energy-efficient routing protocol for wireless sensor networks with static clustering and dynamic...

Wireless Pers Commun (2012) 65:347–367DOI 10.1007/s11277-011-0260-4

Energy-Efficient Routing Protocol for Wireless SensorNetworks with Static Clustering and Dynamic Structure

Huei-Wen Ferng · Robby Tendean · Arief Kurniawan

Published online: 5 March 2011© Springer Science+Business Media, LLC. 2011

Abstract Due to limited energy of sensor nodes in a wireless sensor network, an energy-efficient routing protocol with static clustering and dynamic structure (ERP-SCDS) is pro-posed in this paper. Utilizing virtual points in a corona-based wireless sensor network, staticclusters with dynamic structures are formed in ERP-SCDS. Moreover, next-round clusterheads are selected in advance to avoid a deadlock when the old cluster heads die. Finally,a simple relay node selection mechanism instead of a complicated multi-hop route discov-ery algorithm is further designed for ERP-SCDS. Integrating these mechanisms enablesERP-SCDS to form balanced cluster sizes to prolong the network lifetime. Via simulations,we demonstrate that ERP-SCDS significantly outperforms LEACH, HEED, and Hausdorffpreviously proposed in the literature.

Keywords Wireless sensor network · Static clustering · Energy efficiency ·Dynamic structure · Routing · Network lifetime

1 Introduction

Wireless sensor networks (WSNs) formed by a group of sensor nodes in an area of interestwith the short-range wireless communication have become one of popular networks. In aWSN, sensor nodes gather information and send it to the data processing center called basestation or sink. This task-oriented feature makes WSNs well suit the application in environ-ment monitoring, for example, alarming the forest guard when the forest is on fire or alarmingthe local guard if a dangerous volcanic activity is detected. WSNs also find some applicationsin the surveillance system to detect intruders in a secret military facility or detect smugglersaround the country border. The latest application for WSNs is ubiquitous computing. In thisfield of applications, WSNs may be used for checking the temperature inside the house. When

H.-W. Ferng (B) · R. Tendean · A. KurniawanDepartment of Computer Science and Information Engineering,National Taiwan University of Science and Technology, Taipei 106, Taiwane-mail: [email protected]

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the temperature received from sensor nodes is above a pre-specified threshold, the sink willturn on the air conditioner. Of course, WSNs can be built for checking the water flow, gasflow, house security, etc. These specific applications of WSNs make smart home possibleand visible.

Wireless sensor nodes are usually low-cost electronic devices equipped with sensors,microprocessors, memory, wireless transceivers, and batteries. Because the global position-ing system (GPS) devices are also cost-effective, low-power GPS devices are usually incorpo-rated into sensor nodes as well. Despite the fact that sensor nodes are cheap, they are limitedin power supply, computational capabilities, memory capacities, and communication band-width, and are also prone to errors. Further noting that sensor nodes are frequently denselydeployed in an area without any chance to change batteries, WSNs need to be operated inan energy-efficient manner, for example, employing an energy-efficient routing protocol forrouting packets from sensor nodes to the sink so that the network lifetime can be prolonged.In this paper, such an issue is touched. Hence, let us check the status of routing protocols inthe following paragraphs.

Generally speaking, routing protocols in WSNs can be categorized into routing protocolswithout a specific architecture and those with a specific architecture. Routing protocols with-out a specific architecture are more suitable for WSNs with small deployment areas since amulti-hop routing mechanism is simply used to transmit data from sensor nodes to the sink.In the literature, several multi-hop routing protocols have been proposed for WSNs, e.g.,[4,16,17,19], and [25]. In [4], the estimated remaining battery energy and estimated energyconsumption were used in routing by Block and Baum to prolong the network lifetime.Intanagonwiwat et al. [16] proposed a data-centric multi-hop routing protocol with multiplepaths called directed diffusion in which the data request is named using attribute pairs beforebeing diffused through the network as an interest via re-broadcasting and geographic routingassisted by the GPS receiver to the right direction based on the destination area. The dynamicsource routing (DSR) [17] proposed by Johnson and Maltz is an on-demand multi-hop rout-ing protocol with routing tables in which a node floods the network with a route requestpacket when it does not have a route to the destination. A receiving node then re-broadcaststhis packet if it never received it before. In [19], an energy-efficient routing protocol basedon residual energy and energy consumption rate (REECR) for heterogeneous WSNs wasintroduced by Li et al. Perkins and Royer [25] proposed the ad-hoc on-demand distancevector (AODV) routing protocol to provide loop-free multi-hop routing with the ability torepair broken links. On the other hand, routing protocols with a specific architecture, e.g.,[20] and [35], fit WSNs with large deployment areas since data aggregation is utilized toreduce duplicate transmissions. In [20], a chain-based protocol called power-efficient gath-ering in sensor information systems (PEGASIS) was proposed by Lindsey and Raghavendrato improve the well-known low energy adaptive clustering hierarchy (LEACH) [13] routingprotocol. In [35], Zhou et al. introduced a hierarchical minimum spanning tree routing pro-tocol for WSNs with a fixed percentage of nodes chosen as candidate cluster heads basedon weighted edges. Each node then selects the nearest cluster head using a prim minimumspanning tree algorithm.

Among architectures for routing protocols in WSNs, the architecture of clusters is oneof the most well-known architectures. With such an architecture, information from sen-sor nodes inside the same cluster is directed to the cluster head which then aggregates thedata and send it to the sink through a direct transmission or a specific routing mechanism.In designing routing protocols with clusters, the main challenge is how to create balancednumbers of nodes among clusters (that is, balanced cluster sizes) to equalize the energy dissi-pation among clusters so that the network lifetime can be prolonged. As far as the clustering

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Energy-Efficient Routing Protocol for Wireless Sensor Networks 349

approach is concerned, it can be further classified into static and dynamic ones. Clusters instatic clustering are formed in the beginning, while cluster heads are selected in each round.Static clustering avoids energy dissipation caused by the re-clustering process, if any, in eachround. However, balanced cluster sizes are desired to prolong the network lifetime. On theother hand, dynamic clustering constructs new clusters every round, resulting in much energywastage. Moreover, cluster sizes still affect the network lifetime.

