ieee transactions on mobile computing, vol. 10, no. 6

Breath: An Adaptive Protocol forIndustrial Control Applications

Using Wireless Sensor NetworksPangun Park, Carlo Fischione, Member, IEEE, Alvise Bonivento,

Karl Henrik Johansson, and Alberto L. Sangiovanni-Vincentelli, Fellow, IEEE

Abstract—An energy-efficient, reliable and timely data transmission is essential for Wireless Sensor Networks (WSNs) employed inscenarios where plant information must be available for control applications. To reach a maximum efficiency, cross-layer interaction is

a major design paradigm to exploit the complex interaction among the layers of the protocol stack. This is challenging because latency,reliability, and energy are at odds, and resource-constrained nodes support only simple algorithms. In this paper, the novel protocol

Breath is proposed for control applications. Breath is designed for WSNs where nodes attached to plants must transmit information viamultihop routing to a sink. Breath ensures a desired packet delivery and delay probabilities while minimizing the energy consumption of

the network. The protocol is based on randomized routing, medium access control, and duty-cycling jointly optimized for energyefficiency. The design approach relies on a constrained optimization problem, whereby the objective function is the energy

consumption and the constraints are the packet reliability and delay. The challenging part is the modeling of the interactions among thelayers by simple expressions of adequate accuracy, which are then used for the optimization by in-network processing. The optimal

working point of the protocol is achieved by a simple algorithm, which adapts to traffic variations and channel conditions with negligibleoverhead. The protocol has been implemented and experimentally evaluated on a testbed with off-the-shelf wireless sensor nodes,

and it has been compared with a standard IEEE 802.15.4 solution. Analytical and experimental results show that Breath is tunable andmeets reliability and delay requirements. Breath exhibits a good distribution of the working load, thus ensuring a long lifetime of the

network. Therefore, Breath is a good candidate for efficient, reliable, and timely data gathering for control applications.

Index Terms—Wireless sensor networks, control over multihop WSNs, cross-layer design, duty cycle, optimization.

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1 INTRODUCTION

WIRELESS Sensor Networks (WSNs) are networks of tinysensing devices for wireless communication, mon-

itoring, control, and actuation. Given the potential benefitsoffered by these networks, e.g., simple deployment, lowinstallation cost, lack of cabling, and high mobility, they arespecially appealing for control and industrial applications[2], [3], [4]. The variety of application domains andtheoretical challenges for WSNs has attracted researchefforts for more than a decade. Nevertheless, a livelyresearch and standardization activity is ongoing [3], [4], [5].

Although WSNs provide a great advantage for process,manufacturing and industry, they are not yet efficientlydeployed. This is because the software for these applicationsis usually written by process and software engineers that areexpert in process control technology, but know little of the

network and sensing infrastructure that has to be deployedto support control applications. On the other side, thecommunication infrastructure is designed by communica-tion engineers that know little about process controltechnology. Moreover, the adoption of wireless technologyfurther complicates the design of these networks. Being ableto satisfy high requirements on communication performanceover unreliable communication channels is a difficult task.

Standard practice for control system design over commu-nication networks is as follows: First, deploy the networkedembedded system on a predefined distributed architecture,chosen on the basis of experience and heuristic considera-tions. Then, tweak the software implementation of thecontrol algorithm to meet latency, bandwidth, and reliabilityoffered by the network. In many control designs, the networkimperfections are completely disregarded, assuming insteadthat sensor and control data instantaneously reach thecontroller and actuator node, respectively.

This is far from ideal, because many control systems arehighly cost-sensitive, and using a nonoptimized network isclearly expensive. Moreover, the complexity of largenetworked embedded systems continues to increase, mak-ing heuristic and experience-based design practices inade-quate at best. To bridge this gap and derive a correct andefficient implementation, a system-level approach has beenproposed in [6], [7]. By a system-level design for WSNs, thecontrol algorithm designers impose a set of requirements onreliability, packet delay and energy consumption that thecommunication infrastructure must satisfy.

IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 10, NO. 6, JUNE 2011 821

. P. Park, C. Fischione, and K.H. Johansson are with the ACCESS LinnaeusCentre, Electrical Engineering Department, Royal Institute of Technology,6th Floor, Osquldas Vag 10, KTH, SE-100 44 Stockholm, Sweden.E-mail: {pgpark, kallej}@kth.se, [email protected].

. A. Bonivento is with Fondo Atlante Ventures, Intesa Sanpaolo Group,Piazza Scala 6, 20100 Milano, Italy. E-mail: [email protected].

. A.L. Sangiovanni-Vincentelli is with the Department of ElectricalEngineering and Computer Sciences, University of California at Berkeley,231 Cory Hall MC# 1770, Berkeley, CA 94720-1770.E-mail: [email protected].

Manuscript received 7 Dec. 2009; revised 1 July 2010; accepted 26 Aug. 2010;published online 16 Nov. 2010.For information on obtaining reprints of this article, please send e-mail to:[email protected], and reference IEEECS Log Number TMC-2009-12-0532.Digital Object Identifier no. 10.1109/TMC.2010.223.

1536-1233/11/$26.00 ! 2011 IEEE Published by the IEEE CS, CASS, ComSoc, IES, & SPS

An efficient system-level design process for operations ofWSNs in industrial control applications poses extrachallenges compared to more traditional communicationnetworks, namely:

. Reliability: Sensor information must be sent to thesink of the network with a given probability ofsuccess, because missing these data could preventthe correct execution of control actions or decisionsconcerning the phenomena sensed. However, max-imizing the reliability may increase substantially thenetwork energy consumption [3]. Hence, the net-work designers need to consider the trade-offbetween reliability and energy consumption.

. Delay: Sensor information must reach the sinkwithin some deadline. A probabilistic delay require-ment must be considered instead of using averagepacket delay since the delay jitter can be too difficultto compensate for, especially if the delay variabilityis large. Retransmission of old data to maximize thereliability may increase the delay and is generallynot useful for control applications [8].

. Energy efficiency: The lack of battery replacement,which is essential for affordable WSN deployment,requires energy-efficient operations. Since highreliability and low delay may demand a significantenergy consumption of the network, thus reducingthe WSN lifetime, the reliability and delay must beflexible design parameters that need to be adequatefor the requirements. Note that controllers canusually tolerate a certain degree of packet lossesand delay [9], [10], [11]. Hence, the maximization ofthe reliability and minimization of the delay are notthe optimal design strategies for the control applica-tions we are concerned within this paper.

. Adaptation: The network operation should adapt toapplication requirement changes, varying wirelesschannel, and network topology. For instance, the setof application requirements may change dynami-cally and the communication protocol must adapt itsdesign parameters according to the specific requestsof the control actions. To support changing require-ments, it is essential to have an analytical modeldescribing the relation between the protocol para-meters and performance indicators (reliability, de-lay, and energy consumption).

. Scalability: Since the processing resources are lim-ited, the protocol procedures must be computation-ally light. These operations should be performedwithin the network, to avoid the burden of too muchcommunication with a central coordinator. This isparticularly important for large networks. Theprotocol should also be able to adapt to sizevariation of the network, as, for example, causedby moving obstacles, or addition of new nodes.

In this paper, we offer a complete design approach thatembraces all the factors mentioned above. We propose theBreath protocol, a self-adapting efficient solution for reliableand timely data transmission. Since the protocol adapts tothe network variations by enlarging or shrinking next-hopdistance, sleep time of the nodes, and transmit radio power,we think that it behaves like a breathing organism.

The rest of the paper is organized as follows: In Section 1.1,we motivate our study and summarize existing work.Section 1.2 presents the main contributions of the paper. InSection 2, we define the system scenario. In Section 3, weintroduce Breath in detail. In Section 4, an optimizationproblem is posed to optimize the protocol, whereas inSection 5 the constraints and cost function of the protocol aremodeled. In Section 6, we derive the optimal solution and inSection 7, we present an adaptive algorithm to obtain theworking point of the protocol. The fundamental workinglimits of Breath are given in Section 8. A completeexperimental implementation of the protocol is presentedin Section 9. Finally, in Section 10 concluding remarks andfuture perspectives are given.

1.1 Related WorksThere have been many contributions to the problem ofprotocol design for WSNs, both in academia (e.g., [3], [12])and industry (e.g., [13], [14], [15]). New protocols have beenbuilt around standardized low-power protocols such asIEEE 802.15.4 [5], Zigbee [16], and WirelessHART [17].WirelessHART is a promising solution for the replacementof the wired HART protocol in industrial contexts. However,the power consumption is not a main concern in Wireles-sHART, whereas the data link layer is based on Time DivisionMultiple Access (TDMA), which requires time synchroniza-tion and prescheduled fixed length time slots by a centralizednetwork manager. Such a manager should update theschedule frequently to consider reliability and delay require-ments and dynamic changes of the network, which demandscomplex hardware equipments. WirelessHART is thus incontrast with the necessity of simple protocols able to workwith limited energy and computing resources. In Table 1, wesummarize the characteristics of the protocols that arerelevant for the category of applications we are concernedwithin this paper. In the table, we have evidenced whetherindications as energy E, reliability R, and delay D have beenincluded in the protocol design and validation, and whether across-layer approach has been adopted. We discuss theseprotocols in the following.

