Adaptive Routing in Wireless Sensor Networks: QoS Optimisation for Enhanced Application Performance

7/28/2019 Adaptive Routing in Wireless Sensor Networks: QoS Optimisation for Enhanced Application Performance

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Adaptive Routing in Wireless Sensor Networks: QoS Optimisation for Enhanced

Application Performance

Mohammad Hammoudeha,

, Robert Newmanb

aManchester Metropolitan University, Chester Street, Manchester, M1 5GD, UKbUniversity of Wolverhampton, Wulfruna Street, WV1 1LY, UK

Abstract

One of the key challenges for research in wireless sensor networks is the development of routing protocols that provideapplication-specific service guarantees. This paper presents a new cluster-based Route Optimisation and Load-balancingprotocol, called ROL, that uses various quality of service (QoS) metrics to meet application requirements. ROL com-bines several application requirements, specifically it attempts to provide an inclusive solution to prolong network life,

provide timely message delivery and improve network robustness. It uses a combination of routing metrics that can beconfigured according to the priorities of user-level applications to improve overall network performance. To this end,an optimisation tool for balancing the communication resources for the constraints and priorities of user applicationshas been developed and Nutrient-flow-based Distributed Clustering (NDC), an algorithm for load balancing is proposed.NDC works seamlessly with any clustering algorithm to equalise, as far as possible, the diameter and the membership ofclusters. This paper presents simulation results to show that ROL/NDC gives a higher network lifetime than other sim-ilar schemes, such Mires++. In simulation, ROL/NDC maintains a maximum of 7% variation from the optimal clusterpopulation, reduces the total number of set-up messages by up to 60%, reduces the end-to-end delay by up to 56%, andenhances the data delivery ratio by up to 0.98% compared to Mires++.

Keywords: Wireless Sensor Networks, Routing, Distributed Clustering, Quality of Service, Optimisation, Adaptive,Load-balancing, Application Performance

1. Introduction

Wireless Sensor Networks (WSNs) hold a lot of promisein applications where gathering sensed data in remote orinaccessible locations is required. The stream nature of thegathered data, the limited resources, and the distributednature of WSNs bring new challenges for data routing tech-niques that need to be addressed. Since communicationis often the most expensive operation for a sensor node,the applications underlying data communication paradigmmust be energy efficient.

In WSNs, routing protocols are application dependentand their design goals vary among different applications.For instance, many applications require real-time commu-nication, e.g., a fire fighter may rely on timely temper-ature updates to remain aware of the current fire condi-tions whereas a soil monitoring system may only need toreport measurements every few hours. Therefore, routingprotocols must meet the delay requirements at minimumenergy cost. Hence, routing protocol designers have toconsider the characteristics of sensor nodes along with theapplication and architectural requirements. Many routing

Corresponding authorEmail addresses: [email protected] (MohammadHammoudeh), [email protected] (Robert Newman)

protocols have been proposed in the literature, althoughthe performance of these protocols is promising in termsof energy efficiency, most of them come with no guaran-tee of Quality of Service (QoS) required by real-time andcommunication-heavy applications. As discussed in Sec-tion 2, a protocol that balances, as far as possible thefollowing characteristics is needed: energy efficiency, scal-ability, robustness, and provision of timeliness.

To address these requirements, a new protocol calledRoute Optimisation and Load-balancing (ROL) is pro-posed. Compared to LEACH [1] and Mires++ [2], this

protocol reduces the cost and number of setup messages,and thus extends the network life. ROL improves on therobustness of LEACH and Mires by ensuring that eachnode learns multiple paths to its Cluster Head (CH) andby the electing of CH backup node(s). Energy expendi-ture is further reduced by shortening the distance betweenthe node and its CH. This is done by incorporating a hopcount metric in addition to the transmission backoff delay.Load balancing is achieved at two levels: (1) Network level,through traffic multiplexing over multiple paths; (2) Clus-ter level, by rotation of the CHs.

This paper proceeds as follows. Section 2 introduceshierarchical routing in WSNs. Section 3 outlines the ob-

jectives and motivation for a new clustering protocol. InSection 4, is the main exposition of the ROL, it explains

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different phases in the clustering process. In Section 5,NDC is described. In Section 6, the evaluation metrics forthe ROL/NDC are defined. In Section 7, the simulationresults of ROL/NDC are presented. Finally, conclusionsare drawn and further work suggested.

2. Hierarchical Routing in WSNs

Routing has proved to be a key issue in WSNs research.In the literature, a lot of the effort has been devoted torouting protocols since they differ depending on the natureof user applications and on the architecture of the under-lying network. As clustering approaches are particularlytempting for large-scale high-density WSN applications,a solution for clustering with ad hoc wireless communi-cations is sought. Hierarchical routing is a two or moretier routing scheme. Nodes in the upper tier are calledCHs and act as a routing backbone, while nodes in thelower tier perform sensing tasks. Kulkarni et. al. [3] arguethat multi-tier networks are scalable and offer the follow-ing number of advantages over single-tier networks: lowercost, better coverage, higher functionality, and better reli-ability.

Many clustering algorithms have been previously inves-tigated, both independently and in the context of routingprotocols. In this section, we briefly review some cluster-ing protocols used in this paper. We refer the interestedreader to [4, 5] and references there in for a comprehensivesurvey of recent clustering algorithms.

LEACH [1] is one of the first clustering-based proto-cols that utilises randomised rotation of the CH role toevenly distribute the energy load among nodes in the net-work. LEACH is well-suited for applications where con-stant monitoring is needed and data collection occurs peri-odically to a centralised location. LEACH has been basedon a number of assumptions which in the authors opinionlimit its effectiveness in a number of applications, such assingle hop communication.

Smaragdakis et al. [6] address the issue of heterogeneityof nodes in terms of their energy. The development of theirprotocol was motivated by applications that would benefit

from realising the effect of nodes heterogeneity, e.g. re-energisation of WSNs and applications where the spatialdensity of sensors is a constraint. This protocol assumesthat the sink can be reached directly by all nodes. It re-quires knowledge of the energy levels of other nodes inthe network to weight the CH election probabilities, whichrequires extra communication overhead.

In [7], the authors proposed a protocol called HEEDfor sensor applications requiring efficient data aggregation.HEED produces balanced clusters with low message over-head using information about residual energy and a sec-ond parameter such as node degree. HEED out-performsgeneric clustering protocols on various factors including

energy efficiency. However, HEED is still heuristic in na-ture and suffers a high network delay due to the complex-

ity of the CH selection algorithm. Besides which, HEEDprovides only a two level hierarchy.