In the literature, many clustering protocols have been proposed for WSNs, e.g., [2,5–11,14,15,18,21–23,26,27,29,31–34,36]. In [2], Al-Karaki et al. introduced the optimal aggre-gation points to improve effectiveness of data aggregation. Boukerche et al. [5] proposed aclustering routing protocol using the nearest neighbor approach, alternation of nodes respon-sible for the inter-cluster communication, and alternation of possible routes to the sink. In [6],the maximum energy cluster head (MECH) protocol was proposed by Chang and Kuo. ForMECH, cluster head selection is done based on a pre-specified cluster member threshold toform a balanced cluster. Chen et al. [7] proposed a hierarchical energy-efficient protocol foraggregator selection (EPAS) with a general compression model for data aggregation. Cui etal. [8] improved LEACH [13] by considering balance of the network load, multi-hop routing,and remaining energy of nodes. Another improvement on LEACH was developed by Fanand Yu [9] via further considering the remaining energy of nodes in the cluster head selec-tion. In [10], a robust clustering with cooperative transmission (RCCT) for energy-efficientWSNs to distribute energy loads was proposed by Ghelichi et al. with energy considerationin selecting cluster members. In [11], the geographical-based multi-hop clustering algorithm(GBMCA) was proposed by Hao et al. to divide the network area into small regions by adopt-ing multi-hop links for the inter-cluster communication. In [14], Hong and Liang proposedan access-based energy-efficient (ABEE) clustering protocol by using a request-responsemessage mechanism with the first-come-first-serve policy and the assistance of the GPSsystem to get node positions. Huang et al. [15] introduced a low-energy static clusteringscheme (LESCS) for WSNs to prolong the network lifetime through solving the hot-spotproblem with static clustering and equal energy consumption throughout the network. In[18], a distributed position-based network protocol called minimum energy communicationnetwork (MECN) with the aid of GPS receivers to have node positions was proposed by Liand Halpern. Manjeshwar et al. [21] designed a threshold-sensitive energy-efficient sensornetwork (TEEN) protocol to increase network coverage by employing a hierarchical cluster-ing approach. Furthermore, the adaptive periodic threshold-sensitive energy-efficient sensornetwork (APTEEN) [22] protocol was also proposed by Manjeshwar et al. to improve TEENby using some extra methods for retrieving information from sensor nodes. Nam et al. [23]developed an adaptive cluster head selection by considering positions of cluster heads andcluster members. In [26], Rhazi and Pierre modeled clustering as hyper-graph partitioningand offered a heuristic solution based on a tabu-search. On the other hand, Su and Zhang[27] introduced clustering with an optimal number of clusters based on an energy thresh-old. Wang et al. [29] proposed a dynamic optimal number of clusters based on the currentnumber of nodes in the network. In [31], Xu et al. developed an adaptive clustering proto-col for medium-scale wireless sensor networks (ACPM) using an adaptive backoff schemeto select cluster heads with the highest remaining energy. Ye et al. [32] introduced a two-tier data dissemination model for large-scale wireless sensor networks to provide scalableand efficient data delivery to multiple mobile sinks with the assumption that each node isaware of its location through receiving GPS signals. In the hybrid energy-efficient distrib-uted (HEED) clustering protocol introduced by Younis and Fahmi [33], minimum degreenodes, maximum degree nodes, or average minimum reachability power (AMRP) can bechosen to form a cluster. In [34], Zhang et al. extended the network lifetime by modifying

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LEACH with a weighted clustering algorithm. Zhu et al. [36] proposed a static-clustering-based minimum energy routing protocol with the Hausdorff distance to ensure the networkconnectivity.

Since most of routing protocols with clustering for WSNs do not consider energy dissi-pation in each phase of the network operation, energy efficiency can not be fully exploited.Towards this direction, this paper proposes ERP-SCDS for WSNs. In ERP-SCDS, static clus-ters are constructed in the beginning using well-distributed virtual points inside the network.Additionally, sensor nodes choose the nearest virtual point and keep the virtual point ID.Nodes with the same virtual point ID then belong to the same cluster, resulting in a staticcluster with a dynamic structure. With the two special treatments, balanced cluster sizescan be reached. Furthermore, cluster heads are chosen according to the remaining energyas well as the distance to the nearest virtual point so that a longer network lifetime can beexpected and the in-network connectivity can be ensured. For routing from cluster headsto the sink, every cluster head selects a relay node by considering its remaining energyand distance to the sink to ensure both longer network lifetime and right direction of datatransmission. In the literature, LEACH [13], HEED [33], and Hausdorff [36] are the closestrouting protocols to ERP-SCDS. Let us now take a closer look at the three protocols. InLEACH, the clustering algorithm done in each round builds several clusters according toa pre-specified number of clusters. As for how to select a cluster head, a probability-basedapproach is used. Moreover, the high energy dissipation occurs because every cluster headinforms nodes by broadcasting an advertisement message throughout the network and directtransmission is employed to send data from cluster heads to the sink. In HEED, minimumdegree nodes, maximum degree nodes, or AMRP can be utilized to form a cluster. For thefirst policy, a node selects a cluster head with the minimum members to form balanced clustersizes. As for the second policy, a node selects a cluster head with the maximum members toform a dense cluster. With AMRP, a cluster is formed by considering the minimum powerrequired by all cluster members to reach the cluster head selected based on the remainingenergy and probability. However, HEED involves the re-clustering process, causing energywastage. In Hausdorff, clustering is done based on the Hausdorff distance provided by theGPS receiver to ensure network connectivity. It is still possible to form a single-node clus-ter, leading to a shorter network lifetime. Moreover, the cluster formation mechanism inHausdorff is not energy-efficient since many control messages are employed. Furthermore,a cluster head needs to broadcast a membership update message to its neighboring clus-ters once a confirmation message is received from a joining node. Finally, a cluster headselection mechanism executed by the old cluster head results in a deadlock when the oldcluster head dies. As far as ERP-SCDS is concerned, clustering is done in the beginningusing distributed virtual points to form balanced cluster sizes and avoid the occurrence ofsingle-node clusters. Moreover, the cluster head selection based on the weighted average ofthe remaining energy and distance to the nearest virtual point ensures that a node with theoptimal remaining energy and near the center of the cluster is selected. To avoid the deadlockcaused by the death of an old cluster head, the next-round cluster head is selected in advance.Besides, an advertisement message is broadcasted with low power and only directed to thenodes with the same virtual point ID. Last but not least, the multi-hop route discovery algo-rithm is replaced by the relay node selection mechanism to further achieve better energyefficiency.

