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Wireless Pers Commun (2013) 72:1077–1104
DOI 10.1007/s11277-013-1056-5
A Review on Hierarchical Routing Protocols for Wireless
Sensor Networks
Zahariah Manap ·Borhanuddin Mohd Ali ·
Chee Kyun Ng ·Nor Kamariah Noordin ·Aduwati Sali
Published online: 21 February 2013
© Springer Science+Business Media New York 2013
Abstract The routing protocol for Wireless Sensor Networks (WSNs) is defined as the
manner of data dissemination from the network field (source) to the base station (destina-
tion). Based on the network topology, there are two types of routing protocols in WSNs, they
are namely flat routing protocols and hierarchical routing protocols. Hierarchical routing pro-
tocols (HRPs) are more energy efficient and scalable compared to flat routing protocols. This
paper discusses how topology management and network application influence the perfor-
mance of cluster-based and chain-based hierarchical networks. It reviews the basic features
of sensor connectivity issues such as power control in topology set-up, sleep/idle pairing
and data transmission control that are used in five common HRPs, and it also examines their
impact on the protocol performance. A good picture of their respective performances give an
indication how network applications, i.e whether reactive or proactive, and topology man-
agement i.e. whether centralized or distributed would determine the network performance.
Finally, from the ensuring discussion, it is shown that the chain-based HRPs guarantee a
longer network lifetime compared to cluster-based HRPs by three to five times.
Keywords Routing protocols ·Hierarchical ·Clustering ·Sensor networks
Z. Manap (B
)
Department of Telecommunication Engineering, Faculty of Electronics and Computer Engineering,
Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
e-mail: mz502@yahoo.com; zahariah@utem.edu.my
B. M. Ali ·C. K. Ng ·N. K. Noordin ·A. Sali
Department of Computer and Communication Systems Engineering, Faculty of Engineering,
Universiti Putra Malaysia, Selangor, Malaysia
e-mail: borhan@eng.upm.edu.my
C. K. Ng
v e-mail: mpnck@eng.upm.edu.my
N. K. Noordin
e-mail: nknordin@eng.upm.edu.my
A. Sali
e-mail: aduwati@eng.upm.edu.my
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1078 Z. Manap et al.
1 Introduction
Over the last two decades, a new class of short-range wireless communication networks has
appeared to facilitate the needs of monitoring and control of physical environment especially
in remote and unreachable areas. These networks are called wireless sensor networks (WSNs).
The nature of WSNs is some kind of a personal area network (PAN) which interconnects a
number of devices wirelessly. The physical and MAC layers of WSNs comply with the IEEE
802.15.4 standard that is uniquely designed for low-rate wireless personal area networks
(LR-WPAN) [1,2].
Wireless Sensor Network becomes a new technology trend when human, machines and
the environment are being integrated autonomously. This is made possible by advances in
processor, memory and microelectronics devices which allow the sensing and computing
elements to be integrated together in small devices to perform the programmed tasks [3–5].
These networks are generally used in disaster relief applications, battlefield surveillance, envi-
ronmental control, habitat monitoring, intelligent buildings, facility management, machine
surveillance, preventive maintenance, precision agriculture, medicines and healthcare, and
transport and logistics.
The IEEE 802.15.4 standard allows WSNs to operate at low data rates, low complexity
and low cost, suitable for applications which require random deployment, long operational
duration and mass of sensor motes quantity. This standard supports star and peer-to-peer
network topologies which are two of the most favorite topologies in WSNs. In terms of
energy conservation, the beacon-enabled super frame of IEEE 802.15.4 enables WSNs to
be working in energy save mode which is either sleep or idle mode [1]. IEEE 802.15.4
guarantees a very low hop delay [2], allowing the data transmission to be executed in a
multi-hop architecture and avoiding fast energy drainage due to long range data propagation.
The routing protocol for WSNs is defined as the manner of data dissemination from the
network field (source) to the base station (BS). This protocol takes place in the network
layer of the protocol stack. Many routing protocols have been proposed starting with the
two simplest methods called flooding and gossiping [6,7]. Flooding applies simple multicast
data dissemination whereby the data is forwarded in a broadcast manner until it reaches the
BS. The routing approach in flooding is simple with no routing table needed and has low
transmission delay. However, this protocol drains the energy of the nodes drastically when
all nodes take part in handling a single data packet, thus shortening the network lifetime.
The most critical problem introduced by flooding is data implosion, where a node receives
multiple copies of the same data. Meanwhile the gossiping tries to eliminate data implosion
problem by randomly selecting a neighbor to pass the data with the hope that the data will
eventually arrive at the BS. However, this approach might increase the transmission delay
especially in a large network. These two protocols have shown that energy efficiency and
transmission delay are two important factors to be considered in designing routing protocols
for WSNs.
A more sophisticated approach known as data-centric routing protocol [5] has been intro-
duced to avoid data implosion problem and reduce transmission delay. In these protocols,
the route for data transmission is determined prior to the real data transmission from source
to destination. The BS explores the network by broadcasting small size query packets to the
nodes. The query packets specify the attributes of the required data. The node that owns
the required data will send a feedback to the BS. Then, the most suitable route is chosen to
disseminate the real data to the BS. The weakness of data-centric routing protocols is that
they are only suitable for on-demand data applications such as event detection or intrusion
detection. If this type of routing protocols is applied in monitoring applications, the accuracy
123
Hierarchical Routing Protocols for WSNs 1079
Sensor nodes Sensor field
Internet
User
d2
d1
S1
BS
S2
S3
d3
S4d4d5
S5
Fig. 1 The basic network architecture of a WSN with direct transmission routing protocol
of the data is somewhat doubtful since the data from a single node does not represent the
real condition of the environment. For a small scale network, data-centric routing protocols
offer an acceptable transmission delay. However, in large scale networks, propagation delay
caused by the negotiation process and a very long multi-hop path increases the transmission
delay, thus limiting the scalability of the WSNs.
To overcome the latency problem and promote scalability,some HRPs have been proposed.
They are namely Low-energy Adaptive Clustering Hierarchy (LEACH) [8], Threshold Sen-
sitive Energy Efficient Sensor Network (TEEN) [9], Adaptive Periodic TEEN (APTEEN)
[10], Power-efficient Gathering in Sensor Information Systems (PEGASIS) [11]andPower-
efficient Data Gathering and Aggregation Protocol-power Aware (PEDAP-PA) [12]. This
paper reviews the mechanism and design strategies that influence the performance of these
five HRPs.
The subsequent sections are arranged as follows; the next section gives an overview of
WSNs, followed by an introduction of routing protocols for WSNs in Sect. 3. Section 4
gives related survey works while Sect. 5briefly describes general features of HRPs. Section
6describes the mechanism of each of the five HRPs. The performance of the five HRPs is
evaluated in Sect. 7. Finally, this paper is concluded in Sect. 8.
2 Overview of WSNs
WSN is a type of short range wireless communications network that is ideal for monitoring
and detection applications. Two main categories of WSN applications are space monitoring
such as for the environment, and for event detection such as against intrusion or for wildlife
monitoring. All these generally operate in hostile or unreachable fields. This network provides
the information about the activities in the field to the remote user via the internet. Figure 1
shows the basic architecture of a WSN. It consists of a large number of sensor nodes that
are located randomly to cover a network field, a gateway (or BS), the internet and the user.
Basically, the function of the sensor nodes is to sense the changes or activities around their
vicinity and route the data to a BS, whilst the BS integrates the WSN and the internet.
Every sensor node in a WSN is able to interact among themselves as well as with their
environment by sensing and transmitting physical parameters [6]. Each of the nodes consists
of a memory, communication device, controller, sensor and power supply as shown in Fig. 2.
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1080 Z. Manap et al.
