmetricrepresent the value measured by the sensor as a symmetric triangular fuzzy number Vk= (Vk, VSk). The module Messagetriangular fuzzy number. It follows that the estimate of the Analyser is activated whenever node nk receives a messageaggregated value is a symmetric triangular fuzzy number in its from a neighboring node, say ni. The Message Analyzerturn. The aggregation is performed by each node in the extracts the information from the message and decidesnetwork when either a new value is available from the local whether this information has to be sent either to the Table ofsensor or a new value has been received from a neighboring the Estimates or to the Aggregation and Decision modules.node. The information is the estimation Ei of the global aggregated

The aggregation is implemented as a weighted average function maintained in node ni. The Table of the Estimates hasbetween the estimate and the new value, where weights are an entry i for each neighboring node ni: the entry contains theinversely proportional to the degree of uncertainty of the two last value received from node ni. The Aggregation modulefuzzy numbers. Actually, the aggregation is executed only if aggregates either Vk or Ei with Ek. The output of the module isthe two fuzzy numbers intersect each other. If there is no a new estimate nEk, which replaces the old estimate Ek and isintersection, the new estimate corresponds to the largest or input to the Decision module. Based on the Table of thesmallest fuzzy number depending on the objective of the Estimates, the Decision module decides whether nEk isapplication (estimating a maximum or a minimum). relevant and therefore has to be sent to the neighbors or not. In

Once the new estimate has been computed, the node the case of maximum (minimum) estimation, if the newdecides whether the new estimate must be sent in broadcast to estimate is larger (lower) than at least one entry in the tablethe neighboring nodes or not, based on a local table which and the degree of uncertainty of this entry is higher than thecontains the values received from each neighboring node. one of the new estimate, then the new estimate is sent in

We discuss and evaluate the application of the algorithm broadcast; otherwise no communication occurs. If the variableto the monitoring of the maximum temperature in a 12-node we are monitoring varies slowly, the amount of exchangedreal wireless sensor network deployed in a two-hundred messages is very slow. In the following, we analyze the singlesquare meters flat. We used Tmote Sky motes from Motelv as modules in detail.nodes. We show that our distributed algorithm promptly reactsto changes of temperature, maintaining the total number of Nodenkmessages exchanged among the nodes very low. In the Vk vhypothesis of using the B-MAC protocol, we also compute an F z

estimation of the node life-time. |eboardmon nboard mote Aggregation Decision ENNE1.

Send message11. OVERVIEW OF THE APPLICATION to neighbors

Since nodes are battery-powered, energy consumption is (niEi)one of the critical aspects in the design of wireless sensornetworks. Datasheets of commercial motes show that the radioon board nodes is the main cause of energy consumption. In from a neighbor ni El cl vi

particular, for each reception/transmission of one byte, radios Analyzer (n,E,)typically use a current of the order of some mAs. Thus, in nc Ec cc vc

order to save energy, communication among nodes should be TableoftheEstimates

reduced as much as possible without affecting the accuracy of Fig. 1. Block diagram of the application deployed on each node.the measures performed in the network. To this aim, we III. THE AGGREGATION MODULEpropose a distributed approach based on fuzzy numbers andweighted average operators to enforce nodes to communicate The Aggregation Module compares either Ei (ei, esi) or Vkonly when it is necessary. (Vk, vsk) to Ek (ek, esk) and produces a new estimate nEk (nek,

The basic idea of our approach is that each node nesk). In the following, we identify Ei (ei, esi) and Vk (Vk, vsk)maintains an estimation E of the global aggregated function. with I (i, is). The Aggregation Module behaves differentlyThus, when the node receives a new value from a neighbor or with respect to the following diverse cases:measures a new value from the sensor can decide whether the 1. I r- E = 0 The new value nEk depends on the type ofnew value has to be forwarded or not. To take themeasurement error into account, we represent the value aggregate we are monitoring in the network:measured by the sensor as a symmetric triangular fuzzy i. if the aggregate is the minimum, then nEk= min (, Ek);number V= (v, vs), where v is the central value and vs is the ii. if the aggregate is the maximum, then nEk =max (I,spread of the triangle. The spread is proportional to the Ek);measurement error. It follows that estimation E is a fuzzy 2. I n Ek .0°. The new value nEk is computed as follows:number in its turn and is represented as E =(e, es). nek = ek*Wk+ i * WI (1)

Figure 1 shows the modules deployed on each genericnode nk. The module Fuzzifier is activated by each new value felCS = CSkc WE + ZS W (2)