We categorize first the MAC protocols as follows:random-based, TDMA-based, and hybrid-based accessmechanism. In random-based MAC protocol, each nodesperiodically wake up, listen to the channel, and then goback to sleep again. X-MAC [18] improves B-MAC [19] byusing a strobed sequence of short packets including thetarget ID allowing for fast shutdown and response. Thisscheme addresses the overhearing overhead of longpreambles of Low-Power-Listening (LPL) protocols andsaves the energy consumption of the nontarget receivers. X-MAC also includes a lookup table to adapt the duty cycle ofthe nodes based on the traffic load. However, this issuboptimal solution when there are multiple transmittersand receivers in the network since X-MAC only optimizesthe energy consumption of the network with only onereceiver. The reverse approach to LPL was redesigned forWSNs as the Receiver-Initiated (RI)-MAC [20]. The receiversends out beacon messages at regular intervals and a sendermust wait until it receives one and respond by sending themessage in the rendezvous action to minimize channelusage (i.e., no long preambles). However, the main draw-back is that beacon messages interfere with ordinary trafficas well as with each other.

822 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 10, NO. 6, JUNE 2011

In TDMA-based MAC protocols, nodes only wake upand listen to the channel in assigned slots and then go backto sleep in other slots. Scheduled Channel Polling (SCP)-MAC [21] does not require long preambles of LPL, and isable to operate ultralow duty cycles when traffic is light bysynchronizing the channel polling times based on acommon slot structure. Crankshaft [22] is similar to SCP-MAC and specifically targeted for dense network. Itemploys node synchronization and offset wake-up sche-dules to restraint the main cause of inefficiency in densenetworks. However, the throughput decreases at low trafficload due to idle slots. Furthermore, it is difficult tosynchronize all the nodes of the network to eliminate theclock drifting and adapt to the changes of the topology.Moreover, adapting the slot assignment is not easy within adecentralized environment for traditional TDMA.

Some hybrid MAC protocols are proposed by combiningthe advantages of both a random access with contentionand a TDMA without contention. To offer flexible quality ofservice to several classes of applications, the IEEE 802.15.4standard provides optional hybrid MAC mechanism basedon its superframe structure. Funneling-MAC [23] mainlyuses a CSMA/CA mechanism in the network except alocalized TDMA algorithm of the funneling region closer tothe sink. The sink node manages the TDMA scheduling ofthe funneling region instead of the whole sensor field.

GAF [24] and SPAN [25] consider the energy efficiency asa performance indicator, which is attained by algorithmsunder the routing layer and above the MAC layer so-calledbridge layer. Both protocols are similar since it activates onlya fraction of the nodes in a certain area at any given time. A

major weakness of GAF is precisely the requirement that therouting feature be guaranteed, which results in inefficiencyin terms of latency and energy consumption. In SPAN, theenergy consumption significantly increases as the number ofnodes increases. Simulation results of reliability and delayare reported in [24], [25]. In summary, these protocols [18],[19], [20], [21], [22], [23], [24], [25] have not been designedout of an analytical modeling of reliability and delay, sothere is not systematic control of them.

Fetch [26] and Dozer [27] are designed for monitoringapplication, which mainly deals with lower traffic load thancontrol applications. The latency of Fetch [26] is signifi-cantly dependent on the depth of the routing tree and isaround some hundred seconds. Dozer [27] comprises theMAC and routing layer to minimize the energy consump-tion while maximizing the reliability of the network, but ananalytical approach has not been followed. Furthermore,Dozer requires synchronized sleep schedules and nodes topersistently maintain routing trees. In addition, experi-mental results of Dozer [27] show good energy efficiencyand reliability under very low traffic intensity (with datasampling interval of 120 s) but the delay in the packetdelivery is not considered, which is essential for controlapplications [8], [9]. Koala [28] is a protocol similar toDozer. It coordinates the sleep schedules for bulk transferapplications, whereas it does not consider control applica-tions. Furthermore, the packet delay of Koala is muchhigher than Dozer because Koala does not use synchronizedsleep schedules and maintenance of routing trees. Energyefficiency with delay requirement for MAC and rando-mized routing is considered in GERAF [29], withoutsimulation or experimental validation. Collection TreeProtocol (CTP) [30] is a best-effort tree-based anycastprotocol used to collect the data from sensor nodes. Notethat CTP is mainly targeted for low traffic rates such asmonitoring applications. CTP estimates first the quality ofthe link, then it decides the parent node merely based on thelink quality. However, this approach may incur a loadbalancing problem because the node with good quality linkwill be selected as the preferred parent and consumes moreenergy. Furthermore, CTP uses a very aggressive retrans-mission policy, i.e., the default value of the maximumnumber of retransmissions is 32 times. Note that retrans-mission of old data to maximize the reliability may increasethe delay and is generally not useful for control applications[8]. Backpressure Collection Protocol (BCP) [31] considersalternative approach based on dynamic backpressurerouting of wireless networks. In BCP, the routing andforwarding decision is made on a per-packet basis bycomputing a backpressure weight of each outgoing link thatis a function of localized queue and link state information.Therefore, the overhead due to the backpressure algorithmdepends on the all possible forwarding nodes of the nexthop. Furthermore, the backpressure algorithm does notprevent loops of the routing and may incur in large delay.

The focuses of the protocols mentioned above [24], [25],[26], [27], [28], [29], [30], [31] are the maximization of theenergy efficiency or reliability, or just minimization of thedelay, without considering simultaneously applicationrequirements in terms of reliability and delay in the packet

PARK ET AL.: BREATH: AN ADAPTIVE PROTOCOL FOR INDUSTRIAL CONTROL APPLICATIONS USING WIRELESS SENSOR NETWORKS 823

TABLE 1Protocol Comparison

The letters E, R, and D denote energy, reliability, and communicationdelay. The circle denotes that a protocol is designed by considering theindication of the column, but it has not been validated experimentally.The circle with plus denotes that the protocol is designed by consideringthe indication and experimentally validated. The dot denotes that theprotocol design does not include indication and hence cannot control it,but simulation or experiment results include it. The term “bridge” meansthat the protocol is designed by bridging MAC and routing layers.

delivery. In other words, these protocols are mostlydesigned for monitoring applications and do not supporttypical control requirements. Control and industrial appli-cations are able to cope with a certain degree of packetlosses and delay [9], [10], which implies that the approachesfollowed in the protocols mentioned above are not the idealsolution for these applications. The maximization of theenergy efficiency and reliability may give a long delay,which are bad for the stability of the closed-loop controlsystem. Analogously, the maximization of the reliabilitymay be energy demanding and may give long delay, all ofwhich are not tolerable for control applications. In addition,the protocols mentioned above do not support an adapta-tion to the changes of the reliability and delay, which maybe required by the controllers.

The protocols MMSPEED [32] and SERAN [33] areappealing for control and industrial applications. However,MMSPEED is not energy-efficient because it considers arouting technique with an optimization of reliability anddelay without energy constraints. The protocol satisfies ahigh reliability requirement by using duplicated packetsover multipath routing. Duplicated packets increase thetraffic load with a negative effect on the stability and energyefficiency of the network. In SERAN, a system-level designmethodology has been presented for industrial applica-tions, but even though SERAN allows the network tooperate with low energy consumption subject to delayrequirements, it does not consider tunable reliabilityrequirements nor duty-cycling policies, which are essentialto reduce energy consumption. Furthermore, SERANfocuses on low traffic networks. These characteristics limitthe performance of SERAN both in terms of energy andreliability in our application setup.

Given the availability of numerous techniques to reduceenergy consumption and ensure reliability and low delays,a cross-layer optimization is a natural approach to integratethe protocol layers. Some cross-layer design challenges ofthe physical, MAC, and network layers to minimize theenergy consumption of WSNs have been surveyed in [34],[35], [36]. Many of the cross-layer solutions proposed in theliterature are hardly useful for the application domain weare targeting, because they require sophisticated processingresources, or instantaneous global network knowledge,which are out of reach of the capabilities of real nodes.Network design can be formulated as an optimizationproblem. However, as it was noted in [37], the complexinterdependence of the decision variables (sleep disciplines,clustering, MAC, routing, power control, etc.) leads todifficult problems even in simple network topologies,where the analytical relations describing packet receptionrate, delay, and energy consumption may be highly non-linear expressions. Such a difficulty is further exacerbatedwhen considering non-TDMA scheme [38]. We proposenext a design approach that offers a computationallyattractive solution by simplifications of adequate accuracy.

1.2 Original Contribution

In this paper, we present Breath, an adaptive protocol forWSNs for reliable and timely data gathering. Our systemmodel considers nodes that have to send packets to the sinkvia multihop routing under tunable reliability and delay

requirements. We present a solution based on randomizedrouting, CSMA/CA MAC and randomized sleep disciplinethat are jointly optimized for energy consumption. To thebest of our knowledge, no efficient and simple cross-layerprotocol that includes all the relevant characteristics of thephysical layer, MAC, routing, duty cycling, load balancingthat minimizes the energy consumption of the networkunder reliability and delay requirements has been pro-posed. No protocol in the literature guarantees adaptationto reliability and delay requirements over multihop com-munication, with optimizing the energy consumption.Especially, our original contribution is as follows:

1. We provide explicit analytical relations of thereliability, delay, and total energy consumption asa function of MAC, routing, physical layer, dutycycle, and radio power. The approach is based onsimple yet good approximations whose accuracy issystematically verified.

2. The analytical relations allow us to pose and solve amixed integer-real optimization problem where theenergy minimization is achieved under tunablereliability and delay requirements.

3. Based on this optimization, we develop a novelalgorithm that allows for rapid deployment and self-adaptation of the network to traffic variations andchannel conditions, and guarantees the applicationrequirements without heavy computation or com-munication overhead.