The rest of this paper is organized as follows. The network and energy dissipation modelsare described in Sect. 2. As for the details of the proposed protocol, they are examined inSect. 3. Via simulation, performance of the proposed protocol and comparisons with theclosely related protocols are given in Sect. 4. Finally, Sect. 5 concludes this paper.

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2 Network and Energy Dissipation Models

2.1 Network Model

The corona-based network model widely used in the literature, e.g., [24,28], and [30], isemployed in this paper. As shown in Fig. 1, the sink is located at the center of the areaand the network coverage is divided into k coronas denoted by C1, C2, . . . , Ck , respectively.Denoting the radius of the outer concentric circle associated with corona Ci by ri , we assumeri − ri−1 = r (i ≥ 2) and r1 = r , i.e., equal width of coronas. Furthermore, virtual pointsare deployed on these concentric circles. As for data transmission, a low-power broadcastrange R1 and a high-power broadcast range R2 are used for intra-cluster communication andinter-cluster communication, respectively, in the network. The low-power broadcast rangeis obtained via multiplying the farthest distance between a sensor node and the nearest vir-tual point by two to ensure connectivity inside the cluster. As for the high-power broadcastrange, it is calculated by R2 = max (4R1, R), where R denotes the radius of the area. Notethat 4R1 is needed to ensure the network connectivity between clusters, while R acts asa threshold to limit the high-power broadcast range since the sink is located at the cen-ter of the network. For the data communication before the steady-state phase, the mediumaccess control (MAC) protocol of carrier-sense multiple access with collision avoidance andacknowledgement (CSMA/CA with ACK) [1] is used. As for the intra-cluster communica-tion, the use of the TDMA schedule definitely avoids collisions. In this paper, all nodes areassumed to be time-synchronized by letting the sink broadcast synchronization pulses to allnodes (see [13] for details). Because the direct sequence spread spectrum (DSSS) can beused to reduce the inter-cluster interference, no collisions in the inter-cluster communicationare assumed as well. Therefore, we assume that there is no collision and packet loss forsimplicity. Besides, sensor nodes are assumed to always have data to be sent during theirTDMA time slot. Because a wireless sensor network, which usually employs static sensornodes as those assumptions made by many papers in the literature, is assumed in this paper,the impact caused by mobility of sensor nodes will not be studied in this paper. Finally,sensor nodes are equipped with low-power GPS devices to acquire their positions and sensecontinuously as the assumption made in [14,16,18,32,36]. Let us explain this assumptionas follows. First, many sensor nodes with GPS have been already in the market. Second, theissue of sensor node localization is not the point of the paper. These enable us to pose suchan assumption although it might bring the deployment cost up but might not be unrealistic atall. Of course, such an assumption can be totally replaced by any possible solution on sensornode localization reported in the literature for sure.

2.2 Energy Dissipation Model

Like [13], an adaptive energy model is employed in this paper. Therefore, the energy expen-diture for transmitting an l-bit message from node i to node j denoted by ET x (i, j) can becalculated via

ET x (i, j) ={

lEelec + lεfsd2i j , di j < d0,

lEelec + lεmpd4i j , di j ≥ d0,

(1)

where Eelec is the electronic energy consumed per bit for coding, modulation, filtering, andspreading, d0 is the distance threshold, di j is the distance from node i to node j, εfs is theamplifier energy for a free space (fs) model when the transmission distance is shorter than

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Fig. 1 A WSN with k coronasand a single sink

Sink C1 C2

r1

r2

rk

Ck-1C…

r…

rk-1

Ck

d0, εmp is the amplifier energy for a multi-path (mp) model when the transmission distanceis longer than d0. For receiving a message, the energy dissipation ERx is

ERx = lEelec. (2)

Moreover, the energy spent in data aggregation at the cluster head E Ax can be calculated by

E Ax = lE D A, (3)

where ED A is the energy required to aggregate one bit data.

3 The Proposed Protocol

ERP-SCDS consists of several phases (see Fig. 2). The first phase is the initialization phasefor static clusters. ERP-SCDS employs an architecture with static clusters to reduce theenergy in the clustering process. Because of static clusters, it is critical to determine balancedcluster sizes to maintain a long lifetime. To have an energy-efficient clustering mechanismand well-distributed clusters, the initialization of static clusters is handled by the sink. Ini-tially, the sink forms a balanced distribution of virtual points to be detailed later. Since thesevirtual points act as centers of clusters, balanced distribution of virtual points ensures bal-anced distribution of clusters, thus minimizing the energy for clustering in ERP-SCDS. Thesecond phase in ERP-SCDS is the formation of clusters. Instead of the traditional rectangularstructure for clusters, ERP-SCDS allows each node to select the nearest virtual point to forma cluster, resulting in a dynamic structure. The third phase of ERP-SCDS is the next-roundcluster head selection. ERP-SCDS selects the next-round cluster head in advance to avoidthe deadlock of the cluster head selection mechanism when an old cluster head executing thisphase dies. The fourth phase of ERP-SCDS is the route discovery. Abandoning a route dis-covery algorithm which may be complicated and waste energy, ERP-SCDS adopts a simpleand energy-efficient relay node selection mechanism with the multi-hop routing from clusterheads to the sink. In addition, ERP-SCDS employs two conditions to determine whether anode can be a relay node or not. If no relay node is selected, it means that the distance from thecluster head to the sink is short so that direct transmission is used. This provides ERP-SCDSflexibility since direct transmission is more efficient in a small network, while relay nodes