Unlike previous work targeting the optimisation of en-ergy efficiency and network lifetime, Yang et al. [8] pro-posed Minimum Energy Spanning Tree for Efficient Rout-

ing (MESTER) for maintaining high quality in data col-lection for as long as possible. The design of MESTERis predicated on the idea that the sink or network con-troller is more powerful than the nodes, which are seen ashaving very limited resources. MESTER therefore adoptscentralised algorithms and requires the sink to take con-trol of managing the network topology and calculating therouting path and time schedule for data collection. Thisresults in it being intrinsically non-scalable - or scalableaccording to the capacity of the sink, and dependent onthe reliability of a single sink node.

Mires++ [2] is a clustering service on a pub-lish/subscribe middleware called Mires. It attempts toachieve energy saving, load balancing, and robustness.Mires++ implement two protocols: (1) Creates two tierflat clusters based on residual energy and subscribed datatopics. (2) Performs load balancing by rotating the CHrole and setting a maximum cluster size. It also elects abackup CH to achieve fault tolerance. Mires++ inheritsall features of Mires middleware, including its drawbacks.Publish/subscribe is initiated centrally at the sink, whichintroduces a single point of failure, communication bot-tlenecks, and incurs high communication costs. Usually,nodes are deployed to achieve a common sensing task;therefore, all nodes are likely to subscribe in the same

data topic, which reduces the usefulness of its cluster-ing. Finally, physical location awareness is an importantcondition in Mires++ clustering, which limits its applica-bility. We compare ROL/NDC performance with that ofMires++ as they both aim to achieve the same goal.

LEACH, and its derivatives, have been successful in re-ducing the energy per bit required by each node and thenetwork as a whole. Nonetheless, most of these proto-cols are built upon inflexible assumptions and have seri-ous drawbacks. Many clustering algorithms are heuristicin nature and have a time complexity of O(n), e.g. [9],where n is the total number of nodes in the network. Also

other protocols, such as [6], demand time synchronisationamong nodes, which makes them unsuitable for large-scalenetworks. Another set of protocols requires centralisedmanagement which limits their scalability. Some of theseprotocols have been designed with robustness in mind,however, the level of fault tolerance is usually designed toadapt to occasional node failures and infrequent topologymigration.

The characteristics of the protocols discussed above aresummarised in Table 1.

In summary, it is evident that the desired qualities of arouting protocol are largely determined by the character-istics of the applications they serve. Most of the reviewed

clustering protocols are application specific. Such proto-cols require profound modifications to be flexible enough

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Table 1: Characteristics of existing hierarchical routing protocolsProtocol Heuristic Sink cen-

tralised

Depth

limited

Application

limited

LEACH No No Yes YesHEED Yes No Yes YesMESTER No Yes No YesMires++ No Yes Yes Yes

to meet the diversity of functional and non-functional re-quirements placed on them by applications. Therefore,application requirements combined with the sparse mem-ory and processing resources of a typical node have greatimpact on choosing a suitable routing protocol. As moreWSNs software applications are deployed and the diver-sity of sensor devices increases, there is significant valuein developing a routing protocol that allows system devel-opers to optimise its operation. This provides a way to

provide paths based on different optimisation objectivesas specified by an objective function and the routing met-rics. Thus, a routing protocol that balances as many ofthe following design objectives is desirable:

1. Real-time: An effective protocol should providetimely communication by reducing end-to-end com-munication delays. Timeliness constraints are impor-tant as WSNs often operate in the real world to re-flect the current physical status of the sensing envi-ronment. Data timeliness is normally at odds with en-ergy consumption [10]; data aggregation reduces therouting protocol temporal performance due to the in-

troduced aggregation delay.2. Reliability: A reliable and robust routing protocol

is to be capable of providing correct measurementsat the right moment without interruption. DifferentWSN applications have varied requirements on the re-liability of data delivery, which may evolve over time.For example, a WSN deployed for fire detection ina forest can be used for measuring humidity as well.When the measured temperature is in the range ofnormal temperature it is delivered to the sink toler-ating a certain percentage of loss. Yet, when a fireis detected, the data should be delivered to the sinkwith high priority.

3. Scalability: Scalability is the ability of the network togrow in size without excessive communication over-head. In a larger scale WSN, more overhead is un-avoidable to keep the build of the communicationpaths to the sink. Routing protocols should supportin-network processing, such as lossy aggregation, anddeploy localised algorithms to reduce the amount ofcommunicated data to save energy and reduce band-width utilisation. Besides, they should be able to ex-ploit the density of nodes in saving energy, distribut-ing load, and correcting faults.

4. Energy consumption: Routing protocols should use

resources efficiently to carry out data communicationtasks. This is particularly important for long-term

deployment applications of WSNs.

5. Load-balancing: Routing protocols should be able todistribute energy consumption between nodes to ex-tend network life. Distributing workload will also helpto prevent energy depletion in one part of the network,

which may result in partial coverage of the monitoredenvironment.

6. Clustering requirements: Clustering should be keptfast, simple, and decentralised. The total number ofclusters in the network should be sustained equal oraround the optimal number of clusters defined in [1],which is 5% of the total number of nodes in the net-work.

7. Location information: Routing protocol should beable to function without node geolocation. However,it should be open to utilise location information if theapplication requires.

3. ROL Motivation and Aim

The increasing number of WSN applications, each withtheir own intrinsic needs, present systems designers withsoftware architectural challenges, both at developmenttime and throughout the system life-cycle. WSN research,e.g. [11], has successfully shown the merits of integratingapplication domain knowledge in system design. It is ob-vious that each application imposes different requirementson the routing scheme with respect to the urgency of time-liness, data delivery patterns, QoS, memory consumption,

processing levels, etc. These requirements evince that spe-cial support for accommodating routing efficiently relies ona distinctive set of requirements as it must be optimisedfor the application.

As shown in Section 2, most of the existing routing pro-tocols are developed to meet application-specific design ob-jectives and requirements. The ma jority of these protocolsachieve one objective at the expense of others. This islikely because some objectives are contradictory. For in-stance, energy saving by data aggregation introduces con-siderable communication delays. Moreover, other routingprotocols in the literature focus on achieving one or two

of these design objectives and ignore the others. For ex-ample, [12] is routing protocols with energy efficiency asthe key design factor; [13] aims to handle dynamic topo-logical variation; [14] aims to handle event detection inheterogeneous networks; [15] aims to provide secure datacommunication; [16] is designed for issues that are specif-ically related to topologically linear structure WSNs.

To support various computationally demanding applica-tions for large-scale WSNs, such as data visualisation ppli-cations [11], a routing protocol that considers part or allof the main design objectives (listed in Section ) is needed.This routing protocol is designed generically and targetsa variety of application scenarios by balancing the multi-

ple application objectives based on a global optimisationequation.

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Table 2: Configurable ROL parameters and the applications ob jec-tives they affect

Parameter Design objective

backoff Energy efficiency, timelinessNum. of hops Scalability, robustness, timelinessNum. of backup CHs

Robustness, data delivery ratio

Num. of paths Robustness, data delivery ratio, timeliness, en-ergy efficiency

NDC Energy efficiency, scalability, timeliness

The motivation for developing a new routing protocolthat can be specifically optimised to a range of applica-tions include: (1) financial as software is reused and de-velopment time decreases; (2) easier maintainability as acommon routing protocol can be deployed across a set ofdifferent applications; (3) simpler modelling of routing re-quirements; (4) improved applications performance.