4. The protocol is implemented on a testbed usingTmote sensors [13]. We show by analysis andexperimental evaluation the benefits of our solution.

2 SYSTEM SCENARIO

We consider the scenario depicted in Fig. 1, where a plant isremotely controlled over a WSN [8], [10]. Outputs of theplant are sampled at periodic intervals by the sensors withtotal packet generation rate of ! pcks/s (see Table 2 for mainsymbols used in the paper). We assume that packetsassociated to the state of the plant are transmitted to a sink,which is connected to the controller, over a multihopnetwork of uniformly and randomly distributed relayingnodes. No direct communication is possible between theplant and the sink. Relay nodes forward incoming packets.


Fig. 1. Wireless control loop. An wireless network closes the loop fromsensors to controller. The network includes nodes (black dots) attachedto the plant, h! 1 relay clusters (gray dots), and a sink (blackrectangular) attached to the controller.

When the controller receives the measurements, they areused in a control algorithm to compensate the control output.The control law induces constraints on the communicationdelay and the packet loss probability. Packets must reach thesink within some minimum reliability and maximum delay.These boundaries are denoted as application requirementsthroughout this paper. The application requirements arechosen by the control algorithm designers. Since they canchange from one control algorithm to another, or a controlalgorithm can ask to change the application requirementsfrom time to time, we allow them to vary. We assume thatnodes of the network cannot be recharged, so the operationsmust conserve energy. The system scenario is quite general,because it applies to any interconnection of a plant by amultihop WSN to a controller tolerating a certain degree ofdata loss and delay [9], [10], [11].

A typical example of the scenario described above is anindustrial control application. In particular, a WSN withnodes uniformly distributed in the walls or in the ceiling canbe deployed as the network infrastructure that support thecontrol of the state of the robots in a manufacturing cell.Typically, a cell is a stage of an automation line. Its physicaldimensions range around 10 or 20 meters on each side.

Several robots cooperate in the cell to manipulate andtransform the same production piece. The typical way ofmonitoring the state of a robot is to observe its vibrationpattern of the different parts of a robot. If the values of thesevibrations are above a given threshold, a controller send thecontrol message to these robots. Hence, each node sensesvibrations and has to report the data to the controller within adelay. The decision-making algorithm runs on the controller,which is usually a processor placed outside the cell. Multi-hop communication is needed to overcome the deepattenuations of the wireless channel due to moving metalobjects and save energy consumption.

3 THE BREATH PROTOCOL

The Breath protocol groups all N nodes between the clusterof nodes attached to the plant and the sink with h! 1 relayclusters. Data packets can be transmitted only from a clusterto the next cluster closer to the sink. Clustered networktopology is supported in networks that require energyefficiency, since transmitting data through relays consumesless energy than routing directly to the sink [39]. In [40], adynamic clustering method adapts the network parameters.In [39] and [41], a cluster header is selected based on theresidual energy levels for clustered environments. How-ever, the periodic selection of clustering may not be energy-efficient, and does not ensure the flexibility of the networkto a time-varying wireless channel environment. A simplergeographic clustering is instead used in Breath. Nodes inthe forwarding region send short beacon messages whenthey are available to receive data packets. Beacon messagesare exploited to carry information related to the controlparameters of the protocol. When a node receives a beaconmessage with the updated number of clusters h! 1, thenthe node adapts to its cluster based on a rough knowledgeof its location.

In the following sections, we will describe the protocolstack and state machine of Breath in Sections 3.1 and 3.2,respectively.

3.1 The Breath Protocol Stack

Breath uses a randomized routing, a CSMA/CA mechan-ism at the MAC, radio power control at the physical layer,and sleeping disciplines. We give details in the following.

In many industrial environments, the wireless conditionsvary heavily because of moving metal obstacles and otherradio disturbances. In such situations, routing schemes thatuse fixed routing tables are not able to provide the flexibilityover mobile equipments, physical design limitations, andreconfiguration typical of an industrial control application.Fixed routing is inefficient in WSNs due to the cost ofbuilding and maintaining routing tables. To overcome thislimitation, routing through a random sequence of hops hasbeen introduced in [29]. The Breath protocol is built on anoptimized random routing, where next hop route isefficiently selected at random. Randomized routing allowsus to reduce overhead because no node coordination orrouting state needs to be maintained by the network.Robustness to node failures is also considerably increasedby randomized routing. Therefore, nodes route data packetsto next-hop nodes randomly selected in a forwarding region.


TABLE 2Main Symbols Used in the Paper

Each node, either transmitter or receiver, does not stay inan active state all time, but goes to sleep for a randomamount of time, which depends on the traffic and channelconditions. Since traffic, wireless channel, and networktopology may be time-varying, the Breath protocol uses arandomized duty-cycling algorithm. Sleep disciplines turnoff a node whenever its presence is not required for thecorrect operation of the network. GAF [24], SPAN [25], andS-MAC [42] focus on controlling the effective networktopology by selecting a connected set of nodes to be activeand turning off the rest of the nodes. These approachesrequire extra communication, since nodes maintain partialknowledge of the state of their individual neighbors. InBreath, each node goes to sleep for an amount of time that isa random variable dependent on traffic and networkconditions. Let "c be the cumulative wake-up rate of eachcluster, i.e., the sum of the wake-up rates that a node seesfrom all nodes of the next cluster. The cumulative wake-uprate of each cluster must be the same for each cluster toavoid congestions and bottlenecks.

The MAC of Breath is based on a CSMA/CA mechanismsimilar to the IEEE 802.15.4. Both data packets and beaconpackets are transmitted using the same MAC. Specifically,the CSMA/CA checks the channel activity by performingClear Channel Assessment (CCA) before the transmissioncan commence. Each node maintains a variable NB for eachtransmission attempt, which is initialized to 0 and countsthe number of additional backoffs the algorithm does whileattempting the current transmission of a packet. Eachbackoff unit has duration Tca ms. Before performing CCAs,a node takes a backoff of randomð0;W ! 1Þ backoff units,i.e., a random number of backoffs with uniformly dis-tributed over 0; 1; . . . ;W ! 1. If the CCA fails, i.e., thechannel is busy, NB is increased by one and the transmis-sion is delayed of randomð0;W ! 1Þ backoff periods. Thisoperation is repeated at most Mca times, after which apacket is discarded.

The Breath protocol assumes that each node has a roughknowledge of its location. This information, which iscommonly required for the applications we are targeting[3], can be obtained running a coarse positioning algorithm,or using the Received Signal Strength Indicator (RSSI),which is typically provided by off-the-shelf sensor nodes[43]. Some radio chips already provide a location enginebased on RSSI [44]. Location information is needed fortuning the transmit radio power and to change the numberof hops, as we will see later. The energy spent for radiotransmission plays an important role in the energy budgetand for the interference in the network. Breath, therefore,includes an effective radio power control algorithm.

3.2 State Machine Description

Breath distinguishes between three node classes: edgenodes, relays, and the sink.

The edge nodes wake up as soon as they sense packetsgenerated by the plant to be controlled. Before sendingpackets, the edge node waits for a beacon message from thecluster of nodes closer to the edge. Upon the reception of abeacon, the node sends the packet.

Consider a relay node k. Its detailed behavior isillustrated by the state machine of Fig. 2, as we describein the following:

. Calculate Sleep State: the node calculates theparameter "k for the next sleeping time andgenerates an exponentially distributed random vari-able having average 1="k. After this, the node goesback to the Sleep State. "k is computed such that thecumulative wake-up rate of the cluster "c is ensured.

. Sleep State: the node turns off its radio and starts atimer whose duration is an exponentially distributedrandom variable with average 1="k. When the timerexpires, the node goes to the Wake-up State.

. Wake-up State: the node turns its beacon channelon, and broadcasts a beacon indicating its location.Then, it switches to listen to the data channel, and itgoes to the Idle Listen State.

. Idle Listen State: the node starts a timer of a fixedduration that must be long enough to receive apacket. If a data packet is received, the timer isdiscarded, the node goes to the Active-TX State, andits radio is switched from the data channel to thebeacon channel. If the timer expires before any datapacket is received, the node goes to the CalculateSleep State.

. Active-TX State: the node starts a waiting timer of afixed duration. If the node receives the first beaconcoming from a node in the forwarding region withinthe waiting time, it retrieves the node ID and goes tothe CSMA/CA State. Otherwise, if the waiting timeris expired before receiving a beacon, the node goesto the Calculate Sleep State.

. CSMA/CA State: the node switches its radio to hearthe data channel, and it tries to send a data packet toa node in the next cluster by the CSMA/CA MAC. Ifthe channel is not clean within the maximumnumber of tries, the node discards the data packetand goes to the Calculate Sleep State. If the channelis clear within the maximum number of attempts,the node transmits the data packet using anappropriate level of radio power and goes to theCalculate Sleep State.

The sink node sends periodically beacon messages tothe last cluster of the network to receive data packets.Such a node estimates periodically the traffic rate and the


Fig. 2. State machine description of a relay node executing the Breathprotocol.

wireless channel conditions. By using this information, thesink runs an algorithm to optimize the protocol para-meters, as we describe in Section 4. Once the results of theoptimization are achieved, they are communicated to thenodes by beacons.

According to the protocol given above, the packetdelivery depends on the traffic rate, the channel conditions,number of forwarding regions, and the cumulative wake-up time. In the next sections, we show how to model andoptimize online these parameters.

