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Chapter 9Design Principles of Wireless Sensor NetworksProtocols for Control Applications

Carlo Fischione, Pangun Park, Piergiuseppe Di Marco, Karl Henrik Johansson∗†

Abstract

Control applications over wireless sensor networks require timely, reliable, andenergy efficient communications. This is challenging because reliability and latencyof delivered packets and energy are at odds, and resource constrained nodes supportonly simple algorithms. In this chapter, a new system-leveldesign approach for pro-tocols supporting control applications over wireless sensor networks is proposed.The approach suggests a joint optimization, or co-design, of the control specifica-tions, networking layer, the medium access control layer, and physical layer. Theprotocol parameters are adapted by an optimization problemwhose objective func-tion is the network energy consumption, and the constraintsare the reliability andlatency of the packets as requested by the control application. The design methodaims at the definition of simple algorithms that are easily implemented on resourceconstrained sensor nodes. These algorithms allow the network to meet the reliabilityand latency required by the control application while minimizing for energy con-sumption. The design method is illustrated by two protocols: Breath and TREnD,which are implemented on a test-bed and compared to some existing solutions. Ex-perimental results show good performance of the protocols based on this designmethodology in terms of reliability, latency, low duty cycle, and load balancing forboth static and time-varying scenarios. It is concluded that a system-level design isthe essential paradigm to exploit the complex interaction among the layers of theprotocol stack and reach a maximum wireless sensor network efficiency.

∗ The authors are with the ACCESS Linnaeus Center, ElectricalEn-gineering, Royal Institute of Technology, Stockholm, Sweden. E-mails:carlofi,pgpark,pidm,[email protected]. This work was supported by theEU project FeedNetBack, the Swedish Research Council, the Swedish Strategic ResearchFoundation, and the Swedish Governmental Agency for Innovation Systems.

† Some parts of this book chapter have been presented to IEEE Communications Society Con-ference on Sensor, Mesh and Ad Hoc Communications and Networks (IEEE SECON 08), SanFrancisco, CA, USA, June 2008, and to IEEE International Conference on Communications 2010(IEEE ICC 10), Cape Town, South Africa, May 2010.

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2 Carlo Fischione, Pangun Park, Piergiuseppe Di Marco, Karl Henrik Johansson

Key words: Wireless sensor networks, Networked Control, IEEE 802.15.4, Opti-mization.

9.1 Introduction

Wireless sensor networks (WSNs) are networks of tiny sensingdevices for wirelesscommunication, actuation, control, and monitoring. Giventhe potential benefits of-fered by these networks, as, e.g., simple deployment, low installation cost, lack ofcabling, and mobility, they are specially appealing for control applications in homeand industrial automation [1] – [3]. The variety of application domains and theoret-ical challenges for WSNs has attracted research efforts for more than one decade.Nevertheless, a lively research and standardization activity is ongoing [2] – [5] andthere is not yet a widely accepted protocol stack for WSNs for control applications.

The lack of efficient protocol solutions is due to that the protocols for control ap-plications face complex control and communication requirements. Traditional con-trol applications are usually designed by a top-down approach from a protocol stackpoint of view, whereby most of the essential aspects of the network and sensinginfrastructure that has to be deployed to support control applications are ignored.Here, packet losses and delays introduced by the communication network are con-sidered as non-idealities and uncertainties and the controllers are tuned to cope withthem without having any influence on them. The top-down approach is limited fortwo reasons: 1) it misses the essential aspect of the energy efficiency that is usuallyrequired to WSNs [2], and 2) it can be quite conservative and therefore inefficient,because the controllers are built by presuming worst case wireless channel condi-tions that may be rarely experienced in the reality. On the other side, protocols forWSNs are traditionally designed to maximize the reliabilityand minimize the de-lay. This is a bottom-up approach, where controller specifications are not explicitlyconsidered even though the protocols are used for control. This approach is energyinefficient because high reliability and low latency may demand significant energyconsumption [2, 6]. Therefore, it follows that there is the essential need of a newdesign approach.

Traditional WSNs applications (e.g., monitoring) need a high probability of suc-cess in the packet delivery (reliability). In addition to reliability, control applicationsask also for timely packet delivery (latency). If reliability and latency constraints arenot met, the correct execution of control decisions may be severely compromised,thus creating unstable control loops [7]. High reliabilityand low latency may de-mand significant energy expenditure, thus reducing the WSN lifetime. Controllerscan usually tolerate a certain degree of packet losses and delay [8]: For example, thestability of a closed loop control system may be ensured by high reliable communi-cations and large delays, or by low delays when the packet loss is high. In contrastto monitoring applications, for control applications there is no need to maximize thereliability. A trade-off between latency, packet losses, and stability requirements canbe exploited for the benefit of the energy consumption, as proposed by the system-level design approach [9]. Therefore, we claim that the protocol design for control

9 Design Principles of WSNs Protocols for Control Applications 3

needs a system-level approach whereby the need of a parsimonious use of energyand the typical requirements of the control applications are jointly taken into ac-count and control and WSNs protocols are co-designed.

Exploiting such a trade-off poses extra challenges when designing WSNs pro-tocols for control applications when compared to more traditional communicationnetworks, namely:

• Reliability: Sensor information must be sent to the sink of the network with agiven probability of success, because missing these data could prevent the cor-rect execution of control actions or decisions concerning the phenomena sensed.However, maximizing the reliability may increase substantially the network en-ergy consumption [2]. Hence, the network designers need to consider the trade-off between reliability and energy consumption.

• Latency: Sensor information must reach the sink within somedeadline [6]. Aprobabilistic delay requirement must be considered instead of using averagepacket delay since the delay jitter can be too difficult to compensate for, espe-cially if the delay variability is large [10]. Retransmission of old data to maxi-mize the reliability may increase the delay and is generallynot useful for controlapplication [7].

• Energy efficiency: The lack of battery replacement, which isessential for af-fordable WSN deployment, requires energy-efficient operations. Since high re-liability and low delay may demand a significant energy consumption of thenetwork, thus reducing the WSN lifetime, the reliability anddelay must be flex-ible design parameters that need to be adequate for the requirements. Note thatcontrollers can usually tolerate a certain degree of packetlosses and delay [8] –[12]. Hence, the maximization of the reliability and minimization of the delayare not the optimal design strategies for the control applications we are con-cerned within this chapter.

• Adaptation: The network operation should adapt to application requirementchanges, varying wireless channel and network topology. For instance, the set ofcontrol application requirements may change dynamically and the communica-tion protocol must adapt its design parameters according tothe specific requestsof the control actions. To support these changing requirements, it is essential tohave an analytical model describing the relation between the protocol parame-ters and performance indicators (reliability, delay, and energy consumption).

• Scalability: Since the processing resources are limited, the protocol proceduresmust be computationally light. These operations should be performed withinthe network, to avoid the burden of too much communication with a central co-ordinator. This is particularly important for large networks. The protocol shouldalso be able to adapt to size variation of the network, as, forexample, caused bymoving obstacles, or addition of new nodes.

In this chapter, we propose a design methodology for WSNs protocols for controlapplication that embraces the issues mentioned above. The remainder of the chapteris organized as follow: in Sec. 10.2, we discuss the related literature. In Sec. 10.3,we describe the design method. Such a method is then applied in Sec. 10.4, wherethe Breath protocol is described and experimentally evaluated, and in Sec. 10.5,


where the TREnD protocol is described and experimentally tested. In Sec. 10.6, weconclude the chaper.

9.2 Background

Although the standardization process for WSNs is ongoing, there is not any widelyaccepted complete protocol stack for WSNs for control [2]. The IEEE 802.15.4protocol [4], which specifies physical layer and medium access control (MAC),is the base of recent solutions in industrial environments as WirelessHART [13],ISA100 [2], and ROLL [5]. Hence, we consider IEEE 802.15.4 asthe referencestandard in our investigation.

There have been many contributions to the problem of protocol design for WSNs,both in academia (e.g., [2, 14]) and industry (e.g., [15] – [17]). New protocols havebeen built around standardized low-power protocols such asIEEE 802.15.4 [4], Zig-bee [18] and WirelessHART. WirelessHART is a promising solution for the replace-ment of the wired HART protocol in industrial contexts. However, the power con-sumption is not a main concern in WirelessHART, whereas the data link layer isbased on Time Division Multiple Access (TDMA), which requires time synchro-nization and pre-scheduled fixed length time-slots by a centralized network man-ager. Such a manager should update the schedule frequently to consider reliabilityand delay requirements and dynamic changes of the network, which demands com-plex hardware equipments. WirelessHART is thus in contrastwith the necessityof simple protocols able to work with limited energy and computing resources. InTab. 10.1, we summarize the characteristics of the protocols that are relevant for thecategory of applications we are concerned within this chapter. In the table, we haveevidenced wether indications as energyE, reliability R, and delayD have beenincluded in the protocol design and validation, and whethera cross-layer approachhas been adopted. We discuss these protocols in the following.