Sensor
Communication
Device
Memory
Controller
Power Supply
Fig. 2 Basic components of a sensor node [6]
The function of the controller is to perform data computations before sending it to the next
node. The memory temporarily stores the sensed/aggregated data and other parameters that
are defined in the protocol. The communication device is the radio part which enables com-
munication between nodes and the BS. This device consists of a transmitter and a receiver or a
combined transceiver. The RF-based transceivers with a typical communication frequencies
between 433 MHz and 2.4 GHz [6] are normally used as the transceiver for the sensor nodes.
Most sensor nodes in the market are powered by a very limited voltage source such as an AA
battery or coin cell.
The energy from the power supply on the sensor node board is consumed by three main
components; communication device, controller and sensor. Each of the sensor node’s com-
ponents must consume the energy wisely to optimize the energy conservation. For example,
the communication device is used in data communication regarding how the data should be
routed to the destination. Hence, it is important to ensure that the routing protocol in sensor
networks is energy efficient. The next part, the controller is basically used for data computa-
tion. This part deals with the complexity of algorithm and data overhead. Low computational
complexity and data overhead respectively help to minimize energy consumptions. However,
computation consumes less energy than communications. Therefore, complex computation
is acceptable as long as it can save more energy in data routing.
Since the major portion of the energy is consumed for data communications, the main issue
in WSNs is an efficient way to route the data from sensor nodes to the BS while maintaining
acceptable latency. The network shown in Fig. 1uses the simplest data routing protocol
known as direct transmission (DT). In DT, data is directly transmitted from each and every
node to the BS. To explain the weakness of DT routing protocol, five nodes are designated
in Fig. 1as S1,S2,S3,S4and S5and the distance from each corresponding node to the BS
as d1,d2,d3,d4and d5respectively. The energy consumed by each node to transmit k bits
of data within distance dis [8]:
ETx (k,d)=Eelec ∗k+amp ∗k∗d2(1)
where Eelec is the energy used to run the transmitter circuitry in nJ/bit and amp is the energy
used by transmit amplifier to achieve acceptable signal to noise ratio (SNR) in pJ/bit/m2.The
energy consumed by each node to receive kbits of data is [8]:
ERx (k)=Eelec ∗k(2)
From (1)and(2), energy is needed not only to transmit but also to receive the data. However,
higher energy is expended for data transmission than that of data reception. The energy
consumption for data transmission relies mainly on the distance squared whereby the farther
the node from the BS, the higher the energy is consumed to transmit the data. In Fig. 1,S2,S3
123
Hierarchical Routing Protocols for WSNs 1081
Internet
User
S1
BS
S2
S3
S4
S5
Fig. 3 The multi-hop architecture of a WSN
and S4consume much higher energy compared to S1and S5since the data transmission from
these nodes involves long distances. These nodes will die out much quicker than S1and S5,
leaving the area that are covered by the nodes unattended. Therefore, in WSNs environment
where the sensor nodes are scattered all over the field, DT is only suitable for nodes which are
close to the BS. As an alternative in order to reduce energy consumption in data transmission,
the data is made to travel in short distances as shown in Fig. 3. In this multi-hop architecture,
neighboring nodes perform cooperative tasks by passing the data from one node to another
until the data reaches the BS.
The simplest multi-hop routing protocol is minimum transmission energy (MTE) [13]. In
this protocol, data is routed to the BS through intermediate nodes which act as routers. An
example of protocol that proposes MTE approach is found in [13]. In that paper, the routers
are chosen so that data is routed to the BS with the minimum energy cost. The relationship
between energy cost of MTE and DT for transmitting data packet from a sensor node to the
BS is given by:
Ei→r→BS <Ei→BS (3)
where the left side expression denotes the energy cost from node ito the BS through a router
rand the right side expression denotes the energy cost for DT. To analyze this expression,
we put a weight to represent the distances between nodes as in Fig. 4. Considering the d2in
(1), if S3sends the data directly to the BS, d2equals 81. However, if S1and S2are chosen as
the routers, d2is only equal to 42.
In a multi-hop architecture, the total path seems to be longer than that in DT. However,
when the propagation model of the environment is taken into account, several short-distance
transmissions are better than a single high energy transmission. Hence, it can be said that
by reducing the transmission distances it can improve the energy efficiency. Because of
this, many multi-hop data routing protocols have been proposed such as flooding, gossiping
and data-centric routing protocols as described earlier. However, due to some weaknesses
associated with these protocols, hierarchical architecture is appended on top of multi-hop
architecture to maintain the network performance over a larger field area.
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1082 Z. Manap et al.
Internet
User
9
S1
BS
S3
S2
5 1
4
Fig. 4 Example of MTE routing path
Tab l e 1 Comparison between proactive and reactive networks
Description Proactive networks Reactive networks
Type of applications Periodic monitoring Critical monitoring/event detection
Mode of sensing circuitry Periodically switch on Always on
Mode of communication device Periodically switch on Always on
Data delivery Periodic/continuous Event driven / On demand
3 Routing Protocols in WSNs
The routing protocol depends on the user’s interest or network applications. Based on the
network applications, routing in WSNs are of two types; proactive and reactive. There is
also a possibility to combine both applications in one network called hybrid network. In a
proactive network, the data is collected from the whole network or several regions of the
network field. The idea is to update the BS with the current information of the network field’s
condition, for example on the current temperature or humidity in the monitored area. The
process of data gathering in proactive network involves all or almost all sensor nodes, where
the data sensed by individual node is accounted to determine the current condition. The report
will be sent to the BS periodically regardless of whether there are any changes occurring in
the field. This network is normally used for monitoring applications. Conversely, in a reactive
network, the data is sent to the BS based on user’s request or if there is any critical change
in the network field. Critical change is defined as an event which gives a signal that exceeds
the predetermined threshold value. Only dedicated nodes or the nodes which are close to the
event will take part in data gathering and dissemination. This kind of network is suitable for
critical event detection and monitoring. The differences of the two networks are summarized
in Table 1.
In WSNs, the sensor nodes can be deployed either in a deterministic manner, where they
are placed manually in the network area or by using random deployment [7]. Deterministic
deployment is easier and straight forward since the nodes can be deliberately arranged to
distribute evenly in network area. The nodes can be located so that they are not interfering
with each other or the interference and overlapped coverage between neighboring nodes
is minimized. This eases the protocol stacks implementation. Nonetheless, deterministic
deployment is only applicable for reachable areas such as for local surveillance monitoring,
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Hierarchical Routing Protocols for WSNs 1083
Fig. 5 The implosion problem in
flooding 25
74
8
6
1
3
Sink
parking lot monitoring and patients monitoring applications. For hostile and unreachable
areas such as in the forest or volcanic field, the only option is to randomly deploy the sensor
nodes in the field of interest. Random deployment yields the location of the nodes to be
pervasive all over the field. The nodes can be sparse in one region whilst dense in another or
there might be some regions which are not covered by the nodes. Hence, a large quantity of
the sensor nodes need to be deployed to ensure that every inch of the field of interest falls
under sensing coverage. Yet random and densely located, the most challenging task of the
implemented protocols is to ensure that the sensor nodes are capable of self-organizing.
In dense and random nodes topology, the probabilities for two or more nodes to sense the
activity in the same area is very high. The closer nodes will surely sense the same activity and
send the same data to the BS. To reduce data redundancy, some routing protocols such as the
ones proposed in [14–16] make use of the capability of nodes to adjust the transmitted power
adaptively depending on the algorithm needs. In a proactive network, the data sensed by one
node may represent a certain region, thus, sending the same data from the same region is a
waste of energy. To conserve energy, closer nodes which are sensing the same area are paired
[10] so that they take turn in doing the sensing task. Likewise, in a reactive network, all nodes
which are in the event’s vicinity must work together [17] to ensure the correct and accurate
data is reported to the BS. Implementing idle/sleep pairing may ignore some important data,
thus reducing the accuracy. Hence, the routing protocol must be designed by considering
the type of applications and the user’s requirements to ensure that the performance of the
network can be optimized.