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1. Initialization of static clusters:2. Sensor nodes send their coordinates to the sink. 3. The sink calculates the number of concentric circles k and virtual point coordinates. 4. The sink broadcasts virtual point IDs and coordinates. 5. Each sensor node keeps the nearest virtual point ID and coordinate. 6. While (all nodes are alive) do7. Cluster formation:8. If (IsFirstRound) then9. Perform cluster head selection. 10. Else11. Nodes whose next-round cluster head flag is set update their status to the cluster head. 12. End if13. Each cluster head broadcasts an ADV message. 14. Nodes with the same virtual point ID reply with a JOIN message. 15. Each cluster head creates and broadcasts the TDMA schedule. 16. Next-round cluster head selection:17. Each cluster head calculates the weighted average wi for its members. 18. Each cluster head selects and tells the member with the largest wi that “it is the next-round cluster head”. 19. The member with the largest wi sets its next-round cluster head flag. 20. Route discovery and relay node tables:21. Each cluster head broadcasts a candidate relay node message. 22. Each receiving cluster head checks: 23. If (d(sender, receiver) < d(receiver, sink)) then24. If (d(sender, sink) < d(receiver, sink)) then

25. Calculates and inserts the weighted average ciw and ID of the sender to the relay node table.

26. End if27. End if

28. The candidate relay node with the largest weighted average ciw is then selected as the relay node.

29. Steady state:30. Cluster members send the data to the cluster head. 31. Each cluster head aggregates and sends the data to the sink via multi-hop routing with relay nodes. 32. End while

Fig. 2 Pseudocode of ERP-SCDS

Time 1 2 3 4 • • •

Round Steady-state phase

2 3 4 2 3 4

Frame

1. Initialization of static clusters 2. Cluster formation 3. Next-round cluster head selection 4. Route discovery and relay node tables

Fig. 3 Time diagram of ERP-SCDS

can reduce transmission energy in a large network. As for the last phase of ERP-SCDS, it isthe steady-state phase in which sensor nodes send information directly to their cluster headsand the information is then forwarded to the sink by relay nodes through multi-hop routingor via direct transmission. In Fig. 3, the time diagram of ERP-SCDS is shown. Initializationof static clusters is done in the beginning. Then, each round consists of the cluster formation,next-round cluster head selection, route discovery and relay node tables, and steady-statephase.

3.1 Initialization of Static Clusters

In ERP-SCDS, static clusters are formed using virtual points whose locations are determinedby the sink. First of all, all sensor nodes check their locations using the low-power GPSdevice. Then, they send their coordinates to the sink. Upon receiving, the sink calculates

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- Virtual Point- Sink - Sensor Node

(a) (b)

Fig. 4 Initialization of static clusters. a Placement of virtual points. b Selection of the nearest virtual point

how many concentric circles are needed according to the (expected) number of static clustersgiven initially. Since virtual points serve as centers of clusters, the number of virtual pointsis equivalent to the number of static clusters. Let us further detail the placement of virtualpoints. First, note that an optimal number of virtual points on the i th concentric circle denotedby ni can be calculated by

ni = �2πri

dvp�, (4)

where dvp is the diameter of a cluster and is set to the corona width r . By using (4) repeatedlyfrom the first circle until the total number of virtual points on all concentric circles used isgreater than the expected number of static clusters, the number of concentric circles neededto place virtual points is then determined. Further adding 1 (the outermost concentric circlewhich is not used for placement of virtual points) to this number, we get the total number ofconcentric circles, i.e., the value of k.

Now, the sink is able to determine coordinates (xvp(i, ji ), yvp(i, ji )) of virtual points,where i ∈ {1, 2, . . . , k} is the index of concentric circles, and ji ∈ {1, 2, . . . , ni } denotes theji th virtual point on the i th concentric circle (see Fig. 4a), by

xvp(i, ji ) = xc + ri cos

(2π jini

), (5)

yvp(i, ji ) = yc + ri sin

(2π jini

), (6)

where (xc, yc) is the coordinate of the sink.The placement of virtual points using (5) and (6) as shown in Fig. 5a can be further

improved to have more balanced placement of virtual points. The first improvement onthe placement of virtual points can be achieved by re-arranging the numbers of virtualpoints on the outer concentric circles of coronas Ck−2 and Ck−1. The sink first checksif the total number of virtual points on the two concentric circles is fewer than halfof the optimal number of virtual points. If yes, these virtual points are evenly distrib-uted as possible as it can on these two concentric circles as shown in Fig. 5b (if thetotal number of virtual points on these two concentric circles is odd, the outer concen-tric circle of corona Ck−2 gets one more virtual point). The second improvement on the

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Sink

Virtual Point

(a)

Sink

Virtual Point

(b)

Virtual Point

Sink

(c)

Fig. 5 Distribution of virtual points. a Before improvement. b The first improvement. c The secondimprovement

distribution of virtual points can be done by giving extra initial angles to rotate thevirtual points on the concentric circles of coronas C2, C3, . . . , Ck−1 (see Fig. 5c). As forthe initial angle, it can be set to half of the angle between two consecutive virtual pointson the inner concentric circle right next to the current one. Once the coordinates of vir-tual points have been determined, the sink broadcasts virtual point IDs and coordinates toall sensor nodes in the network. Finally, each sensor node selects and keeps the nearestvirtual point ID and coordinate. As shown in Fig. 4b, this virtual point selection mech-anism lets sensor nodes with the same virtual point ID form a cluster with an irregularshape/structure.