GAF [19], SPAN [20] and X-MAC [21] consider the energy efficiency as a per-formance indicator, which is attained by algorithms under the routing layer andabove the MAC layer so called bridge layer. Simulation results of reliability anddelay are reported in [19,20]. These protocols have not beendesigned out of an ana-lytical modelling of reliability and delay, so there is not systematic control of them.One of the first protocol for WSNs designed to offer a high reliability is RMST [22],but energy consumption of the network or delay have not been accounted for in thisprotocol. The same lack of energy efficiency and delay requirements can be foundin the reliable solutions presented in [23]- [25]. Dozer [26] comprises the MACand routing layer to minimize the energy consumption while maximizing the relia-bility of the network, but an analytical approach has not been followed. Specially,Fetch [25] and Dozer [26] are designed for monitoring application, which mainlydeals with lower traffic load than control applications. Thelatency of Fetch [25] issignificantly dependent on the depth of the routing tree and is around some hun-dred seconds. In addition, experimental results of Dozer [26] show good energyefficiency and reliability under very low traffic intensity (with data sampling inter-


Table 9.1 Protocol comparison. The lettersE, R, andD denote energy, reliability and commu-nication delay. The circle denotes that a protocol is designed by considering the indication of thecolumn, but it has not been validated experimentally. The circle with plus denotes that the protocolis designed by considering the indication and experimentally validated. The dot denotes that theprotocol design does not include indication and hence cannotcontrol it, but simulation or experi-ment results include it. The term “bridge” means that the protocol is designed by bridging MACand routing layers.

Protocol E R D Layer

GAF [19] © · · bridgeSPAN [20] © · · bridgeXMAC [21] ⊕ · · MACFlush [23] ⊕ MACFetch [25] · ⊕ · phy, MAC, routing

GERAF [27] © © MAC, routingDozer [26] ⊕ ⊕ MAC, routing

MMSPEED [28] © © routingBreath ⊕ ⊕ ⊕ phy, MAC, routingTREnD ⊕ ⊕ ⊕ phy, MAC, routing

val of 120 s) but the delay in the packet delivery is not considered, which is essentialfor control applications [7], [8]. Energy efficiency with delay requirement for MACand randomized routing is considered in GERAF [27], withoutsimulation or exper-imental validation.

The focuses of the protocols mentioned above [19] – [27] are the maximization ofthe energy efficiency or reliability, or just minimization of the delay, without consid-ering simultaneously application requirements in terms ofreliability and delay in thepacket delivery. In other words, these protocols are mostlydesigned for monitoringapplications and do not support typical control application requirements. Controland industrial applications are able to cope with a certain degree of packet lossesand delay [8, 10, 11], which implies that the approaches followed in the protocolsmentioned above are not the ideal solution for these applications. The maximizationof the energy efficiency and reliability may give a long delay, which are bad forthe stability of the closed-loop control system. Analogously, the maximization ofthe reliability may be energy demanding and may give long delay, all of which arenot tolerable for control applications. In addition, the protocols mentioned above donot support an adaptation to the changes of the reliability and delay, which may berequired by the controllers.

The protocols MMSPEED [28] and SERAN [9] are appealing for control and in-dustrial applications. However, MMSPEED is not energy efficient because it consid-ers a routing technique with an optimization of reliabilityand delay without energyconstraints. The protocol satisfies a high reliability requirement by using duplicatedpackets over multi-path routing. Duplicated packets increase the traffic load withnegative effect on the stability and energy efficiency of thenetwork. In SERAN, asystem-level design methodology has been presented for industrial applications, buteven though SERAN allows the network to operate with low energy consumptionsubject to delay requirements, it does not consider tunablereliability requirements


sink

node 1

plant 1

WSN

node 2

plant 2

node n

plant n

controller 1

controller 2

controller n

Fig. 9.1 Control over a wireless sensor network. There aren plants andn controllers. A networkcloses the loop from sensors attached to plants to the controllers.

nor duty-cycling policies, which are essential to reduce energy consumption. Fur-thermore, SERAN focuses on low traffic networks. These characteristics limit theperformance of SERAN both in term of energy and reliability in our applicationsetup.

Given the availability of numerous techniques to reduce energy consumption andensure reliability and low delays, a cross-layer optimization is a natural approachfor WSNs protocols. Some cross-layer design challenges of the physical, MAC andnetwork layers to minimize the energy consumption of WSNs have been surveyedin [29] – [31]. However, many of the cross-layer solutions proposed in the liter-ature are hardly useful for the application domain we are targeting, because theyrequire sophisticated processing resources, or instantaneous global network knowl-edge, which are out of the capabilities of real nodes. Moreover, the requirements ofthe control applications are not taken into account.

In the following sections, we present a general approach to the design of WSNsprotocols for control. We illustrate this approach by the protocols Breath andTREnD, which are presented in this chapter. These protocolsembrace simultane-ously the physical layer, the MAC layer, the networking layer and the control appli-cation layer, see Tab. 10.1.

9.3 Protocol Design for Control Applications

In this chapter, we are concerned with the design of protocols for WSNs used toclose the loop between plants and controllers, see Fig. 10.1. The nodes connectedto the plants take state information and transmit it to the sink via a multi-hop WSN.The controllers are attached to the sink of the network. The sink must receive pack-ets from the nodes of the plants with a desired probability ofsuccess and within alatency constraint demanded by the controllers so that the control decision can be


correctly taken. We assume that the communication network must be energy effi-cient to guarantee a long network lifetime.

Give the system model reported in Fig. 10.1, we formulate thenew WSNs proto-col design approach for control applications by the following optimization problem,where the parameters of the physical layer, the MAC layer, the networking layer,and the control application layer are co-designed by a jointoptimization:

minx

Etot(x) (9.1a)

s.t. Ri(x) ≥ Ωi, i = 1, . . . , n , (9.1b)

Pr[Di(x) ≤ τi] ≥ ∆i i = 1, . . . , n , (9.1c)

Each decision variable is a protocol parameter. These decision variables are denotedby the vectorx, which can include the radio power used by the nodes, the MACparameters (e.g., the access probability or the time division multiple access slot du-ration, or the random backoff of IEEE 802.15.4 MAC, etc.) andthe routing param-eters.Etot(x) is the total energy consumption of the network, which is obviouslyfunction of the protocol parameters.Ri(x) is the probability of successful packetdelivery (reliability) from nodei to the sink, andΩi is the minimum desired prob-ability. Di(x) is a random variable describing the delay to transmit a packet fromnodei to the sink.τi is the desired maximum delay, and∆i is the minimum prob-ability with which such a maximum delay should be achieved. Notice thatEtot(x),Ri(x),Di(x), i = 1, . . . , n, are implicit functions of the wireless channel, networktopology, and traffic load generated by the nodes.

We remark thatτi, ∆i, andΩi are thecontrol requirements, andx collects theprotocol parametersthat must be adapted to the wireless channel conditions, net-work topology, and the control requirements for an efficientnetwork operation.Since controllers may need some reliability and latency during certain time inter-vals, and a different pair of requirements during some othertime intervals, the re-liability and latency requirements may change dynamicallydepending on the stateof the plant and the history of the control actions. This means that problem (10.1)must be solved periodically and nodes need to know the optimal solution to adapttheir protocol operation so that a global optimum can be achieved. It follows thatthe proposed design method allows us to perform a systematicco-design betweenthe control requirements of the application and the networkenergy consumption.

Note that problem (10.1) can be a mixed integer-real optimization problem, be-cause some of the protocol parameters can take on integer values. The solution tothis problem can be obtained at the sink node, or by distributed algorithms, whereeach node performs local computation to achieve the optimalglobal solution. How-ever, as it was noted in [32], the complex interdependence ofthe decision variables(sleep disciplines, clustering, MAC, routing, power control, etc.) lead to difficultoptimization problems even in simple network topologies, where the analytical re-lations describing reliability, latency, and energy consumption may be highly non-linear expressions. Such a difficulty is further exacerbated when considering non-TDMA scheme [33]. We propose a design approach that offers a computationallyattractive solution by simplifications of adequate accuracy. We show how to modelthe relations of problem (10.1) and compute the solution to the problem in the fol-


Controller Plant

Sink Edge

Cluster h

Relays

Cluster h-1Cluster 2Cluster 1

Network

Fig. 9.2 Wireless control loop. An wireless network closes the loop fromsensors to controller.The network includes nodes (black dots) attached to the plant, h− 1 relay clusters (grey dots), anda sink (black rectangular) attached to the controller. Note that the lines between the sink and thecontroller, and between the controller and the plant are notcommunication links, but control links.

lowing two sections. Specifically, in Section 10.4 we apply this design method toa system where there is only a plant that needs to be controlled over a multi-hopnetwork, whereas in Section 10.5, the design method is applied to a system wherethere are multiple plants.

9.4 Breath

The Breath protocol is designed for the scenario depicted inFig. 10.2, where aplant is remotely controlled over a WSN [7, 11]. Outputs of theplant are sampledat periodic intervals by the sensors with total packet generation rate ofλ pkt/s. Weassume that packets associated to the state of the plant are transmitted to a sink,which is connected to the controller, over a multi-hop network of uniformly andrandomly distributed relaying nodes. No direct communication is possible betweenthe pant and the sink. Relay nodes forward incoming packets.When the controllerreceives the measurements, they are used in a control algorithm to compensate thecontrol output. The control law induces constraints on the communication delay andthe packet loss probability. Packets must reach the sink within some minimum reli-ability and maximum delay. The application requirements are chosen by the controlalgorithm designers. Since they can change from one controlalgorithm to another,or a control algorithm can ask to change the application requirements from timeto time, we allow them to vary. We assume that nodes of the network cannot berecharged, so the operations must conserve energy. The system scenario is quitegeneral, because it applies to any interconnection of a plant by a multi-hop WSN toa controller tolerating a certain degree of data loss and delay [8] – [12].