4 PROTOCOL OPTIMIZATION

The protocol is optimized dynamically by a constrainedoptimization problem. The objective function, denoted byEtotðh;"cÞ, is the total energy consumption for transmittingand receiving packets from the edge cluster to the sink. Theconstraint are given by the end-to-end packet receptionprobability and end-to-end delay probability. The optimiza-tion problem is

minh;"c

Etotðh;"cÞ ð1aÞ

s:t: Rðh;"cÞ $ !; ð1bÞPr½Dðh;"cÞ & # ' $ "; ð1cÞh $ 2; ð1dÞ"min & "c & "max: ð1eÞ

The decision variables are the cumulative wake-up rate "cof each cluster and the number of relay clusters, h! 1.Rðh;"cÞ is the probability of successful packet delivery(reliability) from the edge cluster to the sink, and ! is theminimum desired probability. Dðh;"cÞ is a random variabledescribing the delay to transmit a packet from the edgecluster to the sink. # is the desired maximum delay, and " isthe minimum probability with which such a maximumdelay should be achieved. Constraint (1d) is due to that thereis at least two hops from the edge cluster to the sink.Constraint (1e) is due to that the wake-up rate cannot be lessthan a minimum value "min, and larger than a maximumvalue "max due to hardware reasons. Note that Problem (1) isa mixed integer-real optimization problem, because "c is realand h is integer. We need to have " and ! close to one. Welet " $ 0:95 and ! $ 0:9, namely we assume that the delay #must be achieved at least with a probability of 95 percent,and the reliability must be larger than 90 percent. We remarkthat # ;", and ! are application requirements, and h;"c, andnodes’ radio transmit power are protocol parameters that mustbe adapted to the traffic rate !, the wireless channelconditions, and the application requirements for an efficientnetwork operation.

In the following, we shall propose an approach to modelthe quantities of Problem (1), along with a strategy toachieve the optimal solution, namely the values of h( and "(cthat minimize the cost function and satisfy the applicationrequirements. As we will see later, the system complexityprevents us to derive the exact expressions for the analyticalrelations of the optimization problem. An approximation ofthe requirements and an upper bound of the energyconsumption will be used.

5 MODELING OF THE PROTOCOL

In this section, we model the reliability, packet delaydistribution, and total energy consumption of the network.

5.1 Reliability Constraint

In this section, we provide an analytical expression for thereliability constraint (1b) in Problem (1).

A data packet can be lost at a hop because of a badwireless channel or packet collisions. The collision prob-ability is determined by the CSMA/CA MAC. Therefore, toanalyze such a behavior, we use a Markov chain. Theapproach is similar to the one proposed in [45] and [46](see also [47], [48], [49]). Let m be the maximum backoffstage, and W be the maximum backoff time of CSMA/CA.Let sðtÞ 2 f0; . . . ;mg and bðtÞ 2 f0; . . . ;W ! 1g be thestochastic processes representing the backoff stage andthe backoff time counter, respectively. The delay spentbefore a node senses the channel idle is modeled by theMarkov chain depicted in Fig. 3. The Markov chain state isðsðtÞ; bðtÞÞ, where bðtÞ ¼ !1 refers to the assessment of thechannel state during CCA. Denote the Markov chain’ssteady-state probabilities by bi;k ¼ PrfðsðtÞ; bðtÞÞ ¼ ði; kÞg.They allow us to compute the probability of successfultransmission in CSMA/CA as the probability that exactlyone node transmits and n! 1 are silent:

scðnÞ ¼$ð1! $Þn!1

1! ð1! $Þn;

where

$ ¼Xm

i¼0

bi;0 ¼2

W þ 3:

From the Markov chain, we derive also the busy channelprobability %ðnÞ, which is

%ðnÞ ¼ 1! ð1! $Þn!1

2! ð1! $Þn!1 : ð2Þ

We will use this probability in Section 5.3.


Fig. 3. Markov chain model for CSMA/CA state evolution of Breath.

The probability of successful transmission in CSMA/CA scðnÞ depends on the number of nodes n that arecontending to transmit packets. We need therefore tocompute the probability sbð"c; nÞ that a generic number nof contending nodes compete within a period 1=! totransmit a data packet. By recalling that the cumulativewake-up rate is exponentially distributed random variablewith intensity "c, and noting that e!"cn=! is the probability tohave more than n contending nodes, we conclude that

sbð"c; nÞ ¼ e!"cðn!1Þ=! ! e!"cn=!: ð3Þ

Hence, the reliability with sbðnÞ and scðnÞ is

Rðh;"cÞ ¼Yh

i¼1

piX1

n¼1

sbðnÞ scðnÞ; ð4Þ

where pi denotes the probability of successful packetreception during a single-hop transmission from cluster ito cluster i! 1.

Since the components of the sum in (4) with n $ 2 give asmall contribution, we set n ¼ 2 and validate (4) byexperimental results. Fig. 4 reports the reliability versus"c, as obtained by (4) with n ¼ 2 and experiments for a two-hop network. We see that (4) provides a good approxima-tion of the experimental results because it is always around5 percent of the experiments for reliability values ofpractical interest (larger than 0.7). The same behavior isfound for h up to 4.

We can rewrite the reliability constraint Rðh;"cÞ $ ! byusing (4) with n ¼ 2, thus obtaining

"c $ frðh;!Þ ¼4! lnð2CrÞ

! ! ln Cr ! 1þffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðCr ! 1Þ2 ! 4Cr !1=h=pmin ! 1ð Þ

q" #;

ð5Þ

where Cr ¼ $ð1! $Þ=ð1! ð1! $Þ2Þ, and pmin ¼ minðp1; . . . ;phÞ. Note that we used the worst channel condition of thenetwork pmin, which is acceptable for optimization purposebecause in doing so we consider the minimum of (4). Since

the argument of the square root in (5) must be positive, anadditional constraint is introduced:

h & hr ¼4 lnð!Þ

lnðpminÞ: ð6Þ

This constraint is a function of the minimum desiredreliability requirement ! and the worst channel conditionpmin. As the reliability requirement becomes stricter or theworst channel condition pmin decreases, the constraint hrdecreases. Note that the logarithm function gives a negativenumerical value when 0 & ! & 1 and 0 & pmin & 1. We willuse (5) and (6) in Section 6 to find the solution ofProblem (1). Now, we turn our attention to the delayconstraint.

5.2 Delay Constraint

The delay Dðh;"cÞ between edge to sink is given by the sumof the delays experienced by a packet at each hop. There aretwo sources of delay:

. Time to wait before the first wake-up of a node in thenext cluster: Let such a time be denoted with &i forcluster i.

. Time to wait for clean channel: Since the Breathprotocol uses CSMA/CA, a node spends a randomtime before sensing idle channel. Denote with "isuch a time for cluster i.

By summing these delays per each hop, we obtain the delaymodel

Dðh;"cÞ ¼Xh

i¼1

ð&i þ "iÞ: ð7Þ

In this equation, &i is an exponentially distributed randomvariable whose intensity "c is the sum of the wake-upintensities of the nodes in the next cluster. Characterizationof "i is more difficult, owing to the backoff mechanism ofthe CSMA/CA algorithm. However, we assume that thebackoff time can be approximated by a Gaussian distribu-tion whose average is matched with the average andstandard deviation of a uniformly distributed randomvariable between 0 and ðMca þ 1ÞðW ! 1Þ. Namely,

"i 2 NðMca þ 1ÞðW ! 1ÞTca

2;ðMca þ 1ÞðW 2 ! 1ÞT 2

ca

12

$ %:

We remark that this approximation significantly reducesthe computation complexity when compared to an accurateanalysis of the probability density function of the packetdelay, which we have proposed in [50]. According to suchan assumption, the delay Dðh;"cÞ is approximated by aGaussian random variable N ð"D;'2

DÞ, where

"D ¼h

"cþ hðMca þ 1ÞðW ! 1ÞTca

2; ð8Þ

'2D ¼

h

"2c

þ hðMca þ 1ÞðW 2 ! 1ÞT 2ca

12: ð9Þ

We validated these approximations by comparing theanalysis to experimental results. Figs. 5a and 5b showthe mean and variance of the delay given by (8) and (9) byexperimental results, respectively. The analytical model


Fig. 4. Reliability as obtained by (4) and experimental results as afunction of "c. Curves refer to traffic rates ! ¼ 5; 10; 15 pcks/s for h ¼ 2hops and N ¼ 15 nodes.

describes well the experimental data because it gives an

upper bound for wake-up rates up to 35 s!1, and then the

model underestimates the experimental result for less than

5 percent. These properties are quite useful for optimization

purposes. Same dependence is found on h up to 4.We are now in the position to express the delay constraint

in Problem (1) by using (8) and (9) that we just derived:

Pr½D & # ' + 1!Q # ! "D'D

$ %$ "; ð10Þ

where QðxÞ ¼ 1=ffiffiffiffiffiffi2(p R1

x e!t2=2dt is the complementary

standard Gaussian distribution. After some manipulations,

it follows that (10) can be rewritten as

"c $12 Cd1 hþ 2

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi3 Cd3 h 12 C2

d1 þ Cd2 h! Cd3ð Þ& 'q

12 C2d1 ! Cd2 Cd3

;

where

Cd1 ¼ # !hðMca þ 1ÞðW ! 1ÞTca

2;

Cd2 ¼ hðMca þ 1ÞðW 2 ! 1ÞT 2ca;

Cd3 ¼ ðQ!1ð1!"ÞÞ2:

Since T 2ca ¼ 0:1024, 10!6 [13], and h;Mca;W are positive

integers, it follows that T 2ca - hðMca þ 1ÞðW 2 ! 1Þ. Then,

Cd2 - Cd1 and (10) is approximated by

"c $ fdðh; # ;"Þ ¼4 2 hþQ!1ð1!"Þ

ffiffiffihp& '

2# ! hðMca þ 1ÞðW ! 1ÞTca: ð11Þ

Inequality (11) has been derived under the additionalconstraint

h & hd ¼4 2#

ðMca þ 1ÞðW ! 1ÞTca: ð12Þ

This constraint is a function of the maximum allowabledelay requirement # and the parameters of CSMA/CAalgorithm such as the maximum number of CSMA/CAtransmission tries Mca, the maximum number of randombackoff W and the unit of backoff period Tca. As the delayrequirement # becomes stricter or the parameters of CSMA/CA algorithm Mca;W; Tca increase, the constraint hd de-creases. We will use (11) and (12) in Section 6 to findthe solution of the optimization problem (1). Now, weinvestigate the total energy consumption.

