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A Survey on Hierarchical Routing Protocols in
Wireless Sensor Network
By
MD. JAVED KHAN
Under the guidance of
TANUMOY NAG
Assistant Professor
Dinabandhu Andrews Institute of Technology & Management
SURVEY REPORT SUBMITTED IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE POST GRADUATE DEGREE OF
MASTER OF SCIENCE IN COMPUTER SCIENCE
2016 – 2018
DINABANDHU ANDREWS INSTITUTE OF TECHNOLOGY
AND MANAGEMENT
DEPARTMENT OF COMPUTER SCIENCE DINABANDHU ANDREWS INSTITUTE OF TECHNOLOGY & MANAGEMENT
[Affiliated to Maulana Abul Kalam Azad University of Technology]
BAISHNABGHATA, PATULI, KOLKATA-700084
1
CERTIFICATE OF APPROVAL
The foregoing Survey is hereby accepted as a credible study of a computer science
subject carried out and presented in a manner satisfactory to warrant its acceptance as a
prerequisite to the degree for which it has been submitted. It is understood that by this
approval the undersigned do not necessarily endorse or approve any statement made, opinion
expressed or conclusion drawn therein, but approve the survey only for the purpose for which
it is submitted.
Signature of Examiner Signature of Head of Department
2
ACKNOWLEDGEMENT
I would like to express my sincere thanks to all the people who have helped me most
throughout my project. First of all, I am grateful to my project supervisor Mr. Tanumoy Nag
for his invaluable guidance and constant support throughout the project.
A special thank of mine goes to Mrs Paromita Roy, Head of the Department
(Computer Science) and also my college authorities for providing me with all the necessary
resources and facilities necessary for carrying out the project.
I also wish to thank my parents for their personal support and attention. Last but not
the least, I would like to thank my friends who treasured me for my hard work and
encouraged me.
Md. Javed Khan
3
CONTENTS
1. Abstract 4
2. Introduction 4
3. Related works
3.1. LEACH 5
3.2. Multi-hop LEACH 7
3.3. CAERP 7
3.4. FLOC 8
3.5. HEED 9
3.6. DWEHC 10
3.7. PEGASIS 11
3.8. Sensor Aggregates Routing 11
4. Future Scope 12
5. Security Goals 13
6. Conclusion 14
7. Reference 14
4
1. Abstract:
Wireless sensor networks (WSNs) are low power light-weight sensor nodes
that are placed remotely for applications like wildlife monitoring, rainforest
monitoring, forest fire detection, military surveillance etc. Energy is a critical issue in
WSN, as nodes cannot be recharged or replaced frequently. In this survey, we have
given an overview of hierarchical routing protocols in wireless sensor networks and
their application domains including the challenges that should be addressed in order to
push the technology further, recent technologies for WSNs and identification of
several open research issues that need to be developed in future.
In this survey paper we focused on developments in wireless sensor network
technologies. We review the leading research projects, standards and technologies,
and platforms. Moreover, we highlight a recent phenomenon in WSN research that is
to explore cooperation between sensor networks and other technologies and explain
how this can help sensor networks achieve their full potential. This paper intends to
help new researchers entering the domain of WSNs by providing a comprehensive
survey on recent developments.
2. Introduction:
WSN consists of small sensor nodes which are equipped with limited energy
[1]. The lifetime of a WSN depends on how fast the sensor nodes are consuming their
stored energy. Researches are being done to control the utilization of energy by the
network. WSN is a group of sensor nodes that sense the information from
environment and send it to the Base Station (BS) where the data is collected,
aggregated and through internet the information is made available to the user. Cluster
based protocol is one of the best protocol to reduce the energy consumption [3].
Clustering is the task of grouping a set of objects in such a way that objects in the
same group called cluster, are more similar to each other than to those in other
clusters. In WSN, nodes are grouped into clusters and each cluster has a cluster head
(CH). All the nodes in a cluster send their data to the cluster head. Then the cluster
head sends the aggregated data to the base station.