A typical example of the scenario described above is an industrial control appli-cation. In particular, a WSN with nodes uniformly distributed in the environment(e.g., in the ceiling) can be deployed as the network infrastructure that support the


control of the state of the robots in a manufacturing cell [13, 34]. Typically, a cellis a stage of an automation line. Its physical dimensions range around10 or 20 me-ters on each side. Several robots cooperate in the cell to manipulate and transformthe same production piece. Each node senses the state and hasto report the data tothe controller within a maximum delay. The decision making algorithm runs on thecontroller, which is usually a processor placed outside thecell. Multi-hop commu-nication is needed to overcome the deep attenuations of the wireless channel due tomoving metal objects and save energy consumption.

The Breath protocol groups allN nodes between the cluster of nodes attached tothe plan and the sink withh− 1 relay clusters. Data packets can be transmitted onlyfrom a cluster to the next cluster closer to the sink. Clustered network topology issupported in networks that require energy efficiency, sincetransmitting data throughrelays consumes less energy than routing directly to the sink [35]. In [36], a dynamicclustering method adapts the network parameters. In [35] and [37], a cluster headeris selected based on the residual energy levels for clustered environments. However,the periodic selection of clustering may not be energy-efficient, and does not ensurethe flexibility of the network to a time-varying wireless channel environment. Asimpler geographic clustering is instead used in Breath. Nodes in the forwardingregion send short beacon messages when they are available toreceive data packets.Beacon messages are exploited to carry information relatedto the control parametersof the protocol.

In the following sections, we will describe the protocol stack and state machineof Breath in Section 10.4.1 and 10.4.2, respectively.

9.4.1 The Breath Protocol Stack

Breath uses a randomized routing, a hybrid TDMA at the MAC, radio power controlat the physical layer, and sleeping disciplines. We give details in the following.

In many industrial environments, the wireless conditions vary heavily becauseof moving metal obstacles and other radio disturbances. In such situations, rout-ing schemes that use fixed routing tables are not able to provide the flexibility overmobile equipments, physical design limitations and reconfiguration typical of an in-dustrial control application. Fixed routing is inefficientin WSNs due to the costof building and maintaining routing tables. To overcome this limitation, routingthrough a random sequence of hops has been introduced in [27]. The Breath pro-tocol is built on an optimized random routing, where next hoproute is efficientlyselected at random. Randomized routing allows us to reduce overhead because nonode coordination or routing state needs to be maintained bythe network. Robust-ness to node failures is also considerably increased by randomized routing. There-fore, nodes route data packets to next-hop nodes randomly selected in a forwardingregion.

Each node, either transmitter or receiver, does not stay in an active state all time,but goes to sleep for a random amount of time, which depends onthe traffic andchannel conditions. Since traffic, wireless channel, and network topology may be


time-varying, the Breath protocol uses a randomized duty-cycling algorithm. Sleepdisciplines turn off a node whenever its presence is not required for the correct op-eration of the network. GAF [19], SPAN [20] and S-MAC [38] focus on controllingthe effective network topology by selecting a connected setof nodes to be active andturning off the rest of the nodes. These approaches require extra communication,since nodes maintain partial knowledge of the state of theirindividual neighbors.In Breath, each node goes to sleep for an amount of time that isa random variabledependent on traffic and network conditions. Letµc be the cumulative wake-up rateof each cluster, i.e., the sum of the wake-up rates that a nodesees from all nodes ofthe next cluster. The cumulative wake-up rate of each cluster must be the same foreach cluster to avoid congestions and bottlenecks.

The MAC of Breath is based on a CSMA/CA mechanism similar to IEEE802.15.4. Both data packets and beacon packets are transmitted using the sameMAC. Specifically, the CSMA/CA checks the channel activity by performing clearchannel assessment (CCA) before the transmission can commence. Each node main-tains a variable NB for each transmission attempt, which is initialized to 0 andcounts the number of additional backoffs the algorithm doeswhile attempting thecurrent transmission of a packet. Each backoff unit has duration Tca ms. Before per-forming CCAs a node takes a backoff ofrandom(0,W − 1) backoff units i.e., arandom number of backoffs with uniformly distributed over0, 1, . . . ,W − 1. If theCCA fails, i.e., the channel is busy, NB is increased by one and the transmission isdelayed ofrandom(0,W − 1) backoff periods. This operation is repeated at mostMca times, after which a packet is discarded.

The Breath protocol assumes that each node has a rough knowledge of its lo-cation. This information, which is commonly required for the applications we aretargeting [2], can be obtained running a coarse positioningalgorithm, or using theReceived Signal Strength Indicator (RSSI), which is typically provided by off-the-shelf sensor nodes [39]. Some radio chip already provide a location engine basedon RSSI [40]. Location information is needed for tuning the transmit radio powerand to change the number of hops, as we will see later. The energy spent for radiotransmission plays an important role in the energy budget and for the interference inthe network. Breath, therefore, includes an effective radio power control algorithm.

9.4.2 State Machine Description

Breath distinguishes between three node classes: edge nodes, relays, and the sink.The edge nodes wake-up as soon as they sense packets generated by the plant

to be controlled. Before sending packets, a edge node waits for a beacon messagefrom the cluster of nodes closer to the edge. Upon the reception of a beacon, thenode sends the packet.

Consider a relay nodek. Its detailed behavior is illustrated by the state machineof Fig. 10.3, as we describe in the following:

• Calculate Sleep State: the node calculates the parameterµk for the next sleep-ing time and generates an exponentially distributed randomvariable having av-


Sleep

CalculateSleep

Active-TX

IdleListenWake-up

End Sleep

Time Out

PacketReceived

PacketSent

Beacon Sent

CSMA/CA

BeaconReceived

Time Out

DiscardedPacket

Fig. 9.3 State machine description of a relay node executing the Breathprotocol.

erage1/µk. After this the node goes back to the Sleep State.µk is computedsuch that the cumulative wake-up rate of the clusterµc in ensured.

• Sleep State: the node turns off its radio and starts a timer whose duration is anexponentially distributed random variable with average1/µk. When the timerexpires, the node goes to the Wake-up State.

• Wake-up State: the node turns its beacon channel on, and broadcasts a beaconindicating its location. Then, it switches to listen to the data channel, and it goesto the Idle Listen State.

• Idle Listen State: the node starts a timer of a fixed duration that must be longenough to receive a packet. If a data packet is received, the timer is discarded,the node goes to the Active-TX State, and its radio is switched from the datachannel to the beacon channel. If the timer expires before any data packet isreceived, the node goes to the Calculate Sleep State.

• Active-TX State: the node starts a waiting timer of a fixed duration. If the nodereceives the first beacon coming from a node in the forwardingregion withinthe waiting time, it retrieves the node ID and goes to the CSMA/CA State.Otherwise if the waiting timer is expired before receiving abeacon, the nodegoes to the Calculate Sleep State.

• CSMA/CA State: the node switches its radio to hear the data channel, andit tries to send a data packet to a node in the next cluster by the CSMA/CAMAC. If the channel is not clean within the maximum number of tries, the nodediscards the data packet and goes to the Calculate Sleep State. If the channelis clear within the maximum number of attempts, the node transmits the datapacket using an appropriate level of radio power and goes to the Calculate SleepState.

The sink node sends periodically beacon messages to the lastcluster of the net-work to receive data packets. Such a node estimates periodically the traffic rate andthe wireless channel conditions. By using this information, the sink runs an algo-rithm to optimize the protocol parameters, as we describe inthe following section.Once the results of the optimization are achieved, they are communicated to thenodes by beacons.


According to the protocol given above, the packet delivery depends on the traf-fic rate, the channel conditions, number of forwarding regions, and the cumulativewake-up time. In the next sections, we show how to model and optimize online theseparameters.

9.4.3 Protocol Optimization

The protocol is optimized dynamically by the constrained optimization problem (10.1).The objective function, denoted byEtot(h, µc), is the total energy consumption fortransmitting and receiving packets from the edge cluster tothe sink. The constraintare given by the end-to-end packet reception probability and end-to-end delay prob-ability. In the Breath specific case, problem (10.1) is written in the following form:

minh,µc

Etot(h, µc) (9.2a)

s.t. R(h, µc) ≥ Ω , (9.2b)

Pr[D(h, µc) ≤ τ ] ≥ ∆, (9.2c)

h ≥ 2 , (9.2d)

µmin ≤ µc ≤ µmax . (9.2e)

The decision variables are the cumulative wake-up rateµc of each cluster and thenumber of relay clusters,h − 1. R(h, µc) is the probability of successful packetdelivery (reliability) from the edge cluster to the sink, and Ω is the minimum de-sired probability.D(h, µc) is a random variable describing the delay to transmit apacket from the edge cluster to the sink.τ is the desired maximum delay, and∆ isthe minimum probability with which such a maximum delay should be achieved.Constraint (10.2d) is due to that there is at least two hops from the edge cluster tothe sink. Constraint (10.2e) is due to that the wake-up rate cannot be less than a min-imum valueµmin, and larger than a maximum valueµmax due to hardware reasons.Note that Problem (10.2) is a mixed integer-real optimization problem, becauseµc

is real andh is integer. We need to have∆ andΩ close to one. We let∆ ≥ 0.95 andΩ ≥ 0.9, namely we assume that the delayτ must be achieved at least with a prob-ability of 95%, and the reliability must be larger than90%. We remark thatτ , ∆,andΩ are application requirements, andh, µc and nodes’ radio transmit power areprotocol parametersthat must be adapted to the traffic rateλ, the wireless channelconditions, and the application requirements for an efficient network operation.