4. ROL Operation

In this section we present the relevant details of the ROLalgorithm and its parameters, which allow ROL to be con-figured. Optimisation in ROL refers primarily to the con-figuration of its routing parameters, which is achieved bysetting their values according to application priorities. Ta-ble 2 summarises the ROL parameters and their impacton various routing design objectives. In the following, webriefly justify the chosen parameters and their relationshipwith the specific applications:

1. Backoff: The backoff achieves energy efficiency byminimising the cluster setup traffic. Each node joinsthe nearest CH and sends it its data over the pathwith the minimum number of hops; this results inlower queuing and propagation delays, reduced band-width utilisation, and decreased energy consumption.In many WSN applications, there is a logical relation-ship between adjacent nodes, i.e. nearby nodes rep-resent related data. Associating nearby nodes witha particular cluster supports intelligent data aggre-gation/fusion, which consequently can reduce energyconsumption and improves the quality of extractedinformation [17].

2. Number of hops: Data packets are transmitted in-directly through multi-hops from the source node tothe destination node. As the number of hops in-crease, the retransmissions become cumulative andsuccessful communication becomes more unlikely [18];the increased transmission range will inevitably causehigher interference, which results in lower throughput.Multi-hop communication can be used to avoid longrange transmission to reduce the transmission power.Moreover, end-to-end delay often can be reduced byreducing the number of hops in a path. Multi-hop

routing is particularly useful in large-scale applica-tions, such as target tracking over large geographic

region, where data sinks can not be reached by nodesdirectly.

3. Number off backup CHs: Sensor nodes can switchto backup CHs when the current CH fails. Thisavoids complete re-clustering when re-assigning an in-

terrupted CH. Moving to the alternative CH incursshort disruptive period, i.e. rapid CH healing reducesthe amount of data loss and network availability time.Failure-prone WSN applications, e.g. where nodes aredeployed in harsh environments, can improve applica-tions performance by utilising backup CHs.

4. Number of paths: Multi-path routing yield a vari-ety of benefits such as fault tolerance and increasedbandwidth. It provides better transmission perfor-mance, fault tolerance and load balancing. Path fail-ure starts a path discovery process to discover newroutes, which introduces communication delays. De-

lay can be reduced in multi-path routing, as backuproutes can be recognised immediately with minimumservice interruption transparently to the application.In time critical applications, splitting data to the samedestination into multiple streams where each streamis routed through a different path can reduce data de-livery delay makes the paths are more fully utilised.

5. NDC: NDC is an effective reconfiguration algorithmto increase the network lifetime by fairly distributingnodes across clusters. Balanced clusters lead to fairenergy consumption of the CHs. Clustering is specif-ically practical for applications that need scalability

to hundreds or thousands of nodes. Scalability in thisperspective means the need for load balancing andefficient resource utilisation. Applications requiringefficient data aggregation (e.g., calculating the aver-age temperature in an area) are likely candidates forclustering. A CH can aggregate the data collected bythe nodes in its vicinity and thus decrease the numberof forwarded packets. Clustering can reduce commu-nication interference.

The first step in implementing an application is primar-ily setting the routing parameters according to specificguidelines provided by the routing protocol vendor. Themost difficult part in this process is making the requiredapplication-related decisions. Configuring those decisionsinto the routing protocol is a relatively simple process.

ROL provides robustness through a multi-path routing.Multiple paths can provide fault-tolerance, load balanc-ing, and higher aggregate bandwidth. When a path fails,an alternative path can be immediately used, which al-lows the protocol to dynamically adapt to failures withoutdelays. Load balancing can be achieved by multiplexingthe traffic along multiple routes. When multiple pathsare used simultaneously, the aggregate bandwidth of thepaths may satisfy the bandwidth requirement of the appli-

cation. Therefore, since there is more bandwidth available,a smaller end-to-end delay may be achieved.

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CH1

CH2

d1

d2

Figure 1: The filled nodes are CHs. Horizontal and vertical barsforming L shape are obstacles. The solid lines are path from noden1 to CH-1 and the dashed lines are path from node n1 to CH-2.

Multi-hop communication is an essential property of anyscalable routing protocol such as ROL. Besides reducingthe radio transmission power, multi-hop communicationalso has the following benefits [19]:

1. Spatial reuse: simultaneous communication sessionsare possible for node pairs that are out of range ofeach other

2. Obstacle conciliation: a multi-hop path can goaround an obstacle that obstructs direct radio con-nection between two nodes as shown in Figure 1

3. Distributed processing: a multi-hop topology allowsthe local processing of sensor data among nearbynodes

In some WSN deployments, e.g. in build areas, theremay be obstacles blocking parts of the transmitters sig-

nal, such as walls, which will attenuate the signal seen atthe receiver. Wireless networks designers have tradition-ally used multi-hop relaying to bypass obstacles, therebygaining improved radio channel conditions [20]. As a re-sult, multi-hop relaying provides an opportunity for perfor-mance improvements in WSN applications. The authors ofthis work proposed a solution in [21] to manage obstaclesbetween nodes: Given a detour of length b[P,D] perpen-dicular to the line between nodes P and D, the effectivedistance between the two points is defined as:

di = {(d[P,D])2+ (b[P,D])2}

12 (1)

where b[P,D] is the strength of the barrier.ROL, defines the hop count metric to measure how far

the sensing node is from its CH. As the hop count in a par-ticular routing path increases, the message delivery delayand the power consumption within the network increasesdue to the higher number of transmissions. The hop countmetric allows nodes to: (1) Select the nearest CH, whichsaves energy by reducing messaging needed to bridge thedistance between the node and its CH. (2) Find the short-est path to its CH.

ROL implements a metric called the backoff delay. Thismetric is used to minimise clusters set-up overhead and

to aid the formation of more geographically uniform clus-ters. During the backoff interval, nodes receive several CH

advertisement messages. When the backoff delay reacheszero, the node transmits the advertisement message(s)with the smallest hop count. This helps in blocking ad-vertisement flooding from CHs that are farther away fromunnecessarily reaching neighbouring nodes.

During the cluster set-up phase, one or more nodes arechosen as CH backup node(s). When the current CH de-cides to hand its role to the backup node, it notifies therespective backup node and forwards to it necessary infor-mation, such as the backup nodes list, to avoid a completecluster set-up round.

4.1. ROL Algorithm Details

The operation of ROL can be split into two phases: thesetup phase and the data transmission phase.