Since the transmission mode of a sensor node is in broadcast manner [18], simple multi-
hop routing protocol such as flooding may cause implosion [19] problem as depicted in Fig. 5.
Implosion is a phenomenon where a node receives several copies of the same data from two
or more neighbors during the data dissemination process. As depicted in Fig. 5, suppose node
1 has the data to be transmitted to the sink. Node 1 transmits the sensed data to node 2, node
3 and node 4 since it considers those three nodes as its neighbors. Node 2 transmits the same
data to node 4 and node 5 whilst node 3 also transmits the same data to node 4 and node 6.
Thus, node 4 receives three copies of the same data from node 1, node 2 and node 3. This
situation continues until the sensed data successfully reaches the destination with so many
nodes receiving several copies of the same data. Hence, along the way, there are so many
unnecessary transmissions that occur.
Although flooding does not require routing algorithm and topology maintenance, the
implosion problem precludes this protocol as energy efficient [6,20]. For power constraint
systems like WSNs, this type of data routing is not acceptable since a lot of energy is wasted
for unnecessary data transmissions.
To overcome implosion, data fusion or data aggregation approach is introduced in data-
centric routing protocols. Some examples of data-centric routing protocols are sensor protocol
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for information via negotiation (SPIN) [19], directed diffusion [3], energy-aware routing [21],
rumor routing [22] and active query forwarding in sensor networks (ACQUIRE) [23]. Gen-
erally, data-centric routing protocols imply the establishment of two-way communications
between neighboring nodes by sending requests and responds to ensure only the data are
sent to the nodes which require it. This method may overcome duplication in data transmis-
sion thus conserve overall network energy. Interestingly, the use of neighbor’s information
in these methods helps to reduce the need for global information such as the nodes’ address
or IP. All nodes need to know certain information of their neighbors such as the current
energy level, energy requirement and the distance between them in order for them to pass the
data.
However, data-centric routing protocols only work well in small scale sensor networks.
In large scale networks, they may cause transmission delay. This is because as the number of
nodes increases, the data is propagated through long multi-hop paths. In addition, the data-
centric approaches need to deal with request and advertisement packet transmissions which
are considered as the communication overheads. As a solution, HRP is introduced with the
objective of reducing the transmissions distances between nodes and BS.
4 Related Survey Works
Routing protocol issues have received more attentions in WSNs with the emerging of various
algorithms to fulfill the requirement of high energy efficiency. This has inspired researchers to
survey the routing protocol algorithms, and highlight their features, mechanisms, techniques
and performance to give the overallpicture of the issue. In this section we distinguish our paper
from the previous surveys and state our scope of review. To the best of authors’ knowledge,
there are six other surveys reviewing the routing protocols in WSN [7,24–28]. The authors
in [7,24–26] reviewed the routing protocols in WSNs in general while the other two surveys
in [27,28] focused on the cluster-based routing protocols.
Akkaya and Younis in [7] gave a broad perspective on three main categories of routing
protocols for WSNs including hierarchical protocols. The authors presented the classification
for various approaches pursued in each category of protocols. That survey is very compre-
hensive and gives a good introduction to routing protocols in WSNs. Al-Karaki and Kamal
[24] addressed the routing challenges and design issues in WSNs. The authors also described
and categorised the different approaches for data routing in WSNs. However, both surveys of
[7]and[24] did not show the simulation based performance results of the routing protocols.
Villalba et al. [25] and Singh et al. [26] highlighted almost the same issues as in [7]and[24],
except that the authors in [25] propose an optimization technique in routing protocol called
Simple Hierarchical Routing Protocol (SHRP).
Abbasi and Younis in [27] gave a broad perspective on clustering algorithms by presenting
a taxonomy and general classification of several published clustering schemes. The compari-
son of clustering algorithms was presented based on certain features of clustering such as con-
vergence rate, cluster stability, cluster overlapping, location awareness and mobility. Singh et
al. in [28] reviewed the mechanisms and features of a few published cluster-based routing pro-
tocols such as energy-efficient hierarchical clustering (EEHC), LEACH, enhanced-LEACH
(E-LEACH), LEACH-centralized (LEACH-C), multi-hop LEACH (M-LEACH), LEACH
with fixed cluster (LEACH-F), PEGASIS, hierarchical PEGASIS (H-PEGASIS), Hybrid,
Energy-Efficient Distributed Clustering (HEED), TEEN and APTEEN. However, there are
no comparisons in the aspect of performance evaluations presented in both surveys [27,28]. In
[28], the authors did not discuss the strengths and weaknesses of each protocol over another.
123
Hierarchical Routing Protocols for WSNs 1085
The significance of this paper compared with the above six surveys is that it focuses
on the basic features of sensor connectivity issues such as power control in topology set-
up, sleep/idle pairing and data transmission control used in the earliest cluster-based and
chain-based HRPs. Another element that distinguishes this paper is that it compares the
performance of the said HRPs in terms of network lifetime. The significant impact, strengths
and weaknesses of the strategies and approaches used in each of the HRPs are also described
in this paper. Furthermore, in analyzing the network lifetime, the performance of HRPs are
compared based on two realistic metrics, which are the First Node Dies (FND) and Half of
the Nodes Alive (HNA) which are introduced in [38] in addition to the last node dies (LND).
5 Hierarchical Routing Protocols (HRPs)
HRPs are found to be very energy efficient when this type of routing protocol was first
introduced in [8]. The special feature of this approach is that it provides self-organization
capabilities to allow large scale network deployment. Basically, in a hierarchical architecture,
some nodes take responsibility to perform high energy transmission while the rest perform
normal task. Power-aware algorithm is used to select eligible high energy nodes to relay
the data from normal nodes to the BS. HRPs can be categorized into two types based on the
topology management, they are cluster-based HRPs [8–10] and chain-based HRPs [11,12]. In
cluster-based HRPs, sensor nodes are grouped into clusters and each of these clusters are led
by one of the nodes, called the cluster head (CH). A CH acts as an intermediate node between
cluster members and the BS. In chain-based HRPs, all nodes in the field are connected in a
chain structure. Then, the most energy healthy node is chosen as the chain leader to mediate
the data transmission from normal nodes and the BS. In both types of HRPs, there are other
design features applied to further enhance the performance such as data fusion, threshold
values set up, and sleep/idle pairing.
In terms of operation, a HRP consists of two phases. The first phase is the set-up phase,
when the sensor nodes are organized to form hierarchical architecture either in a cluster-
based or chain-based manner. The second phase is the steady state phase, when data are
routed from sensor nodes to the BS. The hierarchical architecture of a cluster-based or chain-
based HRP can be set up by using distributed algorithm or centralized algorithm. In [8–11], a
stochastic approach is used to form clusters and chains. This is a fully self-organized approach
where no global knowledge is needed local interactions are needed between nodes. However,
random approach is deemed not capable of producing optimal architecture. Thus, many newer
protocols use centralized approach where the hierarchical architecture is determined by the
BS. For example, LEACH-C [20] and APTEEN [10] use simulated annealing technique to
form optimal clusters. The simulation results from both works show that optimal clusters can
reduce data packet loss, thus increasing the throughput and data accuracy [18]. The drawback
of applying centralized algorithm is it limits the self-organization feature of the WSNs.
However, the great performance of centralized algorithm proves that optimal hierarchical
architecture is essential to guarantee good performance of a WSN. To maintain both self-
organization capability and energy efficiency, HRPs must be accomplished with a distributed
topology control algorithm that is capable of providing optimal architecture.