3.2 Cluster Formation

In the beginning, cluster heads are selected using a mechanism assisted by backoff timers.Each node initially starts a backoff timer according to its distance to the nearest virtual point.For the i th node, the backoff timer is set to ti via

ti = di

2r× TCHS, (7)

where di is the distance from the i th node to the nearest virtual point and TCHS is the timeallocated for the first-round cluster head selection. Note that a distance (di ) is used here toensure that the cluster head is the nearest node to the center of the cluster, i.e., the virtualpoint, so that balanced energy for data transmission from cluster members to the cluster headcan be assured to prolong the network lifetime. As for 2r , it is used to make the ratio di

2r fallwithin (0, 1) because di < 2r,∀i under our corona-based model and the way to place virtualpoints. With a backoff timer, each node can declare itself a cluster head independently. Oncethe backoff timer expires, the node associated with this timer broadcasts an advertisement(or ADV) message to claim that it is the cluster head to its neighboring nodes with lowpower. The first node broadcasting the ADV message then becomes the cluster head. Anynode receiving this ADV message then suppresses its own ADV message to be sent. In thefollowing remark, let us discuss the case that two/multiple nodes have the same distance toa virtual point.

Remark 1 In fact, the probability that two/multiple nodes have the same distance to a virtualpoint is quite low (almost impossible). However, if this case happens, the following two situ-ations may happen. First, the two/multiple nodes posses the shortest backoff timer. Becauseof the same shortest backoff timer, the two/multiple ADV messages sent by the two/multiple

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nodes will collide, causing that nothing is heard by the other nodes. For this situation,suppression of ADV messages will not be done by the other nodes. Second, the two/multiplenodes do not have the shortest backoff timer. Then, the two/multiple ADV messages to besent will be suppressed accordingly.

As for the case that the ADV messages collide or a node’s ADV message is lost, a bit moredetailed discussion is provided in the following remark

Remark 2 If the ADV messages collide (or a node’s ADV message is lost), none of thesenodes becomes the cluster head since nothing is heard by the other nodes. Therefore, sup-pression of ADV messages will not be done by the other nodes. Later on, the node sendingan ADV message first heard by the the other nodes becomes the cluster head.

To avoid the possible problem caused by the same distance to a virtual point for multiplenodes and to alleviate ADV message collisions, an alternative to set the backoff timer isfurther provided in the following remark.

Remark 3 To avoid the aforementioned problems, the backoff timer set by (7) can be slightlymodified as

ti = di

2r× TCHS × rand(1 − ν1, 1 + ν2),

where ν1 and ν2 are two preset values (0 ≤ ν1, ν2 � 1) and rand(1 − ν1, 1 + ν2) stands fora random value selected from the interval (1 − ν1, 1 + ν2).

For the second round and beyond, a next-round cluster head should be determined in theprevious round. This will be explained later. Right before a new round, each node checkswhether its “next-round cluster head” flag is set or not. If yes, this node becomes a new clusterhead for the new round and broadcasts an ADV message with low power to its neighboringnodes to inform them of “it is a cluster head”. When the nodes with the same virtual point IDreceive an ADV message, they will reply with a JOIN message containing the source nodeID, destination node ID, source node coordinate, and source node remaining energy to thecluster head. Once the cluster head receives JOIN messages, it saves these cluster memberIDs. Then, the cluster head creates a TDMA schedule and broadcasts it to the cluster mem-bers. With this TDMA schedule, each cluster member transmits the sensed data to the clusterhead right in its time slot.

3.3 Next-Round Cluster Head Selection

In ERP-SCDS, the next-round cluster head is selected in advance to avoid the deadlock whenan old cluster head dies. After receiving a JOIN message from node i (in the current round),the current cluster head calculates the weighted average wi based on the normalized remain-ing energy and distance for selecting the next-round cluster head (with the largest value ofthis weighted average) via

wi = α1Erem

E0+ α2

(1 − di

2r

), (8)

where α1 and α2 are weighting factors, Erem is the remaining energy, E0 is the initial energy.Note that the first term is used to guarantee that a node with the most remaining energy isselected as the next-round cluster head, while the second term ensures that the next-round

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Sink

Candidate

(a)

Sink

YesYes

No

Candidate

(b)

Sink

YesNo

No

Candidate

(c)

Fig. 6 Relay node selection. a Broadcast of a relay node candidate message. b First condition. c Secondcondition

cluster head is the node closest to the center of the cluster. The current cluster head willinform the node with the largest weighted average value of “it is the next-round cluster head”using a next-round cluster head message. Upon receiving this message, this node sets itsnext-round cluster head flag. Right before the next round, each node checks its next-roundcluster head flag. If its flag is set, it will automatically become the cluster head in the nextround and perform the cluster formation.

3.4 Route Discovery and Relay Node Tables

At the beginning of this phase, each cluster head broadcasts a candidate relay node messagecontaining the source node ID, source node coordinate, and source node remaining energy toits neighboring cluster heads with high power to tell them that “it is a candidate relay node”(see Fig. 6a). Upon receiving this message, the neighboring cluster head checks whether itsdistance to the sending cluster head is shorter than its distance to the sink to ensure thatsending the data to the relay node is nearer than sending it directly to the sink (see Fig. 6b). Ifyes, it further checks whether the distance from the sending cluster head to the sink is shorterthan the distance from it to the sink to ensure the right direction/path to the sink (see Fig. 6c).If yes again, it will insert the sending cluster head ID and the sending cluster head weightedaverage value into its relay node table. Here, the weighted average value wc

i of the i th clusterhead can be obtained via

wci = β1

Ecrem

Ec0

+ β2

(1 − dc

i

R

), (9)

where β1 and β2 are weighting factors, Ecrem is the remaining energy of the cluster head, Ec

0is the initial energy of the cluster head, and dc

i is the distance from the i th cluster head to thesink. As for R, it is used to let dc

i /R fall within (0, 1) because the distance between a clusterhead and the sink is shorter than the area radius. Finally, the cluster head with the largestweighted average value listed in the relay node table of the current cluster head is selectedas the relay node for data forwarding of the current cluster head.