Set-up Phase

During the set-up phase, CHs are selected and clustersare formed. The sink randomly selects 5% of the nodesas CHs and floods the network with this information. Ev-ery node that receives the sinks discovery message changesits state from waiting to discovered and examines the mes-sage to check whether it has been selected as CH or not.If yes, it starts a new cluster by broadcasting an advertise-ment message. Otherwise, it broadcasts the original dis-covery message to its neighbours. The sink flooding stepcan be compared to the seeking of a single route in Dy-namic Source Routing (DSR) [22], but every node knowsonly the next hop not the complete hop-by-hop route to

the sink, and no node knows a route to any other node.Every CH broadcasts an advertisement message with the

hop count set to 0. Upon receiving an advertisement mes-sage, a node does the following: (1) If it already belongsto a cluster, it ignores the received message. (2) Else,if the received message carries a smaller hop count thanthe stored one, the latter is deleted and the former is re-tained and it continues listening for new advertisements.After the backoff delay expires, the node re-broadcasts themessage with the smallest hop count after incrementing itby 1. A node remembers the node from which it receivedthe message as the nearest neighbour to its CH. Then,

the node calculates a value vc based on its available en-ergy to represent its desire to become a CH in the nextclustering round. vc is included in the join request mes-sage that the node sends back to register with the chosenCH. The CH registers the node as a member of the clusterand adds the nodes with the highest vc to its CH backuplist. Compounding different functions into a single multi-purpose message reduces set-up communication overhead.The cluster formation messaging is illustrated in Figure 2.

When the cluster round ends, the current CH hands itsrole to the first node in the backup list. With a single localflood, the new CH continues its predecessors role withoutthe need of further communications. The CH role will also

be handed to the backup node when a fault occurs in thecurrent CH or when its energy level approaches a certain

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: Open arrow represents creating a message: Filled arrow represents forwarding a message

a : backoff time

b : random transmission time

c : time the CH waits before it creates and advertise a TDMA schedule

: ImCH(ch_id, numHop), an advertisement message sent by the CH to declare itsnew role. It contains the CH ID and the number-of-hops to reach it.

: Wish(ch_id, wish_value), this is a join request sent by a sensing node to a CH. It

contains the respective CH ID and a wish value representing its desire to become a

CH in the next round.

: ADV_SCH(order), an advertisement message sent by the CH to all its member

nodes. It contains the TDMA schedule that specifies the order of data transmissionfor each node.

Sink n1 n2 n3 n4

2 2

2 22

22

333

3

4

c

2

2

3

4

4 4444

4

aaa

b2

b

22 2

b

Collision

Figure 2: In this scenario, node n3 was chosen to be a CH. It broad-casts an advertisement message. Receiving nodes wait for time abefore they choose the message with the minimum hop count andsend a join request message to the corresponding CH. After randomdelay b, nodes re-broadcast the received advertisement. The CHwaits for a time, c, to receive all potential join request messages be-fore it creates and broadcast the TDMA schedule. Note the collisionthat happened when nodes n1 and n2 forward the advertisementmessages at the same time. Sub-section 4.1 on page 6 describes howthis type of collisions is managed.

threshold. In the case of faults, such as physical damageor fatal internal errors in the CH, the nodes will transmitdirectly to the sink until a new cluster round is due.

Data Transmission Phase

During this phase, nodes transmit data to their CH.The CH aggregates the received data before transmissionto the sink or immediately multiplexes messages over mul-tiple lines in time critical applications. Each member nodetransmits data on its assigned time slot scheduled by theTDMA schedule. Furthermore, each cluster communicateswith the sink using unique Code Division Multiple Access

d : data transmission time assigned in the TDMA

: Data(data), data message sent by sensing node to its CH. It contains raw data

collected by the node.

: ToS(AGdata), a message sent by the CH to the sink. It contains aggregated data.

d

5

66

6

5

55

d

Sink n1 n2 n3 n4

d

6

5

Figure 3: The CH n3 aggregates all received data messages and sendsthem to the sink.

(CDMA) codes to avoid interference with traffic generatedby other clusters. Figure 3, illustrates data transmissionfrom nodes to their CH. It also illustrates how the ag-gregated data is sent from the CH to the sink throughmulti-hop path.

Energy Efficient Sleep/Wake Scheduling

This paper implements a simplified and modified ver-sion of S-MAC [23]. S-MAC is a complex medium-accesscontrol protocol for WSNs, which aims to reduce energyconsumptions and support self-configuration. Inspired byPAMAS [24], S-MAC sets idle nodes to sleep during trans-missions of other nodes. The design of S-MAC expectsthat applications will have long idle periods and can toler-ate some latency. The implemented MAC protocol differsfrom the S-MAC protocol in the following aspects:

1. For simplicity, our implementation does not require

periodic synchronisation among neighbouring nodesto remedy their clock drift

2. Our implementation utilises the formed real commu-nication clusters, while S-MAC forms nodes into a flattopology or vitrual clusters

3. In S-MAC neighbouring nodes are free to talk to eachother no matter what listen schedules they have. Inour implementation, nodes can only talk during theirallocated time slot.

In the following we present some of the details of our im-plementation, particularly the sleep/wake schedule.

Idle nodes consume significant amount of energy [25].

An effective approach to preserve energy is to set the ra-dio to sleep during the idle times and wake it up preciselybefore message transmission/reception. In a network thatinvolves multi-hop communication, this requires precisesynchronisation between sending and receiving nodes. Inthe design of this sleep/wake scheduling method, we ig-nore the effect of synchronisation errors. We assume thata network-wide time synchronisation protocol maintainsa consistent notion of time between various nodes in thenetworks. Time synchronisation has been extensively in-vestigated and several implementations (e.g. [26, 27]) canachieve synchronisation within microsecond. A related as-

sumption we make is that nodes always have data of fixedlength to send.In ROL, all nodes communicate with their CH through

a TDMA. ROL upgrades the TDMA method used inLEACH to make it suitable for multi-hop communication.The TDMA is created by each CH node based on the num-ber of nodes in the cluster. The time domain is dividedinto several periodic time slots of fixed length, one for eachnode. The sleep/wake scheduling is integrated with theTDMA schedule to achieve greater energy savings (TDMAprotocols avoid energy waste due to contention); the radiois turned off during idle times and wakes up just beforemessage transmission/reception. To sustain a connected

graph, nodes sleep times should be appropriately synchro-nised. If a node is in the path of transmission of other

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nodes, it must not sleep before it sends its own data andforwards its children data. Sensing nodes make up theirchildren set while forwarding the join request messagesduring the cluster setup phase.