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is esk- sent or not. To take this decision, the Decision module usesWhere w/Ak es +is e w, = + the Table of the Estimates. The behavior of the DecisionwhereWE -esk- + is esk- + is

module depends on the type of aggregate, minimum orThe aggregation is therefore performed only if the two maximum, we are interested in. In the following, we consider

input estimates are comparable; otherwise the new value of the two cases separately.the estimate depends on the type of aggregate we are . .moioig * Minimum. The message iS sent if, in the Table of themonitoring. Etmts

IV. THETABLE OF THEESTIMATES 3j: (e1 -nek) >ol, 71 >0, e (es1 -nesk)>> 2

The Table of the Estimates is shown in Fig. 2. * Maximum. The message is sent if, in the Table of theEstimates,

Node Estimate Counter Validity 3j: (nek - ej) > ol, or, > 0, e (es1 - nesk) > U2

ni (ei,esi) Cl bi where ul and u2 are two parameters chosen by the~~~~~~~~~user.

ni (ei,esi) C1 bi In other words, the message is sent only if the modulebased on the Table of the Estimates considers that there existssome neighboring node nj with an estimate that is not updated

nNi (eN1,esN1) CNi bNi with respect to new estimate nEk. If the value nEk has beenFig. 2. Table of the Estimates. computed as a consequence of the reception of a message sent

Each entry of the table collects information about a by node ni, then the fuzzy value E, contained in the message is

neighboring node ni. The first column contains the identifier used to update the corresponding entry in the Table of theof the neighboring node. The second column stores the last Estimates. Note that the table is updated after taking theestimate Ei which node nk has received from node ni. The third decision on sending the message. This allows to send a

column contains a counter c .which is increased whenever message even if all the entries, except for ni, are updated. The

node n1k sends a message. The counter is set to 0 whenever a message is important because it serves as acknowledgementnew estimate Ei is received from node ni. The fourth column for node ni.stores bit bi which indicates if the entry is valid or not. The bit The algorithm used by the Decision module causes theis initialized to 1. When counter c.exceeds a value a fixed by following problem. Let us assume that node nk has Nthe user, bit bi is set to 0. The value of the counter can exceed neighboring nodes and nEk results to be the most updateda when: among all the values in the Table of the Estimates. Then, the

module decides to send a message to update the neighbors.* node ni is dead (for instance, because battery is When one of the neighbors replies, the received message

exhausted); triggers the activation of the Aggregation module with the* node n, cannot (temporally or definitively) communicate consequent generation of a new estimate nEk which is

directly with node nk. The reason can be, for instance, the analyzed by the Decision module. Since the entries in themovement of node ni which brings nk out of the radio Table of the Estimates are not updated, the Decision modulerange of ni. decides to send another message with the same information as

The invalidation of an entry of the table is very important the previous sent message. This behavior would produce atleast N sent messages. The problem is avoided by the Messagefor allowing, as explained in the next section, the Decision . .

module to decide whether the new estimate has to be sent or Analyzer module. Before describing this module, let us

not. When validity bit bi is set to 0, counter c.is reset too. examine the structure of a generic message sent by node nk.Whenever node nk sends a message, counter c1 is increased. Figure 3 shows this structure. The first field is the identifier ofWhen counter ci exceeds a value i fixed by the user, the the sender (actually, this field would not be necessary because

entry i vthe sender is inserted automatically into the message at MACentry is removed from the table. Indeed, we are quite sure that level). The second and third fields contain the central valuenode nTcannot communicate with node rk anymore. and the spread of new estimate nEk. The values of the fields

The Table of the Estimates is generated dynamically: at id replYk and id aggk depend on the event that has activatedeach message received from a neighboring node ni, if the Aggregation module and therefore produced new estimateidentifier ni is not in the table, a new entry is created. In the teArnew entry, the Message Analyser module or the Decision k

module copy the identifier n1 and the estimate Ei. Further, they 1. If the activation of the Aggregation module has beenset counter c, to 0 and validity vi to 1. caused by the reception of a message from a neighboring

node, say n,, then the values depend on the type ofV. THE DECISION MODULE aggregate, minimum or maximum, we are interested in:

The role of the Decision module is to decide whether the *Mnmm fE>E,te drpy 1 dagnew value nEk computed by the Aggregation module has to be

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id aggi and nagg =id_agg1; otherwise, id replyk= nk estimation, if the fuzzification Vk of the value vk measured byand id_aggk = nagg; we consider Ei > Ek if ei > ek. the sensor does not intersect Ek and Vk is lower (higher) than