In the following we shall propose an approach to model the quantities of Prob-lem (10.2), along with a strategy to achieve the optimal solution, namely the valuesof h∗ andµ∗

c that minimize the cost function and satisfy the applicationrequire-ments. As we will see later, the system complexity prevents us to derive the exactexpressions for the analytical relations of the optimization problem. An approxi-mation of the requirements and an upper bound of the energy consumption will beused.


9.4.3.1 Reliability Constraint

In this subsection, we provide an analytical expression forthe reliability con-straint (10.2b) in Problem (10.2). We have the following result

Claim. The reliability constraint (10.2b) is

R(h, µc) =h

∏

i=1

pi

∞∑

n=1

ψsb(n)ψsc(n) , (9.3)

wherepi denotes the probability of successful packet reception during a single-hoptransmission from clusteri to clusteri− 1, n is the number of nodes in the cluster,1/λ is the data packet transmission period,

ψsc(n) =ζ(1 − ζ)n−1

1 − (1 − ζ)n,

where

ζ =

m∑

i=0

bi,0 =2

W + 3.

Proof. A proof is provided in [41].

Since the components of the sum in (10.3) withn ≥ 2 give a small contribution,we setn = 2. In [41], we see that (10.3) provides a good approximation ofthe ex-perimental results because it is always around5% of the experiments for reliabilityvalues of practical interest (larger than 0.7). The same behavior is found forh up to4.

We can rewrite the reliability constraintR(h, µc) ≥ Ω by using (10.3) withn = 2, thus obtaining

µc ≥ fr(h,Ω) , λ ln(2Cr)−

λ ln

[

Cr − 1 +√

(Cr − 1)2 − 4Cr

(

Ω1/h/pmin − 1)

]

, (9.4)

whereCr = ζ(1 − ζ)/(1 − (1 − ζ)2), andpmin = min(p1, . . . , ph). Note thatwe used the worst channel condition of the networkpmin, which is acceptable foroptimization purpose because in doing so we consider the minimum of (10.3). Sincethe argument of the square root in (10.4) must be positive, anadditional constraintis introduced:

h ≤ hr ,ln(Ω)

ln(pmin). (9.5)

We will use (10.4) and (10.5) in Section 10.4.4 to find the solution of Problem (10.2).Now, we turn our attention to the delay constraint.


9.4.3.2 Delay Constraint

In this section, we give the expression for the delay constraint in (10.2c). The delaydistribution is approximated by a Gaussian random variable. We have the followingresult:

Claim. The delay constraint given by Eq. (10.2c) is approximal by

Pr[D ≤ τ ] ≈ 1 −Q

(

τ − µD

σD

)

≥ ∆, (9.6)

where

µD =h

µc+h(Mca + 1)(W − 1)Tca

2, (9.7)

σ2D =

h

µ2c

+h(Mca + 1)(W 2 − 1)T 2

ca

12. (9.8)

andQ(x) = 1/√

2π∫ ∞

xe−t2/2dt.


We are now in the position to express the delay constraint in Problem (10.2)by using Eqs. (10.7) and (10.8) that we just derived. After some manipulations, itfollows that (10.6) can be rewritten as

µc ≥ 12Cd1 h+ 2√

3Cd3 h [12C2d1 + Cd2 (h− Cd3)]

12C2d1 − Cd2 Cd3

,

where

Cd1 = τ − h(Mca + 1)(W − 1)Tca

2,

Cd2 = h(Mca + 1)(W 2 − 1)T 2ca ,

Cd3 =(

Q−1(1 −∆))2.

SinceT 2ca = 0.1024 × 10−6 [15], andh, Mca, W are positive integers, it follows

thatT 2ca ≪ h(Mca + 1)(W 2 − 1). ThenCd2 ≪ Cd1 and (10.6) is approximated by

µc ≥ fd(h, τ,∆) ,2[

h+Q−1(1 −∆)√h]

2τ − h(Mca + 1)(W − 1)Tca. (9.9)

Inequality (10.9) has been derived under the additional constraint

h ≤ hd ,2τ

(Mca + 1)(W − 1)Tca. (9.10)


We will use (10.9) and (10.10) in Section 10.4.4 to find the solution of the optimiza-tion problem (10.2). Now, we investigate the total energy consumption.

9.4.3.3 Energy Consumption

In this subsection, we give an approximation of the energy consumption. Such anenergy is the sum of the energy for data packet communication, plus the energyfor control signalling. First, we need some definitions. Each time a node wakes up,it spends an energy given by the power needed to wake-upAw during the wake-up timeTw, plus the energy to listen for the reception of a data packet within amaximum timeTac. After a node wakes up, it transmits a beacon to the next cluster.Let the wireless channel loss probability be1 − pi of i cluster, then nodes ofi − 1cluster have to wake-up on average1/pi times to create the effect of a single wake-up so that a transmitter node successfully receives a beacon. Recalling that there areh hops and a cumulative wake-up rate per clusterµc, the total cost in a timeT . LetEr be the fixed cost of the RF circuit for the reception of a data packet. LetEca(µc)be the energy spent during the CSMA/CA state.

We have the following result:

Claim. The total energy consumption is

Etot(h, µc) =Tλ

[

Qm

(

S

h− 1

)

+Qm

(

S

h− 1

)

(h− 1)

× u(h− 1) + h

(

Arx

µc+ Eca(µc) + Er

)]

+

Tµc

pmin

[

2Qb

(

S

h− 1

)

+Qb

(

2S

h− 1

)

(h− 2)

×u(h− 2) + h (AwTw +Arx(Tac − Tw))] . (9.11)

wherepmin is the worst reception probability,Qb(di) andQm(di) are the expectedenergy consumption to transmit a beacon message and the energy consumption forradio transmission, respectively, wheredi is the transmission distance to which adata packet has to be sent.


9.4.4 Optimal Protocol Parameters

In this section we give the optimal protocol parameters usedby Breath. Consider thereliability and delay constraints, and the total energy consumption as investigated inSections 10.4.3.1, 10.4.3.2, and 10.4.3.3. The optimization problem (10.2) becomes


minh,µc

Etot(h, µc) (9.12)

s.t. µc ≥ max(fr(h,Ω), fd(h, τ,∆)) ,

2 ≤ h ≤ min (hr, hd) ,

µmin ≤ µc ≤ µmax ,

where the first constraint comes from (10.4) and (10.5), and the second from (10.9)and (10.10). We assume that this problem is feasible. Infeasibility means that for anyh = 2, . . . ,min (hr, hd), thenµc ≥ max(fr(h,Ω), fd(h, τ,∆)) > µmax, namelyit is not possible to guarantee the satisfaction of the reliability and delay constraintgiven the application requirements. This means that the application requirementsmust be relaxed, so that feasibility is ensured and the problem can be solved. Thesolution of this optimization problem,h∗ andµ∗

c , is derived in the following.By using the numerical values given for the Tmote sensors [15] for all the con-

stants in the optimization problem, we see that the cost function of Problem (10.12)is increasing inh and convex inµc. This allows us to derive the optimal solution intwo steps: for each value ofh = 2, . . . ,min (hr, hd), the cost function is minimizedfor µc, achievingµ∗

c(h). Then, the optimal solution if found in the pairh, µ∗c(h) that

gives the minimum energy consumption. We describe this procedure next.Let h be fixed. From the properties the cost function of Problem (10.12), the

optimal solutionµ∗c(h) is attained either at the minimum of the cost function or

at the boundaries of the feasibility region given by the requirements onµc. Theminimum of the cost function can be achieved by taking its derivative with respect toµc. To obtain this derivative in an explicit form, we assume that CSMA/CA energyconsumption can be approximated by a constant value since the numerical value issmaller than other factors. Under this assumption, the minimization by the derivativeis approximated by

µe(h) = (pmin λ Atx)12

[

h− 2

hQb

(

2S

h− 1

)

u(h− 2)+

2

hQb

(

S

h− 1

)

+AwTw +Arx(Tac − Tw)

]− 12

. (9.13)

The approximation was validated in [41], were it was shown that the error is lessthan2%.

By using Eq. (10.13), we see that an optimal solutionµ∗c(h) providedh is given

byµe(h) if µmin ≤ µe(h) ≤ µmax andµe(h) ≥ max(fr(h,Ω), fd(h, τ,∆)), other-wise an optimal solution is given by the value betweenµmax andmax(fr(h,Ω),fd(h, τ,∆))that minimizesEtot(h, µc). Therefore, for anyh = 2, . . . ,min (hr, hd), we com-puteµ∗

c(h). Then, the optimal solutionh∗ andµ∗c is given by the pairµ∗

c(h), h thatminimizes the cost function.


9.4.5 Adaptation Mechanisms

In the previous sections, we showed how to determine the optimal number of clus-ters and cumulative wake-up rate by solving an optimizationproblem. Here, wepresent in detail some adaptation algorithms that the sink must run to determinecorrectlyh∗ andµ∗

c as the traffic rate and channel conditions changes. These al-gorithms allow us to adapt the protocol parameters to the traffic rate and channelcondition without high message overhead.