There are a large number of hierarchical routing protocols (HRPs) for WSNs that has been
proposed in the literature. Most of the HRPs reference to LEACH [8] which is the pioneer
work on HRP, as the benchmark to evaluate the performance of their respective protocols.
Although LEACH was proposed more than 10 years ago, its tremendous performance in
terms of energy conservation has made it a significant reference in almost all later HRPs such
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1086 Z. Manap et al.
Tab l e 2 The parameters used in
the simulations [18]Parameter Value
Network dimension 100 m ×100 m
BS location 75 m from the field
No. of nodes 100
Initial energy 2 J
Eelec 50 nJ/bit
amp 10 pJ/bit/m2
two−ray−ground 0.0013 pJ/bit/m4
do87 m
EDA 5 nJ/bit/signal
Data size 500 bytes
as in [29,30,15,31–36]. Therefore, in this paper, we choose LEACH as the first protocol to be
reviewed. The objective is to compare two types of topology management in HRPs, namely
cluster-based and chain-based on the fixed WSNs. TEEN [9] and APTEEN [10] are among
the earliest cluster-based HRPs that implement different strategies to overcome unnecessary
transmissions in LEACH while maintaining some basic features of LEACH. While PEGASIS
[11] and PEDAP-PA [12] are among the earliest HRPs that implement chained topology. The
five basic HRPs are reviewed in order to show the advantages and tradeoffs of the techniques
and strategies proposed in each of the HRPs as described in the conclusion. A good picture
of their respective performances give an indication how network applications, i.e whether
reactive or proactive, and topology management, i.e. whether centralized or distributed affect
the network performance.
6 Mechanism and Design Features of HRPs
Generally, HRPs are designed to provide scalability to WSNs while maintaining high energy
efficiency. This section discusses and evaluates the mechanism and strategies used in five
HRPs which are LEACH [8], TEEN [9], APTEEN [10], PEGASIS [11] and PEDAP-PA [12].
6.1 Simulation Set-up
The simulations for all five HRPs were done in the network simulator 2 (NS-2). All HRPs
use the energy model as described by (1)and(2) and the parameters used in the simulation
are listed in Table 2. The parameters are based on the simulations done in [18]. We simulate
all five HRPs on a 100 m ×100 m network field with 100 sensor nodes that are randomly
deployed. The BS is located 75m from the sensor field. All nodes are heterogeneous with
initial energy supply of 2J. The energy for the radio electronics, Eel ec is set to 50 nJ/bit. The
energy for radio transmitter, amp is set to 10 pJ/bit/m2for transmission distances less than
87m (do). For the transmission distances greater than 87m, the energy for radio transmitter,
two-ray-ground is set to 0.0013 pJ/bit/m4. The energy consumed for data aggregation, EDA
is set to 5 nJ/bit/signal and the size of each data message is 500 bytes.
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Hierarchical Routing Protocols for WSNs 1087
1 2 3 4 5 6 7 8 9 10 11
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
Number of clusters
Average energy dissipation per round (J)
Fig. 6 Average energy dissipated per number of clusters formed [8]
6.2 Low-Energy Adaptive Clustering Hierarchy (LEACH)
Heinzelman et al. [8] proposed LEACH routing protocol that considers fair load distribution
among all nodes in the network based on a probability function. The operation of LEACH is
broken into rounds. There are two phases involved in each round; the set-up phase, when the
clusters are formed, and the steady-state phase, when the data from all nodes are forwarded
to the BS. At the beginning of the set-up phase, several nodes will independently elect
themselves as a CH based on their current energy level and the threshold value, T(n)which
is determined by:
T(n)=P
1−P∗rmod 1
P,if n ∈G
0,otherwise (4)
where Pis the desired percentage of CH and Gis the set of nodes that have not become CHs
in the last 1/Prounds. The author reported that, for a 100-node WSN, the optimum number
of CHs is about 5 % of the total number of nodes. The result of the simulation from [8]is
redrawninFig.6. The graph shows that the cost is the lowest in terms of average energy
when the 100 nodes are clustered into three to five clusters.
The mechanism of LEACH is described in Fig. 7. For brevity, one BS and only three nodes
are shown in the figure. The BS will broadcast the query to the entire network. Assuming that
in that particular round, S1elects itself as a CH based on the probability function in (4), S1
will advertise its status to the non-CH nodes (S2and S3)by broadcasting an advertisement
(ADV) message. The ADV message contains the information of the CH, required attribute
(A)and report time (TR).S2and S3will choose to join the closest CH in order to minimize
the transmission distance. Since the ADV message from all CHs are sent at the same transmit
energy, the nodes will regard the CH from which it receives the strongest ADV message as
the closest one. If the ADV message received from S1is the strongest one, then S2and S3
join S1by sending an acknowledgement (ACK) message of its membership to S1.
After completing the set-up phase, the steady-state phase begins. S1creates and broadcasts
a time division multiple access (TDMA) schedule which allocates a transmit time slot to each
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BS_REQ
BS S1=CH S3
S2
CH_ADV
JOIN_ACK
JOIN_ACK
TDMA_SCHEDULE
DATA
DATA
COMPRESSED
DATA
Cluster formation
Data transmission
Fig. 7 Flow of mechanism in LEACH
of the cluster members. This schedule allows the cluster members to turn into sleep mode
and activate their transceiver circuit just before the allocated transmit slot. All non-CH nodes
are assumed to have data to transmit during their allocated slot. A CH has to wait until it
receives the data from all cluster members before compressing the data into one single packet
and sending it to the BS.
The packet transmission from CH to BS may involve a long range of distance which
consumes high energy. The CH-BS transmissions will rapidly reduce the energy of the CHs.
To avoid the CHs to die out quicker than other nodes, the role of CH is rotated among nodes
in every round. Once a node has become a CH, it will not be eligible to become a CH again
within the next 1/Prounds. This approach effectively avoids one node to experience frequent
long range transmissions within a short period. This approach balances the load among the
nodes, prolongs network lifetime and guarantees the quality of the network to be maintained
for a longer period.
Heinzelman et al. [8] compare the performance of LEACH with DT and MTE. From the
graphs in Fig. 8, it is obvious that LEACH protocol postpones the first node dies (FND) after
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Hierarchical Routing Protocols for WSNs 1089
0100 200 300 400 500 600 700
0
10
20
30
40
50
60
70
80
90
100
Time (rounds)
Number of sensors still alive
Direct Transmission
MTE
LEACH
Fig. 8 Comparison of the network lifetime for LEACH, DT and MTE
200 rounds whereas all other nodes die out after 600 rounds. This is two times better than
that of DT and four times better than MTE.
LEACH proves that reducing intra-cluster transmission distance may result in higher
energy efficiency. Despite its great performance over those conventional methods, LEACH
does not guarantee that the energy supplied is efficiently used to successfully send the data
to the BS. The random approach of cluster formation in LEACH does not guarantee optimal
clusters that are equal in size and well distributed in the network field. The clusters in LEACH
are formed based on the distance between normal nodes and the CH. Every node will choose
to join the closest CH. An example of clusters formed in LEACH is shown in Fig. 9.The
result was obtained by using Matlab. The network of 100 sensor nodes is grouped into six
clusters. The problem with this cluster formation is that the clusters formed are not uniform
in size. The unbalanced number of cluster members within all clusters can be seen where
there is one cluster consisting of only four nodes including the CH, while other clusters are
cramped with up to 25 nodes. This will lead to unequal length of TDMA frame generated in
each cluster.
The time-line for LEACH operation is shown in Fig. 10. Figure 10a shows the time-line
for a cluster showing the label number of cluster members of up to four nodes; in this scenario
more frames can be sent to the CH. Figure 10b shows the time-line for a cluster of 16 nodes,
where due to longer TDMA frames, only one or two frames can be sent to the CH.