3.5 Steady-State Phase

Once the TDMA schedule has been broadcasted to cluster members and the relay nodeshave been selected, intra-cluster and inter-cluster data transmissions can be then started.For the intra-cluster data transmission, each cluster member sends the sensed data to its

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Table 1 General parameters Parameter Value

Control packet size 25 bytes

Packet header size 25 bytes

Eelec 50 nJ/bit

εfs 10 pJ/bit/m2

εmp 0.0013 pJ/bit/m4

ED A 5 nJ/bit/signal

Threshold distance (d0) 75 m

cluster head using the TDMA schedule. The cluster head receives data from cluster mem-bers in one TDMA frame before aggregating them. Afterwards, the cluster head forwardsthe aggregated data to the relay node. In this manner, the inter-cluster data transmis-sion can be done to finish the data delivery to the sink. It is possible that a relay nodedies earlier than the sending cluster head. For this case, this cluster head needs to re-select a new alive relay node with the largest weighted average value from its relay nodetable. If there is no alive relay node, the data is then directed to the sink using the directtransmission.

4 Simulation Results and Discussions

In this section, the determination of the number of clusters for ERP-SCDS and performancemetrics of ERP-SCDS, LEACH, HEED, and Hausdorff, including ratio of single-node clus-ters which is the ratio of the number of single-node clusters to the total clusters, coefficientof variation of cluster size, clustering energy dissipation per round, route discovery energydissipation per round, number of hops in inter-cluster routing, and network lifetime1 which isdefined as the number of rounds from the beginning until the first sensor node dies, are givenby averaging results of 20 simulation runs built by the C++ programming language. There aretwo scenarios with different parameters: one for the small-area network and the other one forthe medium-area network. The small-area network scenario focuses on the performance ofthe clustering mechanism, while the medium-area network scenario emphasizes the perfor-mance of the inter-cluster communication. As for the general parameters used, they are givenin Table 1. Note that we assume that messages from cluster members to the cluster head areperfectly aggregated into one. Depending on the number of sensor nodes, area, and numberof frames per round, time per round is in the range of a few seconds if sensor nodes contin-uously send data to the sink [33]. For HEED, AMRP is used for clustering and a dynamicsource routing (DSR) is used for route discovery. In the following, let us further detail thesimulation arrangement for these two scenarios and determine the number of clusters forERP-SCDS.

1 Although the network lifetime from the beginning until the instant at which the last node dies is tractable,it not meaningful since the connectivity cannot be guaranteed during this network lifetime. To have meaning-ful and tractable network lifetime, we consider the network lifetime defined in this paper only. In fact, thisdefinition of network lifetime was widely employed in the literature as well.

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Fig. 7 Network lifetime for different number of clusters under different numbers of nodes. a Case of thesmall-area network. b Case of the medium-area network

4.1 Detailed Simulation Arrangement

4.1.1 Small-area Network

For the small-area network, 300–700 sensor nodes are randomly deployed in an area of 100m × 100 m with a sink at the center. Each sensor node is initially given 2-joule energy andthere are 5 TDMA frames in each round. As for the packet size, it is set to 100 bytes. ForLEACH, the optimal number of clusters copt is set to 11 [13]. For HEED, the cluster radiusis set to 25 m as suggested by [33]. As for Hausdorff, the low-power broadcast range R1 isset to 30 m [36]. For ERP-SCDS, copt is set to 17 according to the simulation result to begiven later. In WSNs, energy is an important factor. Therefore, we set α1 = β1 = 0.8 andα2 = β2 = 0.2.

4.1.2 Medium-area Network

In the medium-area network, 300–700 nodes are randomly distributed in an area of 350 m× 350 m with a sink at the center. For this scenario, 5 joule of the initial energy is given toeach node and each round consists of 10 TDMA frames. As for the packet size, it is set to500 bytes. For LEACH, copt is set to 7. As for the cluster radius of HEED, it is set to 30 m.For Hausdorff, the low-power broadcast range is set to 50–75 m. For ERP-SCDS, copt is setto 30 according to the simulation result to be given later. Like the reasoning and setting inthe small-area network, α1 = β1 = 0.8 and α2 = β2 = 0.2.

4.1.3 Determination of the Number of Clusters for ERP-SCDS

In the following simulations, the optimal numbers of clusters for the small-area network andmedium-area network are investigated in a sense of the longest network lifetime. Consid-ering different numbers of clusters, we observe the network lifetime vs. number of nodes.In Fig. 7a, b, the longest network lifetime for the small-area (medium-area) network can bereached when the number of clusters is 17 (30). Therefore, 17 and 30 are chosen for copt inthe remaining small-area and medium-area network simulations.

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Fig. 8 Ratios of single-nodeclusters for different protocolsunder different number of nodes

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4.2 Simulation Results

4.2.1 Ratio of Single-node Clusters

Note that more single-node clusters indicate more unbalanced clusters produced by the rout-ing protocol. Therefore, it can be used to examine the level of balance on clusters. Moreover,single-node clusters shorten the network lifetime. Since the ratio of single-node clusters isrelated to the clustering mechanism, the small-area network is employed. In Fig. 8, ratiosof single-node clusters formed by different routing protocols are given. For ERP-SCDS, itshows the best performance since zero single-node cluster is generated because of balancedcluster distribution based on virtual points. As for LEACH, almost zero single-node clusteris observed as well since it floods the network with advertisement messages to let sensornodes select the nearest cluster head. For HEED, it gives a higher ratio of single-node clus-ters as compared to ERP-SCDS and LEACH. When the main clustering phase fails to selectthe final cluster head, any node with no final cluster head around it will be selected as anew final cluster head in the finalized clustering phase. This gives an opportunity to producesingle-node clusters for HEED. Finally, the rule of the Hausdorff distance in forming a clusterby considering the distance between the cluster with the joining node and its neighboringclusters causes rejection to joining nodes and increases the probability to form single-nodeclusters, resulting in the highest ratio of single-node clusters among the four protocols. Fromthe above discussion, it evidently shows that ERP-SCDS exhibits perfect balanced clustersamong the four protocols.