9.4.5.1 Traffic Rate and Channel Estimation

The sink node estimates the traffic rateλ and the worst channel probabilitypmin ofthe network. To estimate the global minimum of the worst channel condition, eachpi should be estimated at a local node and sent to the sink for each link of the pathi = 1, . . . , h. This might increase considerably the packet size. To avoidthis, wepropose the following strategy. Consider a relay node of thei th cluster. It estimatespi by the signal of the beacon packet. Then the nodes comparespi with the channelcondition information carried by the received data packet and selects the minimum.This minimum is then encoded in the data packet and sent with it to the next-hopnode. After the sink node retrieves the channel condition ofthe route by receiving adata packet, it computes an average of the worst channel conditions among the lastreceived data packets. Using this estimate, the sink solvesthe optimization problemas described in subsection 10.4.4. Afterwards, the return value of the algorithm,h∗

andµ∗c , can be piggybacked on beacons that the sink sends toward therelays closer

to the sink. Then, these protocol parameters are forwarded when the nodes wake-upand send beacons to the next cluster toward the edge nodes. During the initial state,nodes seth = 2 before receiving a beacon.

9.4.5.2 Wake-up rate and Radio Power Adaptation

Once a cluster receivedµ∗c , each node in the cluster must adapt its wake-up rate so

that the cluster generates such a cumulative wake-up rate. We consider the naturalsolution of distributingµ∗

c equally between all nodes of the cluster. Letµk be thewake-up rate of nodek, and suppose that there arel nodes in a cluster. The fairsolution isµk = µ∗

c/l for any node. However, a node does not know and cannotestimate efficiently the number of nodes in its cluster.

To overcome this problem, we follow the same approach proposed in [31], wherean Additive Increase and Multiplicative Decrease (AIMD) algorithm leads to a fairdistribution of the wake-up duties within a single cluster.Specifically, each nodethat is waiting to forward a data packet observes the time before the first wake-upin the forwarding region. Starting from this observation, it estimates the cumulativewake-up rateµc of the forwarding region and it compares it with the optimal valueof the wake-up rateµ∗

c when a node receives a beacon. Note that the node retrievesinformation onh∗, µ∗

c and location information of the beacon node. Ifµc < µ∗c the


node sends by the data packet an Additive Increase (AI) command for the wake-uprate of next-hop cluster, else it sends a Multiplicative Decrease (MD) command.Furthermore, the node updates the probability of successful transmissionpi basedon the channel information using the RSSI and distance informationdk betweenits own location and beacon node. After the node updates the channel conditionestimation, it sets the data packet transmission power toPt(dk), and encodes thechannel estimation in the packet as described in Section 10.4.5.2.

If a data packet is received, the node retrieves informationon wake-up rate up-date: if AI thenµk = µk+θ, elseµk = µk/φ, whereθ andφ are control parameters.From experimental results, we obtained thatθ = 3 andφ = 1.05 achieve good per-formance. Furthermore, the node runs the reset mechanism for load balancing ofwake-up rate as discussed in Subsection 10.4.5.2. The command on the wake-uprate variation is piggybacked on data packets and does not require any additionalmessage.

However, this approach may generate a load balancing problem because of dif-ferent wake-up rates among relays within a short period. Load balancing is a criticalissue, since some nodes may wake-up at higher rate than desired rate of other nodes,thus wasting energy. To overcome this situation, each relaynode runs a simple resetmechanism. We assign an upper and lower bound to the wake-up rate for each node.If the wake-up rate of a node is larger than the upper bound(1 + ξ)µ∗

c(h∗ − 1)/N

or is smaller than the lower bound(1 − ξ)µ∗c(h

∗ − 1)/N , then a node resets itswake-up rate toµ∗

c(h∗ − 1)/N , whereξ assumes a small value and(h∗ − 1)/N is

an estimation of number of nodes per cluster.

9.4.6 Experimental Implementation

In this section we provide an extensive set of experiments tovalidate the Breathprotocol. The experiments enable us to assess Breath in terms of reliability anddelay in the packet transmission, and energy consumption ofthe network both instationary and transitionary condition. The protocol was implemented on a test bedof Tmote sensors [15], and was compared with a standard implementation of IEEE802.15.4 [4], as we discuss next.

We consider a typical indoor environment with concrete walls. The experimentswere performed in a static propagation (AWGN) and time-varying fading environ-ment (Rayleigh), respectively:

• AWGN environment: nodes and surrounding objects were static, with minimaltime-varying changes in the wireless channel. In this case,the wireless channelis well described by an Additive White Gaussian Noise (AWGN) model.

• Rayleigh fading environment: obstacles were moved within the network, alonga line of20 m. Furthermore, a metal object was put in front of the edge node,so the edge node and the relays were not in line-of-sight. Theedge node wasmoved on a distance of few tens of centimeters.

A node acted as edge node and generated packets periodicallyat different rates(λ = 5, 10, and15 pkt/s).15 relays were placed to mimic the topology in Fig. 10.2.


The edge node was at a distance of20 m far from the sink. The sink node col-lected packets and then computed the optimal solution as described in Subsectionsand 10.4.4 and 10.4.5. The delay requirement was set toτ = 1s and the reliabilitytoΩ = 0.9 and0.95. In other words, we imposed that packet must reach destinationwithin 1s with a probability ofΩ. These requirements were chosen as representativefor control applications.

We compared Breath against an implementation of the unslotted IEEE 802.15.4 [4]standard, which is similar to the randomized MAC that we use in this chapter. Insuch an IEEE 802.15.4 implementation, we set nodes to a fixed sleep schedule,defined byCTac wereC is integer number (recall thatTac is the maximum node lis-tening time in Breath). We defined the caseL (low sleep), were the IEEE 802.15.4implementation is set withC = 1, whereas we defined the caseH (high sleep) bysettingC = 4. The caseH represents a fair comparison between Breath and IEEE802.15.4, while in the caseL nodes are let to listen much longer time than nodes inBreath. The power level in the IEEE 802.15.4 implementationwhere set to−5dBm.We set the IEEE 802.15.4 protocol parameters to default valuesmacMinBE= 3,aMaxBE= 5, macMaxCSMABackoffs= 4. Details follows in the sequel.

9.4.7 Protocol Performance

In this subsection, we investigate the performance of Breath about the reliability,average delay and energy consumption that can be achieved ina stationary con-figuration of the requirements, i.e., during the experimentthere was not change ofapplication requirements. Data was collected out of10 experiments, each lasting1 hour. For performance of the protocol when the requirementsare time-varying,see [41].

9.4.7.1 Reliability

Fig. 10.4 indicates that the network converges by Breath to astable error rate lowerthan1 −Ω and hence satisfies the required reliability with traffic rateλ = 10 pkt/s,the delay requirementτ = 1s, andΩ = 0.9, 0.95. IEEE 802.15.4H in AWGNchannel provides the worse performance than the other protocols because of lowerwake-up rate. Observe thatΩ = 0.9 in Rayleigh fading environment gives the betterreliability thanΩ = 0.95 in AWGN channel due to higher wake-up rate to compen-sate the fading channel condition. Notice that the higher fluctuation of reliabilitybetween the number of received packets2500 and2800 for Rayleigh fading envi-ronment withΩ = 0.95 is due to deep attenuations in the wireless channel.

Fig. 10.5 shows the reliability of Breath and IEEE 802.15.4L, H as a functionof the reliability requirementΩ = 0.9, 0.95 and traffic rateλ = 5, 10, 15 pkt/sin AWGN and Rayleigh fading environments, with the vertical bars indicating thestandard deviation as obtained out of10 experimental runs of1 hour each. Observethat the reliability is stable around the required value forBreath, and this holds for


0 1000 2000 3000 4000 5000 60000.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

Number of received packets

Rel

iab

ility IEEE 802.15.4 L : AWGN

IEEE 802.15.4 H : AWGN

Breath: Fading, Ω = 0.90

Breath: AWGN, Ω = 0.90



Fig. 9.4 Convergence over time of the reliability for IEEE 802.15.4L, H in AWGN, and Breathwith reliability requirementsΩ = 0.9, 0.95 and traffic ratesλ = 10 pkt/s in AWGN and Rayleighfading environments.

5 6 7 8 9 10 11 12 13 14 150.4

0.5

0.6

0.7

0.8

0.9

1

Traffic rate (pcks/s)

Rel

iab

ility Breath: Fading, Ω = 0.95

Breath: AWGN, Ω = 0.95Breath: AWGN, Ω = 0.90


IEEE 802.15.4 L : Fading

IEEE 802.15.4 L : AWGN


IEEE 802.15.4 H : Fading

Fig. 9.5 Reliability in IEEE 802.15.4L, H, and Breath with requirementΩ = 0.9, 0.95 for trafficratesλ = 5, 10, 15 pkt/s in AWGN and Rayleigh fading environments.

different traffic rates and environments. However, IEEE 802.15.4L andH do notensure the reliability satisfaction for large traffic rates. Specifically, IEEE 802.15.4H shows poor reliability in any case, and performance worsen as the the environ-ment moves from the AWGN to the Rayleigh fading. Furthermore,even thoughIEEE 802.15.4L imposes that nodes wakes up more often, it does not guaranteeagood reliability in higher traffic rates. The reason is foundin the sleep schedule ofthe IEEE 802.15.4 case, which is independent of traffic rate and wireless channelconditions. The result is that the fixed sleep schedule is notfeasible to support hightraffic and time-varying wireless channels. Moreover, the fixed sleep schedule doesnot guarantee a uniform distribution of cumulative wake-uprate within certain time


5 6 7 8 9 10 11 12 13 14 150.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0.11

0.12

E2E

del

ay (s

)










Fig. 9.6 Temporal average of the delay of Breath and IEEE 802.15.4L, H with reliability require-mentΩ = 0.9, 0.95 and delay requirementτ = 1 s over traffic ratesλ = 5, 10, 15 pkt/s in AWGNand Rayleigh fading environment.

in a cluster, which means that there may be congestions. On the contrary, Breathpresents an excellent behavior in any situation of traffic load and channel condition.