To describe the drawback of non-uniform cluster size, a simple analysis has been done
towards the time-line of LEACH operation. For brevity, let’s assume that all nodes can
generate data at a uniform rate of 5packets/s. If the steady state duration for one round of
operation is set by the network to be 5s, the CH of a smaller cluster as shown in Fig. 10a can
allocate five guarantee time slot (GTS) to each of its cluster members. After the CH send
the first beacon, every node will send one packet during its GTS and go to sleep mode until
the next beacon is sent by the CH. After five beacons have been broadcasted by the CH, all
buffered data packets will be sent to the CH. In this simple analysis, all data packets generated
by the cluster members of smaller cluster are successfully sent to the CH. Therefore, no data
loss occurs in the smaller cluster. In contrast, in the larger cluster as shown in Fig. 10b,
only 20 data packets generated by the cluster members can be successfully delivered to the
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0 20 40 60 80 100
0
10
20
30
40
50
60
70
80
90
100
Fig. 9 The clusters formed in LEACH
(a)
(b)
Fig. 10 The time-line of LEACH operation for (a) a small cluster, and (b) a larger cluster
CH within the steady state time, yielding 75 % data loss. This shows that LEACH is only
suitable for low data traffic applications. For critical environmental monitoring applications
such as fire detection or volcanic field monitoring where the field area needs to be closely
monitored, high data traffic will be generated. Therefore, LEACH is not the best option.
It can be concluded that LEACH conserve more energy compared to conventional routing
protocols but it does not guarantee high data throughput at the BS [18].
Another weakness of LEACH is that it may introduce high data latency. The duration of
one operation round is set by the network based on the longest TDMA schedule in order
to assure that all nodes are able to send at least one packet. Non-uniform cluster size will
lead to long steady state duration that may defer the data transmission from CHs to the
BS. The latency will be very high and thus decreases the network efficiency. Therefore, it
can be said that although TDMA may absolutely prevent data collisions within clusters, it
may also introduce delay. Other than low data throughput and latency, LEACH also suffers
from high energy consumption due to unnecessary data transmissions. LEACH assumes all
nodes to have data to send in every round. If there are no significant changes in one node’s
vicinity throughout several consecutive rounds, the node might be transmitting the same
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Hierarchical Routing Protocols for WSNs 1091
data repeatedly. In this case, not only a lot of energy is wasted for the transmission, this
will also burden the CHs in accomplishing data processing. Another factor that may lead
to energy wastage due to unnecessary transmissions is overhearing. This happens when the
CHs broadcast their ADV message to recruit the cluster members. While the messages are
meant for closest nodes, the distant nodes might also receive the messages. Although these
messages are small in size, the accumulated effect of energy consumption for this purpose
reduces the energy efficiency, thus shortening the average network lifetime.
6.3 Threshold Sensitive Energy Efficient Sensor Network (TEEN)
Manjeshwar and Agrawal [9] proposed TEEN which is meant for reactive network such
as in critical events monitoring applications where the network responds immediately to
drastic changes in the environment. TEEN adopts the cluster formation as in LEACH with
some modifications to the radio model. During cluster change time in TEEN, there are two
other parameters broadcasted by the CHs to the cluster members in addition to Aand TR;
the hard threshold (HT)and the soft threshold ( ST). After clusters are successfully formed,
the operation of TEEN starts. All non-CH nodes are put in the idle mode. Whenever a node
senses any changes in its vicinity that reaches the value of HT, the transceiver of the node is
switched on and the sensed data is sent to the CH. At the same time, the node itself stores
the sensed data for future reference. The stored data is called as sensed value (SV). In the
subsequent rounds, the node will transmit its current sensed data if it differs from SV by
the amount of STor greater. The value of SV is refreshed every time transmission occurs.
By using this approach, the BS will only get the data when there are drastic changes in the
network field, thus the data transmission in TEEN is not a continuous process. This cuts a
great number of unnecessary data transmissions, which therefore conserves more energy.
The introduction of HTand STpositively impacts the network lifetime as shown in Fig. 11.
After 1,000 rounds, there are 50 nodes still alive in TEEN while in LEACH, the network
has died long before. This gives TEEN 163% longer network monitoring time than LEACH.
The FND occurs after 550 rounds, which is longer than in LEACH by a factor of 2.75.
As these protocols are implemented in monitoring applications where the network quality
must be maintained as long as possible, obviously TEEN can guarantee a better network
quality than LEACH does. TEEN proves that the introduction of HTand STmay eliminate
unnecessary nodes-CH and CH-BS transmissions by suppressing the below-threshold data.
The complexity of the computation due to the introduction of the thresholds is tolerable
since it consumes less energy compared to the amount of energy that can be saved. Hence,
the implementation of the threshold parameters is the core feature that promotes the good
performance of TEEN. The introduction of these parameters eliminates data redundancy,
giving a better network quality and prolonging the network lifetime.
The implementation of the threshold parameters in TEEN is intended to reduce unneces-
sary transmissions. However, the weakness of this technique is that it can leave the network
unattended. Since the network will only send a report to the BS whenever the nodes sense a
signal greater than HT, during the time in which the BS does not receive any data from the
network, it will consider that the environment has no significant changes or no critical events
in the network at the moment. However, if this condition continues for such a long period, the
network acts like a dead field where the BS has no idea about the network’s current condition.
The BS is not reported about the current energy level of the nodes whereas the nodes are
still sensing the environment which eventually will drain their energy and cause the nodes
to die out. Therefore, a mechanism that alerts the BS about the current condition must be
introduced to avoid the ‘silent network’ phenomenon.
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0 200 400 600 800 1000 1200
0
10
20
30
40
50
60
70
80
90
100
Time (rounds)
Number of nodes alive
LEACH
TEEN
Fig. 11 Comparison of the network lifetime between TEEN and LEACH
The energy efficiency of TEEN depends on the number of transmissions. This means
that, if there is a lot of data transmission in the network, the amount of energy consumed by
the whole network is high and vice versa. In the environment where the attribute of interest
changes rapidly, the nodes may frequently sense the critical changes and thus activate their
transmitter. The worst case might happen where all nodes sense a signal value that is greater
than HT. In this case, the operation of TEEN will be very similar to the operation of LEACH.
Since TEEN does not apply TDMA scheduling for node to CH transmissions, the possibility
of collisions is very high. On the other hand, in a low traffic environment such that the critical
changes occur in certain regions of the network field, the involvement of all nodes in cluster
formation is not necessary since not all nodes transmit the data to the CH. As TEEN is
targeted for reactive network applications, it is understood that only the nodes around the
event’s vicinity will detect any critical changes. The non-participating nodes will drain their
energy for handshaking process and have to pay for overhead cost, but do not contribute to
the network’s throughput. Therefore, cluster-based topology management is not suitable for
reactive network applications unless the clusters are formed only by several nodes which have
significant observation value over the changes. Several nodes should work cooperatively in
data gathering because the data sent by one single node is not that accurate due to the limited
capability of the sensor circuitry [5]. Thus, the network should set the appropriate size of the
cluster depending on the level of criticalness of the applications.
6.4 Adaptive Periodic TEEN (APTEEN)
Manjeshwar and Agrawal [10] proposed a protocol for hybrid networks to improve the
performance of TEEN, which they called APTEEN. This protocol facilitates both proactive
and reactive network applications. To avoid ‘silent network’ scenario, a new parameter called
count time (TC)is introduced to trigger periodic reports to the BS. By using this approach,
although there are no critical changes in the field, the network is forced to send a report
describing the current condition of the network in every TC.ThevalueofHT,STand TCcan
be adjusted by the user to balance between data accuracy and energy efficiency.