* Maximum. If Ei < Ek, then id replyk = ni, id_aggk Ek, then the central value ek of Ek is preserved, but the spreadid_aggi and nagg id_agg1; otherwise, id replyk= nk esk is increased by a percentage 3 fixed by the user. In thisand id_aggk = nagg; we consider Ei < Ek if ei < ek. way, the spread of the symmetric triangular fuzzy number is

increased at prefixed instants. Increasing the spread, we also2. If the activation of the Aggregation module has been.. enhance the probability that Ek intersects Vk (or E1), thuscoausd byhmeasuringanodhewd vpluk from the senst ond performing the aggregation and certainly generating a value

board the node,thendreplyk -1bydefaultand nEk lower (higher) than Ek. The new estimate would not beid aggk= nagg nk- sent, however, if we do not modify the Decision module too.

Besides the messages sent by using the algorithm described inl nk nek nesk idreplyk idaggk Section V, the Decision module also will send new estimate

nEk if:Fig. 3. Structure of a generic message.

* in the case of Minimum estimation, 3j: (nek - ej) > XThe role of field id_replyk will be clear in the next section. in the Table of the Estimates,

Field id aggk allows propagating the identifier of the node * in the case of Maximum estimation, 3j: (e1 - nek) > Xwhich has measured the maximum (or minimum) value in the

network. ~~~~~~~~~~~~~~~inthe Table of the Estimates,network.

VI. THE MESSAGE ANALYSER MODULE Obviously, values 3 and X are chosen so as to followthe decreasing of the maximum temperature more slowly than

The Message Analyzer Module analyses the message the increasing.received by the node and decides whether the informationcontained in the message has to be forwarded to the VIII. POWER CONSUMPTION: AN EXAMPLEAggregation and Decision modules or simply stored in the The modules described in the previous sections wereTable of the Estimates. Obviously, if the Aggregation and implemented in nesC and were loaded on a network of twelveDecision modules do not receive the information as input, Tmote Sky motes to monitor the maximum temperature in athey are not activated and no transmission occurs. The 200m2 flat. Monitoring maximum temperature can be useful todecision is based on the analysis of the field id_reply. Let us detect possible fires. Fig. 4 shows the plant of the flat and theassume that the message has been received from node ni. position (black circles) of the motes in the rooms. The motes,

* If id_replyi = nk, then the module updates the Table of which are connected to one mote through arrows, representthe Estimates without forwarding the value Ei to the the neighborhood of this mote. Neighborhood is determinedAggregation module; in this case, node nk is receiving a by the radio range. In our experiments, we set the radio rangemessage which is an acknowledgment to a previous to the minimum power (RADIO_POWER=3) so as to allowmessage sent by node nk itself Thus, no further multi-hop communication.estimate, which is not already known by theneighboring nodes, has to be propagated.

* If id_replyi . nk, then the module sends the value Ei tothe Aggregation module; in this case, the message isnot a reply to a message sent by node nk, but rather thecommunication that a new value is being propagatedthrough the network.

VII. HANDLING ESTIMATE STALENESSA

Let us suppose to use the application described previouslyto estimate the trend of the maximum temperature during aday (0-24). The temperature will increase during the firsthours of the day until to achieve the highest value atapproximately 12:00. Then, the temperature will start todecrease. The aforementioned modules allow following theincrease of the maximum temperature, but not the decrease.Thus, at 23:00 we have that the maximum temperature in the 'network iS Still equal to the highest temperature measuredFi4.Patothexmlfa.during the day. In order to allow our application to follow the Fiue5sosterndfthmammtmpauecurrent maximum temperature, we have to slightly modify the mesrdi h ewr uig 4husts.W bev

Aggregationand Decisio modules. As regards. th that the maximum temperature decreases in the time intervalAggregation module, in the case of maximum (minimum)

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[11:00-19:30] because the heating system is turned off. At TABLE II19:30 the heating system is turned on and the maximum TiME AND CURRENT CONSUMPTION OF TmOTE SKYtemperature increases from 17 °C to 20 'C. At 21:00, the Operation Time (ms) I (mA)heating system is turned off and the maximum temperature Initialize radio (b) tINIT 0.47 CINIT 14decreases till 18.5°C. Suddenly, at 23:00 the temperatureincreases till 21°C. This increase is caused by one of the Turnonradio(c) toN 1.42 CON 1inhabitants of the flat who had a shower, so increasing the Switch to RXITX (d) tsw 0.212 csw 14temperature in the bathroom. Time to sample radio (e) tSR 0.288 CSR 21