5.3 Energy Consumption

The total energy consumption is

Etotðh;"cÞ ¼ Epckðh;"cÞ þEwuðh;"cÞ; ð13Þ

where Epckðh;"cÞ is the total energy for transmission andreception of data packets and Ewuðh;"cÞ is the energyconsumption for wake-up, listening, and beaconing dur-ing a time T , which we characterize in Sections 5.3.1 and5.3.2, respectively.

5.3.1 Data Packet Communication Energy

Assuming h hops, and recalling that edge node emits! pcks/s,

Epckðh;"cÞ ¼ T!Xh

i¼1

QmðdiÞ þArx

"cþEcað"cÞ þEr

" #; ð14Þ

where Er accounts for the fixed cost of the RF circuit forthe reception of a data packet. The term QmðdiÞ is theenergy consumption for radio transmission, where di isthe transmission distance to which a data packet has to besent. The term Ecað"cÞ is the energy spent during theCSMA/CA state.

The energy model given by (14) is derived under theassumption that all packets generated at the edge nodesreach the sink. Obviously, some packet may be lost beforereaching the sink, therefore (14) gives an upper bound onthe energy consumption. This is reasonable, since our goalis the minimization of the cost function.

The energy spent for radio transmission is a function ofthe radio power used to transmit packets:

QmðdiÞ ¼ V IðPtðdiÞÞ tm; ð15Þ

where V is the voltage consumption of the RF circuit at thenode, tm is the transmission time of a data packet, IðPtðdiÞÞis the current consumption of the electronic circuit neededto transmit packets at radio power PtðdiÞ, and di is thedistance from the transmitter which a packet must reach to


Fig. 5. Validation of average and variance of delay given by (8) and (9) byexperimental results, respectively. The traffic rates ! ¼ 5; 10; 15 pcks/sare considered w.r.t. wake-up rates "c from 5 to 50, h ¼ 2 hops, andN ¼ 15 nodes. (a) Average delay. (b) Variance of delay.

with some desired probability. The relation between thecurrent consumption and radio power depends on thehardware platform. For Tmote sensors, it holds that [51]

IðPtðdiÞÞ + !19PtðdiÞ4 þ 53PtðdiÞ3 ! 53PtðdiÞ2

þ 29PtðdiÞ þ 8:7:

Given this approximation, minimization of QmðdiÞ isachieved by minimizing PtðdiÞ. PtðdiÞ can be minimized bycomputing the minimum radio power that ensures packets toreach a given distance with a given probability, as we see next.

The optimal transmit power is derived by consideringthe distribution of the Signal to Interference plus NoiseRatio (SINR). By imposing a requirement pcon on theprobability of successful packet reception at a distance dkfrom node k, we can translate the requirement on theaverage SINR, thus obtaining a bound )c on such anaverage SINR. From this, we can then derive the transmitradio power necessary to successfully receive packets at adistance dk with probability pcon. It follows that theminimum transmit power is [1]

PtðdkÞ dB ¼ )c dB þ PLðd0Þ dB þ 10 *k log10dkd0

þ Pn dB !ln 10

20'2)k dB;

where PLðd0Þ dB is the path loss at a reference distance d0;*k isthe path loss decay constant,Pn is the noise floor, and')k is thevariance of the SINR (see [1] for details). We remark that thepower PtðdkÞ dB minimizes the energy spent for radiotransmission in (15). Notice that the actual packet receptionprobability pi may fluctuate around pcon due to the delay ofthis power control and the limited maximum transmit power.

In the following, we characterize Eca. Consider theenergy spent for transmission of a data packet in the ithcluster. Let Eca is the energy spent by a node to check thechannel status by the CSMA/CA algorithm upon thereception of a beacon. This energy, which is due to CCA,is dependent on the maximum number of tries Mca. Wehave two situations: the number of contending nodes nattempting to transmit a data packet is less then Mca, or thenumber of contending nodes is larger than Mca. Ifn < Mca þ 1, all nodes will succeed to sense a clean channelwith the energy Eca1ðnÞ, otherwise we need to consider thetransmission success and failure probabilities to performCCA with the energy Eca2ðnÞ, which is the function of thebusy channel probability %ðkÞ conditioned on k contendingnodes defined by (2), see the details in [1].

By summing the two energy components Eca1ðnÞ; Eca2ðnÞ,the average energy consumption spent by the CSMA/CA is

Ecað"cÞ ¼X1

n¼1

sbð"c; nÞ Eca1ðnÞ uðMca ! nÞ½

þ Eca2ðnÞ uðn!Mca ! 1Þ';ð16Þ

where sbð"c; nÞ is the probability to have n contendingnodes in a cluster given by (3) and uðxÞ ¼ 1 if x $ 0,whereas uðxÞ ¼ 0 otherwise.

5.3.2 Control Signaling Energy

A node randomly cycles between an awake state and asleep state. Each time a node wakes up, it spends an energygiven by the power needed to wake-up Aw during thewake-up time Tw, plus the energy to listen for the receptionof a data packet within a maximum time Tac. After a nodewakes up, it transmits a beacon to the next cluster. Let thewireless channel loss probability be 1! pi of cluster i, thennodes of cluster i! 1 have to wake-up on average 1=pitimes to create the effect of a single wake-up so that atransmitter node successfully receives a beacon. Recallingthat there are h hops and a cumulative wake-up rate percluster "c, the total cost in a time T for wake-ups andbeaconing is

Ewuðh;"cÞ

¼ T"cXh

i¼1

1

piQbðdiÞ þAwTw þArxðTac ! TwÞ½ ';

ð17Þ

where QbðdiÞ is the expected energy consumption totransmit a beacon message at the distance di.

5.3.3 Total Energy Consumption

Here, we put together the energy analysis developed in theprevious two sections. The total energy consumption is

Etotðh;"cÞ ¼ T!"Qm

S

h! 1

$ %þQm

S

h! 1

$ %ðh! 1Þ

, uðh! 1Þ þ h Arx

"cþEcað"cÞ þ Er

$ %#

þ T"cpmin

"2Qb

S

h! 1

$ %þQb

2S

h! 1

$ %ðh! 2Þ

, uðh! 2Þ þ hðAwTw þArxðTac ! TwÞÞ#;

ð18Þ

where we upper bounded (14) and (17) by considering theworst distance to which data and beacon packets must besent, which are S=ðh! 1Þ and 2S=ðh! 1Þ, and the worstreception probability pmin.

Fig. 6 shows the energy given by (18) as a function of thenumber of hops h over different wake-up rates "c. The totalenergy consumption increases with h given "c becauseincreasing h implies higher wake-up rates per node for agiven number of total nodes present in the network. Inother words, increasing the number of hops is energy-inefficient. Observe also that a low wake-up rate does notminimize the total energy consumption, because of thelonger waiting time to receive a beacon message that such arate causes. Hence, there is a trade-off between the energyconsumption for wake-up and waiting to get a beaconmessage. We explore this trade-off for optimizationproblem in the following section.

6 OPTIMAL PROTOCOL PARAMETERS

In this section, we give the optimal protocol parameters usedby Breath. Consider the reliability and delay constraints, andthe total energy consumption as investigated in Sections 5.1,5.2, and 5.3. The optimization problem (1) becomes


minh;"c

Etotðh;"cÞ

s:t: "c $ maxðfrðh;!Þ; fdðh; # ;"ÞÞ;2 & h & min hr; hdð Þ;"min & "c & "max;

ð19Þ

where the first constraint comes from (5) and (6), and thesecond from (11) and (12). We assume that this problem isfeasible. Infeasibility means that for any h ¼ 2; . . . ;minðhr;hdÞ, then "c $ maxðfrðh;!Þ; fdðh; # ;"ÞÞ > "max, namely it isnot possible to guarantee the satisfaction of the reliabilityand delay constraint given the application requirements.This means that the application requirements must berelaxed, so that feasibility is ensured and the problem canbe solved. The solution of this optimization problem, h( and"(c , is derived in the following.

By using the numerical values given for the Tmotesensors [13] for all the constants in the optimizationproblem, we see that the cost function of Problem (19) isincreasing in h and convex in "c. This allows us to derivethe optimal solution in two steps: for each value ofh ¼ 2; . . . ;minðhr; hdÞ, the cost function is minimized for"c, achieving "(cðhÞ. Then, the optimal solution if found inthe pair h;"(cðhÞ that gives the minimum energy consump-tion. We describe this procedure next.