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Hierarchical Routing Protocols for WSNs 1093
The mechanism of APTEEN is divided into two phases as in LEACH and TEEN. How-
ever, to form optimal clusters, APTEEN uses a different approach of topology architecture
algorithm in the set-up phase. APTEEN takes advantage of the unlimited power supply that
the BS has. This protocol uses BS-centralized clustering algorithm for cluster formation state
as described in [37]. The optimum selection of CHs and cluster formation are executed by
the BS based on the information of current energy level provided by the network in the pre-
vious round. By using this technique, a well managed hierarchical architecture is established
whereby the location of CHs is evenly distributed over the network field and the number of
cluster members is uniform in all clusters.
At the steady state phase, APTEEN applies a sleep/idle pairing technique to reduce the
highly correlated data being sent by the nodes to the CH. This technique pairs every two close
nodes by using simulated annealing approach. Each of the nodes in pairs will alternately take
the role to handle queries and send messages from/to the CH. APTEEN uses a modified
version of TDMA frame whereby every sleeping node is allocated a shorter time slot and
every idle node is allocated a longer time slot. This allows sleeping nodes to be able to send
critical data at any time within the cluster time to back up the function of idle nodes. Each
sleep/idle pair may change their status at anytime within the cluster time. With this, the BS
will receive the critical data without delay.
Figure 12 shows the comparison of the network lifetime for APTEEN, TEEN and LEACH.
Generally, the performance of APTEEN lies between LEACH and TEEN. After 800 rounds,
there are 87 nodes still alive in APTEEN, 95 nodes still alive in TEEN while no node are alive
in LEACH. Based on half of nodes alive (HNA) metric described in [38], APTEEN achieves
78 % of the network lifetime compared to TEEN. However, this is much better than LEACH
as such it extends the network lifetime by more than 100%. Sleep/idle pairing does not seem
to help much in enhancing the performance since sleep nodes are still allocated with GTS
and able to send data. Although modified TDMA schedule used in this protocol is simple
(in terms of data computation) and collision-free MAC protocol [39], it causes high latency
due to sleep delay [40] introduced by sleeping nodes. In addition, sleeping nodes may also
introduce idle listening at the CH side if no drastic changes occur during their allocated slots.
Apart from that, the option for every node to change their operation mode at any time within
the cluster time, obviates the sleep/idle pairing process. Sleep/idle pairing can significantly
affect the network if a sleep node remains in that mode until it receives a wake up signal from
its partner while certain conditions hold. Nevertheless, APTEEN deserves appreciation for
the ability to accommodate both on demand and periodic queries. Therefore, APTEEN shows
a high versatility while maintaining acceptable performance in terms of energy consumption.
The BS-centralized cluster formation approach used in APTEEN is able to form optimal
clusters where the location of the clusters is well distributed in the network field and the
number of cluster members is uniform for all clusters. The optimal clusters can produce
high throughput, decrease delay and improve energy efficiency. However, it ignores the most
important feature that a WSN should possess, that is the capability of self-organization.
Applying centralized algorithm in routing protocol can nullify the self-organizing capability
in WSNs. However, it is not easy to implement self-organized topology control that can
provide optimal clusters, therefore, the results obtained by APTEEN at least may provide
some ideas of the design of a distributive topology control.
6.5 Power-Efficient Gathering in Sensor Information Systems (PEGASIS)
Lindsey and Raghavendra [11] proposed PEGASIS, a chain-based HRP for proactive net-
works. PEGASIS is a fully distributed HRP where it applies distributed algorithm during
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0 500 1000 1500 2000 2500
0
10
20
30
40
50
60
70
80
90
100
Time (rounds)
Number of nodes alive
APTEEN
TEEN
LEACH
Fig. 12 Comparison of the network lifetime for APTEEN, TEEN and LEACH
topology set-up phase and steady state phase. The main difference between PEGASIS and
previously discussed HRPs is the way it manages the sensor nodes topology. Instead of
grouping sensor nodes into clusters, PEGASIS applies chain forming at the beginning of
each round. Upon deployment, each node is assumed to know its neighbors. The greedy
algorithm is used to select a start node which will initiate the chain forming process. This
node is normally the farthest node from the BS. The start node will then select the closest
neighbor to which it will set a connection path. The second node that joins the chain then
will select its closest neighbor and set a new connection path. The process goes on until all
nodes have joined the chain.
An example of the PEGASIS chain is shown in Fig. 13. For brevity, let us assume that
there are only nine sensor nodes in the field. The nodes are denoted as S1,S2,S3,S4,S5,S6,
S7,S8and S9. To check the closest neighbor of each node, a weighted number is considered
to represent the distance between nodes. Assuming S8as the start node, a connection path is
set between S8and S6, indicated by a solid line. The connection path setup continues until
all nine nodes have joined the chain, with the condition that a node that has already joined
the chain cannot be revisited. After the chain is constructed, the most energy healthy node is
selected randomly as the chain leader. Let’s assume that S7is selected as the chain leader for
that particular round. S7will pass a token frame to the end node, S8. Upon receiving the token
frame, S8transmits its data and forward the token frame to S6which will fuse the received
data with its own data. Then S6transmits the fused data and forwards the token frame to S2.
The process of data fusion continues until all nodes transmit their data and the token frame
reaches S7. The direction of data flow is indicated by the arrows. After receiving the token,
S7will fuse the received data packet with its own data before sending a single data packet to
the BS.
The simulation result in Fig. 14 depicts the performance of PEGASIS in comparison to
LEACH and DT. Considering HNA metric, PEGASIS prolongs the network lifetime by up
to four times over that of LEACH and by up to 30 times better than DT. The first node
dies FND of PEGASIS occurs after 1,200 rounds, which is 1,000 rounds later than the FND
in LEACH. After the FND in PEGASIS, the number of nodes that are still alive decreases
gradually till 2,500 rounds of operation. Then the nodes rapidly die out until the last node
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Hierarchical Routing Protocols for WSNs 1095
S1
S3
S4
S6
S2
S7
S5
S9
S8
4
7
5
4
3
5
10
3
7
6
7
5
4
4
2
7
2
Fig. 13 Chain construction and data aggregation in PEGASIS
0 500 1000 1500 2000 2500 3000
0
10
20
30
40
50
60
70
80
90
100
Time (rounds)
Number of sensors still alive
PEGASIS
LEACH
Direct Transmission
Fig. 14 Comparison of network lifetime for PEGASIS, LEACH and DT
dies at 2,900 rounds. On the other hand, in LEACH the number of nodes that are still alive
decreases rapidly just after the FND. This shows that PEGASIS can maintain quality network
for a longer period of time.
The superior performance of PEGASIS is a result of the energy saving and load
balancing strategies. Chain topology reduces transmission distances and data receptions
per sensor node. In PEGASIS, a node only has to transmit and receive from its neigh-
bor nodes in the chain. In addition, PEGASIS performs distributed data fusion and
chain leader role’s rotation that balance the load distribution among the nodes. These
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strategies postpone the FND, thus guaranteeing a good quality for the network over a
longer time. Apart from eliminating overhead cost for cluster formation, chain form-
ing and other complementary strategies implemented in PEGASIS prolong the network
lifetime.
The drawback of PEGASIS is that it uses relative neighborhood graph (RNG) [6]to
determine the closest neighbor at each node. This algorithm is performed in a distributive
manner where the interaction occurs between neighboring nodes within one-hop distance.
However, since a node that has joined the chain cannot be revisited, the nodes that join the
chain at a later time may become very distant from each other as the chain grows. Therefore,
in large scale networks, the energy consumption are stretched exponentially since the energy
to transmit data is proportional to distance squared. The RNG algorithm may also result in
suboptimal paths where data from some nodes have to travel through several hops whereas
alternatively, there is a shorter path is available; Fig. 13 explains this problem. For example,
data from node S8has to travel through S6,S2,S1,S3,S4before finally reaching S7,giving
the distance weight of 26. If the algorithm is able to optimize the path by searching two hops
ahead, the data from S8can be routed to S6and S9before reaching S7, giving a distance
weight of 12. This reduces the distance by about 54 % which may exponentially reduce the
energy consumption.