9.4.7.2 Delay

In Fig. 10.6, the sample average of the delay for packet delivery of Breath, IEEE802.15.4L andH are plotted as a function of the reliability requirementΩ andtraffic rateλ in AWGN and Rayleigh fading environments, with the vertical barsindicating the standard deviation of the samples around theaverage. The samplevariance of the delay exhibits similar behavior as the average. The delay meets quitewell the constrains. Observe that delay decreases as the traffic rate rises. This is dueto that Breath increases linearly the wake-up rate of nodes when the traffic rate in-creases (see Eq. (10.4)). The delay is larger for worse reliability requirements. Notethat Eq. (10.4) increases as the reliability requirementΩ increases. IEEE 802.15.4L has lower delay than IEEE 802.15.4H because nodes have higher wake-up time.Breath has an intermediate behavior with respect to IEEE 802.15.4L andH af-ter λ = 7. From these experimental results, we conclude that both Breath andIEEE 802.15.4 meet the delay requirement. However, notice that the delay for IEEE802.15.4 is related to only packet successfully received, which may be quite few.

9.4.7.3 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 of the node to the total experimental time. Obviously,


5 6 7 8 9 10 11 12 13 14 150

5

10

15

20

25

30

35

40

45

50


Du

ty C

ycle

(%)









Fig. 9.7 Sample average of the node’s duty cycle in IEEE 802.15.4L,H and Breath with reliabilityrequirementΩ = 0.9, 0.95 for traffic ratesλ = 5, 10, 15 pkt/s in AWGN and Rayleigh fadingenvironments.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

0.5

1

1.5

2

2.5

3

Du

ty C

ycle

(%)

Node ID

Fig. 9.8 Distribution of the duty cycle in each node withN = 15 relays. The reliability require-ment isΩ = 0.95 and traffic rate isλ = 5 pkt/s.

the lower is the duty cycle, the better is the performance of the protocol on energyconsumption.

Fig. 10.7 shows the sample average of duty cycle of Breath, IEEE 802.15.4LandH with respect to the traffic ratesλ = 5, 10, 15 pkt/s andΩ = 0.9, 0.95,both in AWGN and Rayleigh fading environments, with the vertical bars indicat-ing the standard deviation of the samples. Note that IEEE 802.15.4L andH donot exhibit a clear relationship with respect to traffic rateand have almost flat dutycycle around42% and18%, respectively, because of fixed sleep time. ConsideringBreath, observe that the duty cycle increases linearly withthe traffic rate and reli-ability requirement. As for the delay, this is explained by Eq. (10.4). Since Breathminimizes the total energy consumption on the base of a tradeoff between wake-uprate and waiting time of beacon messages (recall the analysis in Section 10.4.3.3),lower wake-up rates do not guarantee lower duty cycle. Observe that choosing a


5 6 7 8 9 10 11 12 13 14 150

5

10

15

20

25

30

35

40

45

Du

ty C

ycle

(%)

Total number of relays

Breath: AWGN, Ω = 0.90Breath: AWGN, Ω = 0.95



Fig. 9.9 Sample average of the duty cycle in IEEE 802.15.4L, H and Breath with reliabilityrequirementΩ = 0.9, 0.95 and traffic ratesλ = 5, 10, 15 pkt/s in AWGN environment for differentnetworks, each with a different number of relaying nodes.

lower active time for the nodes of the IEEE 802.15.4 implementation would obvi-ously obtain energy savings comparable with Breath, however, the reliability of theIEEE 802.15.4 implementation would be heavily affected (recall Fig. 10.5). In otherwords, ensuring a duty cycle for the IEEE 802.15.4 implementation comparablewith Breath would be very detrimental with respect to the reliability.

Fig. 10.8 shows the experimental results for the duty cycle of each relay node forλ = 5 pkt/s andΩ = 0.95. A fair uniform distribution of the duty cycles amongall nodes of the network is achieved. This is an important result, because the smallvariance of the wake-up rate among nodes signifies that duty cycle and load areuniformly distributed, with obvious advantages for the network lifetime.

Fig. 10.9 reports the case of several networks, where each network corresponds toa number of relays between the edge and the sink in an AWGN environment. Fromthe figure it is possible to evaluate how much Breath extends the network lifetimecompared to IEEE 802.15.4L andH. Observe that the duty cycle is proportional tothe density of nodes. Hence, the network lifetime is extended fairly by adding morenodes without creating load balancing problems.

Finally, Breath uses a radio power control, so that further energy savings areactually obtained with respect to the IEEE 802.15.4 implementation.

9.5 TREnD

In this section, we present the TREnD protocol. The acronym remarks the signif-icant characteristics simultaneously embraced by the protocol as opposed to othersolutions available from the literature: timeliness, reliability, energy efficiency anddynamic adaptation. TREnD is an energy-efficient protocol for control applicationsover WSNs organized into clusters.


A:814

A:822

*

A:824

A:834

*

A:830

A:810AA:810T

A:810B

A:836A

A:840

A:836T

NC

1

32

64

10

1112

1415

1

32

1

644444

A:81410

1112

A:814A:8

1414151515 141414141414

C1

C2

C3

C4

C5

Fig. 9.10 Experimental Setup.

9.5.1 System Model

TREnD considers a general scenario for an industrial control application: the stateof a plant must be monitored at locations where electrical cabling is not available orcannot be extended, so that wireless sensor nodes are an appealing technology.

Information taken by nodes, which are uniformly distributed in clusters, are sentto the sink node by multi-hop communication. The clustered topology is motivatedby the energy efficiency, since transmitting data directly to the sink may consumemore than routing through relays. The cluster formation problem has been thor-oughly investigated in the literature and is out of the targets of this chapter (see,e.g., [37], [35]).

In Fig. 10.10, the system model is reported. Nodes are deployed in an indoorenvironment with rooms. Each dotted curve defines a cluster of nodes. Nodes ofa cluster can send packets only to the nodes of next cluster toward the controller,which takes appropriate actions upon the timely and reliable reception of sourceinformation. Hence, nodes not only send their sensed data, but also forward packetscoming from clusters further away from the sink. The networkcontroller is thesink node, which, being a node of the networks, is equipped with light computingresources.

We assume that the controller knows cluster locations and the average number ofnodes in each cluster, and nodes know to which cluster they belong. The controllercan estimate the amount of data generated by each cluster, which is used to adaptthe protocol to the traffic regime. These assumptions are reasonable in industrialenvironments [2].

9.5.2 TREnD Protocol Stack

In this section, we introduce the protocol stack of TREnD.Similarly to SERAN [9], the routing algorithm of TREnD is hierarchically sub-

divided into two parts: a static route at inter clusters level and a dynamical routingalgorithm at node level. this is supported at the MAC layer byan hybrid time divi-sion multiple access and carrier sensing multiple access (TDMA/CSMA) solution.


The static schedule establishes which one is the next cluster to which nodes of agiven cluster must send packets by calculating the shortestpath from every clusterto the controller. The network controller runs a simple combinatorial optimizationproblem of latency-constrained minimum spanning tree generation [42]. Alterna-tively, if the number of clusters is large, the static routing schedule is pre-computedoff-line for a set of cluster topologies and stored in the sink node in a look-up array.No intra cluster packet transmission is allowed.

The static routing algorithm is supported at MAC level by a weighted TDMAscheme that regulates channel access among clusters. Nodesare awake to transmitand receive only during the TDMA-slot associated to the cluster for transmissionand reception, respectively, thus achieving consistent energy savings. The organiza-tion of the TDMA-cycle must consider the different traffic regimes depending onthe cluster location. Since clusters closer to the sink may experience higher trafficintensity, more than one transmitting TDMA-slot can be assigned to them. It is nat-ural to first forward packets of clusters close to the controller, since this minimizesthe storage requirement in the network. To minimize the global forwarding time,the evacuation of packets of a cluster is scheduled path-by-path. By following theserules, the controller is able to generate an appropriate TDMA scheduling table.

The dynamic routing is implemented by forwarding the packets to a node withinthe next-hop cluster in the path chosen at random, as proposed in [27] and [35]. Insuch an operation, no cluster-head node is needed within clusters, and nodes needto be aware only of the next-hop cluster connectivity. The procedure for random se-lection of next-hop node is performed by considering a duty cycling in the receivingcluster combined with beacon transmissions.

The communication stage between nodes during a TDMA-slot ismanaged atMAC layer by ap-persistent CSMA/CA scheme, to offer flexibility for the introduc-tion of new nodes, robustness to node failures, and support for the random selectionof next-hop node. As we will see in Sec. 10.5.5, in hybrid TDMA/CSMA solutionsthep-persistent MAC gives better performance than the binary exponential backoffmechanism used by IEEE 802.15.4.