The proposed sleep/wake schedule aims to reduce the

sleep latency by having a periodic receive-send-sleep cycle.This is achieved through level-by-level offset schedules, inwhich data flows in step by step from the leaves of the treetowards the CH, with nodes going to sleep once they sendtheir packets to their parents, and waking up just in timeto receive the next round of packets. In this design, weassume that the number of slots available to the cluster isfixed. This in effect defines the duration of the periodicsleep cycle, and the wake cycle, which are assumed to bedetermined a priori by many factors including:

1. Application-specific requirements for energy effi-ciency, e.g. end-to-end delay

2. The size and type of the collected data, e.g. numeri-cal, image, or video data

3. The packet generation rate

4. Limitations on sleep/wakeup times of the radio hard-ware involved

5. The processing time, especially in systems that in-volve complex patterns of in-node processing (e.g.lossy data aggregation)

6. The average distance between a node and its parent(propagation delay)

7. Manufacturing imprecision and aging, the clock fre-

quency is affected by environmental factors includingtemperature, pressure, and voltage [28]

In the absence of a network-wide synchronisation protocol,a guard interval can be added to the slot to compensatefor synchronisation errors. The sleep/wake schedule willresult in every node having a path on which all interme-diate nodes have sequentially increasing slots. The nodewake up time is calculated as the transmission interval plusthe number of hops between the leaf node times a singleslot length. For instance, if nodes send their data every mminutes and the slot length is slsec, then, a node that is nh

hops from the leaf node will wake up at timet= m+(s

ln

h).In large scale WSNs, multiple nodes should be allowed

to transmit at the same time slot if their receivers are innon-conflicting parts of the network, i.e. a receiver of aspecific transmission is also within the radio range of an-other transmission intended for another node. If a nodecommunication may interfere with another node commu-nication, these nodes should not transmit simultaneously.To avoid interference and unnecessary communication de-lay, each sensor node obtains the slot assignments of itsneighbours. Then, the two nodes can synchronise to wakeup at different times. This sleep/wake pattern is similarto that of the S-MAC protocol. In S-MAC each node fol-

lows a periodic active/sleep schedule, synchronised withits neighbouring nodes.

Figure 4: ROL interactive tool

As in LEACH and S-MAC, ROL also uses CDMA tominimise interference between clusters such that each clus-ter uses a different set of CDMA codes. When a node is inthe multi-hop path for transmitting the aggregated datacoming from the CH to the sink, it has to schedule care-fully to participate in each communication. One way toachieve this is by creating a secondary sleep/wake sched-ule. The secondary schedule is created sequentially start-ing at the CH. Since the CH knows the next hop towardsthe sink node, it synchronises with that node to wake upat time tCH = m + ot(sl nhmax) where nhmax is the longestpath between the CH and a leaf node and ot is a fixed timeoffset. This allows the CH to collect and aggregate data

from all of its member nodes. Then, each node on the pathto the sink increments the tCH by sl and forwards it to thenext hop in the path. This process terminates at the sinknode.

For the duration of the network setup phase, CH ad-vertisement communication is not guaranteed collision-freeas TDMA schedules are only created after clusters are es-tablished. S-MAC handles collisions resulting from inter-ference with other signals by having nodes backoff for arandom duration before transmission. The full SMAC im-plementation also deals with collisions through the use oftraditional mechanisms such as RTS/CTS exchange andvirtual listening according to a network allocation vector.

In delay sensitive applications or in applications thatinvolve heavy bidirectional traffic, i.e. frequent networkquerying, all nodes in the network wake up at the sametime according to a simple periodic pattern with a fixedperiod such as the IEEE 802.11.

4.2. A tool for Performance Optimisation

To find values for the routing parameters that are bestfor a particular application, system designers are providedwith an interactive tool (see Figure 4). This tool, aids inresolving the performance differentiation of applicationsby incorporating the configurability of the ROL parame-

ters. It can be used by system designers, who have advanceknowledge of application requirements, to order different

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routing design objectives according to their importance forthe application and automatically calculates the routingparameters values. System designers can also use the toolto change different parameter values to manually optimiseROL performance. As the user changes the values, the im-

pact of these changes on the various protocol performanceareas is visually reflected on a scale as a percentage from 0to 100.

Theoretically, finding the best routing parameter valuesis a global optimisation problem. According to [29], theglobal optimisation is to find the best possible element x

from a set X according to a set of criteria F= {f1, f2, .., fn}.These criteria, called objective functions, are expressedas functions. An objective function can be expressedas f : x Y with Y R. Objective functions canbe simple mathematical expressions as well as complex al-gorithms. Global optimisation covers all methods that can

be used to find the best element x in X such that f F.Because the relation between the various routing param-eters and their effect on the routing performance is clear,routing optimisation can be solved using deterministic al-gorithms. Such deterministic algorithms use problem de-pendent heuristics based on a model of some natural phe-nomenon or physical process. In this work, global opti-misation is applied to sets F (many criterion) consistingof n = |F| objective functions fi, each representing one de-sign objective to be optimised.

F= fi : X Yi : 0 < i n, Yi R (2)

This class of optimisation algorithms is usually named withthe prefix multi-objective. ROL achieves optimisation bycomputing a weighted sum g (x) of all functions fi (x) F.Each objective fi is multiplied with a weight wi correspond-ing to its priority. Weighted sum seeks Pareto optimal so-lutions [30] by systematically changing the weights amongthe objective functions, i.e. trading-off conflicting objec-tives. Signed weights can be used to give some objectivesmore weight or influence on the routing performance thanother objectives in the same set. An example from [29]assumes that a weight wa = 1 is applied to an objectivefunction fa and the weight wb = 1 to the objective func-

tion fb. Decreasing g (x), results in reducing the influenceof fa and increasing that of fb and vice versa. In thisway, multi-objective problems can be simplified to single-objective ones. In [29], g (x) is presented as:

g (x) =

ni=1

wifi (x) =fiF

wifi (x) (3)

andx X g

x g (x)x X (4)

To generalise to an arbitrary number of objective and anydimensionality state space, we replace the scalar x by a

vector field x (z) defined over domain x. The choice of avector field for x allows modelling more than one quantity

of interest. The new n objective functionals is

f(x) =

L (x (z)) dz,

where L is a set of operators on the vector field x. Theweights are given by the vector , such that the overallcost function is e(,x) = Tf(x). The minimum cost solu-tion for a given set of weights is x (), resulting in a totalcost of e () = minze (,x) = e (,x

()). The weightfunction must be convex, regardless of the convexity of in-dividual objective functions. The above weight function isconvex in . The proof is as follows:

Proof: Let e (1) = e1

and e (2) = e2.e () is convex

if for any [0, 1], e (1 + (1 )2) e1+(1 ) e

2.

e (1 + (1 )2) = min ex (1 + (1 )2,x)

= minz (1 + (1 )2)T f (x)

minzT1 f (x)

= e1 + (1 ) e2

Sensor network designers can choose the configurationsthat best suit their application requirements. If the defaultvalues of the parameters are used, ROL/NDC attempts toachieve a good level of the following main design objec-tives: energy efficiency, timeliness, and robustness.