5
In previous years [4], intensive research that addresses the potential of
collaboration among sensors in data gathering and processing of the sensing activities
were conducted. Basically, sensor nodes are reserved in energy supply and
communication bandwidth. So, innovative techniques are highly required to eliminate
energy inefficiencies that shorten the lifetime of the WSN. Such limitations combined
with a typical deployment of large number of sensor nodes pose many challenges to
the design and management of WSNs, so that the lifetime of the network is
maximized.
3. Related works:
In hierarchical routing protocols entire network is allocated into multiple
clusters [5]. One node in each cluster plays leading rule as Cluster Head, which is the
only node that can communicate to Base Station in clustering routing protocols. This
significantly reduces the routing overhead. Some hierarchical routing protocols are
discussed below.
3.1: LEACH (Low Energy Adaptive Clustering Hierarchy)
In [6], Heinzelman and al. have proposed a distributed clustering algorithm
called Low Energy Adaptive Clustering Hierarchy (LEACH), for routing in
homogeneous sensor networks. By analysing the advantages and disadvantages of
conventional routing protocols they have developed LEACH, a clustering-based
protocol that minimizes energy degeneracy in sensor networks. The use of clusters for
transmitting data to the base station influences the advantages of small transmit
6
distances for most of the nodes and requiring only a few nodes to transmit far
distances to the base station. LEACH is a self-organizing, adaptive clustering protocol
that uses randomization to distribute the energy load evenly among the sensor nodes
in the network. In LEACH, the nodes organize themselves into several local clusters,
with one node acting as the cluster-head. If the cluster-heads were chosen a priori and
fixed throughout the system lifetime, it is easy to see that the nodes which were
chosen to be cluster-heads would drained the energy quickly, ending the useful
lifetime of all nodes belonging to those clusters. Thus LEACH includes randomized
rotation of the high-energy cluster-head position such that it rotates among the various
sensors in order to not drain the power of a single sensor. LEACH also performs local
data fusion to “compress” the amount of data being sent from the clusters to the base
station, further reducing energy degeneracy and enhancing system lifetime.
First [5], cluster heads are selected and clusters are formed, second, data
transfer to the base station. During the first phase, the process of electing cluster-
heads is triggered to select future cluster-heads. Thus, a predetermined fraction of
nodes connected as cluster heads according either 0 or 1. If the random number is less
than a threshold „Ts‟ then the node becomes a cluster head in the current round,
otherwise the node n is expected to join the nearest cluster head in its neighbourhood.
The threshold is set as:
{
Where r is the current round number (starting from round 0), p the probability for
each node to become cluster heads and G is the set of nodes that have not been
cluster-head in the last 1/p round. The election probability of nodes G to become
cluster heads increases in each round in the same epoch and becomes equal to 1 in the
last round of the period.
Advantage:
LEACH is completely distributed, no control from the base station, and the
nodes do not require the knowledge of global network for LEACH operating.
LEACH reduces communication energy by 8x compared with direct
transmission and minimum transmission-energy routing.
The first node death in LEACH occurs over 8 times later than the first node
death in direct transmission, minimum-transmission-energy routing and a static
clustering protocol.
7
The last node death in LEACH occurs over 3 times later than the last node
death in the other protocols.
Based on MATLAB simulations, this hierarchical routing protocol „LEACH‟ will
overtake conventional communication protocols, in terms of energy dissipation, ease
of configuration and extend lifetime of the network. Providing such a low-energy, this
will help pave the way for future micro-sensor networks.
3.2: Multi-hop LEACH
Multi-hop LEACH (M-LEACH) [7] is an improved version of LEACH, in
which members of a cluster may be more of a leap from their corresponding cluster-
head and communicates with it in multi-hop mode. Each sensor must be able to
aggregate data, which increases the overhead for all sensors. To improve this
protocol, in [8], the authors have focused on heterogeneous sensor networks, in which
two types of sensors are deployed: (i) High Capacity Sensors (Super Sensor) and (ii)
Simple Sensors. The Super Sensors have large capacity and capabilities of processing
and communicating and act as cluster-heads, while others are simple sensors with
limited power, grouped to the closest cluster-head in their neighbourhood and
communicate with it directly or in multi hop.