Another weakness of PEGASIS is caused by the MAC protocol employed. The token
passing MAC protocol that is implemented during steady state phase requires the nodes to be
awake at all time in order not to miss the token frame. Thus, the transceiver circuit must be put
in either transmit state or receive state. The transmitting node which temporarily possesses
the token frame must be in the transmit state while the other nodes are in the receive state.
With this requirement, PEGASIS cannot implement energy saving strategy through power
management. It is even worse if PEGASIS is employed in large scale networks where the
chain will be very long. If only one token frame circulates in the logical ring, high amount
of energy will be wasted for idle listening. Moreover, if a large scale network is considered,
PEGASIS also results in high latency, and as the sensors are prone to failure due to energy
drainage or loss of connectivity, it is very difficult to maintain the logical token ring or bus.
If a node fails during the token passing process, the token frame will be lost and the logical
ring needs to be reconfigured.
6.6 Power Efficient Data gathering and Aggregation Protocol-Power Aware (PEDAP-PA)
Tan and Körpeoglu [12] propose PEDAP and PEDAP-PA. Both protocols are centralized
chain-based HRPs. Like the abovementioned four HRPs, the main objective of PEDAP is
also to prolong the network lifetime. The mechanism of PEDAP and PEDAP-PA is similar
to PEGASIS except for the algorithm used in the chain forming process. Instead of forming
the chain by using local interaction between nodes, PEDAP and PEDAP-PA apply the com-
putation of the link costs all over network before constructing the minimum spanning trees
(MST). The MST is the best tree that gives the shortest paths in interconnecting all nodes in
the field. The approach used in PEDAP and PEDAP-PA can reduce the overall link cost of
data communications. Taking the same scenario as in Fig. 13, an optimized chain is formed
by PEDAP and PEDAP-PA as shown in Fig. 15. The MST approach eliminates long distance
data transmissions by optimizing the link paths, this is done by changing the direction of data
propagation.
This paper focuses on PEDAP-PA since this version is more significant compared to its
original version, as far as energy conservation is concerned. As the quality of the network
decreases rapidly after the FND, PEDAP-PA implements a load balancing strategy to post-
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S1
S3
S4
S6
S2
S7
S5
S9
S8
4
7
5
4
3
5
10
3
7
6
7
5
4
4
2
7
2
Fig. 15 Possible chain construction and data aggregation in PEDAP and PEDAP-PA
pone the occurrence of FND. The computation of the link costs is done with reference to the
remaining energy of each node as follows:
Cij (K)=
2∗Eelec +k+amp ∗k∗d2
ij
ei(5)
Cvi(K)=Eelec ∗k+amp ∗k∗d2
ib
ei(6)
Equation (5) computes the link cost between two neighboring nodes iand jwhere Eel ec
and amp are mentioned in Table 2,kis the data size and d2
ij is the distance between node
iand node j. Equation (6) computes the link cost between node iand the BS where d2
ib is
the distance between node iand the BS. The term eirepresents the normalized value of the
remaining energy of node iwith respect to the maximum possible energy in the battery. By
using (5), nodes with lesser residual energy will be included later in the chain so that they
receive less number of messages. Figure 16 shows the comparison of the network lifetime for
PEDAP-PA, PEGASIS and LEACH. Considering HNA metric, it is clear that the PEDAP-PA
outperforms PEGASIS by 4 % and about 3 times better than LEACH. The FND in LEACH
occurs after 200 rounds and in PEGASIS after 1200 rounds. With the load balancing strategy
implemented in PEDAP-PA, the FND happens after 2,100 rounds. This means that PEDAP-
PA guarantees full network coverage for 75% longer period compared to PEGASIS and
about 10 times longer compared to LEACH. Thus it is clear that PEDAP-PA guarantees a
good quality of the network for longer period than LEACH and PEGASIS.
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0 500 1000 1500 2000 2500 3000
0
10
20
30
40
50
60
70
80
90
100
Time (rounds)
Number of nodes alive
PEDAP-PA
PEGASIS
LEACH
Fig. 16 Comparison of the network lifetime for PEDAP-PA, PEGASIS and LEACH
A very clear drawback of PEDAP-PA is that the topology management is done at the BS
level. As discussed before, centralized algorithm limits the self-organization capability of
the WSNs, thus it limits the scalability of the network.
7 Performance Evaluation of Various HRPs
Generally, all HRPs described in Sect. 6aims to prolong the network lifetime by enhancing
the efficiency of energy consumption. However, depending on the applications, there is a need
to tolerate between data throughput and longevity. For example, in critical applications such
as in disaster relief or volcanic monitoring, data accuracy is more important. This needs some
energy to be sacrificed for performing frequent data transmissions. The approach of giving
the authority to the user in adjusting the threshold values in TEEN [9] and APTEEN [10],
makes these protocols more versatile. With this, the protocols can be implemented in various
applications ranging from non-critical monitoring to critical event detection applications.
Every protocol described in Sect. 6has different strategy in achieving their respective
objectives. Heinzelman et al. [8] apply nodes grouping strategy to reduce the transmission
distances between nodes and CH role rotation strategy to balance the energy consumption
among the nodes. These two strategies have been proven to successfully increase the energy
conservation by two times compared to DT. Manjeshwar and Agrawal [9] introduce HTand
STon the same cluster-based topology as in LEACH to eliminate unnecessary transmissions.
This strategy saves significant amount of energy and prolongs network lifetime by up to
163 % compared to LEACH. APTEEN [10] extends the performance of TEEN by forming
optimal clusters to improve data throughput and applying sleep/idle pairing to eliminate
redundant data. The performance of APTEEN lies between LEACH and TEEN. Lindsey and
Raghavendra [11] dwell on the topology setup algorithm and local computation to evenly
distribute the load among sensor nodes. This protocol shows a much better performance
compared to cluster-based protocols. The fifth protocol, PEDAP-PA [12] applies centralized
path determination algorithm to reduce transmission distances. This delays the occurrence
of FND and provides the longest network lifetime compared to the other four protocols.
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Hierarchical Routing Protocols for WSNs 1099
0 500 1000 1500 2000 2500 3000
0
10
20
30
40
50
60
70
80
90
100
Time (rounds)
Number of nodes alive
PEDAP-PA
PEGASIS
APTEEN
TEEN
LEACH
Fig. 17 System lifetime using LEACH, TEEN, APTEEN, PEGASIS, PEDAP-PA
Tab l e 3 Performance evaluation of HRPs
Performance parameters LEACH [8] TEEN [9] APTEEN [10] PEGASIS [11] PEDAP-PA [12]
Energy Efficiency Poor Moderate Moderate Good Best
Network Lifetime Shortest Long Long Longer Longest
Self-organization capability High High Low High Low
Network Quality Maintenance Poor Moderate Moderate Good Best
Throughput Low Low High Low High
Latency High Low Moderate High Moderate
The graphs in Fig. 17 show the network lifetime for each of all five HRPs described in
Sect. 6. Generally, the graphs have the same trend where at the early stage of operation,
all nodes remain alive up to certain rounds before the FND. The FND point is different for
each protocol. After the FND, the number of living nodes degrades until all of them die
out due to energy drainage. The FND describes the ability of a protocol to maintain a good
quality of the network’s function. The later the occurrence of FND, the longer the quality of
network’s function. However, depending on the applications, the FND sometimes is not the
main interest of the protocols. For example, in monitoring applications, a more interesting
metric to look at is the HNA. Considering both FND and HNA metrics, the graphs shows that
PEDAP-PA has the best performance, followed by PEGASIS, TEEN, APTEEN and LEACH.