5. Optimal Cluster Balancing

In this section we propose an optional cluster balancing

plug-in called Nutrient-flow-based Distributed Clustering,NDC. This plug-in can be used with any clustering al-gorithm. The aims of the NDC algorithm are: (1) Toequalise, so far as is possible, the diameter of the clusters.(2) To equalise, so far as is possible, the membership of theclusters. The distributed model described here is basedaround a metaphor of nutrient flow supporting some sim-ple life-form, such as a mould. The concept is to providea limited supply of nutrient and allow the nodes to allythemselves with a CH which will provide the largest nu-trient supply. If properly regulated, this should lead toclusters broadly equalising their membership. In order to

minimise the radius of a cluster, it is arranged that someof the supply of nutrient is lost in transit between nodes -the further the distance travelled, the more is lost. It isimportant that some advantage be given to nodes that joinin a cluster, rather than communicating directly with thesink. For this reason, it is necessary to provide some ad-vantage associated with clustering, as opposed to directcommunication. The simplest way to do this is to makethe loss of nutrient super-proportional to the distance ofa link. Given that in real life, radio propagation obeysan inverse square law, it seems reasonable to make theloss of nutrient proportional to the square of the distancetravelled.

Like many distributed route discovery algorithms, NDCoperates in distinct phases with the network reconfiguring

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itself from phase to phase. During each phase, nodes tryto improve the amount of nutrient available to them. Theydo this by contacting a local node at random and if clus-tering with that node will offer a better supply than thatwhich is currently available, then the node changes alle-

giance. Nodes receiving requests for nutrient from othernodes make an offer back to that node, giving an estimateof the nutrient that would have been available had thatnode been a member of its cluster. The estimate dependsboth on the amount of nutrient available, and the numberof nodes dependent on that CH.

Another consideration is that the algorithm has togive encouragement for clusters to grow, that is that theamount of nutrient available becomes greater as nodes jointhe network. To effect this in each phase the sink hasan available amount of nutrient proportional to the totalnumber of connected nodes. The starting conditions are

as follows: (1) Some initial store of nutrient available atthe sink, nsink. (2) Current state of all other nodes is tohave no nutrient, nav = 0.

From the initial state, some nodes (Nc) will by chancehave direct contact with the sink. These become the initialCHs, and each is given an equal share of the nutrient avail-able nav =

nsink|Nc |

, which is available to them as attenuatedby the square of the distance from the sink. Across thenetwork the sequence of events in each phase is as follows:Nutrient allocation: Each node transmits to its depen-dents (if any) the total amount of nutrient available tothat cluster and the current number of members (includ-ing the CH) at that level of the hierarchy. Each dependentcalculates its share of nutrient, S, for this phase, which is

n

m k d2

where n is the total nutrient available to the cluster, m isthe number of members, k is a constant of proportionalityfor the distance adjustment and d is the distance betweenthe node and the CH.

CH advertisement: Each node which has a supply ofnutrient selects another node (or set of nodes, to speed upthe evolution of the system) at random and forwards theabove information, along with the identity of the CH.

Nutrient estimation: The receiving node calculates theamount of nutrient, S, it could have received in this phaseas a member of that cluster. If the amount is greater thanits actual allocation in this phase it communicates withthe CH and joins the cluster (also communicates with itsold CH to leave that cluster).

CH propagation: CHs propagate upward through thenetwork the number of members. The sink calculates theamount of nutrient available to each clustered for the nextphase using the formula

nn =no mn

mowhere nn is the nutrient available for the next phase, no

Algorithm 1 NDC cluster balancing1. some nodes become initial CHs2. the sink gives each CH nutrient share nav3. each CH sends the values ofn and m4. nodes receiving the CH message do the following:

5. calculate S6. forward current CH id & the received n and m values7. nodes receiving the forwarded message do the following:8. calculate S

9. IF S > S THEN10. leave current CH11. join the CH in the forwarded message12. END IF13. all CH send their m value to the sink14. the sink calculate nnext15. the sink broadcast nnext value

Figure 5: DAG based on nutrient transfer.

is the nutrient available this phase, mn is the membershipreported, mo is the membership reported for the previousphase. The effect of this re-distribution of nutrients is toadvantage CHs gaining members, in order to avoid cyclicalmovement of members between clusters. The operation ofthe NDC clustering is summarised in Algorithm 1.

From the viewpoint of theory, the resource allocationpolicy of a communication link can be depicted by a Di-rected Acyclic Graph (DAG) [31] with a single root repre-senting the sink and leaf nodes representing individual traf-fic streams. Middle nodes represent organisational entities.

Each node gets resources from its parent and identifieshow its resources are distributed to its children. Examplesof policies include fair resource sharing at various gran-ularities, traffic priorities, and communications distances.DAGs can be used by various applications to specify howtraffic streams or groups of nodes should share bandwidth.By merging sub-graphs, the management policies of vari-ous routing resources (e.g. timeliness) can be characterisedsimultaneously. NDC lends itself naturally to dealing withfailure recovery in an integrated mode during the resourcedistribution process. For instance, applications can pre-pare for a quick response to the failure of nodes and linksduring the formation of the graph, e.g. use another sub-

graph with highest resource availability. Figure 5 (a) showsa simple 2-cluster network scenario modelled as a DAG

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based on the received nutrient values. In Figure 5 (b),the optimised DAG based on the highest nutrient avail-ability is shown. To accommodate nutrient flow in thenetwork scenario, the problem of local maxima need to besolved. This means that NDC has to ensure that there

exists a path from any node to its CH and the nutrientlevels of all nodes along the path are strictly increasingtowards the CH, i.e. there exists a neighbour ofi, j,and Si < S j. This is achieved by normalising the nodeto CH distance (d). When d > 1, the probability of localmaxima is 0. Therefore, the original function is modifiedto use dNorm 0, 1). By negation, assume that there is noneighbour with Si < S j, for every node j, Si S j. Then,from the iterative calculation of the nutrient, Si, we canwrite:

Si =nParent

mParent k d2

Norm

S j S j

Thus nParent

mParent k d2

Norm

S j & dNorm 1

which contradicts the previous assumption that dNorm 0, 1). Therefore, for every sensor node connected to thenetwork, there exists at least one link from that node tothe CH.