3.3: CAERP (Cluster Arranged Energy Efficient Routing Protocol)
In [10] the Quadrature LEACH (Q-LEACH) is a Clustering based protocol for
a homogenous network, which is partition into four quadrants. During data
communication time the energy unbalancing occurs and this protocol in not suitable
for large networks. In [9], authors have proposed a cluster arrangement routing
protocol for minimum energy consumption during the data communication time. They
have considered a sensor network consisting of N sensor nodes uniformly deployed
over a vast field to continuously monitor the environment. The protocol is:
Base Station is located far from the sensing field.
Base Station and Sensors are all immobile after deployment.
All nodes have similar processing capabilities with equal significance.
Once the nodes are deployed then they are left unattended.
Sensors can operate in active mode or low-power sleeping mode.
Sensors use power control according to the distance to the desired recipient to
vary the transmission power.
Sensor node can compute the approximate distance to another node based on
the received signal strength.
8
There are mainly four phases in CAERP: Clustering, CH selection, Routing and
Data transmission. The novel clustering arrangement consist of a centralized
cluster head selection algorithm, a cluster formation scheme for balancing energy
load among cluster heads and an energy efficient multi hop routing algorithm for
data transmission from cluster heads to the base station.
3.4: Fast Local Clustering Service (FLOC)
FLOC [12] is a distributed technique that produces almost equal sized clusters
with minimum overlap. The model classifies nodes based on their proximity to the
CH into inner (i-band) and outer (o-band). I-band nodes will suffer very little
interference communicating with the CH, while message from o-band nodes may be
lost. FLOC favours i-band membership in order to increase the robustness of the intra-
cluster traffic.
A node stays idle, wait for some random duration to receive a request from
any potential CH. If there is no request, it becomes a candidate CH and
broadcasts a candidacy message.
Receiving the candidacy message a recipient node „„k‟‟ that is already a i-band
member of a cluster Ck, will reply back to inform the candidate CH about such
membership. The candidate CH will then realize the conflict and join Ck as an
o-band node.
If the candidate CH receives no conflict messages, it becomes a CH and starts
requesting members to its cluster.
An idle node would join a cluster as an o-band node if it does not receive any
request from a closer CH. That decision can be changed, if the node later
receives an request from a closer CH, i.e. the node switch its membership to a
better clustering.
FLOC scales very well converging in a constant time, anyway the size of the network.
It also exhibits self-healing capabilities as o-band nodes can switch to i-band node in
another cluster. In addition, new nodes can execute the algorithm and either joins one
of the existing clusters or forms a new one that possibly would attract some of the
current o-band nodes in neighbouring clusters.
9
FLOC Clustering Algorithm
3.5: Hybrid Energy-Efficient Distributed Clustering (HEED)
HEED [13] is a distributed clustering scheme where CH nodes are picked
from the deployed sensors. HEED considers a hybrid of energy and communication
cost to select CHs. In HEED only the sensors which have high remaining energy, can
become cluster-head nodes. HEED has three main characteristics:
The probability that two nodes within each other‟s transmission range
becoming CHs is minor. Unlike LEACH, this means that CHs are well
distributed in the network.
Energy consumption is not assumed to be uniform for all the nodes.
For a given sensor‟s transmission range, the probability of CH selection can be
adjusted to ensure inter-CH connectivity.
In HEED, each node is mapped to exactly one cluster and can directly communicate
with its CH. The algorithm has three phases:
Initialization phase: First sets an initial percentage of CHs among all sensors.
This percentage value is used to limit the initial CH announcements and each
sensor sets its probability of becoming a cluster-head.
Repetition Phase: In this phase, every sensor goes through several iterations
until it finds the CH.
CH
candidate
idle o-band
i-band
10
Finalization phase: During this phase, each sensor makes a final decision. It
either picks the least cost CH or pronounces itself as a CH.
3.6: Distributed Weight-Based Energy-Efficient Hierarchical Clustering (DWEHC)
Ding et al. [14] have proposed DWEHC to achieve more goals than those of
HEED by generating balanced cluster sizes and optimizing the intra-cluster topology.