4.2.2 Coefficient of Variation of Cluster Size

The coefficient of variation of cluster size cv shows the normalized variance of memberscovered by a cluster. It is defined by

cv =

√1

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N(xi − μ)2

μ, (10)

where N is number of clusters formed, xi is number of sensor nodes in the i th cluster, and μ

is the sample mean of sensor nodes in a cluster. Likewise, the small-area network scenariois employed since it shows the balance of sensor nodes between clusters. The coefficientsof variation of cluster size for different protocols can be seen in Fig. 9. As we can see from

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Fig. 9 Coefficients of variationof cluster size for differentprotocols

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this figure, ERP-SCDS has the lowest coefficient of variation of cluster size as compared toLEACH, HEED, and Hausdorff because of the fact that the balanced distribution of virtualpoints makes more balanced distribution of cluster members. Compared to LEACH, ERP-SCDS has about 34% of decrease in this performance metric since advertisement messagesin LEACH are broadcasted to the entire network and the cluster head selection is done basedon the probability, giving the possibility to form unbalanced clusters. Compared to HEED,ERP-SCDS gains 36% or so of decrease in this performance metric because there is possi-bility in HEED to construct unbalanced clusters with a similar reasoning like that given forthe ratio of single-node clusters. As for Hausdorff, it performs worst since a sensor node inHausdorff is perhaps rejected by clusters around it, forcing it to declare itself as a clusterhead.

4.2.3 Clustering Energy Dissipation Per Round

Energy used for clustering can be used to illustrate the efficiency of the clustering mechanismused in WSN routing protocols. Note that the simulation scenario used for this purpose isthe small-area network. As shown in Table 2, HEED shows the worst performance in termsof clustering energy dissipation per round because of the re-clustering mechanism and ERP-SCDS outperforms the other protocols because of the nature of static clustering. Comparedto HEED, about 96% lower clustering energy dissipation per round is gained by ERP-SCDS.As mentioned previously, HEED consumes much more energy in the re-clustering process

Table 2 Clustering energydissipation (J) per round

# Of nodes LEACH HEED Hausdorff ERP-SCDS

300 0.05 0.22 0.028 0.013

350 0.06 0.29 0.032 0.015

400 0.07 0.37 0.035 0.017

450 0.08 0.46 0.044 0.020

500 0.09 0.57 0.044 0.022

550 0.10 0.68 0.047 0.025

600 0.11 0.81 0.057 0.027

650 0.12 0.94 0.058 0.029

700 0.13 1.09 0.060 0.031

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Table 3 Total energy dissipation(J) per round

# of nodes LEACH HEED Hausdorff ERP-SCDS

300 0.25 0.43 0.24 0.19

350 0.29 0.53 0.28 0.22

400 0.34 0.65 0.32 0.25

450 0.38 0.78 0.40 0.29

500 0.42 0.93 0.41 0.32

550 0.47 1.07 0.45 0.36

600 0.52 1.26 0.54 0.39

650 0.56 1.43 0.56 0.42

700 0.60 1.61 0.58 0.45

Table 4 Route discovery energydissipation (J) per round

Number of nodes HEED Hausdorff ERP-SCDS

300 0.012 0.008 0.003

350 0.012 0.009 0.003

400 0.012 0.011 0.003

450 0.012 0.011 0.003

500 0.011 0.012 0.003

550 0.011 0.013 0.003

600 0.011 0.015 0.003

650 0.011 0.016 0.003

700 0.011 0.017 0.003

which needs to select temporary cluster heads along with several cluster head advertise-ment messages to assure the final cluster heads and stable clusters in its main clusteringphase. ERP-SCDS consumes 76% or so lower energy in clustering per round as compared toLEACH since much energy for broadcasting advertisement messages to the entire network iswasted by LEACH. As for Hausdorff, it performs better than LEACH and HEED but worsethan ERP-SCDS. Compared to Hausdorff, ERP-SCDS still gets about 51% lower clusteringenergy dissipation per round since clustering in Hausdorff uses more control messages. Ofcourse, one may have the previous performance comparison based on the ratio of clusteringenergy dissipation per round which is the ratio of the clustering energy dissipation per roundto the total energy dissipation per round (shown in Table 3 for reference). For brevity, weomit this part of comparison.

4.2.4 Route Discovery Energy Dissipation Per Round

Despite the fact that clustering and the steady-state phase consume most of the energy, routediscovery energy dissipation still shows the efficiency of the route discovery mechanism usedin WSN routing protocols for HEED, Hausdorff, and ERP-SCDS (since LEACH uses directtransmission from cluster heads to the sink, this performance metric excludes it). Again,the small-area network scenario is used here. Table 4 shows the route discovery energydissipation per round and reveals that ERP-SCDS has the lowest route discovery energy dis-sipation per round since it employs the simple relay node selection mechanism instead of a

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Fig. 10 Number of hops ininter-cluster routing

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complicated and high energy-consumed route discovery algorithm. Although the distributedDijkstra algorithm with estimated energy consumption and remaining energy parameters forroute discovery is employed by Hausdorff, it still consumes more energy than ERP-SCDSwhose route discovery energy dissipation per round is about 77% lower than that of Haus-dorff. For HEED, the highest route discovery energy dissipation per round is observed amongthe three protocols because it executes the route discovery mechanism for each cluster headto the sink. Moreover, the route discovery algorithm DSR used by HEED is mainly designedfor the ad hoc network without energy and cost consideration [17].

4.2.5 Number of Hops in Inter-Cluster Routing

The number of hops in inter-cluster routing can be used to examine the efficiency of multi-hoprouting in the WSN routing protocol as well. Similarly, only HEED, Hausdorff, and ERP-SCDS are considered because LEACH uses direct transmission for routing from cluster headsto the sink. To get this performance metric, the medium-area network scenario is employed.Illustrated by Fig. 10, ERP-SCDS shows the lowest number of hops in inter-cluster rout-ing since it employs two conditions in selecting relay nodes to ensure the optimal distanceand right direction to the sink, causing the lowest number of hops needed to reach the sink.ERP-SCDS has about 78% fewer hops than HEED since no distance consideration is takenby HEED in the route discovery algorithm. For Hausdorff, a bit more hops are required ascompared to ERP-SCDS since Hausdorff considers power consumption in its route discoveryalgorithm.