6. Evaluation Metrics

In this section, the performance of ROL is to be evalu-ated via simulations with respect to the following metrics:

Data Delivery Ratio

Data Delivery Ratio (DDR) is a service level param-eter that indicates the network effectiveness in transmit-ting offered data in one direction of virtual connection [32].It is considered as one of the prime measures of robust-ness. DDR is a ratio of successful distinct payload octetsreceived to attempted payload octets transmitted [32].When calculating DDR, the packets which arrived late atthe destination are considered ineffective. The DDR for asingle node Si is denoted as DDR(Si) and defined as:

DDR(Si) = data delivered to the sink

data offered by Si

100% (5)

where data delivered is successfully delivered payloadoctets and data offered is the attempted payload octetstransmitted. The overall DDR of a network with a num-ber of nodes n, denoted as DDRN, is the average DDR ofall nodes:

DDRN =1

n

ni=1

DDR(Si) (6)

Timeliness

Timeliness is measured as the time normalised againstthe average time for a single-hop along the shortest path

from a node to the sink [33]. Recent studies in WSNs fo-cus on timeliness as a QoS metric (e.g., [34]). The average

delay taken by the first copy of a packet from the sourcenode, Si, to the sink is denoted as T(Si). T(Si) includes allpossible delays that are caused by queuing in the interfacequeue, retransmission at the MAC layer and the propaga-tion through the environment. The average delay of all n

nodes, denoted as TN, is given by:

TN =1

n

ni=1

(ta ts) (7)

where ta is the time a packet arrives at the sink and ts is thetime a packet sent at the source. The delay depends also onthe scale of the network [33]. The average network delay isthe total delay divided by the number of connection pairsthroughout the simulation to eliminate the effect of thenetwork scale. Then timeliness is measured as follows [33]:

TNetwork =TN

1n

ni=1 h(Si)

=ni=1 T(Si)n

i=1 h(Si)=

1

n

ni=1

T(Si)

h(Si) (8)

where h(Si) is the minimum hop count from Si to the sink.TNetwork is the average time it takes a packet to be deliv-ered. When TNetwork = 1, it means that the packets havean equivalent average delay of the same packets deliveredthrough the shortest paths with a delay of 1 on each hop.

Energy Efficiency

Since radio communication is the most power hungry op-eration [35], the cost that ROL imposes on the networksis considered in terms of number of messages sent. This

measure also gives an indication of the bandwidth usagebesides the energy consumption. Recall that all messages,including the ones originating from the CHs and carryingaggregated data, have a fixed data length. In this work, weare only interested in studying the effect of using the back-off time metrics on the cluster setup traffic. Therefore, thesetup traffic for a single cluster formation round, T rRound iscalculated as:

T rRound=number of sent messages

Cl i f e(9)

where Cl i f e is the time spanning from the moment the CH

sends the first advertisement message to the moment allcluster members receive the TDMA schedule.

7. Simulation Experiments

As ROL is derived from LEACH, we adopt the same net-work and energy model for better comparison. We use thespecification of MICAz [36], a popular sensor node proto-type, to make the simulation adhere to the real hardwareparameters of WSNs. Random graphs were dispersed ina 100m 100m region such that no two nodes share thesame location and the transmission range of each node is

bound to 75m. The bandwidth of the channel was set to250kbps, each data message was 50 bytes long, and the

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Figure 6: CH advertisement traffic vs backoff delay

packet header for various message types was fixed to 30bytes. A simple model for radio hardware energy dissi-pation is also assumed. All the nodes were given an ini-tial 0.5J supply of energy. For the experiments describedhere, both the free space (d2power loss) and the multi-path finding (d4power loss) channel models were used. Theprocessing delay for transmitting a message is randomlychosen between 0 and 5ms. Using this network configura-tion, we simulated in the Dingo WSN simulator [37] theROL/NDC, Mires++, and LEACH with 5% of the nodesbeing CHs.

7.1. Energy Consumption

This experiment studies the effect of the ROL/NDC pa-

rameters on the energy consumption. ROL/NDC was sim-ulated with various backoff delay values. Figure 6 plotsthe total number of setup messages versus the backoff de-lay. When the backoff delay is set to 0 the behaviour ofROL/NDC will be similar to LEACH. The figure showsthat as the backoff delay increases, the number of setupmessages decreases until the time becomes large enoughfor nodes to receive advertisements from all CHs. Theoptimal backoff delay is calculated from Figure 6 to bearound 20ms. Using this value, the total number of set-upmessages is reduced by up to 65%.

Figures 7 shows ROL/NDC, Mires++, and LEACH net-

work life. The network life is studied as the number ofrounds until the first node dies (Figures 7 (a)) and thenumber of rounds until the last node dies (Figures 7 (b)).ROL/NDC achieves energy gains by reducing the clus-tering overhead, load balancing across clusters, and usingshortest multi-hop communication paths.

In Mires++ and LEACH the network life trend was flatand considerably lower than ROL/NDC. Mires++ incurshigh energy consumption due to heavy setup communica-tion. Often adapting synchronous communication, such asthe publish/subscribe style used in Mires++, is computa-tionally intensive process. Mires++ also spend a signifi-cant amount of energy on updating nodes topics subscrip-

tion. In LEACH, the long range radio transmission resultsin nodes energy depletion. Also, LEACH requires nodes

(a) Number of rounds until the first node dies

(b) Number of rounds until the last node dies.

Figure 7: Energy performance analysis.

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to continuously listen to the channel, which results in con-siderable energy waste. Generally, clusters formed basedon the shortest distance will consume minimum commu-nication energy at the beginning, but energy consumptionwill eventually increase due to the re-clustering overhead.

7.2. Load-balancing

This experiment covers network load-balancing offeredas a main design objective of ROL/NDC. We study the ef-fect of the hop count, the backoff delay, and the use of NDCon clusters formation (geographic span and population).LEACH, Mires++, and ROL/NDC were respectively ap-plied on randomly generated network topologies. Due tothe effect that nodes distribution have on the results, mul-tiple simulation runs are combined to estimate uncertain-ties in the simulations. In other words, to demonstratethat the load is balanced for any setup we ran the sameexperiments for 5 different distributions. This makes oursimulation a Monte Carlo simulation, as repeated samplingfrom a distribution is performed. Also, to accommodateto the effect of CH locations on nodes distribution, CHlocations were kept fixed in all runs (original location weredetermined nondeterministically). In each protocol run,randomly distributed nodes are organised into five clus-ters (optimial percentage of CH as calculated in LEACH).Then, node distribution between different clusters is stud-ied. In ROL/NDC the backoff delay were set to 20ms andthe hop count initially set to 0.

Figure 8 shows nodes distribution among clustersformed using LEACH, Miress++, and ROL/NDC. The

population density (the number of nodes per unit area)of various LEACH generated clusters varies largely (seeFigure 8 (a); the first and fifth clusters hosted over 65%of the total number of nodes on 62.5% of the applicationarea, while the other three clusters hosted less than 35% ofthe total network population on the rest of the applicationarea. LEACH poor performance is mainly due to definingnetwork regions by inconsistent wireless connectivity. Itdoes not deploy any mechanism that manages the span orthe membership of clusters.

In Miress++ (Figure 8 (b)), nodes were distributed morefairly among clusters when compared to LEACH. However,

two of its clusters were underpopulated (hosted 10% of thetotal population). This is possibly due to nodes distribu-tion. Miress++ ignores the location and distance betweennodes when forming its clusters. The unfair cluster pop-ulation is critical as Miress++ relies only on rotating CHto acheive load balancing, i.e. without balanced clustermembership it will be load unbalanced.