It is also clear that chain-based protocols perform much better than cluster-based protocols,
and the BS-centralized protocols give better performance compared to distributed protocols.
The performance evaluation of HRPs is summarized in Table 3, while Table 4lists the design
strategies and features of the HRPs.
In summary, the five HRPs can be categorized into two types of protocol depending on
the hierarchical topology; namely cluster-based and chain-based. The HRPs that fall under
cluster-based are LEACH, TEEN and APTEEN while the HRPs that fall under chain-based
are PEGASIS and PEDAP-PA. Obviously, the great performance of the two chain-based
HRPs is caused by two factors. Firstly, chain-based architecture provides the protocol with
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Tab l e 4 Design strategies of HRPs
Description LEACH [8] TEEN [9] APTEEN [10] PEGASIS
[11]
PEDAP-
PA [12]
Hierarchical topology Cluster-based Cluster-based Cluster-based Chain-based Chain-based
Hierarchical level Single level Single level Single level Multi-level Multi-
level
Intra-cluster transmissions Direct
transmission
Direct
transmission
Direct
transmission
Multi-hop Multi-hop
Data fusion/aggregation CH-centralized CH-centralized CH-centralized Distributed Distributed
Topology set up algorithm Distributed Distributed BS-centralized Distributed BS-centralized
Periodic monitoring √X√√√
Critical event detection X √√XX
Rotation of coordinator’s role √√√√√
Power aware algorithm √√√√√
Type of network Fixed Fixed Fixed Fixed Fixed
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Hierarchical Routing Protocols for WSNs 1101
a multi-level hierarchy which allows multi-hop data transmission from ordinary nodes to the
coordinator. Conversely, cluster-based HRPs apply direct transmission within the clusters
which may involve long range distances. This result depicts the radio model as discussed
in Sect. 2. Secondly, distributed data fusion that is applied in chain-based HRPs balances
the load among the nodes, thus optimizing their current energy level. On the other hand, in
cluster-based HRPs, data fusion is burdened to the CH alone which quickly drains its energy.
Another positive effect of the load balancing strategy applied in the chain-based HRPs is that
the occurrence of the FND can be postponed for a significant number of rounds compared
to cluster-based HRPs. With more living nodes around, the quality of the network can be
guaranteed for a longer period.
The algorithm used in topology setup may also affect the HRP’s performance. For chain-
based HRPs, the best performance shown by PEDAP-PA proves that BS-centralized algo-
rithm produce optimal topology compared to distributed algorithm. Likewise in cluster-based
HRPs, BS-centralized cluster formation algorithm used in APTEEN enhances its performance
over LEACH. The effect of forming optimal topology is that the network can produce higher
throughput. The tradeoff of applying centralized algorithm is that the protocols will lose their
self-organization capability which limits the network scalability.
8Conclusion
This paper reviews the design strategies used in five HRPs and examines their contribu-
tion to the protocol performance. These HRPs are namely Low-energy Adaptive Clustering
Hierarchy (LEACH), Threshold Sensitive Energy Efficient sensor Network (TEEN), Adap-
tive Periodic TEEN (APTEEN), Power-efficient Gathering in Sensor Information Systems
(PEGASIS) and Power Efficient Data Gathering and Aggregation Protocol-power Aware
(PEDAP-PA). These HRPs can be categorized into two types which are cluster-based and
chain-based depending on the hierarchical topology formed to facilitate the routing proto-
col. Two main factors that can drain a sensor node’s energy quickly are the transmission
distance and number of received data packets that the node has to handle. It is found that the
chain-based HRPs guarantee a longer network lifetime by three to five times when compared
to cluster-based HRPs. Centralized topology management algorithms can produce optimal
hierarchical topology to enhance the network performance. Load balancing strategies such
as rotation of coordinator’s role and distributed data fusion can postpone the occurrence of
FND which guarantees a good quality of the network for a longer period.
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Author Biographies
Zahariah Manap received her B.E and M.E. in Communication and
Computer Engineering from Universiti Kebangsaan Malaysia, Selan-
gor, Malaysia in 2000 and 2003 respectively. She has been working
as a lecturer at UTeM, Melaka, Malaysia since 2003, teaching many
subjects in the field of electronics and telecommunications engineer-
ing. She is currently a Ph.D. student at Department of Computer and
Communication Systems Engineering, Faculty of Engineering, Univer-
siti Putra Malaysia. Her research interests include clustering algorithms
and routing protocols in Wireless Sensor Networks.
Borhanuddin Mohd Ali obtained his B.Sc. (Hons) Electrical and
Electronics Engineering from Loughborough University in 1979; M.Sc.
and Ph.D. from University of Wales, UK, in 1981 and 1985, respec-
tively. He became a lecturer at the Faculty of Engineering UPM in
1985, made a Professor in 2002, and Director of Institute of Multime-
dia and Software, 2001-2006. He has been with MIMOS as a Princi-
pal Researcher, heading the Wireless Networks and Protocol Research
Lab in 2007-2010 before came back to UPM. He was then heading the
Institute of Advanced Technology (ITMA), UPM as director in 2010-
2012. In 1997 he co founded the national networking testbed project
code named Teman, and became Chairman of the MYREN Research
Community in 2002, the successor to Teman. His research interest is
in Wireless Communications and Networks where he publishes over 80
journal and 200 conference papers. He is a Senior Member of IEEE, a
member of IET and a Chartered Engineer.
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1104 Z. Manap et al.
Chee Kyun Ng received his Bachelor of Engineering and Master
of Science degrees majoring in Computer & Communication Systems
from Universiti Putra Malaysia, Serdang, Selangor, Malaysia, in 1999
and 2002 respectively. He has also completed his PhD programme in
2007 majoring in Communications and Network Engineering at the
same university. He is currently undertaking his research on wireless
multiple access schemes, wireless sensor networks and smart antenna
system. His research interests include mobile cellular and satellite com-
munications, digital signal processing, and network security. Along the
period of his study programmes, he has published over 100 papers in
journals and in conferences.
Nor Kamariah Noordin received her B.Sc. in Electrical Engineering
majoring in Telecommunications from University of Alabama, USA,
in 1987. She became a tutor at the Department of Computer and Elec-
tronics Engineering, Universiti Putra Malaysia, and pursued her Mas-
ters Degree at Universiti Teknologi Malaysia and Ph.D. at UPM. She
then became a lecturer in 1991 at the same department where she was
later appointed as the Head from year 2000 to 2002. She is currently
the Director of Corporate Planning Division at the Office of the Vice
Chancellor. During her more than 20 years at the department she has
been actively involved in teaching, research and administrative activi-
ties. She has supervised a number of undergraduate students as well as
postgraduate students in the area of wireless communications, which
led to receiving some national and UPM research awards. Her research
work also led her to publish more than 100 papers in journals and in
conferences.
Aduwati Sali is currently a Lecturer at Department of Computer
and Communication Systems, Faculty of Engineering, Universiti Putra
Malaysia (UPM) since July 2003. She obtained her PhD in Mobile
Satellite Communications form University of Surrey, UK, in July 2009,
her MSc in Communications and Network Engineering from UPM in
April 2002 and her BEng in Electrical Electronics Engineering (Com-
munications) from University of Edinburgh in 1999. She worked as an
Assistant Manager with Telekom Malaysia Bhd from 1999 until 2000.
She involved with EU-IST Satellite Network of Excellence (SatNEx) I
& II from 2004 until 2009. She is the principle investigator for projects
under the funding bodies Malaysian Ministry of Science, Technology
and Innovation (MOSTI), Research University Grant Scheme (RUGS)
UPM and The Academy of Sciences for the Developing World (TWAS-
COMSTECH) Joint Grants. Her research interests are radio resource
management, MAC layer protocols, satellite communications, wireless
sensor networks, disaster management applications, 3D video transmis-
sions.
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