Evaluate radio sample (0 tEV 0.197 CEV 14

U ,,0u 0 S ; wF v 0 -V--H0W-0i0XX-'-l-1--11--1IReceive]ebytetRXB0.032 CRXB 19.7

Transmit] byte tTXB 0.032 CTXB 17.4

The total energy E consumed by a node can be expressedas:

'4 3 1X' 2S '2 2 $ 2StggSggtEEs + ERX + ETX + ELISTEN + ESLEEP (3)Fig. 5. 24 hours test where Es, ERX, ETX, ELISTEN and ESLEEp are the amount of

Table I shows the number of sent and received messages energy consumed for sampling sensor, receiving andfor each node in the network during the time duration T= 24h transmitting messages, listening the radio channel and= 86400s of the test. We can observe that the motes (9, 10, 11 sleeping.and 12), which have a wider neighborhood, exchange a higher Energy ES is computed asnumber of messages. This is a direct consequence of our E = V c t (4)algorithm. Indeed, when a node sends a message, it receives S -cDATA Dan acknowledgement from each neighbor. If the number of where V = 3Volt is the battery voltage, CDATA = 2.95 mA is theneighbors is high, the number of received messages is high current consumption to sample the temperature sensor and tDtoo. Since the life-time of a mote depends on the number of is the total time spent to sample the sensor during 24 hourssent and received messages, the data shown in Table I are test. Ifwe assume that the sensor is sampled every 15 seconds,essential to estimate the network life-time. tD = 5760 tDATA = 1152 s, where tDATA = 200ms is the time

TABLE I needed for each sampling. It follows that ES = 10 1 95.2 mJ.SENT AND RECEIVED MESSAGES DURING THE 24 HOURS TEST Energy ERX is expressed as:node-ID received messages sent messages ERX= V CB tRX (5)

1 1180 4842 (sink) 2059 1129 where tRX nRmSG * (LPREAAIBLE + LPACKET) * tRXB is the total time

3 1150 503 spent to receive nRMSG messages arrived during T, LPREAMBLE +4 1583 674 LPACKET is the length of the message (length of the preamble5 2078 954 plus length of the packet) expressed in bytes and tRxB is the6 2041 902 time spent to receive one byte. The value of LPREAMBLE depends7 1090 401 on the MAC protocol. Since we adopt the send and receive8 1260 611 primitives of TinyOS, LPACKET 38 bytes.9 3672 1650 Similar to ERX, energy ETX is computed as10 4058 1867 ETX= V-CTXB *tTX (6)1 1 3181 1493 where tTX = nSMsG * (LPREAMBLE + LPACKET) * tTXB is the total time

spent to transmit nSMSG messages during T.The MAC protocol is responsible for minimizing listening

A. Estimation of the mote life-time time tLISTEN. The B-MAC protocol, adopted in ourTo estimate the mote life-time, we hypothesize to use the experiments, periodically samples the channel for activity

B-MAC protocol [18]. Our estimation is based on the model [18]. To avoid loosing messages, the sample period t1 has toproposed by Polastre et al. and requires the knowledge of be smaller than the duration of the preamble [18]. Thus,some values of current consumption, which can be extracted F 7from the datasheet of the Tmote Sky. We have rearranged LPREAMBLE - t .

some equations to fit them to our application.I RXBIsome eqationsto fit hem toour appicatio. The sampling of the channel should adopt a low powerTable II shows times andl consumptions for completing litng(L )aprch

primitive operations of a Tmote Sky. stng(L )aprch

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Figure 6 shows the sequence of operations required to is able to reduce the number of received and sent messagesperform a LPL using the CC2420 radio. Though the current without affecting the quality of the aggregate estimation. Weprofiles are slightly different from those shown in [18] for the have applied our algorithm to monitor the maximumCC1000 radio, we assume the same power consumption temperature in a two-hundred square meters flat using a 12-ESAMPLE= 17.3 UJ computed in [18] for each single LPL. We Tmote Sky nodes wireless sensor network. In the hypothesisnote that the LPL option is not implemented for the CC2420 of using the B-MAC protocol, we have determined that theradio at the moment. Thus, we could not verify experimentally life-time of the network is approximately 418 days with athe theoretical computations shown in this section. We can battery capacity of 2500mAh.deduce from the figure that the time tLISTEN spent for REFERENCESperforming one LPL of the channel is

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