4.2.6 Network Lifetime

The network lifetime reflects the energy efficiency of a routing protocol. As one can seefrom Fig. 11a, ERP-SCDS outperforms the other protocols under the small-area networkscenario. In a small-area network, multi-hop routing seems to be unnecessary since thedistance between the cluster head and the sink is shorter than the distance threshold d0.Hence, the free space energy model is used for data transmission, leading to lower energydissipation and a long network lifetime. One can note that HEED and Hausdorff gain ashorter network lifetime as compared to those of ERP-SCDS and LEACH. More specif-ically, ERP-SCDS can outperform HEED and Hausdorff by 153 and 28%, respectively.Compared to LEACH, ERP-SCDS still shows 16% or so of improvement in the networklifetime since more balanced clusters are formed in ERP-SCDS. In Fig. 11b, ERP-SCDS

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Fig. 11 Network lifetime. a Small-area network. b Medium-area network. c Under different side lengths ofthe network area

exhibits a much better network lifetime than the other protocols under a medium-area net-work. For LEACH, about 87% of reduction in the network lifetime as compared to thatof ERP-SCDS is observed since each cluster head in LEACH broadcasts an advertisementmessage to the entire network. Moreover, there is no routing algorithm for LEACH to for-ward the data from cluster heads to the sink. As for HEED, it shows 73% or so of reductionin the network lifetime than that of ERP-SCDS because of no energy consideration in themulti-hop routing and too much energy wastage in the re-clustering process and unbal-anced cluster sizes. Finally, the network lifetime for Hausdorff is about 64% shorter thanthat of ERP-SCDS since Hausdorff produces many single-node clusters, seriously causingunbalanced cluster sizes. To have a detailed study on the impact caused by the distancebetween the sink and the network, which is defined as the shortest distance from the sinkto the network perimeter, on the network lifetime, the network lifetime under different sidelengths of the network area, which is two times of the distance between the sink and thenetwork, is further shown in Fig. 11c. Here, the node density is fixed at 0.01 (nodes persquare meter). Fig. 11c shows that the network lifetime of ERP-SCDS is much better thanthose of the other protocols except the case when the distance between the sink and thenetwork is fixed at 50 (m). Under such a case, ERP-SCDS gets 23% (22%) of decline innetwork lifetime as compared to LEACH (Hausdorff) and 37% of improvement in net-work lifetime as compared to HEED. As the distance between the sink and the network

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goes up, the percentages of improvement in network lifetime gained by ERP-SCDS overLEACH, Hausdorff, and HEED fall within [12%, 165%], [84%, 406%], [124%, 479%],respectively.

5 Conclusions

An energy-efficient routing protocol, i.e., ERP-SCDS, has been proposed in this paperby adopting the architecture of static clusters but dynamic structures. Introducing well-distributed virtual points to the network, evenly distributed clusters can be expected, reachingbalanced cluster sizes. Besides, a cluster head selection mechanism with energy and distanceconsideration in ERP-SCDS ensures that sensor nodes with higher remaining energy andnearer the center of the cluster are selected to prolong the network lifetime. Moreover, asimple and energy-efficient relay node selection mechanism instead of a complicated andenergy-wasted route discovery algorithm is employed in ERP-SCDS. With these designideas, ERP-SCDS significantly outperforms LEACH, HEED, and Hausdorff in terms ofbalanced cluster size, clustering energy dissipation (per round), route discovery energy dis-sipation (per round), and network lifetime. With the superiority of ERP-SCDS over the otherthree closest protocols in the literature, ERP-SCDS is highly recommended for use in WSNsfor sure.

In fact, considering the impact caused by sensor node mobility may form another issue.Therefore, it serves as a possible future direction of this paper.

Acknowledgements This work was supported by the National Science Council (NSC), Taiwan underContracts NSC 96-2221-E-011-020-MY3 and NSC 97-2221-E-011-045-MY3.

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Author Biographies

Huei-Wen Ferng received the B.S. degree in electrical engineeringfrom the National Tsing Hwa University, Hsinchu, Taiwan, in 1993and the Ph.D. degree in electrical engineering from the NationalTaiwan University, Taipei, in 2000. He joined the Department of Com-puter Science and Information Engineering, National Taiwan Univer-sity of Science and Technology, Taipei, as an assistant professor inAugust 2001. Since February 2005, he has been an associate profes-sor. Funded by the Pan Wen-Yuan Foundation, Taiwan, he spent thesummer of 2003 visiting the Department of Electrical Engineering andComputer Science, University of Michigan, Ann Arbor. His researchinterests include wireless networks, mobile computing, high-speed net-works, design of fair scheduling, teletraffic modeling, queuing theory,and performance analysis. He was a recipient of the research awardfor young researchers from the Pan Wen-Yuan Foundation, Taiwan, in2003 and was a recipient of the Outstanding Young Electrical EngineerAward from the Chinese Institute of Electrical Engineering (CIEE),Taiwan, in 2008. He is a member of the IEEE.

Robby Tendean received his B.S. degree in information engineer-ing from the Surabaya Institute of Technology and Applied Science,Indonesia, in 2006 and M.S. degree in computer science and infor-mation engineering from the National Taiwan University of Scienceand Technology, Taipei, Taiwan, in 2009. His research interests includewireless sensor networks and performance analysis.

Arief Kurniawan received his B.S. and M.S. degrees in electricalengineering from the Institut Teknologi Sepuluh Nopember, Surabaya,Indonesia, in 1998 and 2006, respectively. Since 2009, he has beenpursuing his Ph.D. degree in computer science and information engi-neering at the National Taiwan University of Science and Technology,Taipei, Taiwan, where he is currently a Ph.D. candidate. His researchinterests include wireless sensor networks and protocol design.

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