MAC operations of nodes are described in the following. Eachnode in the trans-mitter cluster having a packet to be sent wakes up in CSMA-slots with probabilityα and enters in listening state. At the receiver cluster, eachnode wakes up withprobabilityβ and multicasts a small length of beacon message to the nodes in thetransmitter cluster. An awake node that correctly receivesthe beacon at the trans-mitter side, senses the channel and, if clean, tries to unicast its packet to the beaconsender. An acknowledgement (ACK) may conclude the communication if a retrans-mission mechanism is implemented. If no beacon is sent or there is a collision, theawake nodes in the transmitter cluster keep on listening in the next CSMA-slot withprobabilityα or go to sleep with probability1 − α.

If we compare TREnD to SERAN [9], we see that SERAN has the drawback thatnodes in the receiver cluster have to be listening for the overall TDMA-slot duration,due to a contention-based transmission of the ACKs. In TREnD, the selection ofthe forwarding nodes follows a random policy regulated byβ. The main advantageof this novel solution is the absence of delays between packets exchange during a


CSMA-slot. This allows TREnD to work with a much higher traffic regime whencompared to SERAN.

TREnD offers the option of data aggregation to fairly distribute the traffic loadand energy consumption among clusters. The aggregation hasthe advantage of re-ducing the number of TDMA-slots per cluster and of the trafficfor clusters closerto the sink. However, packet aggregation gives significant advantages only when thetraffic is sufficiently high, as we will see in Sec. 10.5.5, because nodes have to idle-listen longer to catch more than one packet per time and perform the aggregation,and idle-listening is energy inefficient.

9.5.3 Protocol Optimization

In this section, optimization problem (10.1) to select the TREnD protocol parame-ters assumes the following form:

minS,α,β

Etot(S, α, β) (9.14a)

s. t R(S, α, β) ≥ Ω (9.14b)

Pr[D(S, α, β) ≤ Dmax] ≥ Π . (9.14c)

In this problem,Etot(S, α, β) is the total energy consumption of the network andR(S, α, β) is the reliability constraint andΩ is the minimum desired reliability im-posed by the control application. We denote byD(S, α, β) the random variabledescribing the distribution of the latency, byDmax the maximum latency desiredby the control application, and byΠ the minimum probability with which such amaximum latency should be achieved. The parametersΩ, Dmax, andΠ are therequirements of the control application. The decision variables of the optimizationproblem are the TREnD parameters, namely the TDMA-slot duration S, the accessprobabilityα and the wake up probability in receptionβ. In the following, we de-velop the expressions needed in the optimization problem, and derive the solution.

9.5.3.1 Energy Consumption

The total energy consumed by the network over a period of timeis given by thecombination of two components: listening and transmittingcost3.

Listening for a timet gives an energy consumption that is the sum of a fixed wakeup costEw and a time dependent costElt. The energy consumption in transmissionis given by four components: beacon sendingEbc, clear channel assessmentEcca,packet sendingEpkt and ACK sendingEack.

Consider a general topology withN nodes per cluster and suppose that there areG paths in the static routing scheduling (recall Sec. 10.5.2). Lethi be the number of

3 Note that the costs for the initialization of the network are negligible in the energy balance


clusters (hops) per path, we definehmax = maxi=1,...,G hi. We define alsoW , asthe number of listening TDMA-slots in a TDMA-cycle.

Recalling that the TDMA-cycle isTcyc = SMs whereMs is the number ofTDMA-slots in a TDMA-cycle, we have the following result:

Claim. Given traffic rateλ, the total energy consumed in a periodTtot is

Etot(S, α, β) =Ttot

γ S

λTcyc∑

j=1

jαβ Ecca + TtotMsλ (Epkt + Eack)

+ βN W Ttot

Ms

(

Ebc

δ+Ew

S+ El

)

. (9.15)


9.5.3.2 Reliability Constraint

Considering thep-persistent slotted CSMA MAC and the duty cycling in reception,we have the following result:

Claim. The probability of successful transmission in a CSMA-slot while there arek packets waiting to be forwarded in the cluster is

pk = γ pbc (1 − (1 − α)k) (1 − pcl)α(k−1) , (9.16)

wherepbc = γNβ (1 − β)N−1 is the successful beacon probability andpcl is theprobability of an erroneous sensing of a node, when it competes with another node.


In TREnD, a radio power control is implemented, so that the attenuation of thewireless channel is compensated by the radio power, which ensures a desired packetloss probability, as proposed in [44] and [45]. As a consequence of the power con-trol, the channel can be abstracted by a random variable withgood channel probabil-ity γ. Such a modelling has been adopted also in other related works (e.g., [9], [22]).Considering the collision probabilitypcl, we observe that for optimization purposesan upper bound suffices. Experimental results shows that a good upper bound ispcl = 0.2.

By using Claim 10.5.3.2, we can derive the following result:

Claim. Let V (n) = 1 − pn, 1 − pn+1, ..., 1 − pk, wherepn is the generic termgiven in Eq. (10.16) andA(n) = [ai,j ]

S−k+nMc

be a matrix containing all theMc

combinations with repetition of the elements inV (n), taken in groups ofS − k+ nelements. Lethmax be the maximum number of hops in the network. Then, thereliability of TREnD is

R(S, α, β) =

k∑

n=0

k − n

k

k∏

l=n+1

pl

Mc∑

i=1

S−k+n∏

j=1

ai,j

hmax

. (9.17)



With packet aggregation enabled, the following result holds:

Claim. Let hi be the number of hops in the pathi. LetRz be the reliability in a sin-gle hop whenz packets are aggregated. The reliability of a packet that experiencesj hops to the controller is

Ragj (S, α, β) = Rag

j−1 rBi−j+1 , (9.18)

whererj =∑j

i=1(1 − ri−1)∏j−i+1

z=1 Rz , with r0 = 0 .


If the data aggregation is disabled or the size of aggregatedpackets does notchange significantly, then we can simplify Eq. (10.18) and obtain the relation inEq. (10.17). The previous claims are illustrated and verified by experiments in [43].

9.5.3.3 Latency Constraint

The furthest cluster from the controller is the one experiencing the highest latency.Therefore, the latency of packets coming from such a clustermust be less than orequal to a given valueDmax with a probabilityΠ.

Recalling that the maximum number of hops in the network ishmax, an upperbound on the TDMA-slot durationS isSmax = Dmax/hmax. Then, we can providethe following result:

Claim. The latency constraint in Eq. (10.14c) is well approximatedby

Pr[D(S, α, β) ≤ Dmax] ≈ 1 − 1

2erfc

(

A− µ

σ

)

, (9.19)

where A =

S if S ≤ Dmax

hmax

Dmax − (hmax − 1)S if S > Dmax

hmax

µ =∑k

j=1 1/pj , andσ2 =∑k

j=1(1 − pj)/p2j .


9.5.3.4 Protocol Optimization

In the previous subsection, we have established the expressions of the energy con-sumption in Eq. (10.15), the reliability in Eq. (10.18) and the latency constraint inEq. (10.19). We observe that all this expressions are highlynon-linear in the de-cision variables. Sensor nodes are not equipped with a high processing capacity touse these equations, therefore, we provide a computationally affordable sub-optimal


solution to the optimization problem. In the following, we show that such a strategystill gives satisfactorily results.

First, we provide an empirical result on the access probability α and wake upprobabilityβ, for a given TDMA-slot durationS.

Claim. Let N be the number of nodes is a cluster. Letλ be the traffic rate, theaccess probabilityα∗ and the wake up probabilityβ∗, that optimize the reliabilityin Eq. (10.17), are

α∗ =c1

λSMs + c2β∗ =

1

N. (9.20)

with coefficientsc1 = 2.17, c2 = 1.81.


We note here that such choices are sub-optimal because are limited to strategies withconstant access and wake up probabilities per each node.

By using Eq. (10.20) for the access probability and wake up probability, and byassumingS as a real-valued variable, we can show thatEtot, given in Eq. (10.15),is a convex and monotonically decreasing function ofS. It follows that a simplesolution for the TDMA-slot duration,S∗, is given by the maximum integer valueof S that satisfies one out of the two constraints in the problem (10.14a), which aregiven explicitly by Eqs. (10.18) and (10.19), respectively. The search of the optimalS can be done by a simple additive increasing multiplicative decreasing algorithm,which we initialize by observing thatS∗ ≤ Smax. Indeed, as shown in Sec. 10.5.3.3,the maximum latency requirementDmax provides an upper bound forS, given bySmax = Dmax/hmax.

9.5.4 Protocol Operation

Suppose that the network user deploys a WSN of nodes implementing the TREnDprotocol, and sets the desired control application requirementsΩ,Dmax andΠ.During an initial phase of operation the sink node retrievesthe traffic and the clustertopology by the received packets. After computing or reading from a look-up arraythe static routing schedule and TDMA-cycle, the sink computes the optimal param-eters as described in Sec. 10.5.3.4. Then, the sink communicates these values to thenodes of the network by tokens. Such a token passing procedure ensures synchro-nization among nodes and allows for initializing and self configuring of the nodesto the optimal working point of the protocol. The token are then forwarded by thenodes closer to the sink to other nodes of the clusters far away by using the ACKmechanism described in [9]. Such tokens need also to be updated so that our proto-col adapts dynamically to new nodes added in the clusters, variations in the sourcetraffic, control application requirements, and time drift of the clocks. We experi-enced that a20 TDMA-cycles period for the refreshing procedure gives satisfactoryperformance to maintain an optimal network operation with negligible extra energyconsumption.