DWEHC proceeds in a distributed manner and has O(1) time complexity. After
locating the neighbouring nodes in its area, each sensor calculates its weight which is
a function of the sensor‟s energy reserve and the proximity to the neighbours. In a
neighbourhood, the node with largest weight (highest reserved energy) would be
elected as a CH and the remaining nodes become members. At this stage, nodes are
considered as first-level members since they have a direct link to the CH. A node
progressively adjusts such membership in order to reach a CH using the least amount
of energy. Basically, a node checks with its non-CH neighbours to find their minimal
cost for reaching a CH. The process continues until nodes settles on the most energy
efficient intra-cluster topology. Both DWEHC and HEED are similar in many ways
like every node in the network participates in the clustering process; they do not make
any assumption about the network size and consider energy reserve in CH selection
etc. But there are many performance differences between DWEHC and HEED. For
example, Clusters generated by DWEHC are more well-balanced than HEED.
DWEHC achieves significantly lower energy consumption in intra-cluster and inter-
cluster communication than HEED.
DWEHC generates a multi-hop intra-cluster topology with CH
CH
11
3.7: Power-Efficient Gathering in Sensor Information Systems (PEGASIS)
In [15], an improvement over the LEACH protocol was proposed; it is called
Power-Efficient Gathering in Sensor Information Systems (PEGASIS). PEGASIS is a
near optimal chain-based protocol. The basic idea of this protocol is that in order to
extend network lifetime, nodes only can communicate with their closest neighbour
nodes and they take turns in communicating with the BS. When the round of all
nodes‟ communication with the BS ends, a new round starts, and so on. This reduces
the power required to transmit data per round as the power draining is spread
regularly over all nodes. Hence, PEGASIS has two main objectives: (i) Increase the
lifetime of each node by using collaborative techniques and (ii) It allows only local
coordination between nodes that are close to each other so the bandwidth consumed in
communication is reduced. PEGASIS avoids cluster formation and uses only one
node in a chain to transmit instead of multiple nodes. To find the closest neighbour
node, each node uses the signal strength to measure the distance to all neighbouring
nodes and then adjusts the signal strength so that only one node can be received. The
chain in PEGASIS will consist of those nodes that are closest to each other and form a
path to the BS. The aggregated data will be sent to the BS by any node in the chain
and the nodes in the chain will take turns for sending to the BS. The chain constructs
in a greedy method. According to simulation results, PEGASIS is able to increase the
lifetime of the network to twice that under the LEACH protocol. Although the
clustering overhead is avoided in PEGASIS, it still requires dynamic topology
adjustment. A sensor node needs to know about the energy status of its neighbours in
order to know where to route its data. Such topology adjustment can introduce
significant overhead for highly utilized networks. In practical cases, sensor nodes use
multi hop communication to reach the BS. PEGASIS assumes that each sensor node is
able to communicate with the BS directly, all sensor nodes have the same level of
energy and are likely to die at the same time and some sensors may be allowed to
move. Hence it affects the protocol functionality.
Hierarchical PEGASIS (an extended PEGASIS), was introduced in [16] with
the objective of decreasing the delay incurred for packets during transmission to the
BS. The chain-based protocol with CDMA-capable nodes constructs a chain of nodes
and forms a tree-like hierarchy; each selected node transmits data to a node in the
upper level of the hierarchy at a particular level. This method ensures data
transmitting in parallel and reduces delay. Such a hierarchical extension has been
shown to perform better than the regular PEGASIS.
3.8: Sensor Aggregates Routing
In [17], a set of algorithms for constructing and maintaining sensor aggregates
were proposed. The objective is to monitor target activity in a certain environment. A
sensor aggregate comprises those nodes in a network that satisfy a grouping predicate
12
for a cooperative processing task. The parameters of the predicate depend on the task
and its resource requirements. Sensors are divided into clusters according to their
sensed signal strength in the field, so there is only one peak per cluster and cluster
leaders are elected. One peak may represent one target, multiple targets, or no target if
the peak is generated by noise sources. To elect a leader, information exchanges
between neighbour sensors. This leader-based tracking algorithm assumes that the
unique leader knows the geographical region of the collaboration. Sensor aggregates
routing has three algorithms:
The protocol comprises a decision predicate P for each node to decide if it
should participate in an aggregate and a message exchange scheme M about
how the grouping predicate is applied to nodes. Distributed Aggregate
Management (DAM) is for forming sensor aggregates for a target monitoring
task. A node determines if it belongs to an aggregate based on the result.
