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

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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

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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

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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

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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.

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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

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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.

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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.

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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.

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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

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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

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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

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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.

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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.

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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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