Let h be fixed. From the properties the cost function ofProblem (19), the optimal solution "(cðhÞ is attained either atthe minimum of the cost function or at the boundaries of thefeasibility region given by the requirements on "c. Theminimum of the cost function can be achieved by taking itsderivative with respect to "c. To obtain this derivative in anexplicit form, we assume that CSMA/CA energy consump-tion can be approximated by a constant value since thenumerical value is smaller than other factors. Under thisassumption, the minimization by the derivative is approxi-mated by

"eðhÞ ¼ pmin ! Atxð Þ12h! 2

hQb

2S

h! 1

$ %uðh! 2Þ

"

þ 2

hQb

S

h! 1

$ %þAwTw þArxðTac ! TwÞ

#!12

:

ð20Þ

In Fig. 7, we check the validity of this approximation. Thefigure reports the approximated minimum of the costfunction as obtained by (20) compared to the wake-up ratethat minimizes the actual energy consumption as obtainedby a numerical minimization algorithm. The approximationis tight because the error is less than 2 percent.

Equation (20) tells us that, provided h, an optimal solution

"(cðhÞ is given by "eðhÞ if "eðhÞ is in the feasible range "min &"eðhÞ & "max and "eðhÞ satisfies the two constraints of both

reliability and delay requirement "eðhÞ $ maxðfrðh;!Þ;fdðh; # ;"ÞÞ. If "eðhÞ is not in the feasible range ð"eðhÞ $"maxÞ [ ð"eðhÞ & "minÞ or "eðhÞ does not satisfies two the

constraints "eðhÞ & maxðfrðh;!Þ; fdðh; # ;"ÞÞ but maxðfrðh;!Þ; fdðh; # ;"ÞÞ is in the feasible range "min & maxðfrðh;!Þ;fdðh; # ;"ÞÞ & "max, then an optimal solution is given by

maxðfrðh;!Þ; fdðh; # ;"ÞÞ. Otherwise, an optimal solution is

given by "max as the best-effort mode since the network does

not satisfies the constraints. Therefore, for any h ¼ 2; . . . ;

minðhr; hdÞ, we compute "(cðhÞ. Then, the optimal solution h(

and "(c is given by the pair "(cðhÞ; h that minimizes the cost

function. This procedure to compute the optimal solution is

illustrated by Algorithm 1, which summarize one of the main

contribution of this paper.

Algorithm 1. Algorithm for the computation of the optimalsolution of Problem (19)


Fig. 6. Total energy consumption given by (18) for a different number ofhops ðh ¼ 2; 3; 4; 5Þ over wake-up rates "c from 1 to 100 in traffic rate(! ¼ 5 pcks/s) and N ¼ 15 nodes.

7 ADAPTATION MECHANISMS

In the previous sections, we showed how to determine theoptimal number of clusters and cumulative wake-up rate bysolving an optimization problem. Here, we present in detailsome adaptation algorithms that the sink must run todetermine correctly h( and "(c as the traffic rate and channelconditions changes. These algorithms allow us to adapt theprotocol parameters to the traffic rate and channel conditionwithout high message overhead.

7.1 Traffic Rate and Channel Estimation

The sink node estimates the traffic rate ! and the worstchannel probability pmin of the network. To estimate theglobal minimum of the worst channel condition, each pishould be estimated at a local node and sent to the sink foreach link of the path i ¼ 1; . . . ; h. This might increaseconsiderably the packet size. To avoid this, we propose thefollowing strategy. Consider a relay node of the ith cluster.It estimates pi by the signal of the beacon packet. Then, thenodes compares pi with the channel condition informationcarried by the received data packet and selects theminimum. This minimum is then encoded in the datapacket and sent with it to the next-hop node. After the sinknode retrieves the channel condition of the route byreceiving a data packet, it computes an average of theworst channel conditions among the last received datapackets. Using this estimate, the sink solves the optimiza-tion problem running Algorithm 1. Afterwards, the returnvalue of the algorithm, h( and "(c , can be piggybacked onbeacons that the sink sends toward the relays closer to thesink. Then, these protocol parameters are forwarded whenthe nodes wake up and send beacons to the next clustertoward the edge nodes. During the initial state, nodes seth ¼ 2 before receiving a beacon.

7.2 Wake-Up Rate and Radio Power Adaptation

Once a cluster received "(c , each node in the cluster mustadapt its wake-up rate so that the cluster generates such a

cumulative wake-up rate. We consider the natural solutionof distributing "(c equally between all nodes of the cluster.Let "k be the wake-up rate of node k, and suppose that thereare l nodes in a cluster. The fair solution is "k ¼ "(c=l for anynode. However, a node does not know and cannot estimateefficiently the number of nodes in its cluster.

To overcome this problem, we follow the same approachproposed in [36], where an Additive Increase and Multi-plicative Decrease (AIMD) algorithm leads to a fairdistribution of the wake-up duties within a single cluster.Specifically, each node that is waiting to forward a datapacket observes the time before the first wake-up in theforwarding region. Starting from this observation, itestimates the cumulative wake-up rate ~"c of the forwardingregion and it compares it with the optimal value of thewake-up rate "(c when a node receives a beacon. Note thatthe node retrieves information on h(;"(c , and locationinformation of the beacon node. If ~"c < "(c , the node sendsby the data packet an Additive Increase (AI) command forthe wake-up rate of next-hop cluster, else it sends aMultiplicative Decrease (MD) command. Furthermore, thenode updates the probability of successful transmission pibased on the channel information using the RSSI anddistance information dk between its own location andbeacon node. After the node updates the channel conditionestimation, it sets the data packet transmission power toPtðdkÞ, and encodes the channel estimation in the packet asdescribed in Section 7.1. If a data packet is received, thenode retrieves information on wake-up rate update: if AI,then "k ¼ "k þ +, else "k ¼ "k=,, where + and , are controlparameters. From experimental results, we obtained that+ ¼ 3 and , ¼ 1:05 achieve good performance. The com-mand on the wake-up rate variation is piggybacked on datapackets and does not require any additional message.

However, this approach may generate a load balancingproblem because of different wake-up rates among relayswithin a short period. Load balancing is a critical issue, sincesome nodes may wake up at higher rate than desired rate ofother nodes, thus wasting energy. To overcome this situation,each relay node runs a simple reset mechanism. We assign anupper and lower bound to the wake-up rate for each node. Ifthe wake-up rate of a node is larger than the upper boundð1þ -Þ"(cðh( ! 1Þ=N or is smaller than the lower boundð1! -Þ"(cðh( ! 1Þ=N , then a node resets its wake-up rate to"(cðh( ! 1Þ=N , where - assumes a small value and ðh( ! 1Þ=Nis an estimation of the number of nodes per cluster.

8 FUNDAMENTAL LIMITS

Understanding the fundamental limits of Breath is critical forits appropriate use. This section focuses on the minimumnumber of relays required to support the protocol, and theminimum delay that can be set by the application.

8.1 Minimum Number of Nodes per Cluster

The minimum number nmin of nodes per cluster to supportthe protocol with given reliability and delay requirements is

nmin $"(c

"k;max;


Fig. 7. Wake-up rate that minimizes the total energy consumption andapproximated wake-up rate as obtained by (20) for different number ofhops ðh ¼ 2; 3; 4Þ, traffic rates ! from 1 to 30 pcks/s, and N ¼ 15. They-axis was normalized by 15.

where "k;max is the maximum wake-up rate of node k. Byconsidering the worst active time for the duty cycle, we have

"k;max ¼ Tac þ Tbeð Þ!1;

where Tac ¼ 30 ms is the maximum listening time to receivea data packet and Tbe ¼ 500 ms is the maximum waitingtime before receiving a beacon [13].

8.2 Minimum Delay

The minimum delay that the application can set is achievedby considering a very high wake-up rate per cluster. Thisminimizes the waiting time before receiving a beacon.Hence, by summing the delays of the CSMA/CA state andphysical limits of the wireless channel, the minimum delay is

# $ h 2ðMca þ 1ÞðW ! 1ÞTca þ 2 Tprop þ tm þ tb& '

;

where the fist term is the maximum delay of CSMA/CAstate, Tprop is propagation delay, and tm and tb are, respec-tively, the transmission delay of data and beacon packets.Since Tprop ¼ 0:875 ms, tm ¼ 1:5 ms, and tb ¼ 0:64 ms [13],they can be basically ignored because they are negligiblewith respect to other delays.

9 EXPERIMENTAL IMPLEMENTATION

In this section, we provide an extensive set of experimentsto validate the Breath protocol. The experiments enable usto assess Breath in terms of reliability and delay in thepacket transmission, and energy consumption of thenetwork both in stationary and transitionary condition.The protocol was implemented on a testbed of Tmotesensors [13], and was compared with a standard imple-mentation of the IEEE 802.15.4 [5], as we discuss next.

We consider a typical indoor environment with concretewalls. The experiments were performed in a static propaga-tion (AWGN) and time-varying fading environment (Ray-leigh), respectively:

. AWGN environment: nodes and surrounding ob-jects were static, with minimal time-varying changesin the wireless channel. In this case, the wirelesschannel is well described by an Additive WhiteGaussian Noise (AWGN) model.

. Rayleigh fading environment: obstacles were movedwithin the network, along a line of 20 m. Further-more, a metal object was put in front of the edgenode, so the edge node and the relays were not inline-of-sight. The edge node was moved on adistance of few tens of centimeters.