9.5.5 Experimental Implementation and Validation

In this section, we present a complete implementation of TREnD by using TinyOS2.x [46] and Tmote Sky nodes [15]. In order to benchmark our protocol, we imple-mented also SERAN [9] and IEEE 802.15.4 [4]. Recall that IEEE802.15.4 is thebase for WirelessHart and other protocols for industrial automation, and that thereis no other protocol available from the literature that is energy efficient, and ensuresreliable and timely packet transmission, as we summarized in Tab. 10.1. We usedthe default MAC parameters of IEEE 802.15.4 so that the protocol fits in the higherlevel TDMA structure and routing algorithm of SERAN and TREnD.

We reproduced the reference test-bed topology reported in Fig. 10.10, whereclusters are placed in an indoor environment. Each cluster is composed by3 sensors,deployed at random within a circle with one meter radius. We analyze different sce-narios with different sets of traffic rateλ and control application requirements (Ω,Dmax, andΠ), which we report in Tab. 10.2. For each scenario, Tab. 10.2 shows alsothe optimal TDMA-slot duration, access and wake up probabilities as obtained bythe optimization in Sec. 10.5.3.4. We measured the duty cycle of nodes as indicatorof the energy efficiency.

9.5.5.1 Performance Comparison

In the first set of experiments, we show the performance improvements in TREnD,when compared to SERAN. In Fig. 10.11, the reliability is reported as function ofthe traffic rateλ, by fixingΩ = Π = 95%, andDmax = 3, 9 s. TREnD has highreliability for all traffic rate conditions and SERAN is significantly outperformed. Inparticular withDmax = 3s, as traffic rate increases overλ = 0.3pkt/s, the reliabilityof SERAN significantly decreases.

In Fig. 10.12 we compare the energy consumption of the two protocols, showingthe average duty cycle of each node, for fixedΩ = Π = 95%, Dmax = 3 s andλ = 0.3 pkt/s. As discussed above, in this operative condition bothSERAN andTREnD meet the reliability and latency constraints. By implementing TREnD withdata aggregation, we observe a more balanced duty cycle among clusters, partic-ularly for the last hop clusters. However, the price to pay for having a better loadbalancing is a slight increasing of the average duty cycle. In fact, TREnD presentsa slightly higher duty cycle for most of the nodes, but it reduces of about 30% theenergy consumption for nodes 7, 8 and 9 (clusterC3), which are critical for the net-work operation since they also forward information from clustersC1 andC2. Thissuggests that packet aggregation is a viable choice only forthe clusters supportinghigh traffic, as those next to the sink. Hence, it is recommended to implement packetaggregation only for those clusters, while for the others noaggregation is needed. Inconclusion, TREnD ensures higher reliability, load balancing and a longer networklifetime than SERAN, without any significant difference in the complexity of thescheme.

Given these results, in the following performance evaluation of TREnD we dis-regard SERAN and consider IEEE 802.15.4. We present two setsof experimental


0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Traffic Rate (pkt/s)

Relia

bili

ty

TREnD (Dmax

= 3 s)

SERAN (Dmax

= 3 s)

TREnD (Dmax

= 9 s)

SERAN (Dmax

= 9 s)

Fig. 9.11 TREnD and SERAN: reliability vs. traffic rateλ, for Ω = Π = 95%.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

1

2

3

4

5

6

7

Node ID

Du

ty C

ycle

(%

)

SERAN

TREnD

Fig. 9.12 TREnD and SERAN: duty cycle distribution among nodes forλ = 0.3pkt/s, Ω = Π =

95%.

results, evaluated for scenariosLR andHR as specified in Tab. 10.2. Fig. 10.13reports the average values of reliability, latency and dutycycle as achieved by theexperiments for TREnD and IEEE 802.15.4. Data of clusters belonging to the samepaths are joined by lines. We see that TREnD always ensures the satisfaction of thereliability and latency constraints specified in Tab. 10.2.TREnD guarantees muchbetter reliability, in particular for clusterC1 (3 hops). In fact inC1, IEEE 802.15.4does not fulfill the requirement. The average latency of IEEE802.15.4 is slightlylower than TREnD, but observe that the latency of IEEE 802.15.4 is computed onlyfor packets arriving successfully at the sink. We observe similar behavior also forother scenarios.

Finally, we present some results about the duty cycle. According to the trafficload supported by the clusters and their allotted TDMA time slots, we observe thatthe duty cycle depends on the number of times a cluster wakes up for the forwarding


C1 C2 C3 C4 C5

0.8

0.9

1

Rel

iabil

ity

0

5

10D

elay

(s)

0

5

10

Du

ty C

ycl

e (%

)

C1 C2 C3 C4 C5

C1 C2 C3 C4 C5

Path1 Path2

TREnD, LR

TREnD, HR

IEEE 802.15.4, HR

IEEE 802.15.4, LR

Fig. 9.13 TREnD and IEEE 802.15.4: reliability, latency and duty cycle for scenariosLR andHR.

Table 9.2 Application requirements and experimental results

Scenario λ Dmax Π Ω S∗ α∗ β∗

LR 0.1 pkt/s 9 s 95% 95% 3.3 s 0.41 0.33HR 0.3 pkt/s 3 s 95% 95% 1.2 s 0.43 0.33

procedure. The duty cycle is the same for the clusters far away from the sink (C1 andC4, see Fig. 10.10), but for all other clusters TREnD gives a consistent reduction ofthe duty cycle with respect to IEEE 802.15.4.

We remark here that the duty cycle strongly depends on the traffic load in thenetwork. In Dozer [26], an average duty cycle0.2% is achieved for a network of40nodes with a packet generation period of120 s each (total traffic load≃ 0.3 pkt/s).TREnD gives an average duty cycle2.5%, but the total traffic load is much higher(≃ 5 pkt/s) than Dozer.

9.5.5.2 Dynamic Adaptation

In the previous section, we used a static network topology where each node is placedat fixed position and the application requirements do not change with time. In thissection, we show the dynamical behavior of the protocol. As we discussed before,no protocol in literature allows for a dynamical adaptationof the parameters to theapplication requirements.

We present the experimental results of dynamic changes between two scenar-ios (LR andHR in Tab. 10.2) in static and time-varying channel conditions. A


0 10 20 30 40 50 60 700.8

0.82

0.84

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

Time (TDMA-cycles)

Relia

bili

tyAverage measurements

Reliability requirement

Fig. 9.14 TREnD: Reliability trace given by the experiments.

0 10 20 30 40 50 60 700

1

2

3

4

5

6

7

8

9

10

11

Time (TDMA-cycles)

Dela

y (

s)

Cluster 1

Cluster 2

Cluster 3

Latency requirement

Fig. 9.15 TREnD: Latency trace given by the experiments.

Rayleigh fading channel is obtained by moving the nodes around their initial po-sition and also by placing metal obstacles in front of the source nodes so that theline-of-sight with the sink is lost. The network starts withscenarioLR and staticchannel, then after 20 TDMA-cycles we introduce a Rayleigh fading channel whichpersists until the TDMA-cycle 60. At TDMA-cycle 40, the application requirementschange to scenarioHR.

Figs. 10.14 and 10.15 report the resulting snapshot of the experiment in termsof reliability and latency. The reliability is measured at the sink node as averageon each TDMA-cycle, while the latency is measured for each successfully receivedpacket. In Fig. 10.14, we observe that TREnD guarantees the reliability requirementfor both static and Rayleigh fading conditions, continuously adapting to the severefading. The protocol is also robust to the change of scenarioat TDMA-cycle 40.In Fig. 10.15 a snapshot of the latency is reported for clusters at different hops tothe controller. We observe that the peaks of delay are limited due to the TDMA


structure, the average and dynamics of the delay are slightly increasing in the time-varying stage but the latency constraint is fulfilled. Moreover, the protocol adaptswell to the change of scenario at TDMA-cycle 40.

9.6 Conclusions

We proposed a novel approach to the design of protocols for control applicationsover WSNs. The approach guarantees the respect of control requirements on re-liability and latency while minimizing energy consumption. Duty-cycle, routing,medium access control, and physical layers were consideredall together to max-imize the network lifetime by taking into account the tradeoff between energyconsumption and application requirements for control applications. The design ap-proach was based on optimization problems to select the protocol parameters bysimple algorithms that can run on resource constrained nodes.

The design methodology was illustrated by the proposal of two protocols: Breathand TREnD. The Breath protocol was designed for scenarios where a plant must becontrolled over a multi-hop network. The TREnD protocols was designed for envi-ronments where multiple plants have to be controlled by a multi-hop network. Forthese protocols, we developed the analytical expressions of the total energy con-sumption of the network, as well as reliability and delay forthe packet delivery.These relations allowed us to pose constrained optimization problems to select op-timally the number of hops in the multi-hop routing, the wake-up rates of the nodes,and the transmit radio power as a function of the routing, MAC, physical layer,traffic generated by the plants, and hardware platform.

We provided a complete test-bed implementation of the protocols that we de-signed on the base of the method proposed in this chapter. We built a WSN withTinyOS and Tmote sensors. An experimental campaign was conducted to test thevalidity of Breath and TREnD in an indoor environment with both AWGN andRayleigh fading channels. Experimental results showed that the protocols achievethe reliability and delay requirements, while minimizing the energy consumption.They outperformed a standard IEEE 802.15.4 implementationin terms of both en-ergy efficiency and reliability. In addition, the protocolsshowed good load balanc-ing performance, and is scalable with the number of nodes. Given these good per-formance, Breath and TREnD are good candidates for many control and industrialapplications, since these applications ask for both reliability and delay requirementsin the packet delivery.

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