ROL/NDC formed energy efficient clusters by reducingthe bridging distance between nodes and their CH (seeFigure 8 (c)). The number of nodes at every cluster main-tained a maximum of7% difference from the optimal pop-ulation (20%). Proportional non-overlapping clusters ofphysically close nodes reduce the cluster management over-

head and therefore the total network energy consumption.The average variance, from the optimal, of the coverage

(a) Cluster geographic span and population in LEACH

(b) Cluster geographic span and population in Mires++

(c) Cluster geographic span and population in ROL/NDC

Figure 8: Load-balanced cluster formation.

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(a) DDR against network density

(b) Average end-to-end delay against netwrok density

Figure 9: Robustness comparison in terms of DDR and average end-to-end delay.

polygon of the 10 clusters in ROL/NDC networks is 3.1075.This variance is approximately 3 times smaller than thatof LEACH (11.818).

7.3. Robustness

This experiment aims to measure ROL/NDCs ability tomaintain a high DDR within a given timeliness constraintin the presence of communication failures.

This evaluation scenario simulates permanent commu-nication failures caused due to hardware failure or energydepletion. In Dingo, node attributes include the expectedtime of node failure and failure type (e.g. PHY D indi-

cates physical node damage by an external event). Theseattributes is used to break communication links during thesimulation at a given times. During simulations, faultynodes are selected randomly based on their node ID (aninteger value between 1 and n), where n is the total numberof nodes in the network. The selection of nodes is not cor-related with the node location. Failures occur during thefirst data transmission phase; after the node sends its data,its energy level drops to 0J, consequently its radio will beswitched off. Other nodes continue to function normallyaccording to their assigned TDMA slot.

Figure 9 (a) shows the DDR for ROL/NDC, Mires++,

and LEACH. The DDR of the three protocols decreasesas the node density increases. ROL/NDC maintains very

high DDR, close to 1, in all tested network densities. InROL/NDC, nodes know multiple paths to their CH. If theprimary path fails, one of the standby paths will be used.Moreover, CH failures can be quickly recovered without in-curring large data loss. The short distance between nodes

and their CH helps in localising traffic, which results inless congestion and data loss.

LEACH, however, offers lower DDR, first is ROL/NDC.The long range communication increases the potential forcollisions. Additionally, if the sink is out of nodes commu-nication range, data will not be delivered.

Mires++ does not define any mechanism to deal withnode or communication paths failures, it only focuses onCH failures. Moreover, excessive delay due to heavy in-network processing, data aggregation and fusion, signifi-cantly reduced the DDR.

Figure 9 (b) plots the average end-to-end delay for the

ROL/NDC, Mires++, and LEACH at different node den-sities. At each point, the end-to-end delay of five runs wasaveraged. ROL/NDC has lower average end-to-end de-lay than Mires++ and LEACH. There are several factorsaccounting for this outcome. First, ROL/NDC forwardsdata on the shortest path to the CH, therefore, reducespropagation and queuing delays. Second, in time criticalapplications ROL/NDC multiplexes data over multi-pathsto avoid congested paths.

However, in LEACH, nodes can only transmit their dataaccording to a TDMA schedule that includes the entirenetwork due to the single hop communication. Major de-lays can be caused by the time period during which failed

or lost paths can be recovered.In Mires++, the publish/subscribe communication and

the message handling process introduces considerable de-lays. First, incoming messages has to be validated by ex-amining several header fields. Then, valid messages aresent to the corresponding message handler based on themessage type. After parsing the message payload, themessage handler updates the state of the node and re-spond accordingly. To reduce raw data transmissions, cHsand sensor nodes run sophisticated computing algorithms,such as data aggregation and fusion, which introduces con-siderable message delivery delays.

7.4. Selection According to Application

From the discussion above, it can be seen thatROL/NDC outperforms the other protocols with regard toenergy consumption, load balancing and robustness. Thus,there are few WSN applications for which it would notproduce a better functioning system than the others, solong as they are based on a set of nodes with reasonablysimilar resource, since ROL/NDC is based around distri-bution of the workload. For systems based on a powerfulnetwork controller and small, computationally weak nodes,a simpler centralised protocol such as MESTER would be

appropriate. Networks using more fully resourced nodeswill tend to be those using nodes carrying a diverse sensor

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load, including sensors requiring extensive data process-ing by the node. This type of network will also tend toproduce a greater volume of data, favouring the higherDDR and lower end-to-end delay that ROL/NDC offers.The differences in performance are also less pronounced

for small networks, below a few hundred nodes. So, whileROL/NDC is advantageous for all non-centralised net-works, it comes into its own for very large networks, com-posed of relatively powerful nodes, in which each node gen-erates substantial amounts of data. WSN of this type arestill relatively rare in practice, with most consisting of afew tens of nodes. However, in many applications networksare installed incrementally, and may be expected to growto a considerable size over a few years. For instance a re-cent application [38] known to the authors places a smallsensor network, of around ten nodes in a vineyard as partof a food quality traceability system. Each node carriestemperature, humidity, sunlight and leaf wetness sensors,and currently transmits data to a single base station. Inits present configuration, any of the protocols discussedhere would be adequate. But, the vineyard in which it isinstalled has a number of similar fields, so if this installa-tion is successful in returning on investment, the networkwill grow to over a hundred nodes quite quickly. This isabout the size where the advantages of ROL/NDC becomeapparent. Furthermore, this vineyard is a member of a co-operative, and if all members adopt the same system thenthe network will grow to over a thousand nodes, and anROL/NDC system will outperform one based on the otherprotocols. Particularly, the longer life due to lower en-

ergy usage and the greater robustness would be expectedto translate directly to a lower cost of ownership, and thisan increased return on investment.

8. Conclusion

This paper presented a distributed clustering protocol,called ROL/NDC. This protocol groups nodes into clus-ters and build routing paths based on localised metricsthat are linear in the number of nodes and linear in thenumber of links, which makes ROL energy and computa-tionally efficient. This work, distinguishes itself from cur-

rent state of the art solutions in three respects. First, ituses a combination of optimisable routing metrics to buildenergy efficient clusters at low cost. These parameters canbe configured and managed to allow user applications toperform better and coexist with each others. Second, itdefines a new cluster balancing method. Third, unlike ex-isting work that focuses on one design goal, ROL/NDCcan achieve comparable results in all of the above designaims.

In future we intend to test the proposed algorithmswith other applications and network topologies. Mostimportantly, we will consider the inclusion of more de-sign objectives into ROL/NDC and research more sophis-

ticated methods to optimise it for specific applications.An interesting extension of this work would be designing

a sleep/wake scheduling algorithm that considers variablesize messages. This is particularly useful for applicationswhere a node may have considerably more data to sendthan some other nodes, e.g. aggregated data or image ofdetected event. In this case per-hop MAC level fairness is

not important as long as application-level performance isnot degraded.

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Adaptive Routing in Wireless Sensor Networks: QoS Optimisation for Enhanced Application Performance

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