A node acted as edge node and generated packetsperiodically at different rates (! ¼ 5, 10, and 15 pcks/s).Fifteen relays were placed to mimic the topology in Fig. 1.The edge node was at a distance of 20 m far from the sink.The sink node collected packets and then computed theoptimal solution using Algorithm 1. The delay requirementwas set to # ¼ 1 s and the reliability to ! ¼ 0:9 and 0.95. Inother words, we imposed that packet must reach destina-tion within 1 s with a probability of !. These requirementswere chosen as representative for control applications.

We compared Breath against an implementation of theunslotted IEEE 802.15.4 [5] standard, which is similar to the

randomized MAC that we use in this paper. In such an IEEE802.15.4 implementation, we set nodes to a fixed sleepschedule, defined by CTac where C is integer number (recallthat Tac is the maximum node listening time in Breath). Wedefined the case L (low sleep), where the IEEE 802.15.4implementation is set with C ¼ 1, whereas we defined thecaseH (high sleep) by setting C ¼ 4. The caseH represents afair comparison between Breath and the IEEE 802.15.4, whilein the case L nodes are let to listen much longer time thannodes in Breath. The power level in the IEEE 802.15.4implementation where set to !5 dBm. We set the IEEE802.15.4 protocol parameters to default valuesmacMinBE ¼3; aMaxBE ¼ 5;macMaxCSMABackoffs ¼ 4. Details fol-low in the sequel.

9.1 Protocol Behavior for Stationary Requirements

In this section, we investigate the performance of Breathabout the reliability, average delay, and energy consump-tion that can be achieved in a stationary configuration of therequirements, i.e., during the experiment there was notchange of application requirements. Data were collected outof 10 experiments, each lasting 1 hour.

9.1.1 Reliability

Fig. 8 indicates that the network converges by Breath to astable error rate lower than 1! ! and hence satisfies therequired reliability with traffic rate ! ¼ 10 pcks/s, the delayrequirement # ¼ 1 s, and ! ¼ 0:9, 0.95. IEEE 802.15.4 H inAWGN channel provides the worse performance than theother protocols because of lower wake-up rate. Observe that! ¼ 0:9 in Rayleigh fading environment gives the betterreliability than ! ¼ 0:95 in AWGN channel due to higherwake-up rate to compensate the fading channel condition.Notice that the higher fluctuation of reliability between thenumber of received packets 2,500 and 2,800 for Rayleighfading environment with ! ¼ 0:95 is due to deep attenua-tions in the wireless channel.

Fig. 9 shows the reliability of Breath and IEEE 802.15.4L;Has a function of the reliability requirement ! ¼ 0:9; 0:95 andtraffic rate! ¼ 5; 10; 15 pcks/s in AWGN and Rayleigh fading


Fig. 8. Convergence over the number of received packets of thereliability for IEEE 802.15.4 L;H in AWGN, and Breath with reliabilityrequirements ! ¼ 0:9; 0:95 and traffic rates ! ¼ 10 pcks/s in AWGN andRayleigh fading environments.

environments, with the vertical bars indicating the standarddeviation as obtained out of 10 experimental runs of 1 houreach. Observe that the reliability is stable around the requiredvalue for Breath, and this holds for different traffic rates andenvironments. However, the IEEE 802.15.4 L and H do notensure the reliability satisfaction for large traffic rates.Specifically, the IEEE 802.15.4 H shows poor reliability inany case, and performance worsen as the environment movesfrom the AWGN to the Rayleigh fading. Furthermore, eventhough the IEEE 802.15.4 L imposes that nodes wakes upmore often, it does not guarantee a good reliability in highertraffic rates. The reason is found in the sleep schedule of theIEEE 802.15.4 case, which is independent of traffic rate andwireless channel conditions. The result is that the fixed sleepschedule is not feasible to support high traffic and time-varying wireless channels. Moreover, the fixed sleep sche-dule does not guarantee a uniform distribution of cumulative

wake-up rate within certain time in a cluster, which meansthat there may be congestions. On the contrary, Breathpresents an excellent behavior in any situations of traffic loadand channel condition.

9.1.2 Delay

In Fig. 10, the sample average of the delay for packet deliveryof Breath, IEEE 802.15.4 L andH are plotted as a function ofthe reliability requirement ! and traffic rate ! in AWGN andRayleigh fading environments, with the vertical barsindicating the standard deviation of the samples aroundthe average. The sample variance of the delay exhibits similarbehavior as the average. The delay meets quite well theconstrains. Observe that delay decreases as the traffic raterises. Because Breath increases linearly the wake-up rate ofnodes when the traffic rate increases (see (5)). The delay islarger for worse reliability requirements. Note that (5)increases as the reliability requirement ! increases. IEEE802.15.4 L has lower delay than IEEE 802.15.4 H becausenodes have higher wake-up time. Breath has an intermediatebehavior with respect to IEEE 802.15.4 L and H after ! ¼ 7.From these experimental results, we conclude that bothBreath and the IEEE 802.15.4 meet the delay requirement.However, notice that the delay for the IEEE 802.15.4 is relatedto only packet successfully received, which may be quite few.

9.1.3 Duty Cycle

In this section, we study the energy consumption of the nodes.As energy performance indicator, we measured the

node’s duty cycle, which is the ratio of the active time ofthe node to the total experimental time. Obviously, thelower is the duty cycle, the better is the performance of theprotocol on energy consumption.

Fig. 11 shows the sample average of duty cycle of Breath,IEEE 802.15.4 L and H with respect to the traffic rates ! ¼ 5,10, 15 pcks/s and ! ¼ 0:9; 0:95, both in AWGN andRayleigh fading environments, with the vertical barsindicating the standard deviation of the samples. Note thatthe IEEE 802.15.4 L andH do not exhibit a clear relationship


Fig. 10. Temporal average of the delay of Breath and IEEE 802.15.4L;H with reliability requirement ! ¼ 0:9; 0:95 and delay requirement # ¼1 s over traffic rates ! ¼ 5; 10; 15 pcks/s in AWGN and Rayleigh fadingenvironment. The vertical bars indicate the standard deviation asobtained out of 10 experimental runs of 1 hour each.

Fig. 11. Sample average of the node’s duty cycle in IEEE 802.15.4 L;H,and Breath with reliability requirement ! ¼ 0:9; 0:95 for traffic rates ! ¼5; 10; 15 pcks/s in AWGN and Rayleigh fading environments.

Fig. 9. Reliability in IEEE 802.15.4 L;H, and Breath with requirement! ¼ 0:9; 0:95 for traffic rates ! ¼ 5; 10; 15 pcks/s in AWGN and Rayleighfading environments. The vertical bars indicate the standard deviationas obtained out of 10 experimental runs of 1 hour each.

with respect to traffic rate and have almost flat duty cyclearound 42 and 18 percent, respectively, because of fixedsleep time. Considering Breath, observe that the duty cycleincreases linearly with the traffic rate for a given reliabilityrequirement, which is explained by (5). Since Breathminimizes the total energy consumption on the base of atrade-off between wake-up rate and waiting time of beaconmessages (recall the analysis in Section 5.3), lower wake-uprates do not guarantee lower duty cycle. Observe thatchoosing a lower active time for the nodes of the IEEE802.15.4 implementation would obviously obtain energysavings comparable with Breath, however, the reliability ofthe IEEE 802.15.4 implementation would be heavily affected(recall Fig. 9). In other words, ensuring a duty cycle for theIEEE 802.15.4 implementation comparable with Breathwould be very detrimental with respect to the reliability.

Fig. 12 shows the experimental results for the duty cycle ofeach relay node for ! ¼ 5 pcks/s and ! ¼ 0:95. A fairuniform distribution of the duty cycles among all nodes ofthe network is achieved. This is an important result, becausethe small variance of the wake-up rate among nodes signifiesthat duty cycle and load are uniformly distributed, withobvious advantages for the network lifetime.

Fig. 13 reports the case of several networks, where eachnetwork corresponds to a number of relays between the edgeand the sink in an AWGN environment. From the figure, it ispossible to evaluate how much Breath extends the networklifetime compared to the IEEE 802.15.4 L and H. Observethat the duty cycle is proportional to the density of nodes.Hence, the network lifetime is extended fairly by addingmore nodes without creating load balancing problems.

Finally, recall that Breath uses a radio power control(Section 5.3.1), so that further energy savings are actuallyobtained with respect to the IEEE 802.15.4 implementation.

9.2 Protocol Behavior for Time-VaryingRequirements

Performance of Breath protocol is based on the applicationrequirements and estimation of the channel condition. In thissection, we investigate the dynamic adaptation of Breathwhen the reliability ! and delay # requirements change.Fig. 14 shows the dynamic adaptability of reliability, packet

delay, and energy consumption when the requirements arechanged for given traffic rate and number of nodes. Figs. 14a,14c, 14e and 14b, 14d, 14f present the behavior of thereliability, packet delay, and average active time when thereliability and delay requirements change, respectively.More specifically, the average active time is defined as theaverage time nodes are active. We observe performance interms of reliability, delay, and average active time inFigs. 14a, 14c, and 14e for a reliability requirement variation.When ! increases from 0.9 to 0.95 at a time corresponding tothe number of received packets is 2,000 the reliabilityconverges to 0.95. At the same time, packet delay decreasesand average active time increase since optimal wake-up rateincreases to guarantee the higher reliability requirement.Analogously, Breath adapts the network by considering thedelay requirement variation in Figs. 14b, 14d, and 14f. It isclear that the average delay is under 60 ms since we considerthe distribution of the delay probability. The average activetime increases when the delay requirement changes due to ahigher optimal wake-up rate. Hence, in the optimizationproblem, the delay requirement 60 ms gives a stricterconstraint than reliability constraint computed at a require-ment of 0.9. From this analysis, we can conclude that Breathadaptively achieves its target (i.e., minimization of powerconsumption) while guaranteeing the reliability and delayrequirements. Furthermore, we observe clearly the trade-offbetween the application requirements and energy consump-tion, i.e., as application requirements become strict, energyconsumption increases.