Aggregates are formed when the process eventually converges.
Energy-Based Activity Monitoring (EBAM) estimates the energy level at each
node by computing the signal impact area. It combines a weighted form of the
detected target energy at each impacted sensor, assuming that each target
sensor has constant energy level.
Expectation-Maximization Like Activity Monitoring (EMLAM) removes the
constant target energy level assumption, estimates the target positions and
signal energy using received signals and uses the resulting estimates to predict
how signals from the targets may be mixed at each sensor. This process is
iterated until the estimate is sufficiently good.
The distributed track initiation management scheme, combined with the leader-based
tracking algorithm, forms a scalable system; works well in tracking multiple targets
when the targets are not interfering and it can recover from inter target interference
when the targets move apart.
4. Future Scope:
The future vision [4] of WSNs is to embed numerous distributed devices to
monitor and interact with real world. Extensive efforts have been applied so far on the
routing problem in WSNs. But still there are some challenges in the routing problem.
Sensors are embedded in unattended places or systems. This is
different from traditional Internet, PDA etc.
Sensors are characterized by a small footprint, and as such nodes
present stringent energy constraints as they are equipped with limited energy
sources.
13
Communication is the primary consumer of energy in this
environment, even the performance of these protocols is promising in terms of
energy efficiency.
Further research is needed to address issues such as QoS (Quality of Service) posed
by video and imaging sensors and real-time applications. Energy-aware QoS routing
in sensor networks will ensure guaranteed bandwidth and provide the use of the most
energy efficient path.
Another interesting issue for routing protocols is the mobility of BS. There
might be situations such as battle environments where the BS and possibly the sensors
need to be mobile. In such cases, frequent update of the position of the command
node and sensor nodes and broadcast of that information through the network may
drain the energy of nodes quickly. So, new routing algorithms are needed to handle
the overhead of mobility and topology changes in such an energy-constrained
environment. Future trends in routing techniques in WSNs focus on different
directions.
5. Security Goals:
Data security is the major risk in WSN. If any malfunction occurs that corrupts the
original data then all these approaches will be wasted. Some security goals are proposed
in [18].
Confidentiality: Data should not leak by the sensor nodes to other network.
Cryptography technique is the standard way to keep the sensitive data secret.
Integrity: Data should reach to the intended receiver without any alteration. Data loss
or damage can occur due to the communication environment. The integrity
mechanism should ensure that no opponent can manipulate the communicated data.
Authenticity: Authentication is necessary for maintaining the network, coordinating
with the sensor node and sending or receiving the information. An opponent can
easily inject the messages in the network, so the receiver must ensure that the received
message is originated by the correct source. Authenticity allows a receiver to verify
the data sent by the authorized user.
Availability: The services of a network should be available always even in presence
of an internal or external attacks such as a denial of service attack (DoS).
Freshness: Receiver receives the recent and fresh data and ensures that no opponent
can replay the old data. This requirement is especially important when the WSN
nodes use shared keys for message communication in the WSN.
14
6. Conclusion:
The past few years have attracted a lot of attention on clustering method for
wireless sensor networks and introduced unique challenges. In this survey, the energy
efficient clustering algorithm for wireless sensors network has been introduced. A
growing list of civil and military applications can employ WSNs for increased
effectiveness; especially in unfriendly and remote areas like disaster management,
border protection, combat field surveillance etc. WSN requires careful architecture
and management of the network. Grouping nodes into clusters has been the most
popular approach in WSNs. Significant attention has been paid to clustering strategies
and algorithms yielding a large number of publications. In this paper, we surveyed the
state of the research and the different schemes. We categorized the different schemes
according the objectives, the desired cluster properties and clustering process,
highlighted the effect of the network model on the followed approaches and
summarized a number of schemes. Although many of these routing techniques look
promising, there are still many challenges that need to be solved in sensor networks.
7. References:
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for Energy-Efficient Routing in Wireless Sensor Network, 2016 IEEE 6th International
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Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297:
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Sensor Networks: recent developments and potential synergies
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Hawaii International Conference on System Sciences – 2000
15
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