10 CONCLUSIONS

We designed and implemented Breath, a protocol that isbased on a system-level approach to guarantee explicitlyreliability and delay requirements in wireless sensornetworks for control and actuation applications. Theprotocol considers duty cycle, routing, MAC, and physicallayers all together to maximize the network lifetime by


Fig. 12. Distribution of the duty cycle in each node with N ¼ 15 relays.The reliability requirement is ! ¼ 0:95 and traffic rate is ! ¼ 5 pcks/s. Fig. 13. Sample average of the duty cycle in IEEE 802.15.4 L;H, and

Breath with reliability requirement ! ¼ 0:9; 0:95 and traffic rates ! ¼5; 10; 15 pcks/s in AWGN environment for different networks, each with adifferent number of relaying nodes. The vertical bars indicate thestandard deviation as obtained out of 10 experimental runs of 1 houreach.

taking into account the trade-off between energy consump-tion and application requirements for control applications.

We developed an analytical expression of the totalenergy consumption of the network, as well as reliabilityand delay for the packet delivery. These relations allowedus to pose a mixed real-integer constrained optimizationproblem to optimize the number of hops in the multihoprouting, the wake-up rates of the nodes, and the transmitradio power as a function of the routing, MAC, physicallayer, traffic, and hardware platform. An algorithm for thedynamic and continuous adaptation of the network opera-tions to the traffic and channel conditions, and applicationrequirements, was proposed.

We provided a complete testbed implementation of theprotocol, building a wireless sensor network with TinyOSand Tmote sensors. An experimental campaign was con-ducted to test the validity of Breath in an indoorenvironment with both AWGN and Rayleigh fadingchannels. Experimental results showed that the protocolachieves the reliability and delay requirements, whileminimizing the energy consumption. It outperformed astandard IEEE 802.15.4 implementation in terms of bothenergy efficiency and reliability. In addition, Breath showedgood load balancing performance, and is scalable with thenumber of nodes. Given its good performance, Breath is a

good candidate for many control and industrial applica-tions, since these applications ask for both reliability anddelay requirements in the packet delivery. A practicalapplication of the protocol was illustrated in [11].

We are currently investigating the extension of thedesign methodology to consider mesh networks such ascoexisting ad hoc and wireless sensor networks.

ACKNOWLEDGMENTS

A preliminary version of this work was presented at IEEESECON 2008 [1]. Alvise Bonivento was with the Depart-ment of Electrical Engineering and Computer Sciences,University of California at Berkeley, when contributing tothis work. Pangun Park, Carlo Fischione, and Karl HenrikJohansson were supported by the Swedish ResearchCouncil, Strategic Research Foundation, and SwedishGovernmental Agency for Innovation Systems.

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Fig. 14. Reliability, packet delay, and active time behavior for ! ¼ 10 pcks/s, N ¼ 15 when reliability and delay requirements vary from ! ¼ 0:9to ! ¼ 0:95 and from # ¼ 1 s to # ¼ 60 ms at a time instant corresponding to the number of received packets is 2,000 in AWGN environment.(a) Reliability behavior when ! changes from 0.9 to 0.95. (b) Reliability behavior when # changes from 1 s to 60 ms. (c) Delay behavior when! changes from 0.9 to 0.95. (d) Delay behavior when # changes from 1 s to 60 ms. (e) Active time behavior when ! changes from 0.9 to0.95. (f) Active time behavior when # changes from 1 s to 60 ms.

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Pangun Park received the BS degree in elec-trical engineering from Ajou University, Suwon,Korea, in 2005 and the MS degree in electricalengineering from the Royal Institute of Technol-ogy, Stockholm, Sweden, in 2007. Since 2007,he has been working toward the PhD degree inthe School of Electrical Engineering at the RoyalInstitute of Technology (KTH), Sweden. Hereceived the Best Paper award at the IEEEInternational Conference on Mobile Ad-Hoc and

Sensor Systems 2009. His research interests include ad hoc and sensornetworks, and networked control systems.

Carlo Fischione received the DrEng degree inelectronic engineering (Laurea Summa CumLaude) and the PhD degree in electrical andinformation engineering from the University ofL’Aquila, Italy, in April 2001 and May 2005,respectively. He is currently an assistant pro-fessor in the Department of Electrical Engineer-ing, ACCESS Linnaeus Centre, Royal Instituteof Technology (KTH), Stockholm, Sweden. Hishistory includes holding research positions at the

University of California at Berkeley, California (2004-2005 and 2007-2008) and Royal Institute of Technology, Stockholm, Sweden (2005-2007). He is supervising a number of doctorate and graduate students atthe Royal Institute of Technology (KTH), Sweden, and the University ofL’Aquila, Italy. He received the Best Paper award from the IEEETransactions on Industrial Informatics in 2007, the best paper awards atthe IEEE International Conference on Mobile Ad-Hoc and SensorSystem 2005 and 2009 (IEEE MASS 2005 and IEEE MASS 2009), andis a recipient of the “Ferdinando Filauro” award from the University ofL’Aquila, Italy, and the “Alta Formazione” Award from the AbruzzoRegion Government, Italy. He has chaired or served as a technicalmember of program committees of several international conferencesand is serving as referee for technical journals. Meanwhile, he has alsooffered his advice as a consultant to numerous technology companiessuch as the Berkeley Wireless Sensor Network Lab, Ericsson AB,Synopsys, and the United Technology Research Center. His researchinterests include optimization, wireless sensor networks, and system-level design of wireless networks. He is a member of the IEEE.

Alvise Bonivento received the Laurea degreein telecommunication engineering from theUniversity of Padova, Italy, in 2002 and theMS and PhD degrees in electrical engineeringand computer science from the University ofCalifornia, Berkeley, in 2004 and 2007, respec-tively. His research interest is in communica-tion protocols for embedded systems, withemphasis on system-level design and protocolsynthesis for wireless embedded networks and

wireless industrial networks. He is a coauthor of several technicalpapers in major communication and embedded systems conferencesand he received the best paper award from the IEEE Transactions onIndustrial Informatics in 2007. He was a management consultant atMcKinsey & Co. from 2007 and 2009 and joined a venture capital firmin 2009 where he currently is an investment manager.

Karl Henrik Johansson received the MSc andPhD degrees in electrical engineering fromLund University, Sweden, in 1992 and 1997,respectively. He is the director of the ACCESSLinnaeus Centre and professor at the School ofElectrical Engineering, Royal Institute of Tech-nology, Sweden. He is a Wallenberg scholar andholds a senior researcher position with theSwedish Research Council. He has held visitingpositions at the University of California at

Berkeley, California (1998-2000) and the California Institute of Technol-ogy (2006-2007). His research interests are in networked controlsystems, hybrid and embedded control, and control applications inautomotive, automation, and communication systems. He has been thechair of the International Federation of Automatic Control (IFAC)Technical Committee on Networked Systems since 2008. He hasserved on the executive committees of several European researchprojects in the area of networked embedded systems. He is on theeditorial boards of the IEEE Transactions on Automatic Control and IETControl Theory & Applications, and previously of the IFAC journalAutomatica. He was awarded an Individual Grant for the Advancementof Research Leaders from the Swedish Foundation for StrategicResearch in 2005. He received the triennial Young Author prize fromIFAC in 1996 and the Peccei award from the International Institute ofSystem Analysis, Austria, in 1993. He received Young Researcherawards from Scania in 1996 and from Ericsson in 1998 and 1999.

Alberto L. Sangiovanni-Vincentelli receivedthe degree in electrical engineering and compu-ter science (“Dottore in Ingegneria”) summa cumlaude from the Politecnico di Milano, Italy, in1971. He holds the Edgar L. and Harold H.Buttner chair of electrical engineering andcomputer sciences at the University of Californiaat Berkeley. In 1987, he was a visiting professorat the Massachusetts Institute of Technology.He was a cofounder of Cadence and Synopsys,

the two leading companies in the area of Electronic Design Automation.He is a member of the board of directors of Cadence and the chair of itsTechnology Committee, UPEK, Sonics, and Accent. He was a memberof the HP Strategic Technology Advisory Board and is a member of theScience and Technology Advisory Board of General Motors. He is amember of the High-Level Group, the steering committee, the governingboard, and of the Public Authorities Board of the EU Artemis JointTechnology Undertaking. He is a member of the scientific council of theItalian National Science Foundation (CNR) and a member of theexecutive committee of the Italian Institute of Technology. He receivedthe Kaufman award of the Electronic Design Automation Council for“pioneering contributions to EDA” and the IEEE/RSE Wolfson JamesClerk Maxwell Medal “for groundbreaking contributions that have had anexceptional impact on the development of electronics and electricalengineering or related fields.” He is an author of more than 800 papers,15 books, and three patents. He is a fellow of the IEEE and a member ofthe National Academy of Engineering.

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